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https://mcpress.mayoclinic.org/parenting/are-video-games-and-screens-another-addiction/?mc_id=global&utm_source=webpage&utm_medium=l&utm_content=epsmentalhealth&utm_campaign=mayoclinic&geo=global&placementsite=enterprise&invsrc=other&cauid=177193

This source illustrates how our everyday screen time can lead to screen addiction. This is applicable to this unit because the reason for addictions is due to the brains ability to release dopamine. As we get addicted to our screens the dopamine that we need to feel the same "high" during and after spending time on our phones becomes greater and our neurotransmitters struggle to send more and more dopamine. Our screen dependency makes it difficult to connect with others because the dopamine we receive from human interaction is not the same as when playing on a device, and I think this will have major consequences within society, and we are already seeing it with children younger, and younger getting addicted to the screen.

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In an increasingly digitalized world, where most people ― even children ― own electronic devices with screens, many parents worry about the effects of screen use on themselves and their children.

To complicate matters, some screen time can be educational for children and support their social development. With the COVID-19 pandemic, many children and teens have spent even more time playing video games to socialize with friends, since they couldn't get together in person.

With screens virtually everywhere, controlling a child's screen time can be challenging. How can you manage your children's screen time? How will you know if you or your children are addicted to screens or video games?

Is screen time damaging?

It's difficult to avoid screens completely. However, excessive screen time can affect people's mental, social and physical health.

Too much screen time has been linked to:

Obesity

Poor sleep or insomnia

Behavioral problems, including impulsive actions

Loss of social skills

Violence

Less time for play

Eye strain

Neck and back problems

Anxiety

Depression

Difficulties with work or school

Many people have cried, laughed or been startled while watching a movie. This is because their brains process and react to the sensory input as if it were happening to them. This same type of engagement is possible when a person plays a video game.

While gaming, a gamer's brain is processing the scenario as if it were real. If the game depicts a dangerous or violent situation, the gamers' bodies react accordingly. Their fight-or-flight response to that perceived danger is triggered by exposure to intense stimulation and violence in the game. Excessive video game use can lead to the brain being revved up in a constant state of hyperarousal.

Hyperarousal looks different for each person, and it can include difficulties with paying attention, managing emotions, controlling impulses, following directions and tolerating frustration. Some adults or children struggle with expressing compassion and creativity, and have a decreased interest in learning. This can lead to a lack of empathy for others, which can lead to violence. Also, kids who rely on screens and social media to interact with others typically feel lonelier than kids who interact in person.

Chronic hyperarousal can have physical symptoms, as well, such as decreased immune function, irritability, jittery feelings, depression and unstable blood sugar levels. In children, some can develop cravings for sweets while playing video games. Combined with the sedentary nature of gaming, children's diet and weight can be negatively affected, as well. Sometimes children will even avoid stopping the game to go to the restroom, which can lead to hygiene issues.

How can gaming become an addiction?

An addiction is defined as a person's inability to control use of a substance or behavior, despite negative consequences. Some people who are engrossed in screen time or video games while ignoring other normal activities could be close to meeting this definition.

So why does this happen? The reward center in the brain releases dopamine in response to a pleasurable experience or hyperarousal. If a person experiences hyperarousal while playing video games, the brain associates the activity with dopamine. The person develops a strong drive to seek out that same pleasure again and again.

Dopamine is a powerful neurotransmitter in the brain. It helps sustain people's interest and attention, which is why it can be hard for people to tear themselves away from a situation or behavior. It's also self-reinforcing. The more times people experience the behavior, the more dopamine is released, and the more driven they are to return to the behavior.

Symptoms of screen time or video game addiction

Similar to tobacco, alcohol or drugs, screen time or video games can become an addiction if it damages your health and relationships, and you are unable to control it.

Some symptoms could include:

Having intense urges for screen time or to play video games, and these urges block out other thoughts

Spending money on video games or screens, even though you can't afford it

Cutting back on social or recreational activities because of preference for screen time or video games

Continuing to play video games or participate in screen time, even though you know it's causing problems in your life, such as poor performance at school or work, or letting household responsibilities go

Displaying signs of irritability, anxiety or anger when forced to stop playing, even for brief periods of time

Lying to others about the extent of your use

Needing more screen time over time to get the same level of enjoyment

Neglecting your appearance, including lack of interest in grooming or clothing

Continued excessive use of screens can result in long-term or permanent changes in the brain that require extensive behavioral and medical treatment to reverse.

What can you do?

As your child grows, a one-size-fits-all approach doesn't work as well. Not all kids are the same when it comes to screens and technology. Some children can self-regulate and might even put the screens down to go outside without you prompting them. Other kids become noticeably more anxious and quicker to lose their tempers when they spend a lot of time on screens. You'll need to decide how much media to let your child use each day and what's appropriate.

Consider applying the same rules to your child's real and virtual environments. In both, play with your child, teach kindness, be involved, and know your child's friends and what your child does with them. Also, keep in mind that the quality of the media your child is exposed to is more important than the type of technology or amount of time spent. Encourage active screen time over passive screen time.

Active screen time is when you are interacting with other people that you know or when you are cognitively or physically engaged. For example, play educational games, or games that require players to build something together ― or fitness-type games that require movement while playing. Passive screen time includes watching screens with minimal cognitive engagement, such as scrolling through social media, watching online videos or playing simple games.

Set reasonable limits for your child's screen time and video game types, especially if your child's use of screens hinders involvement in other activities.

Consider these tips:

Follow guidelines for screen time, such as those suggested by the American Academy of Pediatrics

Model healthy use of screens and video games. Consider unplugging when you first get home from work, at dinner and when driving. Model other methods of relaxation and entertainment, such as taking a walk, playing a game, having a dance party or reading a book.

Encourage a balance between screen time and activities that require in-person social interactions, such as family activities or extracurricular activities.

Create structured, screen-free times, such as during mealtimes, in the mornings and before bedtime.

Consider using apps that control the length of time your child can use a device.

Keep screens out of bedrooms.

Require that all devices be charged outside of bedrooms at night.

Learn about the game rating categories and only allow your children to play video games suitable for their ages.

If you're concerned about a child or loved one's use of screen time, consulting a behavioral or addictions specialist can help determine treatment options.

Relevant reading

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Are video games and screens another addiction? - Mayo Clinic Press

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In an increasingly digitalized world, where most people ― even children ― own electronic devices with screens, many parents worry about the effects of screen use on themselves and their children. To complicate matters, some screen time can be educational for children and support their social development.

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https://youtu.be/1l4FHqHQXE8

This video perfectly incapsulates the principles raised in the article. The speaker, Lundblade describes how the current generation of teenagers have always been around technology, and therefore have grown up learning how to make connections through a digital medium. During this talk Lundblade ponders on the question, How smartphones have ruined the latest generations? As someone who has grown up in one of the newer generations it is easy to see and experience the dopamine that is released when I play a game and advance to the next level, watch a funny video or FaceTime family members in other states, all from the conscience of a little screen that fits in my pocket. Now, like so many others in my generation I find social interactions exhausting and unpredictable, this possibly could be due to the fact that my brain is so used to the dopamine released when I am on my phone that its harder for me to enjoy having social interactions with people in person. I believe that explanation has a lot to say about my generation and the ones to follow, and like the speaker I believe that the instant gratification found through the use of cellphones have forever changed the landscape in which the world interacts with each other.

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Have Smartphones Ruined Us Forever? | Eric Lundblade | TEDxYouth@SRDS

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In this article the source links how mental health disorders can be linked to our genetic makeup. This shows how in a research study looking at the patterns in a family when it comes to having a mental disorder. A international research consortium conducted an analysis that incorporated data from genome-wide association studies or GWAS of 5 major disorders. The results showed variations associated with all 5 disorders. These included variations in 2 genes that code for the cellular machinery that helps regulate the flow of calcium into neurons. This article perfectly incapsulates how research can help us better understand what puts people at risk for certain disorders and this can help them be identified and treated.

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Major mental disorders traditionally thought to be distinct share certain genetic glitches, according to a new study. The finding may point to better ways to diagnose and treat these conditions.

Scientists have long recognized that many psychiatric disorders tend to run in families, suggesting potential genetic roots. Such disorders include autism, attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depression and schizophrenia. Symptoms can overlap and so distinguishing among these 5 major psychiatric syndromes can be difficult. Their shared symptoms suggest they may also share similarities at the biological level. In fact, recent studies have turned up limited evidence of shared genetic risk factors, such as for schizophrenia and bipolar disorder, autism and schizophrenia, and depression and bipolar disorder.

To take a broader look, an international research consortium conducted an analysis that incorporated data from genome-wide association studies (GWAS) of the 5 major disorders. This type of study involves scanning through thousands of genetic markers in search of tiny variations that appear more often in people who have a particular condition than in those who don't. The research received primary funding from NIH's National Institute of Mental Health (NIMH), along with other NIH components.

As reported online in the Lancet on February 28, 2013, the scientists screened for evidence of illness-associated genetic variation among over 33,000 patients. All had been diagnosed with at least 1 of the 5 disorders. A comparison group included about 28,000 people who had no major psychiatric diagnosis.

The analysis revealed variations significantly associated with all 5 disorders. These included variations in 2 genes that code for the cellular machinery that helps regulate the flow of calcium into neurons. Variation in one of these, called CACNA1C, had previously been linked to bipolar disorder, schizophrenia and major depression. CACNA1C is known to affect brain circuitry involved in emotion, thinking, attention and memory - functions that can be disrupted in mental illnesses. Variation in another calcium channel gene, called CACNB2, was also linked to the 5 disorders.

In addition, the researchers discovered illness-linked variation for all 5 disorders in certain regions of chromosomes 3 and 10. Each of these sites spans several genes, and causal factors haven't yet been pinpointed. The suspect region along chromosome 3 had the strongest links to the disorders. This region also harbors certain variations previously linked to bipolar disorder and schizophrenia.

"Although statistically significant, each of these genetic associations individually can account for only a small amount of risk for mental illness," says study co-author Dr. Jordan Smoller of Massachusetts General Hospital. Because of this, the variations couldn't yet be used to predict or diagnose specific conditions. But these results may help researchers move closer to making more accurate diagnoses. They may also help lead to a better understanding of the factors that cause these major mental disorders.

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Common Genetic Factors Found in 5 Mental Disorders

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Major mental disorders traditionally thought to be distinct share certain genetic glitches, according to a new study. The finding may point to better ways to diagnose and treat these conditions. Scientists have long recognized that many psychiatric disorders tend to run in families, suggesting potential genetic roots.

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This video describes a research study that looked at the 5 main mental illnesses autism, ADHD, bipolar disorder, major depression and schizophrenia. The researchers analyzed people who had these disorders and found that due to a calcium disorder their neurotransmitters were firing off differently. This research showed that these disorders are more alike than previously perceived. This analysis can lead to new ways to treat people and can lead to a better understanding about how this disorders even occur inside our brains in the first place.

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National Institute of Mental Health (NIMH)

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Five mental disorders share some of the same genes

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In this article, scientists at MIT conducted an experiment that utalized brightness and how it effects our visual perception and how we analyze spacial awareness. What they found was that our brain perceives a certain brightness at each location of the image. "Surprisingly, however, our brightness percepts are not always proportional to the amount of light emanating from image regions, but instead, our perception is the product of the object’s actual color and the amount of light that is shining on it." This shows that estimating brightness can possibly be categorized as a high-level process. It also shows how different optical illusions and many other enviornments can have perceptions about them changed based off of how the eye and brain work together to process brightness.

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MIT News | Massachusetts Institute of Technology

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It's a classic visual illusion: Two gray dots appear on a background that consists of a gradient from light gray to black. Although the two dots are identical, they appear very different based on where they are placed against the background.

Scientists who study the brain have been trying to figure out the mechanism behind this illusion, known as simultaneous brightness contrast, for more than 100 years. An MIT-led study now suggests that this phenomenon relies on brightness estimation that takes place before visual information reaches the brain's visual cortex, possibly within the retina.

"All of our experiments point to the conclusion that this is a low-level phenomenon," says Pawan Sinha, a professor of vision and computational neuroscience in MIT's Department of Brain and Cognitive Sciences. "The results help answer the question of what is the mechanism that underlies this very fundamental process of brightness estimation, which is a building block of many other kinds of visual analyses."

As one part of their investigation, the researchers studied blind children in India and found that they were susceptible to this illusion almost immediately after their sight was initiated after surgery, offering further evidence that brightness estimations are likely based on simple neural circuitry that doesn't require any prior visual experience to be set up.

Sinha is the senior author of the study, which appears in the August issue of Vision Research. Other authors of the paper are Sarah Crucilla, who worked with Sinha while she was in high school and is now a Caltech undergraduate; Tapan Gandhi, a faculty member at the Indian Institute of Technology and a former postdoc at MIT; Dylan Rose, a recent Northeastern University PhD recipient; Amy Singh of Google, who is also a former MIT postdoc; Suma Ganesh and Umang Mathur of Dr. Shroff's Charity Eye Hospital in New Delhi; and Peter Bex, a professor of psychology at Northeastern University.
Estimating brightness
When we look at an image, our brain perceives a certain brightness at each location of the image. Surprisingly, however, our brightness percepts are not always proportional to the amount of light emanating from image regions, Sinha says. Instead, our perception is the product of the object's actual color and the amount of light that is shining on it.

"You could have a really dark piece of cloth under a bright spotlight, and the amount of light that you get from it could be the same as, or even more than, the amount of light from a white piece of paper under dim light," Sinha says. "The brain is presented with the challenge of figuring out how light or dark a surface is based on just the amount of energy it's receiving. In essence, the brain has to figure out the two numbers that were multiplied (illumination level and surface darkness) to produce the one number it is receiving (incoming energy) - a seemingly impossible task since infinitely many pairs of numbers can all yield the same product."

Some scientists, including the 19th century German physicist Hermann von Helmholtz, an early pioneer of vision studies, suggested that estimating brightness is a "high-level" process. That is, the brain estimates brightness based on a high-level understanding of the lighting conditions, shapes, and shadows in the environment that it's seeing.

Many visual tasks, such as identifying faces or objects, do rely on our previous experiences or expectations about what we're seeing. However, the experiments that Sinha and his colleagues performed in this study suggest that in the case of brightness estimation, high-level processing does not play a significant role.

In their first set of experiments, the researchers created an image of a cube that appeared to be lit from the side, with one face appearing a little brighter than the other. In reality, using a clever trick that Chinese ceramic painters knew over 800 years ago, the face that looked brighter actually had lower luminance than the face that looked darker. In this display, the researchers found that when identical gray dots were placed on the two cube faces, the dot that was on the face that seemed to be in shadow actually appeared darker than an identical dot placed on a face that was receiving more light.

"This is the opposite of what happens in standard simultaneous contrast displays, in which a dot on a dark background appears brighter than a dot on a light background," Sinha says. "This result runs counter to the idea that high-level analysis of lighting conditions contributes to brightness estimation."

The second set of experiments was designed to localize the processes of brightness estimation. It built on the curious fact that the unified view of the world we experience, constructed by merging images from the two eyes, is accompanied by an almost complete loss of "eye of origin" information. We do not know what the original images were, and which eye they came from; we are only aware of the merged view (sometimes called the "cyclopean" image, after the one-eyed monster Cyclops of Greek mythology). However, using specially designed images and stereo glasses, the researchers found that brightness estimation did not need to wait until information from the two eyes was merged; it had already occurred by that point.

This finding suggests that brightness estimation occurs very early, before information coming from each eye is combined into one visual stream. The combination occurs in a part of the brain's cortex called V1 (so named because it represents the first stage of visual processing in the cortex). This places a tight constraint on the location of processing; the researchers hypothesize that significant brightness computation most likely takes place in the retina.

"The implication of results from the first two sets of studies was that if brightness estimation is really a low-level process, and the circuitry is located as early as the retina, then perhaps this is an innate dispensation," Sinha says. "This is something that the visual system comes prepared to do, right from birth."
"An innate mechanism"
The researchers were able to explore this hypothesis by studying blind children who had recently had their sight restored. Sinha runs an effort in India called Project Prakash, whose mission is to treat children suffering from preventable forms of blindness such as congenital cataracts. Many of the treated children go on to participate in scientific studies of visual development, although treatment is not contingent on such participation.

"The prediction was that if brightness estimation is truly an innate mechanism, then right after sight is initiated in children who were congenitally blind, they should fall prey to the simultaneous contrast illusion," Sinha says.

That is exactly what the researchers found, in a study of nine children who had cataracts surgically removed between the ages of 8 and 17. All of the children were susceptible to the illusion, in tests done just 24 to 48 hours after their surgical bandages were removed.

In a 2015 study, Sinha showed that recently sighted children are also immediately susceptible to two other visual illusions, known as the Müller-Lyer and Ponzo illusions, which involve judging the length of lines based on visual cues.

"The account that emerged from that work also seems to be consistent with the account that's emerging from the brightness studies. That is, many of the phenomena that we are so quick to ascribe to high level inferential processes may actually be instantiated in some very simple circuit mechanisms of the brain that are innately available," Sinha says. "These results are contributing to the quest for understanding how our nervous systems solve the complex challenge of perceiving and understanding the world around us."

The research was funded by the National Eye Institute/National Institutes of Health, the Nick Simons Family Foundation, the Sikand Foundation, and the Halis Family Foundation.

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Study sheds light on a classic visual illusion

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It's a classic visual illusion: Two gray dots appear on a background that consists of a gradient from light gray to black. Although the two dots are identical, they appear very different based on where they are placed against the background.

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In this video a scientist explains how our brains perceive space based- off of optical illusions. Utalizing a picture of two monsters in a hallway, that is seen in the thumbnail, which our brain precieves as two monsters that are different sizes. However, once the background is taking away we can now see that they are the same size. This shows that our brain and eyes process size based off of previous knowledge as our brains mature but also that our eyes can be decieved based off of drawings. This is similar to life like chalk images that are made to look like holes.

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Ask a Scientist: What Is an Optical Illusion?

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In this article the author brings together the studies of original critical period thought of Chomsky and the new mass media age. This article asks the vital question of how language and technology can work hand and hand. A psychologist in the article, sites educational programming such as Sesame Street has been able to help children develop language skills, and how many other children's programming is trying to implement the same effect. It also talks about how technological learning can be a detriment to a child's development due to the fact that it can lessen the human interaction that is critical to human development. In my opinion I believe that there is a happy median between the two. I think that this article is asking all the correct questions and it will be interesting to see how human development will pan out with the increase of technology.

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Language Acquisition and Language Processing Lab, Department of Language and Literature, Norwegian University of Science and Technology, Trondheim, Norway

School of Computing and Mathematics, Plymouth University, Plymouth, United Kingdom

Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, United States

Introduction

The digital age is changing our children's lives and childhood dramatically. New technologies transform the way people interact with each other, the way stories are shared and distributed, and the way reality is presented and perceived. Parents experience that toddlers can handle tablets and apps with a level of sophistication the children's grandparents can only envy. In Great Britain, a recent survey of preschoolers shows that a rising number of toddlers are now put to bed with a tablet instead of a bedtime story. In the USA, a telephone survey of 1,009 parents of children aged 2-24 months ( Zimmerman et al., 2007a) documents that by 3 months of age, about 40% of children regularly watched television, DVDs or videos, while by 24 months the proportion rose to 90%. Moreover, with the advance and exponential use of social media, children see their parents constantly interacting with mobile devices, instead of with people around them. Still, research in the US indicates that assistive social robots seem to have a favorable effect on children's language development ( Westlund et al.).

Existing theories of language acquisition emphasize the role of language input and the child's interaction with the environment as crucial to language development. From this perspective, we need to ask: What are the consequences of this new digital reality for children's acquisition of the most fundamental of all human skills: language and communication? Are new theories needed that can help us understand how children acquire language? Do the new digital environment and the new ways of interaction change the way languages are learned, or the quality of language acquisition? Is the use of new media beneficial or harmful to children's language and cognitive development? Can new technologies be tailored to support child growth and, most importantly, can they be designed to enhance language learning in vulnerable children?

These questions and issues can only be addressed by means of an interdisciplinary approach that aims at developing new methods of data collection and analysis in a longitudinal perspective. This type of research is however not yet documented.

Past and Current Research

The question of how the ecology of the child affects the acquisition of competencies and skills has been approached from different perspectives in different disciplines. In linguistics, the central question addressed concerns the specific role of exposure to language. Two influential types of theory have been proposed. One view is that the capacity to learn language is hard-wired in the human brain ( Chomsky, 1965; Pinker, 1994); linguistic input is merely a trigger for language to develop. From an alternative view, language acquisition depends on the linguistic environment of the child, and specifically on language input provided through child-adult communication and interaction ( Tomasello, 2003). The latter view further specifies that factors in interaction are crucial for language learning to take place. Such views are aligned with overarching theories of human development in cognitive science and psychology. These theories (known as embodied and situated cognition theories) hold that knowledge is acquired by humans through rich physical and social interaction with their environment ( Barsalou, 2008). This interaction leaves multiple traces provided by a number of modalities (auditory, visual, haptic etc.) and helps consolidate knowledge in the brain by strengthening the neural networks that support learning and the use of knowledge. Exactly how input received from multiple, and multi-sensory in nature sources, interacts in both knowledge acquisition and use is, however, still poorly understood.

A current theme in the fields of information technology, artificial intelligence and robotics is to create robots that develop, as children do, and to establish how embodiment and interaction support language learning in these machines. These artificial models will eventually inform us about child development and vice versa ( Cangelosi and Schlesinger, 2015, forthcoming). In the field of human-machine interaction, research is investigating whether using a physical robot, rather than a virtual agent or a computer-based video, has a positive effect on language development. Kennedy et al. (2015), for example, investigate how toy-like robots, such as, the Aldebaran Nao, are used in the classroom instead of, or together with, digital tools such as tablets, to show how a richer embodied technology method further improves language learning. Vogt envisage that, in the digital age, social robots will increasingly be used for educational purposes, such as, second language tutoring. They propose a number of design features to develop a child-friendly social robot that can effectively support children in second language learning, and discuss the technical challenges for developing such tutors.

In education research, the main question is the extent to which the use of tablets can facilitate learning to read and write, and how this type of learning compares to traditional learning. In this context, Guerra and Mellado observe that implementing information and communication technologies for educational contexts that have robust and long-lasting effects on student learning outcomes is still a challenge. They further suggest that any such system must be theoretically motivated and designed to tackle specific cognitive skills (e.g., inference making) supporting a given cognitive task (e.g., reading comprehension), and must be able to identify and adapt to the user's profile. Furthermore, a field that combines the concerns of education and digital technology is newly emerging, where one of the questions is how games should be designed to facilitate learning. Zhang et al. provide a review of the educational application of Massive Multiple Online Role-Playing Games (MMORPGs) based on relevant macroscopic and microscopic studies, showing that gamers' overall language proficiency or some specific language skills can be enhanced by real-time online interaction with peers and game narratives or instructions embedded in the MMORPGs. Mechanisms underlying the educational assistant role of MMORPGs in second language learning are discussed from both behavioral and neural perspectives, highlighting the role of attentional bias. Child-media interaction has also been approached in psychology, raising the issue of how new technologies change behavior and interaction, including values and communication patterns.

A recurrent problem in most recent research, however, is that the topic has been approached from a single disciplinary perspective, and often with a single theory in mind. Accounts are piecemeal and explain only one phenomenon at a time. Despite considerable advances in the past 20 years, we miss a holistic model of language development that also integrates the impact of digital technology on its outcomes. Such a model must take into account the weighting of all factors involved. One major challenge is the nature and amount of data that need to be collected and analyzed to build such a model. These data are, in their nature, multi-modal, complex, and dense. It then becomes mandatory to develop new analytic methods and to integrate the complex data needed in order to answer the following three fundamental questions:

* How should traditional theories and models of language acquisition be revised to account for the multimodal and multichannel nature of language learning in the digital age?

* How should existing and future technologies be developed and transformed so as to be most beneficial for child language learning and cognition?

* Can new technologies be tailored to support child growth, and most importantly, can they be designed in order to enhance specifically vulnerable children's language learning environment and opportunities?

First Language Development

Early Research on the Mass Media and Language Development

Interest in the impact of the mass media on language development started as early as the late 70-ies. One of the questions that was asked was "Does the language of the mass media contribute a "new" language compared to traditional forms of communication (e.g., books or oral language)?" It was suggested that the new mass media (film, radio, TV) offer "new" languages whose grammar was yet unknown ( McLuhan, 1964; Willie, 1979), and, as such, were potentially qualitatively different form oral human-to-human communication. One specific aspect where this difference was particularly salient is the multimodal nature of media, such as television and film. It has been observed that the vehicles of messages in these media involve the marriage of two languages with completely different characteristics (auditory/oral & visual/pictures) ( Willie, 1979).

Some results from this early research indicate that there are certain behavioral consequences. For instance, TV-viewing appears to lead to less reading, yet subject to individual variation ( Himmelweit et al., 1960). Furthermore, TV-viewing leads to less listening to the radio, and, in particular, with more adverse effect for "brighter" children (greater loss). In contrast, a study on the popular children's programme Sesame Street found a positive effect of TV viewing on language development, however, only in combination with adult intervention ( Winn, 1977). Other research suggests that TV viewing overall has a negative effect on the development of children's attention and cognition and the American Academy of Pediatrics has recommended that children below 2 years of age not watch any television ( Anderson and Pempek, 2005).

A valid question if we should expect any impact of mass media on language development is the extent to which the content provided through the media is comprehensible. How much of what children view on TV do they understand? Studies have shown that comprehension tends to increase with age with only 20% understanding among 4-year olds. Also, since this kind of input is mediated through both modalities, the visual and the auditory, advance in language development ought to depend both on the child's non-verbal (visual cognition) and verbal cognitive status at point of exposure. As evidenced by the papers in the current volume, tailoring the features of the technology used to the individual level of cognitive and language skills of the learner is a major prerequisite for successful outcomes. Moreover, as argued by Acerbi, one needs to understand how cultural transmission processes (e.g., transmission biases), of which language learning is arguably one instance, function in the new context of digital media.

When comparing the effects of TV and radio exposure, there is a crucial difference between language experience that requires no reciprocal participation (radio, TV) in contrast to active exchange with another person. Furthermore, TV-images do not go through a complex symbolic transformation; the mind does not decode or manipulate information, as with other types of oral or written language input.

Later research has focused on the extent to which first language acquisition from exposure exclusively to the mass media (radio and TV) deviates from typical language acquisition through interaction with care-givers and peers. Several findings suggest that overwhelming exposure to the kind of input from the radio or TV can have adverse effects, especially for very young children (toddlers). Thus, in a longitudinal study, Zimmerman and Christakis (2005) document that early TV exposure in children younger than 3 years of age was associated with deletirious effects on cognitive development, such as reading at age 7, while infant exposure (between 8 and 16 months) to videos/DVDs was associated with a 16.99-point decrement in CDI score ( Zimmerman et al., 2007b). Tanimura et al. (2007) studied 18-month old infants ( n = 1,900) and found that those who engaged in frequent TV-viewing (>4 h per day), even when accompanied by parental talking, had delayed language development/speech production (in terms of meaningful words). An observational study of 14 pairs of children (age range 7-24 months) and parents videotaped while watching television together shows that both the quality and quantity of parental utterances (Child-directed Speech) significantly declined while the TV was on, and especially when the infants were watching. This also led to an increase of frequency of 1-word sentences, quite often only short phrases, such as nouns (names). From a broader perspective, there is evidence that educational programmes targeting infants and toddlers have not achieved their purported learning goals (cf. Hirsh-Pasek et al., 2015 for a review).

Given that what children watch is important for subsequent vocabulary development ( Anderson, 1998; Linebarger and Walker, 2005), and how children watch (with parent or not) is also relevant ( Jordan, 2004; Anderson and Pempek, 2005), such findings are extremely pertinent for current research to follow up on. Moreover, the results from the study by Zimmerman et al. (2007b) reporting a negative correlation between DVD viewing and vocabulary development have been challenged by a recent re-analyses of the data set from that study ( Ferguson and Donnellan, 2014). This replication found that effect sizes were negligible between analyses for positive, neutral, and negative effects. Interestingly, infants exposed to no media had lower levels of language development compared to infants with some exposure. Thus, it seems that more variables are necessary to take into account in the equation.

From TV and Radio to Tablets and Robots

Modern digital technology has attracted the attention of scholars due to its favorable affordances. It allows for multi-sensory interaction and provides rich input in the form of visual, auditory, and haptic stimuli ( Belpaeme et al., 2012). A recent study by Allen et al. (2015) exploits the multi-modal nature of the input provided by iPads. The main question addressed in that study is whether iPads might promote symbolic understanding and word learning in children with autism in comparison with age-matched typically developing controls. The hypothesis was that multiple, differently colored exemplars of target referents, as afforded by the iPad technology, might promote phonological pattern-meaning/referent associations, e.g., compared to single exemplars. The study included four conditions, contrasting the use of an iPad vs. a Book, and exposing the children to single vs. multiple exemplars of the target items. Participating children were tested on whether they would associate the word to a 3-D referent (real life object) and whether they would generalize it to another member of the same category, but shown in a different color. The results indicated no differences between the two types of media (iPad or book) in symbolic understanding and level of generalization. They further demonstrate that exposure to multiple exemplars increases the rate of extension from picture to 3-D object.

Other studies have focused on how technology can assist exposure to language through reading. Chang and Breazeal (2011) propose to combine a basic primer book with interactivity in order to support parent-child reading interactions during shared book-reading. The design targets very young children (2-5 years) and offers a variety of features: it enables physical proximity, is visually accessible, responds to touch, is navigable to both child and parent, and encourages vocal expression. One specific aspect deserves mention, the Multisensory Contextual Selections. Thus, speech and touch combine to alter the content, and the reader can change story elements using a combination of touch and speech, encouraging creativity and variation. This design is based on interviews and suggestions thereof with educational experts, designers and researchers and exploits the interactive affordances of digital technology. From the point of view of child-parent interactions, Kucirkova et al. (2014) suggest that multimedia story sharing resembles interactions similar to those when experiencing a piece of art in terms of its holistic nature. Furthermore, there is some evidence that personalization of digital multimedia formats leads to more spontaneous speech production in children ( Kucirkova et al., 2014).

Second Language Learning

Westlund et al. (2016) investigated the role of social robots in second language learning. The study had two main goals. The first one was to test whether a socially assistive robot could help children learn new words in a foreign language more effectively by personalizing its affective feedback. The second aim was to demonstrate the feasibility of creating and deploying a fully autonomous robotic system at a school for several months. The design included a socially assistive robotic learning companion to support English-speaking children's acquisition of a new language (Spanish). In a two-month microgenetic study, 34 preschool children played an interactive game with a fully autonomous robot and the robot's virtual sidekick, a Toucan shown on a tablet screen. Two aspects of the interaction were personalized to each child: (1) the content of the game (i.e., which words were presented), and (2) the robot's affective responses to the child's emotional state and performance. The results from the study indicate that the children learned new words and affective personalization led to greater positive responses from the children.

Vogt et al. propose a number of features for an L2 robot tutor including ways to develop the robot such that it can act as a peer to motivate the child during second language learning and build trust at the same time, while still being more knowledgeable than the child and scaffolding that knowledge in adult-like manner. The authors suggest that the first impression of the child are crucial for building trust and common ground, thus supporting child-robot interactions in the long term. Other important features relate to the ability to adapt to the language proficiency level of the individual child, respond contingently, both temporally and semantically, provide effective feedback and monitor children's learning progress, as well as establish joint attention, and use meaningful gestures. There are a number of technical challenges associated with such an optimal design, such as, automatic speech recognition (ASR) for children, reliable object recognition to facilitate semantic contingency and establishing joint attention, and developing human-like gestures with a robot that does not have the same morphology as humans. The paper presents an experiment which investigates how children respond to different forms of feedback from such a robot.

Child-Robot Interaction

While we still lack in-depth longitudinal studies of the effects of current digital technologies on language learning, child-robot interaction has been studied recently. Breazeal et al. (2016) looked at children ranging from 3 to 5 years who were introduced to two anthropomorphic robots that provided them with information about unfamiliar animals. This study found that the children treated the robots as interlocutors: they supplied information to the robots and retained what the robots told them. Children also treated the robots as informants from whom they could seek information. Consistent with children's early sensitivity to an interlocutor's non-verbal signals, children were especially attentive and receptive to whichever robot displayed the greater non-verbal contingency. Selective information seeking is consistent with recent findings showing that although young children learn from others, they are selective with respect to the informants that they question or endorse.

Other research in this domain indicates that children readily treat anthropomorphic robots as social companions ( Shiomi et al., 2006). Kahn et al. (2013) document that children often respond verbally to robots (beyond what one might give to an automated system). This research also shows that robots are often attributed mental attributes (emotions etc.), and further that young participants readily engage in verbal exchange with (e.g., speak to) robots.

Movellan et al. (2009) assessed learning from a robot. In that study toddlers (18-24 months) interacted with a sociable robot which displayed images of 4 objects. At pre-test the toddlers' choices were a little better than chance. Over a 2-week period a modest learning outcome was observed, in that there was a significant improvement on taught words, but no improvement on control words. Tanaka and Matsuzoe (2012) studied word learning in the context of a social robot in the age range between 3 and 6 years. The robot responded either correctly of incorrectly to test questions about the novel words. Children reacted and spoke to the robot, and tried to teach the novel words to the robot. Furthermore, they learned the meaning of some novel action words in the company of the robot. However, the results of this study remain unclear as the children's utterances were not analyzed.

All of the studies investigating Child-Robot interaction indicate that the features of the robot are important, and that children differentiate among potential informants. Thus, accent ( Kintzler et al., 2011), familiarity ( Corriveau and Harris, 2009), turn-taking behavior: contingent responsiveness ( Murray and Trevarthen, 1985; Nadel et al., 1999) have all been implicated as central for the interaction and learning outcomes. These findings are consistent with factors in early language development. Thus, contingent responsiveness has been shown to be essential for language learning in infancy ( Kuhl, 2007), even though earlier studies have suggested that children acquire native competence regardless of whether spoken to by parents or not. Still, this topic has remained largely out of the focus of current research, and the role of child-directed speech is still to be assessed. Other factors with clear impact on language development are joint attention and accompanying gestures ( Tomasello, 2006; Esteve-Gibert et al., 2016). Thus, implementing those features in social robots is likely to have a positive effect on language learning as well.

Interim Summary

The current review has revealed the following findings. Children readily interact with robots. While current research has focused on child-parent interaction while engaging with tablets/iPads, as well as learning in educational contexts, little is known about interaction and language learning from digital devices when the child is the sole agent. The level and quality of interaction largely depends on robot features. As pointed out by Belpaeme et al. (2012), for robots to interact effectively with humans, they need to be capable of coordinated and timely behavior in response to social context. Moreover, they need to display adaptive behavior. Children are likely to interact and engage in verbal exchange (e.g., speak to robots), provided robots feature contingency of responses, provide effective feedback and monitor children's learning progress, as well as establish joint attention, and use meaningful gestures. Yet, very few studies document specific advances in language learning. Thus, so far we see only modest language learning and primarily restricted to vocabulary, but only in experimental settings ( Westlund et al.). Nothing is known about "outside of laboratory settings." Overall, there is almost no research on language development per se.

In a recent detailed review and discussion of educational apps and their affordances, Hirsh-Pasek et al. (2015) emphasize the role of experience and the environment in the process of acquiring knowledge in early development: whether involving language or not. In particular, the path from sensori-motor experience to symbolic learning, as envisaged in approaches influenced by the Piagetan tradition, appears to be of crucial importance for unpacking the impact of digital technologies on the language learning infant. Similar perspectives need to be in focus when assessing the role of tablets (iPads) in early education ( Kucirkova, 2014).

New Research Agenda

The study of language learning in rich environments, including digital tools, poses specific challenges to theoretical and empirical research. Traditional theories of language acquisition emphasize characteristics of the learner, such as innate structures and maturational constraints, as well as of the input (its quality, quantity, and variation), but typically they do not take into account the different channels through which linguistic and contextual data are provided to the learner. The standard channel is human face-to-face interaction, accompanied by books or printed or recorded material later during childhood. However, the digital age is making new channels available to children earlier on. Each such channel provides input to infants and children through multiple sensory modalities simultaneously-not just hearing, but vision, touch etc. Should empirical research show that vocabulary or grammar learning modes or outcomes vary, depending on the channels through which the linguistic input is provided to the child, theories of language acquisition would have to be expanded, so as to include explicit models of how these effects come about. In particular, learning theories (modeling the input and learner) should be accompanied by transmission theories (also modeling the input's sources and transmission channels).

Research on language development in the digital age requires us to understand better the standard modes and channels of language transmission, i.e., vertical social learning. In most modern experiments on (artificial) language learning, the learner is exposed to linguistic or related stimuli that are "produced" by machines, e.g., a computer, not by other human beings. Implicitly, much research on language learning involving exposure or training phases is already research on learning from digital tools. There is research on language learning and use in social contexts ( Tomasello, 2003), however these two lines of work have not yet been integrated: what is needed are experiments in which learning from others and learning from digital tools are directly compared, i.e., where the learning channel is an explicit experimental factor. This approach may help understanding the cognitive and behavioral consequences of learning in digital ecologies, while keeping other factors under experimental control. For example, one could directly test whether digital tools are simply increasing the amount of information that is made available to children, or whether instead they are facilitating or impeding learning (e.g., of new vocabulary) when information quantity is held constant. The same mutatis mutandis would hold for information quality and variation. A further set of questions is whether the effects of digital tools on learning are short-lived or long-lasting, and whether they manifest themselves invariably or only early during development: would the child's brain eventually adapt to the multiplicity of channels and respective modalities through which language is experienced? Longitudinal designs are necessary to answer such questions.

The development of robot tutors to support early language development, as well as L2 language acquisition, offers innovative ways of exploiting the digital age technologies for language tutoring purposes, and in general, for child-robot interaction. Research has consistently demonstrated that the physical presence (embodiment) of a robot (e.g., Kennedy et al., 2015; Cangelosi and Schlesinger, forthcoming), as well as some of its anthropomorphic features (robot appearance with human-like shape; e.g., Walters et al., 2008) and behavior (shared gaze, gestures; e.g., Zanatto et al., 2016), improve the outcome of the tutoring and companionship objectives. Moreover, multimodal approaches to human-robot interaction, such as, those combining tablet-based interfaces with the robot's speech communication capabilities and behavioral feedback strategies, improve the acceptability and efficacy of robot companions ( Belpaeme et al., 2012; Di Nuovo et al., 2016). As such, future research directions in robot tutors for language development will benefit from the investigation of hybrid robot and digital technologies, strategically exploiting the benefits from the robot's anthropomorphic features.

Robot companions also offer the opportunity to support language acquisition in children with atypical development. Pioneering studies have looked at social assistive robotics for children with autism spectrum disorder (ASD) (e.g., Dautenhahn, 1999; Scassellati et al., 2012). For example, Scassellati et al. (2012) suggest that the improvement of social skills development via robot interaction is the consequence of the fact that robots provide novel sensory stimuli to the ASD child. Robot companions have also been used for the support of children with diabetes ( Belpaeme et al., 2012) and with mobility and motor disabilities ( Sarabia and Demiris, 2013). Thus, future work combining robot tutors with populations with atypical cognitive and motor development will contribute to the challenges of language skills acquisition in children with disabilities.

Future research should harvest evidence of language development in interaction with digital tools (including social robots). It should compare children who are often exposed to ICT to children who are not. It should investigate how new media/digital tools impact on the development of lower level language skills (e.g., vocabulary, grammar); how new media/digital tools impact on the development of "higher" skills (e.g., discourse comprehension) and explore the development of dimensionality ( Language and Reading Research Consortium, 2015), and specifically, the effect of digital technology on oral and reading comprehension, and figurative language skills. A broader and overarching issue is the effect of new digital environments on brain plasticity and learning ( Bavelier et al., 2010). Future research on this topic is also in need of novel methods for data analyses.

Author Contributions

MV prepared the initial draft and worked on subsequent editing. GB and AC worked on specific sections. LS completed final editing and alignment of ideas.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

Cangelosi, A., and Schlesinger, M. (2015). Developmental Robotics: From Babies to Robots. Cambridge, MA: MIT Press.

Cangelosi, A., and Schlesinger, M. (forthcoming). From babies to robots: the developmental robotics contribution to child psychology. Child Dev. Perspect.

McLuhan, M. (1964). Understanding Media: The Extensions of Man, 1st Edn. New York, NY: McGraw Hill.

Murray, L., and Trevarthen, C. (1985). "Emotional regulation of interactions between two-month- olds and their mothers," in Social Perception in Infants, eds T. Field and N. Fox, (Norwood, NJ: Ablex Publishers. Na), 177-197.

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Editorial: Language Development in the Digital Age

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AbstractThe digital age is changing our children's lives and childhood dramatically. New technologies transform the way people interact with each other, the way stories are shared and distributed, and the way reality is presented and perceived. Parents experience that toddlers can handle tablets and apps with a level of sophistication the children's grandparents can only envy.

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In this video, there is a great point made when it comes to the development of thinking skills in children and how technology and language development can be accelerated through the use of technology. It states that it can introduce children to a wider range of languages, outside of the home, and can help develop critical thinking skills through educational games. Throughout the video there is a key distinction between screen time and screen education. They are not advocating to setting a child in front of a screen and assuming that they will develop, it is intentional screen time. Thought out and supervised screen time that allows children to engage in the screen activity along with off screen connection with an adult. However, that all being said the psychologist in the video is clear to state that we still do not know all the pros and cons when it comes to children raised solely in the digital age. Hopefully, this will lead to more education during the critical period.

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How do technology and screen time affect early childhood development?

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One of the most infamous studies in human behavior ever conducted was the Stanford Prison Experiment. However, 40 years later new details now show insight to the actual human behavior of this study.
This article is discussing the aftermath that the prisoners of the experiment experienced, and many are revising the narrative of the story.
One man, the one who was the first to break down in solitary confinement reveals that he was faking it in order to escape the hellish existence of the 36 hours he spent in the experiment. Another man discussed what led him and the other prisoners to bond, which led to an attempt revolt against the guards who were abusing their power.
While many may say that this calls into question the who'll experiment, I think that this proves about the same thing. Everyone wanted out and would do anything to do so, and this willingness to survive this fight or flight mode coupled with the group dynamics really show how people will bound together in order to get out of situations such as the abuse of power in the Stanford Prison Experiment.

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It's arguably one of the most controversial experiments.

It all started in the basement of the psychology building at Stanford University on August 17, 1971 after psychologist Phil Zimbardo and colleagues took an ad out in the paper stating: " Male college students needed for psychological study of prison life. $15 per day for 1-2 weeks."

Over 70 people volunteered for the Stanford Prison Experiment. Twenty-four healthy, smart college-aged men were picked and randomly assigned either to be a guard or a prisoner. The aim of the study was to explore the psychology of prison life and how specific situations affect people's behavior.

But the experiment didn't last very long - six days to be exact. Zimbardo was forced to pull the plug because of the disturbing behavior of the guards and the downright despair and other negative reactions of the prisoners.

According to a piece in Stanford Magazine:

For six days, half the study's participants endured cruel and dehumanizing abuse at the hands of their peers. At various times, they were taunted, stripped naked, deprived of sleep and forced to use plastic buckets as toilets. Some of them rebelled violently; others became hysterical or withdrew into despair. As the situation descended into chaos, the researchers stood by and watched-until one of their colleagues finally spoke out.

The magazine features interviews with "some of the key players," including Zimbardo, his wife (the "whistle-blower" who called for the study to stop), a guard (who was "the most abusive") and a prisoner.

Like the fake guards, Zimbardo got caught up in the study, and started embodying the role of the prison's warden. He told the magazine:

There was zero time for reflection. We had to feed the prisoners three meals a day, deal with the prisoner breakdowns, deal with their parents, run a parole board. By the third day I was sleeping in my office. I had become the superintendent of the Stanford county jail. That was who I was: I'm not the researcher at all. Even my posture changes-when I walk through the prison yard, I'm walking with my hands behind my back, which I never in my life do, the way generals walk when they're inspecting troops.

We had arranged for everyone involved-the prisoners, guards and staff-to be interviewed on Friday by other faculty members and graduate students who had not been involved in the study. Christina Maslach, who had just finished her PhD, came down the night before. She's standing outside the guard quarters and watches the guards line up the prisoners for the 10 o'clock toilet run. The prisoners come out, and the guards put bags over their heads, chain their feet together and make them put their hands on each other's shoulders, like a chain gang. They're yelling and cursing at them. Christina starts tearing up. She said, "I can't look at this."

I ran after her and we had this argument outside Jordan Hall. She said, "It's terrible what you're doing to these boys. How can you see what I saw and not care about the suffering?" But I didn't see what she saw. And I suddenly began to feel ashamed. This is when I realized I had been transformed by the prison study to become the prison administrator. At that point I said, "You're right. We've got to end the study."

Soon after the experiment ended, Zimbardo became a sought-after speaker and expert on prison issues. He also stated that the experience helped him become a better person. He retired from Stanford in 2007 after nearly 40 years there as a psychology professor.

Zimbardo's wife, now a professor of psychology at the University of California at Berkeley, talked about the changes she witnessed in him as the study went on and how she finally persuaded him to end it.

At first Phil didn't seem different. I didn't see any change in him until I actually went down to the basement and saw the prison. I met one guard who seemed nice and sweet and charming, and then I saw him in the yard later and I thought, "Oh my God, what happened here?" I saw the prisoners being marched to go down to the men's room. I was getting sick to my stomach, physically ill. I said, "I can't watch this." But no one else was having the same problem.

Phil came after me and said, "What's the matter with you?" That's when I had this feeling like, "I don't know you. How can you not see this?" It felt like we were standing on two different cliffs across a chasm. If we had not been dating before then, if he were just another faculty member and this happened, I might have said, "I'm sorry, I'm out of here" and just left. But because this was someone I was growing to like a lot, I thought that I had to figure this out. So I kept at it. I fought back, and ended up having a huge argument with him. I don't think we've ever had an argument quite like that since then.

I feared that if the study went on, he would become someone I no longer cared for, no longer loved, no longer respected. It's an interesting question: Suppose he kept going, what would I have done? I honestly don't know.

The interview with Dave Eshelman, the abusive guard, was one of the most interesting. With little remorse, he recounted how he made a calculated decision to play a role and wanted to give the researchers something to work with.

What came over me was not an accident. It was planned. I set out with a definite plan in mind, to try to force the action, force something to happen, so that the researchers would have something to work with. After all, what could they possibly learn from guys sitting around like it was a country club? So I consciously created this persona. I was in all kinds of drama productions in high school and college. It was something I was very familiar with: to take on another personality before you step out on the stage. I was kind of running my own experiment in there, by saying, "How far can I push these things and how much abuse will these people take before they say, 'knock it off?'" But the other guards didn't stop me. They seemed to join in. They were taking my lead. Not a single guard said, "I don't think we should do this."

The fact that I ramped up the intimidation and the mental abuse without any real sense as to whether I was hurting anybody- I definitely regret that. But in the long run, no one suffered any lasting damage. When the Abu Ghraib scandal broke, my first reaction was, this is so familiar to me. I knew exactly what was going on. I could picture myself in the middle of that and watching it spin out of control. When you have little or no supervision as to what you're doing, and no one steps in and says, "Hey, you can't do this"-things just keep escalating. You think, how can we top what we did yesterday? How do we do something even more outrageous? I felt a deep sense of familiarity with that whole situation.

Another guard, John Mark, felt as though Zimbardo was trying to manipulate the experiment to go out with a bang.

I didn't think it was ever meant to go the full two weeks. I think Zimbardo wanted to create a dramatic crescendo, and then end it as quickly as possible. I felt that throughout the experiment, he knew what he wanted and then tried to shape the experiment-by how it was constructed, and how it played out-to fit the conclusion that he had already worked out. He wanted to be able to say that college students, people from middle-class backgrounds-people will turn on each other just because they're given a role and given power.

The only prisoner interviewed, Richard Yacco, helped to instigate a revolt against the guard. He told the magazine:

I don't recall exactly when the prisoners started rebelling. I do remember resisting what one guard was telling me to do and being willing to go into solitary confinement. As prisoners, we developed solidarity-we realized that we could join together and do passive resistance and cause some problems. It was that era. I had been willing to go on marches against the Vietnam war, I went on marches for civil rights, and was trying to figure out what I would do to resist even going into the service. So in a way I was testing some of my own ways of rebelling or standing up for what I thought was right.

Yacco was paroled a day before the experiment ended, because he was showing signs of depression. He's now a teacher at an Oakland public high school and wonders if the students who drop out and come unprepared are doing so because they're also filling a role that society has created for them, just like the Prison Experiment.

I highly suggest learning the ins and outs of the experiment here. You really get an appreciation for the lengths the researchers went to simulate an authentic prison environment. The site even features a slideshow that explains how the experiment officially began: Participants were picked up at their homes by real police officers and then booked! (Here's a clip.)

Plus, learn more about Zimbardo and his incredibly interesting research. And here's more than you ever wanted to know about the experiment, Zimbardo's research, media articles, imprisonment and more.

And, last but not least, check out this short BBC clip that interviews Zimbardo, Eshelman and another prisoner and has clips from the experiment 40 years ago.

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Zimbardo's Infamous Prison Experiment: Where the Key Players Are Now

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It's arguably one of the most controversial experiments. It all started in the basement of the psychology building at Stanford University on August 17, 1971 after psychologist Phil Zimbardo and colleagues took an ad out in the paper stating: " Male college students needed for psychological study of prison life.

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This video discusses the Milgram Experiment and what the precedent for human capacity to do things they wouldn't otherwise. I personally believe that humans are neutral and it is our moral's our personal compass that makes us who we are. I do agree with the results of the Milgram experiment because time and time again the group mentality of entire societies have led to vast monstrosities. It is frightening that under the right conditioning and social pressure your neighbors, your friends, your family can turn on you. It really shows how malleable the human brain is and how many of those who are leaders of atrocious movements know how to lock into that. How they can feed into prejudices and main grievances to turn people from everyday shop owners, and farmers to people who would turn their own family in for the sake of the main leader.

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Why Do We Obey Authority? - The Milgram Experiments

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In the article the author delivers clear insight when it comes to first identifying the cognitive disease of dementia. However, makes it clear and nuanced when it comes to the differences of someone being forgetful versus having this disease. Throughout this article the points made were concise and clear and explained how when it comes to those with dementia how the disease is diagnosed and distinguishable to those who are both impacted and those who care for them. It is important to know the symptoms and how to best care for those diagnosed. I found this article very interesting as someone who has never been directly impacted by this and this prompted a deeper dive into this particular memory disease.

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Fisher Center for Alzheimer's Research Foundation

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Everyone becomes forgetful from time to time: forgetting where you placed the car keys, not remembering to pick up an item at the grocery store, forgetting to return a friend's phone call. And as we age, most of us become increasingly forgetful. At least half of those over age 65 say that they are more forgetful than they were when they were younger, experiencing "senior moments" about things like where they put things or recalling somebody's name.

But when does an ordinary memory lapse indicate something more serious, like early Alzheimer's disease or another form of dementia? Can you brush it off as "just being forgetful," or might it be mild cognitive impairment, a more pronounced form of memory loss that sometimes precedes dementia?

"We now know the early warning signs of Alzheimer's disease can begin some 15 years before symptoms of mild cognitive impairment, or long before the beginning signs of a dementia surface," said Dr. Barry Reisberg, director of the Fisher Alzheimer's Disease Education and Resources Program at New York University Langone Medical Center. Because the onset of dementia can be so insidious, forgetfulness and other symptoms may develop over a period of many years.

Increasingly, research indicates that feeling you are forgetful may be cause for concern. A study conducted by Dr. Reisberg and colleagues found that seniors with subjective memory complaints are, over many years, 4.5 times more likely to develop mild cognitive impairment or dementia than those who do not have such memory complaints. That's one reason why it's important to pay attention for signs of being forgetful, and to seek medical attention about early signs of dementia and a possible dementia evaluation and work-up.

In order to distinguish the ordinary forgetfulness that comes with aging from more serious problems like Alzheimer's disease, it helps to consider some key symptoms of mild cognitive impairment and the early stages of dementia.

Forgetting a friend's name or not remembering a lunch date is something that most people without dementia do from time to time. Someone with early dementia, though, might repeatedly forget names or plans, and forget all about the incident soon afterward. Curiously, while someone with early dementia may forget something that happened the previous evening, they may recall in detail events that happened in the more distant past, last year, say, or during their childhood.

At these early stages of dementia, family members, friends and colleagues may begin to notice that something seems wrong. Maybe your spouse or partner complains that you are forgetting social engagements at an increasing rate, or that you repeat questions often. Maybe colleagues at work have expressed concern when you forget to attend a meeting or send an important memo, or are unable to learn a new computer program. Such situations may, understandably, trigger feelings of anger and defensiveness. They can also produce anxiety, which can in turn make anyone even more forgetful. The anxiety may be particularly pronounced in someone in the early stages of dementia.

In addition to being forgetful, those in the early stages of dementia may also have problems with judgment and planning. Someone with early dementia might, for example, become distracted in preparing a recipe or forget the rules of a card game. People with dementia are also much more likely to have traffic accidents than those who do not have dementia. And while many of us are challenged when it comes to finances, someone with early dementia may find it impossible to do everyday chores, like balancing a checkbook, that they used to find easy.

As dementia progresses, people get even more forgetful. Someone with dementia might, for example, get lost in the neighborhood when driving home from the grocery store or forget what day it is. Those with dementia may forget simple words or replace them with strange substitutes, making them difficult to understand. Someone with dementia might also misplace things, like placing a cell phone in the refrigerator, or get confused while getting dressed. These behaviors are not common in someone without dementia.

Unusual changes in personality can also occur, like showing bursts of anger for no reason, becoming depressed or confused, or uncharacteristically clinging to a family member. And while many of us plop down on the couch to watch TV after a long day at work, someone with dementia may show little or no initiative in reaching out to friends and stare at the TV for hours or sleep all day.

Anyone who has concerns about being forgetful or has signs or symptoms like those described, particularly if they are over age 65, should speak with their doctor immediately. "If you are experiencing memory complaints, it is more important than ever to have honest conversations with your physician so he or she can monitor your symptoms and offer treatment therapies if applicable," Dr. Reisberg said. "We often see patients who would gladly talk to their doctors if they felt they were in pain or experiencing other health issues, but conversations about memory are avoided, and this is a dangerous mistake."

While most people who are forgetful don't have dementia, a professional assessment can aid families in determining if this is forgetfulness or possible dementia. In some cases, medications or other environmental factors may be contributing to somebody becoming forgetful. Dosages can be adjusted, or new treatments prescribed, to ease the memory problems. Medical and mental health conditions, like depression or a deficiency in vitamin B12, can also make someone forgetful. These conditions are treatable and reversible.

Even if the diagnosis is Alzheimer's disease or another form of dementia, steps can be taken to improve quality of life. Counseling, for example, can help the person who has early dementia to assess situations like whether the patient should stay in his or her job. Taking steps like early retirement may ease anxiety and improve day-to-day functioning. Medications to ease symptoms can also be prescribed, and may be most effective, during the early stages of dementia and families can take the necessary legal and financial steps to plan more effectively for the future.

By ALZinfo.org, The Alzheimer's Information Site. Reviewed by William J. Netzer, Ph.D., Fisher Center for Alzheimer's Research at The Rockefeller University.

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Everyone becomes forgetful from time to time: forgetting where you placed the car keys, not remembering to pick up an item at the grocery store, forgetting to return a friend's phone call. And as we age, most of us become increasingly forgetful.

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This video describes how different outside elements impact ones memory as they age. Our mental capacity begins to decline as soon as the brain stops developing. However there are things that can help slow the decay of our brains. These factors include; diet, medication and cognitive exercises. This showed that simple tasks such as memory games, puzzles and cognitive exercises used daily can help slow the rate of decline for our brains. I found this video very interesting and I will definitely be added several of these to my lifestyle. I think the more aware the public is the more people will be less effected by cognitive disorders as they age, especially as more of the population is living longer like the video explains.

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Preventing Cognitive Decline and Dementia: A Way Forward

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In this article, the author discusses the effects that technology has on the development of children. Technology can be a blessing and a curse when it comes to kids, it can be an educational tool, and distraction, however what is the cost. Some of the major effects were the development on social skills, if children are addicted to screen time they can be less privy to the want to interact with other children, and this can lead to less social skills, and this can cause them to be even more addicted to screens. Another major development is the idea of self image. Many children that grow up on social media and technology are linked to worse body image and confidence, and this can lead to mental health issues. Overall, there are many advantages to having technology available to children, but many development problems are just starting to come to fruition.

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From smartphones and social media to TV and tablet-based toys, today's kids are constantly inundated by technology. While it's important for children and teens to develop an aptitude for technology, after all, they will use computers their whole lives, too much technology use can have detrimental health and physical effects. The negative effects on children's health run the gamut from increased risk of obesity to loss of social skills and behavioral problems. Of course, this doesn't mean parents need to ban technology entirely, but it's important for parents to be aware of the potential effects of technology on children and develop strategies to limit their children's screen time.

How Technology Affects Children

Technology is everywhere, and while we cannot and should not ban technology entirely, we can set limits for our children to offset some of the negative effects. Too much screen time can affect your child's physical health as well as their behavior. Learn more about the physical and mental effects of too much of a good thing (read: technology) and discover what you can do to help your child.

Childhood Obesity and Technology

With children spending more time typing or tapping on a screen, they're naturally spending less time outside or engaged in physical activities. Research has also found that children and adolescents may engage in more mindless eating while watching TV or playing video games. As a result, we're seeing increased obesity rates in children and adolescents.

While children will naturally be interested in watching various programs, playing video games, and using app-based technology, it's vital for parents to make sure children are spending time outside and getting enough physical activity. Encourage your kids to play outside. If time permits, join your kids in outdoor play. You can play games, like hide-and-seek or tag, or grab a mitt and a ball and play catch, or throw a frisbee around at a park. Your kids will enjoy the extra time they get to spend with you and the exercise and outdoor time will help them burn off energy and sleep better.

Negative Effects of Technology on Children's Social Development

Technology has completely changed the way we interact with each other. Even as adults, we are more prone to send a text vs. make a phone call. Similarly, we often behave differently on social media than we would in person. These differences aren't exclusive to adults. When kids spend a significant amount of time on social media, it can lead to lower self-esteem . Teens are seeing curated content, a digital highlight reel, which can lead to distress due to teens comparing themselves to their peers. In general, technology use can cause social and behavioral problems in children because it minimizes the amount of time kids spend interacting with others.

Make sure to monitor your child's social media use and be aware of the types of websites they're visiting and the games they're playing online. Set up parental controls on computers, smartphones, and tablets to block inappropriate websites and apps. Also, try to keep the computer, game console, or TV in a common room so you can supervise your child's technology use.

In addition to monitoring your child's screen time, it's also important to make sure they engage in social activities. Schedule playdates with friends and encourage your child to interact with others. Another great way to limit screen time and encourage physical activity and social interaction is to enroll your kids in a sports league. This way, they will have organized practices and games that will allow them to not only exercise but be social with other kids their age.

Technology and Attention Span

Gone are the days where we have to rewind a VHS cassette tape to watch a movie or listen to the radio all day to hear our favorite song. With the Internet and YouTube, you can watch a show or listen to a song almost instantly, and you can even skip through the commercials. As a result, kids today are wired for instant gratification; they want what they want, and they want it now.

Electronics, like smartphones and tablets, make it harder for people to concentrate and easier for people to be distracted by constant sounds and notifications. According to a research study in Canada, since technology use increased (around the year 2000) the average attention span has decreased from 12 seconds to eight seconds.

It's challenging enough to teach kids patience, but how can we combat the negative effects of this always-on culture? One way is to limit technology use while children are focused on other tasks. Prohibit smartphone use while working on homework, encourage conversation and interaction during dinner time, turn the TV off while children are reading. Encourage your kids to take time to unplug and disconnect. Get outside, interact with others, and engage in activities that don't involve screens, like board games, books, and puzzles.

Managing Screen Time: How Much is Too Much?

Do you know how much time your kids spend per day on their devices? According to the American Academy of Child & Adolescent Psychiatry (AACAP), teenagers spend up to nine hours per day watching or using screens, and children ages eight to 12, spend four to six hours a day using screens. While some electronic use is vital for schoolwork, especially with distance learning, parents should try to limit screen time, be aware of the websites their kids are visiting, and make sure kids are spending their time watching high-quality educational content.

Mayo Clinic recommends limiting screen time to one hour per day for children between the ages of two and five. While there aren't specific hour guidelines for every age group, The American Academy of Pediatrics (AAP) recommends that parents of kids between five and 18 place consistent limits on technology use.

Work With Your Child to Manage Technology Use

Without hard-fast rules, it's important to assess how much screen time you're comfortable with, and which activities you allow your child to engage in. Many of our National University students are parents, and when you're managing work, college courses, and parenting, some technology use outside of schoolwork is definitely necessary. If your kids are getting more screen time than normal because they're home from school and you're working and taking care of them, that's OK, just try to monitor what they're doing and that it's not interfering with their schoolwork, health, or behavior. Your kids will grow up surrounded by screens and technology. If you establish good habits early on, you can offset the negative effects of technology on children. Remember, not all technology is bad, learn more about how technology can help children learn and develop.

Sources:
https://journals.sagepub.com/doi/abs/10.1177/2167702617723376?journalCode=cpxa& https://www.webwise.ie/parents/parental-controls-2/
https://www.mayoclinic.org/healthy-lifestyle/childrens-health/in-depth/screen-time/art-20047952#:~:text=Developing%20screen%20time%20rules&text=If%20you%20introduce%20digital%20media,doesn't%20work%20as%20well .
https://www.apa.org/topics/healthy-technology-use-children https://childmind.org/article/social-media-and-self-doubt/

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The Negative Effects of Technology on Children | NU

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From smartphones and social media to TV and tablet-based toys, today's kids are constantly inundated by technology. While it's important for children and teens to develop an aptitude for technology, after all, they will use computers their whole lives, too much technology use can have detrimental health and physical effects.

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This video discusses the effects that alcohol has on an infant after it is born. Alcohol has an effect on everyone but the effects are the most harmful to a developing brain of teenagers and kids, however these effects are even worse when it comes to infants addicted to alcohol from birth. Alcohol can cause dopamine levels to not be as connected, neurons are not firing off as they should and can be severely damage and once they are cut off from their mother they go through withdraws symptoms. These effects can lead to mental health issues later down the line, and other disorders that greatly effect individuals down the line.

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In this article the American Psychological Association reveals the identity of Little Albert and what happened to him after the experiment. One of the most notorious psychological experiments to be performed. This article discusses the untimely death of Douglas Merritte from  acquired hydrocephalus. However, there is no evidence showing whether or not the conditioning of fear persisted for the short remainder of Douglas's life. Overall, it does illustrate the very real human side of this experiment and while the results of this conditioning on humans was inconclusive it is interesting to note the circumstances in which it was allowed to happen. During an era when psychology was not as understood, so humanity has now learned how to condition some behaviors we can see through military training but also how society has moved away for more inhumane experimentation in the pursuit of psychological discovery.

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One of psychology's greatest mysteries appears to have been solved. "Little Albert," the baby behind John Watson's famous 1920 emotional conditioning experiment at Johns Hopkins University, has been identified as Douglas Merritte, the son of a wetnurse named Arvilla Merritte who lived and worked at a campus hospital at the time of the experiment - receiving $1 for her baby's participation.

In the study, Watson and graduate student Rosalie Rayner exposed the 9-month-old tot, whom they dubbed "Albert B," to a white rat and other furry objects, which the baby enjoyed playing with. Later, as Albert played with the white rat, Watson would make a loud sound behind the baby's head. After a number of conditioning trials, Watson and Rayner reintroduced the animals and furry items without the scary noise. Through the conditioning, the animals and objects that were once a source of joy and curiosity had become a trigger of fear.

Watson had no reason to reveal Albert's true identity, and he never de-conditioned the child. (Watson was also dismissed from the university around the same time because of an affair with Rayner.) Since then, Little Albert's fate and identity have been a recurring question among psychology scholars, including Appalachian State University psychologist Hall P. Beck, PhD, who with a team of colleagues and students, sought answers. For seven years, Beck and his associates scoured historical materials, conferred with facial recognition experts, met with relatives of the boy they theorized was Albert.

Eventually, the pieces of the puzzle came together. The attributes of Douglas and his mother matched virtually everything that was known about Albert and his mother. Like Albert's mother, Douglas's mother worked at a pediatric hospital on campus called the Harriet Lane Home. Like Albert, Douglas was a white male who left the home in the early 1920s and was born at the same time of year as Albert. What's more, a comparison of a picture of Albert with Douglas' portrait revealed facial similarities.

Sadly, the team also discovered that Douglas died at age 6 of acquired hydrocephalus, and was unable to determine if Douglas' fear of furry objects persisted after he left Hopkins.

The team, which also included Sharman Levinson, PhD, of The American University in Paris, and Gary Irons, the grandson of Arvilla Merritte, published their findings in the October American Psychologist (Vol. 64, No. 7). The article not only satisfies a long-held curiosity, but also reflects a growing interest in the fate of research participants, says Cathy Faye, of the Archives of the History of American Psychology at the University of Akron. Participants in such famous, controversial studies "have become unwitting protagonists whose stories are told over and over again in psychology textbooks," she says. "So people become very curious: Who were they, and how did they feel about the experiment?"

Beck is pleased his students have answered some of those questions, but the real bonus, he believes, is what they gained in the research process.

"The search took them beyond the memorization of their lectures and textbooks, and for the first time, into the creative world of psychological research," he says. "In the end, that was even more important to them than finding Albert."
-T. DeAngelis

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baby behind john watson's famous 1920 emotional conditioning experiment

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Mystery solved: We now know what happened to Little Albert

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One of psychology's greatest mysteries appears to have been solved. "Little Albert," the baby behind John Watson's famous 1920 emotional conditioning experiment at Johns Hopkins University, has been identified as Douglas Merritte, the son of a wetnurse named Arvilla Merritte who lived and worked at a campus hospital at the time of the experiment - receiving $1 for her baby's participation.

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This  video explains the principle of Skinner's Operant Conditioning. While watching the video I was thinking about training my own dog, and how she has been conditioned to sit and lay down. However, me and my mom inadvertently conditioned our dog. Whenever me and my mom go out of town we take our dog to a friend of ours who watches dogs, and my dog Maple loves it there. We take her stuff in this purple bag, so now every time Maple sees that bag she gets very excited, showing the conditioning she has undergone to have that reaction.

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Skinner’s Operant Conditioning: Rewards & Punishments

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This article discuses a new molecular finding about how some human characteristics could be biological rather than learnt. This shows how much we are still learning about the brain and what is actually nature versus nurture. This can also lead to treatments for people who have more aggressive behaviors and help us better understand development, especially that of personality and how that can help psychologist and doctors study the human mind better. This is also very interesting because it shows that we can inherit traits from our parents, and makes you wonder how much of individuality is actually there.

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This article discusses the benefits of Facial Feedback Hypothesis. Along with discussing what it is, and how it effects the stimuli in ones face, this article also discusses the development of this treatment and how it has been implemented by psychologists in order to understand the facial feedback from emotions. This was never something that I have ever thought about but the fact that our emotions can be communicated through our faces is very interesting, and its really interesting how we are still trying to understand why that is the case. Overall, this is a topic I will be looking more thoroughly into but this article is a very good in-depth introduction point to better understand this fascinating phenomenon.

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Journal of Nonverbal Behavior

Abstract

The facial feedback hypothesis states that facial actions modulate subjective experiences of emotion. Using the voluntary facial action technique, in which the participants react with instruction induced smiles and frowns when exposed to positive and negative emotional pictures and then rate the pleasantness of these stimuli, four questions were addressed in the present study. The results in Experiment 1 demonstrated a feedback effect because participants experienced the stimuli as more pleasant during smiling as compared to when frowning. However, this effect was present only during the critical actions of smiling and frowning, with no remaining effects after 5 min or after 1 day. In Experiment 2, feedback effects were found only when the facial action (smile/frown) was incongruent with the presented emotion (positive/negative), demonstrating attenuating but not enhancing modulation. Finally, no difference in the intensity of produced feedback effect was found between smiling and frowning, and no difference in feedback effect was found between positive and negative emotions. In conclusion, facial feedback appears to occur mainly during actual facial actions, and primarily attenuate ongoing emotional states.

Keywords: Facial feedback, Facial expressions, Emotion, Emotional stimuli

Introduction

The facial feedback hypothesis is based on the idea that a facial expression not only expresses an emotion, but also that expression and experience are linked in that afferent sensory feedback from the facial action influences the emotional experience. Elevating your cheeks can make you happier, just as furrowing your brow can make you angrier. The idea was introduced by Darwin ( 1872) when he noted that the experience of an emotion seemed to be intensified when the emotion was freely expressed, but softened when repressed. James ( 1884) also presented ideas along the same lines, but it took almost a century before the specific facial feedback hypothesis was formulated by Tomkins ( 1962), Gellhorn ( 1964), Izard ( 1971), Ekman ( 1973) and Buck ( 1980). Recently, Niedenthal ( 2007) and (Niedenthal et al. 2009, 2010) provided the theoretical framework of embodied emotion to explain, among other things, afferent feedback from facial actions. Empirical research has found that facial feedback has the ability to both modulate present emotions and to initiate emotions (for reviews see Adelmann and Zajonc 1989; Buck 1980; McIntosh 1996). This has been firmly established for happiness and anger and the corresponding facial expressions (e.g., Dimberg and Söderkvist 2011; Laird 1974; Rutledge and Hupka 1985). Also, feedback mechanisms seem involved in the expression of fear and sadness (e.g., Flack et al. 1999), as well as for surprise and disgust (e.g., Lewis 2012).

Recent facial feedback research has sought to improve knowledge of basic mechanisms of facial feedback, and attempted to demonstrate clinical benefits from manipulating the facial muscles. One insight to the basic mechanisms has been provided by Hennenlotter et al. ( 2009), who demonstrated that facial feedback modulates neural activity in the amygdala. Using fMRI they found that amygdala activation was attenuated during imitation of angry facial expressions in participants with Botox-induced paralysis of their corrugator muscles, the muscles used when frowning. Kim et al. ( 2014) further demonstrated that facial feedback modulates activity in the amygdala in an fMRI study where participants were passively exposed to happy and angry facial expressions. Amygdala activity was attenuated when corrugator muscles were paralyzed by Botox, as compared to tests before injections and also to tests after the effects had worn off.

Furthermore, Mori and Mori ( 2010, 2013) have demonstrated significant effects from a passive form of facial feedback, where facial expressions were formed, without any muscle action, on the face of participants with the help of elastic adhesive bandages. Interestingly, since no muscles were activated, any feedback must have originated from sensations in the skin or from proprioceptive signals in the cutaneous muscles. Moreover, Dimberg and Söderkvist ( 2011) demonstrated that facial feedback was more effective at modulating emotions, as compared to initiating emotions, and that feedback was equally effective at modulating both positive and negative emotions. That study also found that effects of facial feedback actions could be detected several minutes later, a result that calls for further research.

Clinical benefits from manipulating facial muscles have been demonstrated in several studies, where Botox injections were used to paralyze the corrugator muscles of clinically depressed patients. The results showed significant mood improvements in patients, and these effects were attributed to reduced facial feedback from the corrugator muscles (Finzi and Rosenthal 2014; Magid et al. 2014; Wollmer et al. 2012, 2014).

Yet, it remains unclear how facial feedback actually works. The most commonly held theory states that when certain facial muscles are activated, afferent feedback from muscular proprioceptive patterns activates corresponding affect programs (Adelmann and Zajonc 1989; Tomkins 1962). However, sensory feedback from the facial skin has also been proposed as a possible contributor to facial feedback (Tomkins 1980). Furthermore, the vascular theory of emotional efference provides another explanation suggesting that facial actions can regulate brain blood temperature by altering nasal air flow, which in turn could produce changes in emotional state (Zajonc et al. 1989). However, it is inherently difficult to identify the neurological basis of the above theories and it also follows that it is difficult to specifically test the theories. Alternatively, one way forward could be to further explore different effects of facial feedback and in this way improve the description of how facial feedback effects occur and thereby extend the boundary conditions of the hypothesis.

Therefore, in two experiments the present study addressed a series of questions that together could provide a more detailed description of how facial feedback effects occur. The two experiments used the voluntary facial action technique, which was developed in Dimberg and Söderkvist ( 2011), based on the method used in Dimberg et al. ( 2002). In this paradigm participants are told a cover story that the purpose of the experiment is to measure the reaction times of different facial muscles. The cover story has proved effective in hiding the true purpose of the manipulation, which in fact is to get the participants to perform particular facial actions associated with specific emotions. In facial feedback experiments it is common to use a cover story in order to lead the participants' attention away from facial expressions and emotions to ensure that they are unconsciously affected by the feedback, which also serves to avoid demand characteristics. In the present paradigm participants are then required to react with their zygomatic muscles, which are the muscles used when smiling, and/or corrugator muscles, which are used when frowning, to different kinds of pictures, which encourage facial actions that resemble naturally occurring facial expressions. When testing the modulating ability of facial feedback, emotion evoking stimuli, such as pictures of happy and angry faces, have been successfully used (Adelmann and Zajonc 1989; Dimberg and Söderkvist 2011; McIntosh 1996). While reacting to the pictures, the participants also rate how pleasant/unpleasant they find them, and after completion each participant has thus smiled one time and frowned one time at each picture. If they rate the pictures as more pleasant and less unpleasant when they smile, as compared to when they frown, a general facial feedback effect has been demonstrated.

In Experiment 1, a question first raised by Dimberg and Söderkvist ( 2011) was further investigated, and focused on whether facial feedback actions only affect us when they are performed, or if the effect also remains to affect us later.

In Experiment 2, three questions were addressed. First, does facial feedback primarily enhance or attenuate present emotions? We know that facial feedback can both enhance and attenuate emotions, but it has not previously been specifically investigated if feedback is more effective in modulation where the facial action is congruent with the present state to enhance it, or if it is more effective in incongruent modulation to attenuate the emotion. The second question was if smiling or frowning generally produces a stronger feedback effect than the other, or if they produce equally strong feedback effects. It is well established that these two major facial actions both produce facial feedback, but it remains unclear if they are equally effective. Third, another question that was also first addressed in Dimberg and Söderkvist ( 2011) was if positive or negative emotions are easier to modulate with facial feedback or if both are modulated equally effective by the feedback.

Experiment 1

The aim of the first experiment was to further investigate if the effect of facial feedback actions could remain and also affect us later. In Dimberg and Söderkvist ( 2011), a significant effect of facial feedback was found about 4 min after the facial action. The design in that experiment, based on the voluntary facial action technique, let participants in a first phase react with smiles/frowns to pictures with positive or negative valence, without rating the pictures. In a second phase they viewed the pictures once again, and this time they did not voluntary move their facial muscles but just rated how pleasant/unpleasant they found the pictures. A facial feedback effect was demonstrated, because the rating of a picture in the second phase was affected by which facial action the participant had performed to that picture in the first phase. That is, participants found pictures to be more pleasant if they had smiled to them 4 min earlier, than if they had frowned.

However, the experiment by Dimberg and Söderkvist ( 2011) had some limitations. Specifically, no effects were measured during the facial actions in the experiment, and no effects later than 4 min were evaluated. It is therefore unclear if the intensity of the effect had changed during the 4 min, and it is also unclear for how long the effect remained. In order to investigate this question more extensively, the present Experiment 1 was designed to measure the facial feedback effects both during the facial actions, 5 min after the actions and approximately 1 day after the actions. The procedure and cover story in the present experiment was essentially the same as the above presented voluntary facial action technique, but some changes were introduced in order to facilitate the measurement of effects at the three different points in time.

If feedback effects would be detected after the initial action, they could be explained in terms of embodied emotion (e.g., Niedenthal 2007) and/or evaluative conditioning (e.g., Hofmann et al. 2010). Considering the empirical literature on facial feedback, a feedback effect during the facial actions was expected. Based on the results in Dimberg and Söderkvist ( 2011), a feedback effect after 5 min could be expected as well. However, we had no specific expectations for the outcome after 1 day since this has not been investigated before.

Method

Participants

Thirty-two persons (mean age = 25.40, SD = 5.20), balanced by gender, participated in the experiment and most of them were students at Uppsala University. No one that studied psychology or previously had participated in similar studies was included. Five persons were excluded from the study due to not following the instructions correctly, and they were replaced by other participants. As compensation, participants were given three movie vouchers at a total value of 300 SEK (approximately 30 USD).

Experimental Design

Because the aim of the experiment was to examine facial feedback effects over time, the two main independent variables were muscle (smile vs. frown) and time (during action vs. after 5 min vs. after 1 day). A secondary independent variable was stimulus (happy vs. angry). Thus, the experiment had a within subject 2 × 3 × 2 factorial design. The dependent variable was the ratings of how pleasant/unpleasant participants experienced stimuli.

Apparatus

Much of the apparatus served only a deceptive purpose in order to strengthen the cover story. The psychophysiological laboratory, for example, played an important role for the credibility of the cover story. The participants were individually tested in a small room inside the laboratory, where they were seated in a comfortable chair with a 17 inch computer monitor placed 70 cm in front of them. A PC placed outside the room was used to display the pictures. In accordance with the cover story, electrodes to ostensibly measure EMG were attached over the corrugator supercilii (frowning) and the zygomatic major (smiling) muscle regions.

The stimulus material consisted of six pictures of happy faces and six pictures of angry faces, selected from Ekman and Friesen's Pictures of facial affect ( 1976). Even if some studies (e.g., Wangelin et al. 2012) have demonstrated that pictures of facial expressions do not typically induce strong emotional engagement, other studies performed with similar exposure techniques and experimental situations (e.g., Dimberg 1982, 1990; Dimberg et al. 1998, 2000, 2002) have demonstrated that pictures of facial stimuli can reliably induce positive and negative emotions of equal intensity. One further reason for using these stimuli was to maintain consistency with Dimberg and Söderkvist ( 2011), where the same material were used. The pictures in the present study were presented in the form of PowerPoint slideshows, where each picture was shown for 8 s and then followed by a black screen for 20, 25 or 30 s before the next picture. Participants rated picture experience using the two categories pleasantness and unpleasantness on a scale from 0 to 100 ( not at all to very much). The reason for using two opposing scales was that we could not rule out the possibility that they would be differently sensitive to the different muscle and stimulus conditions in the experiment. For each picture, a sheet containing both scales was used.

As part of a distraction task, performed between experimental phases in order to prevent carry-over effects, participants pressed a button on a handheld device to seemingly register their reaction time in response to displayed pictures of circles and triangles, three of each.

Cover Story

The cover story was essentially the same as in Dimberg and Söderkvist ( 2011), with a couple of small adjustments for the new procedure. Information on signup posters and in scheduling emails had briefed the participants that the experiment would measure reaction times in different muscles and to different kinds of stimuli. At arrival, they were given a more detailed introduction to the experiment, including a presentation of the psychophysiological apparatus. The experiment was presented as the latest in a run of experiments that aimed to clarify how fast human beings could react. The claimed purposes of this specific experiment were to (first) compare the reaction times in facial muscles with the more traditional reaction time measurement of pressing a button with your thumb, and (second) to see how much the reaction time varied over different days. The stimulus material was presented as pictures of human faces and of geometrical figures and the reason for using them was to compare with previous experiments that had used other kinds of stimuli, like audio signals. Participants were told that the reason for rating the pleasantness and unpleasantness of the stimuli was to control for the possibility that the reaction time is affected by how we like the picture. The purpose of this explanation was to mislead participants that might suspect a hidden agenda into believing that the agenda was in fact to see if different kinds of pictures affected the reaction time.

Procedure

Participants attended the laboratory on three separate days within the same workweek. They performed two nearly identical cycles of testing (congruent/incongruent reactions) and in each cycle they rated the stimuli at three different points in time (during action, after 5 min, after 1 day). Half of the participants began with the congruent reactions, and the other half began with the incongruent reactions. In the congruent cycle they were instructed to react by elevating their cheeks as fast as possible if the displayed picture was of a happy person, and to contract their eyebrows as fast as possible if the picture was of an angry person. In the incongruent cycle they received the reversed instructions and thus elevated their cheeks to angry persons and contracted their eyebrows to happy persons. Consequently, and as can be seen in Fig. 1 , the procedure consisted of six different phases: 1-congruent (or incongruent) reactions, ratings during action, 2-no reactions, ratings after 5 min, 3-no reactions, ratings after 1 day, and then a new cycle started with 4-incongruent (or congruent) reactions, ratings during action, 5-no reactions, ratings after 5 min, and 6-no reactions, ratings after 1 day. Additionally, phases performed during the same day had distraction tasks between them to prevent carry-over effects.

Day One

After the initial presentation of the experiment, the first phase began (congruent or incongruent reactions, ratings during action). The participants were asked to sit down in the smaller room where the electrodes were attached on their face. Half of them were randomized to receive the congruent instructions for how to react, while the other half received incongruent instructions. All were told that in order for the apparatus to register their reaction time properly they needed to make a distinct muscle contraction and keep it contracted for a couple of seconds. Finally, they were introduced to the rating scales and instructed to rate for each picture, during the display, how pleasant and unpleasant they experienced it. They were then left alone and exposed to all the six happy and six angry pictures.

After this first phase, a 4 min distraction task followed where they first received instructions for how and when to use the handheld device. Half of the participants were instructed to press the button if the displayed picture was of a circle, and to do nothing if it was of a triangle. The other half received the opposite instructions, to press the button if the picture was of a triangle and to do nothing if it was of a circle. During this task they did not rate the displayed pictures. All subsequent distraction tasks had similar structure.

After the distraction task, the second phase (no reactions, ratings after 5 min) followed. The participants received instructions that they would see the pictures of human faces once again, but this time their reaction time would not be measured. Instead they were instructed to keep their facial muscles still and relaxed. New rating sheets were handed to them together with a repetition of the rating instructions. The stated reason for this extra rating session was to ensure stable and valid measurements. Participants were then exposed to the pictures in a different order as compared to the first phase.

Day Two

When the participants returned for day two, the third phase began (no reactions, ratings after 1 day). They were again asked to sit down in the smaller room and the electrodes were attached. They were then informed that the day would begin with a rating session similar to the one that ended day one, where they did not react with their facial muscles. Rating sheets were handed out and they were exposed to the pictures in a different order compared to the first and second phase.

After a distraction task, the fourth phase began (incongruent or congruent reactions, ratings during action) and it was identical to the first phase, but with the critical muscle instructions reversed. After another distraction task, the fifth phase followed (no reactions, ratings after 5 min), which was identical to the second phase.

Day Three

Just as in previous days, all participants were asked to sit down in the smaller room while electrodes were attached. This sixth and final phase (no reactions, ratings after 1 day) was identical to the third phase, and picture ratings were performed with muscles relaxed.

After the rating session, participants were informed that they had completed the experiment. They were then interviewed in order to determine if they had followed the instructions correctly, and if they at any point had realized the true purpose of the experiment, which none of them had. Finally, they were informed about the true purpose and were asked to keep it secret in conversation with others.

Transformations and Analysis

As a result of the experimental procedure, each participant had rated how pleasant and unpleasant they found the six happy and six angry pictures during smiling respectively frowning within each of the three different conditions of time (during action, after 5 min, after 1 day). Similar to the procedure in earlier studies (Dimberg and Söderkvist 2011) we compared the pleasantness and unpleasantness ratings in a preliminary analysis to determine if there were any major differences between them. When we correlated the two ratings within each participant the median correlation coefficient for all participants was −0.85 ( SIQR = 0.11), indicating that the two measures seemed to reflect the same emotional quality. Furthermore, the results provided by the pleasantness and the unpleasantness ratings pointed in the same direction and the interpretations of them were quite similar. Consequently, to provide a more distinct presentation of the results we therefore merged the two ratings into a single bipolar rating score by subtracting the unpleasantness ratings from the pleasantness ratings. The resulting bipolar pleasantness rating scale ranged from −100 to 100, with a positive value thus indicating pleasantness, and a negative unpleasantness.

Because the purpose of the study was to measure and compare the magnitude of facial feedback effect at different points in time a new dependent variable specifically representing this was created. The facial feedback effect ( FFE) score was calculated for each rated picture by subtracting the frown rating from the corresponding smile rating within each condition of time. A positive value in the resulting score thus indicated a facial feedback effect, since the picture had been rated as more pleasant during or after the smile, as compared to during or after the frown. Also, for each participant, the ratings of the six happy and the six angry pictures within each condition of time were collapsed into one mean for the happy and one mean for the angry pictures. Consequently, each participant had six mean FFE scores, with one score during positive emotions (to happy pictures) and one score during negative emotions (to angry pictures) for each of the three conditions in time. To analyze the data a 3 × 2 within subject ANOVA was performed, with the factors time (during action vs. after 5 min vs. after 1 day) and emotion (positive vs. negative). The significance level was set to 0.05 for all tests.

Results

A first analysis revealed that there was no main effect of emotion and no interaction between emotion and the time factor, both F:s < 1. Therefore, to simplify the analysis and the presentation of results, the data for positive and negative emotions were collapsed so that in the final analysis only the mean FFE scores as a function of time are reported.

There was a significant main effect of time, F(2,62) = 4.57, MS e = 147.12, p < 0.05, η p 2 = 0.13, and as can be seen in Fig. 2 this was due to a much higher mean FFE score during action, as compared to the scores after 5 min and after 1 day. Subsequent t-tests further confirmed that the mean FFE score during action [ M = 7.42, SD = 19.28, 95% CI (0.47, 14.37)] was significantly higher than the score after 5 min [ M = −1.01, SD = 12.84, 95% CI (−5.64, 3.62)], t(31) = 2.78, p < 0.05, d = 0.69, and after 1 day [ M = 0.08, SD = 9.40, 95% CI (−3.31, 3.47)], t(31) = 2.42, p < 0.05, d = 0.61. Additional one-sample t-tests also demonstrated that only the mean FFE score during action differed significantly from zero, t(31) = 3.46, p < 0.05, d = 0.61, while the mean FFE scores after 5 min and after 1 day did not differ from zero, both t:s < 1. That is, facial feedback effects were demonstrated only during action, but no remaining effects during the two later phases.

Discussion

The aim of the experiment was to investigate if the effect of facial feedback actions remained to affect us at a later time. As expected, and consistent with earlier findings (Adelmann and Zajonc 1989; Dimberg and Söderkvist 2011; McIntosh 1996), there was a significant facial feedback effect, with a medium to large effect size, during the facial action. However, there were no effects after 5 min or 1 day. Thus, the present experiment did not replicate the result of the previous experiment by Dimberg and Söderkvist ( 2011), where an effect of facial feedback was detected about 4 min after the facial action. The two experiments had slightly different designs though, where the present experiment measured the effect at three different points in time and also included specific distraction tasks. It is possible that these differences could account for the diverging results, and the most likely explanation would then be that any remaining effects were abolished by the distraction tasks. If that is the case, it would suggest an initially strong and influential effect of facial feedback, but an effect that is also short-lived and easily dissipated once the facial action is over. The present results interpreted in terms of embodied emotion (e.g., Niedenthal 2007) would suggest that the reactivation of the facial action performed at the first exposure was not strong enough to affect the experience at the later exposures. Similarly, the results interpreted in terms of evaluative conditioning (e.g., Hofmann et al. 2010) would suggest that the affective signature tied to the stimulus by the facial action was not strong enough to affect the evaluation of the stimulus at later exposures.

As compared to the original voluntary facial action technique, the procedure in the present experiment spanned over several days, which presented a challenge for the cover story. Whereas participants in previous experiments only had to believe the cover story for an hour, participants in the present experiment had to believe it for half a week, and this of course increased the risk that they would realize the true purpose. However, not a single participant realized the true purpose, which further confirms that the cover story used in the voluntary facial action technique is robust and can be used in different experimental approaches and designs.

In conclusion, the results in the present Experiment 1 suggest that the effect of facial feedback primarily occurs at the same time as the facial action is performed.

Experiment 2

In Experiment 1, there was a significant effect of facial feedback during the facial actions. However, similar to many previous studies (Adelmann and Zajonc 1989; Dimberg and Söderkvist 2011; McIntosh 1996), this feedback effect was calculated as the difference in ratings between smiling actions and frowning actions. That is, without any ratings during a neutral action, to which the smiling and frowning ratings could be separately compared, it is not possible to conclude to what degree this feedback effect was the result of enhancing or attenuating modulation, and if smiling or frowning produced stronger effect than the other. Therefore, Experiment 2 introduced a neutral action (see below) with the aim to address these questions and provide a more detailed description of how facial feedback effects occur. The experiment addressed three different questions and they will first of all be presented in more detail.

The first question was if facial feedback primarily enhances or attenuates a present emotion. For example, with a positive emotion present, such as during exposure to pictures of happy faces, smiling could further enhance positivity, while frowning could attenuate it. And in contrast, with a negative emotion present, such as during exposure to pictures of angry faces, smiling could attenuate negativity, while frowning could further enhance it. That is, enhancing modulation consists of both smiling during positive emotions and frowning during negative emotions, while attenuating modulation consists both of frowning during positive emotions and smiling during negative emotions. In order to determine if facial feedback primarily enhances or attenuates emotions the magnitude of facial feedback effect in the enhancing respectively the attenuating modulation need to be measured and compared, which means that we need to determine the specific feedback effects of smiling and frowning. As mentioned above, with the help of a neutral muscle condition these effects can be calculated by separately comparing the ratings when smiling and the ratings when frowning to the ratings in a neutral condition. For example, there would be a specific feedback effect of smiling if the ratings when smiling were higher than the ratings in a neutral condition, since feedback from a smile is expected to increase the ratings of pleasantness. On the other hand, there would be a specific feedback effect of frowning if the ratings when frowning were lower than the ratings in a neutral condition, since feedback from a frown is expected to decrease the ratings of pleasantness. Consequently, it would also be possible to conclude if the feedback effect of smiling is stronger with a positive emotion present (enhancing) or with a negative emotion present (attenuating), and if the feedback effect of frowning is stronger with a negative emotion present (enhancing) or with a positive emotion present (attenuating).

In the literature on facial feedback, both enhancing and attenuating modulation have been demonstrated, but there are no studies, as far as we know, that have compared the two in a manner that could investigate if feedback is more effective to enhance or attenuate emotions (Adelmann and Zajonc 1989; McIntosh 1996). Even though many of the first studies on facial feedback included neutral muscle conditions (e.g., Laird 1974; Rutledge and Hupka 1985), their focus was primarily on testing the main hypothesis of facial feedback, and studies were typically not designed or analyzed in a way that could answer the present questions at hand.

In a classical study by Strack et al. ( 1988), a neutral muscle condition was included in a design where participants were instructed to rate the funniness of cartoons with the help of a pen either held between the teeth, between the lips or in the non-dominant hand. Holding the pen between the teeth facilitated smiling since the participants needed to activate the zygomaticus major muscle in order to keep the lips from the pen. Holding the pen between the lips, on the other hand, inhibited smiling and also activated the orbicularis oris muscle, involved in the expression of anger (Ekman and Friesen 1975; Hjortsjö 1970). Holding the pen in the non-dominant hand was in this case the neutral muscle condition. The results demonstrated significant differences, where the funniness ratings were highest for participants who had been facilitated to smile, lowest for participants who had been inhibited to smile, and with the neutral condition perfectly placed in the middle. However, a recent attempt to directly replicate this experiment found no effects on the funniness ratings at all between the teeth and the lip positions of the pen (Wagenmakers et al. 2016). The absence of effects was unexpected since numerous studies over the years have used variations of the original method to successfully manipulate emotions, even if no direct replication had been done previously (Strack 2016).

Even though the present-day effectiveness of the original method must be questioned in light of the failed replication, the original study by Strack et al. ( 1988) still remains of interest for the present study since it demonstrated both enhancing and attenuating feedback effects. That is, with a positive emotion present, a smile enhanced the rated funniness while a mouth that expressed anger attenuated the rated funniness. In other words, the original results demonstrated that facial feedback was equally effective at enhancing and attenuating a positive emotion in this setting. However, it is necessary to note that holding a pen between the lips, and thereby activating the orbicularis oris, does not constitute a complete and valid expression of anger (Ekman and Friesen 1975; Hjortsjö 1970). An essential part of expressing anger is to frown and this action was not included in the study by Strack et al. ( 1988), and by extension not in the replication effort (Wagenmakers et al. 2016). Therefore, it is unclear if it is effective enough to compare these two muscle groups to each other, since it is possible that the rated funniness had been lower if the participants had also been instructed to frown to the cartoons. In summary, the original study by Strack et al. ( 1988) demonstrated that facial feedback had the ability to both enhance and attenuate present positive emotions, even though their relative effectiveness remained unknown. Note however that this result could not be replicated by Wagenmakers et al. ( 2016).

Soussignan ( 2002) adopted almost the same method as Strack et al. ( 1988), but with two separate intensity levels of smiling, one low and one high. The study had both positive and negative emotional stimuli, and the neutral muscle condition was to hold the pen in the mouth in a way that relaxed most muscles. The results showed that the high intensive smile group had significantly higher ratings than the other groups, but only to positive emotional stimuli. There were no feedback effects at all to negative stimuli. Furthermore, there was no difference in ratings between the group that had the pen between the lips and the neutral control group. That is, with a positive emotion present, a smile enhanced the ratings, but a mouth that expressed anger did not attenuate them, a result suggesting that facial feedback primarily enhances present positive emotions. However, since the study by Soussignan ( 2002) used a similar method to Strack et al. ( 1988), the same limitations apply to the angry mouth condition, which means that an attenuating effect remained a possibility even though no effects were found.

Thus, the present study addressed the question if facial feedback primarily enhances or attenuates emotions by introducing two neutral muscle control conditions to the voluntary facial action technique. In line with the cover story of measuring reaction times, the neutral conditions were to press a button on a handheld device and/or to do nothing. Consequently, the participants would ostensibly test their reaction times in two different phases: one facial phase where they would react with either a smile or a frown to pictures of happy and angry faces, and one neutral phase where they would react by either pressing a button or doing nothing to the same set of pictures. By comparing the effects of the smile and the frown conditions with the neutral conditions, it would then be possible to evaluate (1) if facial feedback primarily enhances emotions, a situation where smiles enhance positive emotions and frowns enhance negative emotions, or (2) if feedback primarily attenuates emotions, a situation where smiles attenuate negative emotions and frowns attenuate positive emotions, or (3) if feedback both enhances and attenuates emotions.

The second question, if smiling or frowning produces a stronger feedback effect than the other, is closely related to the first question. The happy smile and the angry frown are powerful emotional expressions and it is well established that both induce facial feedback. However, it remains unknown if smiling and frowning are equally effective when it comes to modulating our present emotions. In Experiment 1, as well as in Dimberg and Söderkvist ( 2011), a general feedback effect was calculated by the difference in ratings between smiling and frowning conditions. With such procedure however it is not possible to tell if the feedback comes from smiling, from frowning, or from both. Thus, as in the above first question, a neutral muscle condition is necessary in order to discern the specific feedback effects of smiling and frowning. Consequently, the present study addressed this question with the help of the two neutral muscle conditions described above.

The third question, if positive or negative emotions are easier to modulate with feedback, was addressed by Dimberg and Söderkvist ( 2011) who found no difference between positive and negative emotions in regards to feedback effects. However, in the study by Soussignan ( 2002) mentioned above, significant feedback effects were only found during positive stimuli, with no effects during negative stimuli. Moreover, and in contrast, a study by Davis et al. ( 2009) found feedback effects during negative stimuli, but not during positive, when they compared inhibited and non-inhibited facial reactions to emotional stimuli. The aim of the present study was to further investigate this question. Note that this third question is intimately related to the first and second questions in the sense that some specific outcomes of those two questions could provide the answer to this question. For example, if facial feedback primarily enhances present emotions, at the same time as feedback effects from smiling are stronger, then it follows that facial feedback modulates positive emotions more effectively. Considering that previous studies have demonstrated inconsistent results, the present study had no specific expectations regarding the outcome.

Method

Participants

Sixty-four persons (mean age = 23.75, SD = 3.98), balanced by gender, participated in the experiment and most of them were students at Uppsala University. No one that studied psychology or previously had participated in similar studies was included. Five persons were excluded from the study due to not following the instructions correctly, and five persons were excluded due to realizing the true purpose of the study, and they were all replaced by other participants. As compensation, participants were given a movie voucher at a value of 100 SEK (approximately 10 USD).

Experimental Design

The two independent variables of the experiment were muscle (smile vs. frown vs. button vs. nothing) and stimulus (happy vs. angry), and it thus had a within subject 4 × 2 factorial design. The dependent variable was the ratings of how pleasant/unpleasant the participants experienced the stimuli.

Apparatus

Similar to the procedure in Experiment 1, the participants were individually tested in a small room inside the psychophysiological laboratory, where they were seated in front of a computer monitor. In the facial phase, where they reacted with either a smile or a frown, electrodes to ostensibly measure EMG were attached over the zygomatic major (smiling) and the corrugator supercilii (frowning) muscle regions. In the neutral phase, where they reacted by either pressing a button or doing nothing, a handheld device was used to seemingly register their reaction times.

The stimulus material consisted of the same six pictures of happy faces and six pictures of angry faces (Ekman and Friesen 1976) that were successfully used in Experiment 1, and another reason for using these stimuli was once again to maintain consistency with the experiments in Dimberg and Söderkvist ( 2011). The pictures were presented in the form of PowerPoint slideshows, where each picture was displayed for 8 s and then followed by a black screen for 20, 25 or 30 s. Participants rated on two different scales of 0-100 ( not at all to very much) how pleasant and unpleasant they experienced the pictures. For each picture, a sheet containing both scales was used.

Procedure

As with the cover story in Experiment 1, participants were told that the aim of the experiment was to measure the reaction times in different muscles. Half of the participants were randomized to begin with the facial phase, where they had electrodes attached on their facial muscles and were instructed to react with either their smile or their frown muscles. After a short break, they continued with the neutral phase where they were introduced to the handheld device and were instructed to react either by pressing a button on the device or by doing nothing. The other half of participants began with the neutral phase, and then continued with the facial phase after a short break. Two different neutral conditions were needed in order for the neutral phase to be as similar as possible to the facial phase, and to seem a plausible test of reaction times.

Within each phase, the participants performed two different rating tasks. In each of these tasks they were exposed to all the six happy and six angry pictures, in different orders, and were instructed to rate how pleasant and unpleasant they experienced them. In the facial phase, one task was to react by elevating the cheeks as fast as possible if the displayed picture was of a happy person, and to contract the eyebrows as fast as possible if the picture was of an angry person. The other task was accompanied by the reversed instruction, that is, to elevate the cheeks if the picture was of an angry person and to contract the eyebrows if the picture was of a happy person. The order of tasks was balanced amongst the participants. In the neutral phase, one task was to react by pressing a button on the handheld device as fast as possible if the displayed picture was of a happy person, and to do nothing if the picture was of an angry person. The other task was with the reversed instructions, that is, to press a button on the handheld device if the picture was of an angry person and to do nothing if the picture was of a happy person. After the experiment, participants were interviewed in order to determine if they had followed the instructions correctly and if they at any time had realized the true purpose of the experiment.

Transformations and Analysis

As a result of the procedure, each participant had rated how pleasant and unpleasant they found the six happy and six angry pictures during the four different muscle conditions: smiling, frowning, pressing a button, and doing nothing. Before further analysis, similar to the procedure in Experiment 1 and earlier studies (Dimberg and Söderkvist 2011), we compared the pleasantness and unpleasantness ratings to see if there were any major differences between them. When we correlated the two ratings within each participant the median correlation coefficient for all participants was −0.80 ( SIQR = 0.13), and the results provided by the pleasantness and the unpleasantness ratings pointed in the same direction. As in Experiment 1, we therefore merged the two ratings to a single rating score by subtracting the unpleasantness ratings from the pleasantness ratings, resulting in the same bipolar scale measuring from −100 to 100 used in Experiment 1. Moreover, the ratings of the two neutral muscle conditions were compared and it was found that there was no significant difference between them, t(63) = 0.01, ns, and also that they had a strong positive correlation, r = 0.90, p < 0.05. Thus, in order to simplify the analysis, the two ratings were merged into a single neutral rating.

Because the purpose of the study was to measure and compare the magnitude of the facial feedback effects of smiling and frowning in enhancing and attenuating modulation respectively a new dependent variable specifically representing each muscle action was created. The specific facial feedback effect (SFFE) scores were calculated by comparing the ratings when smiling and the ratings when frowning to the ratings in the neutral condition. That is, the SFFE score for smiling was calculated by subtracting the ratings in the neutral condition from the ratings when smiling. This procedure created a score that had a positive value if the ratings when smiling were higher than the ratings in the neutral condition. The SFFE score for frowning, on the other hand, was calculated by subtracting the ratings when frowning from the ratings in the neutral condition, which created a score that had a positive value if the ratings when frowning were lower than the ratings in the neutral condition. To summarize, these calculations transformed the ratings into a score that expresses absolute values of feedback effect. That is, with this score a positive sign indicates a feedback effect for both smiling and frowning independent of if the facial action originally increased or decreased the rated pleasantness. As a first step in creating these new dependent variables, the SFFE scores for smiling and frowning were calculated for each rated picture. Secondly, for each participant the scores of the six happy and the six angry pictures were collapsed into one mean for the happy and one mean for the angry pictures, for smiling and frowning respectively. This resulted in four mean SFFE scores for each participant, that is, one score for smiling and one score for frowning during positive emotions (to happy faces), and one for smiling and one for frowning during negative emotions (to angry faces).

The three questions in the present study were first, if facial feedback primarily enhances or attenuates emotions, second, if smiling or frowning produces a stronger feedback effect than the other, and finally if positive or negative emotions are easier to modulate with feedback. In the analysis, these three questions were evaluated using three different factors, one for modulation (enhancing vs. attenuating), one for muscle (smiling vs. frowning), and one for emotion (positive vs. negative). Note however that due to inherent properties of these factors there exists an inevitable interdependency between them. For example, smiling as an enhancing modulation is only possible during a positive emotion and never during a negative emotion, while frowning as an enhancing modulation is only possible during a negative emotion. Similarly, smiling during a positive emotion is per definition an enhancing modulation and cannot be attenuating, while frowning during a positive emotion is always an attenuating modulation. That is, the muscle factor is intertwined and linked together with the modulation and emotion factors in these cases, which illustrates the fact that the conditions within each of the three factors by nature are confounded with each other. Thus, due to this interdependency between the factors it was not possible to simultaneously evaluate the effects in a three-factorial analysis of variance. Instead the three questions were addressed in three separate one-factorial analyses. That is, in each analysis, the four SFFE scores for each participant were collapsed in a different way into two mean scores. First, the mean score for enhancing modulation consisted of the SFFE scores for when participants smiled during positive emotions and frowned during negative emotions, while the mean score for attenuating modulation consisted of the SFFE scores for when participants smiled during negative emotions and frowned during positive emotions. Second, the mean score for smiling consisted of the SFFE scores for when they smiled during positive and during negative emotions, while the mean score for frowning consisted of the SFFE scores for when they frowned during positive and during negative emotions. Finally, the mean score for positive emotions consisted of the SFFE scores for when they smiled and frowned during positive emotions, while the mean score for negative emotions consisted of the SFFE scores for when they smiled and frowned during negative emotions.

Results

As explained in the method section above, the three main questions were evaluated in three separate one-factorial ANOVAs, with the data reconstructed for each analysis. The mean SFFE scores for the two conditions of each of the three factors are presented in Fig. 3 .

Enhancing Versus Attenuating

As detailed above, in order to compare the feedback effects between enhancing and attenuating modulation, the four mean SFFE scores of each participant were collapsed into two mean scores, one for enhancing and one for attenuating modulation. To analyze the resulting data, a one-way within subject ANOVA with the factor modulation (enhancing vs. attenuating) was performed. The result demonstrated a significant effect of modulation, F(1,63) = 4.15, MS e = 210.58, p < 0.05, η p 2 = 0.06, and as seen in the left panel of Fig. 3 this effect was due to higher SFFE scores in attenuating [ M = 4.29, SD = 12.45, 95% CI (1.18, 7.40)] as compared to enhancing modulation [ M = −0.93, SD = 9.68, 95% CI (−3.35, 1.48)]. In order to verify that the attenuating effect per se was significant, we performed additional one-sample t-tests to determine if the scores differed from zero. The tests demonstrated that the attenuating score, t(63) = 2.36, p < 0.05, revealed a significant feedback effect, while the enhancing score did not differ from zero, t < 1.

Smile Versus Frown

To compare the feedback effects between smiling and frowning, the four mean SFFE scores of each participant were collapsed into two mean scores, one for smiling and one for frowning. The data were analyzed with a one-way within subject ANOVA with the factor muscle (smile vs. frown). The result showed no effect of muscle, F(1,63) = 0.31, MS e = 153.58, ns, η p 2 = 0.01, and as seen in the middle panel of Fig. 3 there was no major difference in SFFE scores between when participants smiled [ M = 1.07, SD = 10.11, 95% CI (−1.46, 3.59)] and frowned [ M = 2.29, SD = 9.47, 95% CI (−0.08, 4.65)].

Positive Versus Negative

To compare the feedback effects during positive emotions with the effects during negative emotions, the four mean SFFE scores of each participant were collapsed into a final set of two mean scores, one for positive emotions and one for negative emotions. To analyze the data, a one-way within subject ANOVA with the factor emotion (positive vs. negative) was performed. As can be seen in the right panel of Fig. 3 there was a non-significant trend towards slightly larger scores to positive [ M = 2.77, SD = 6.60, 95% CI (1.12, 4.42)] than negative emotions [ M = 0.59, SD = 6.43, 95% CI (−1.02, 2.19)], F(1,63) = 3.27, MS e = 46.65, p < 0.10, η p 2 = 0.05.

Discussion

Our findings suggest that facial feedback primarily affects present emotions when the facial action is incongruent with the emotional valence. In fact, there were no indications of feedback effects at all when the facial action and the emotion were congruent. This absence of a feedback effect in enhancing modulation is unexpected considering that several previous studies have demonstrated enhancing effects (e.g., Soussignan 2002; Strack et al. 1988). One explanation for the diverging results could be that the present Experiment 2 was specifically designed to evaluate possible differences between enhancing and attenuating modulation. That is, in contrast to previous studies, the present study did not only let participants both smile and frown to positive and to negative stimuli, but instead, data were transformed and analyzed in a way making it possible to combine enhancing smiles and frowns, and combining attenuating smiles and frowns, in order to evaluate the overall enhancing and attenuating effects per se. It seems that this approach was effective in revealing attenuating effects. Still, since the attenuating effect in the present study was unexpected, it would be valuable with more research on the subject before drawing definitive conclusions. In light of this we realized that the data in the present Experiment 1 in fact could be transformed and reanalyzed in a way that would allow us to further test the main questions of Experiment 2. This reanalysis is presented in the Reanalysis of Experiment 1 section below.

Regarding the two other questions of the present study, no significant differences were found. That is, the acts of smiling and frowning appeared to produce feedback effects to a similar degree. Moreover, there was no significant difference in feedback effects during positive and negative emotions, which is in line with earlier results (Dimberg and Söderkvist 2011).

Finally, five participants in Experiment 2 realized the true purpose of the study, as compared to Experiment 1 in which no participants realized the true purpose. However, in the interviews after the experiment this was explained by the fact that these participants had heard about and were familiar with the facial feedback theory.

Reanalysis of Experiment 1

If the significant difference in feedback effects between enhancing and attenuating modulation in Experiment 2 represents a genuine attenuating effect it should be possible to replicate this effect in other experiments where the SFFE scores for smiling and frowning can be calculated for enhancing and attenuating modulation. As it happens, and mentioned above, Experiment 1 in the present study can be considered such an experiment, which presents us with an immediate opportunity to test if the unexpected attenuating effect in Experiment 2 can be replicated.

That is, in the results of Experiment 1 it was demonstrated that there were no effects at all of facial feedback after 5 min and after 1 day. Consequently, these later conditions, and especially the condition after 1 day, could be assumed to reflect a more neutral muscle condition where participants were not influenced by voluntary performed facial actions. As detailed in Experiment 2, a neutral muscle condition makes it possible to discern the specific feedback effects of smiling and frowning, which in turn enable us to address the present questions.

Following the same procedure for transformations as in Experiment 2, and using the ratings after 1 day as the neutral muscle condition, new dependent variables were created in the form of SFFE scores for smiling and frowning. As a result of the transformations, four mean SFFE scores for each participant were created, that is, one score for smiling and one score for frowning during positive emotions, and one for smiling and one for frowning during negative emotions. Similar to Experiment 2, the three questions were evaluated by help of three different factors, one for modulation (enhancing vs. attenuating), one for muscle (smiling vs. frowning), and one for emotion (positive vs. negative), and the factors were analyzed in three separate one-factorial ANOVAs, with the data reconstructed for each analysis.

To compare the feedback effects between enhancing and attenuating modulation, the four mean SFFE scores of each participant were, similarly as in Experiment 2, collapsed into two mean scores, one for enhancing and one for attenuating modulation. The result demonstrated a significant effect of modulation, F(1,31) = 10.92, MS e = 255.49, p < 0.05, η p 2 = 0.26, with higher SFFE scores in attenuating [ M = 10.31, SD = 16.40, 95% CI (4.40, 16.23)] as compared to enhancing modulation [ M = −2.89, SD = 13.13, 95% CI (−7.63, 1.84)]. In order to verify that the attenuating effect per se was significant, we performed additional one-sample t-tests to determine if the scores differed from zero. The tests demonstrated that the attenuating score, t(31) = 3.65, p < 0.05, reflected a significant feedback effect, while the enhancing score did not differ from zero, t < 1.05.

Regarding the two other questions, there was no significant difference in SFFE scores between when the participants smiled [ M = 6.60, SD = 11.92, 95% CI (2.30, 10.90)] and frowned [ M = 0.82, SD = 13.20, 95% CI (−3.94, 5.58)], F(1,31) = 4.09, MS e = 130.53, p < 0.10, η p 2 = 0.12, and there was no significant difference during positive emotions [ M = 4.75, SD = 12.00, 95% CI (0.42, 9.07)] or negative emotions [ M = 2.67, SD = 14.44, 95% CI (−2.54, 7.88)], F(1,31) = 0.42, MS e = 166.57, ns, η p 2 = 0.01.

The results of this reanalysis demonstrate that facial feedback almost exclusively attenuated emotions, with quite a large effect size. This replicates the results of Experiment 2 and further confirms that facial feedback primarily affects ongoing emotions when the facial action is incongruent to the emotion. Just as in Experiment 2 there were no indications of any feedback effects when the facial action was congruent to the emotion.

General Discussion

The purpose of the present study was to further detail the description of how facial feedback effects occur. In two experiments, four different questions were addressed. In Experiment 1, it was demonstrated that the effect of facial feedback primarily occurs at the same time when the facial action is performed. In Experiment 2 it was demonstrated that facial feedback primarily attenuates ongoing emotions, and the same pattern was evident in the Reanalysis of Experiment 1. Notably, there seems to be no systematic difference in feedback effects between smiles and frowns, or between positive and negative emotions.

Regarding the finding that facial feedback primarily attenuates present emotions there are a number of studies in the literature on facial reactions to emotional stimuli (e.g., Dimberg et al. 2000, 2002) that provide important insights. First, when people are exposed to emotional facial stimuli they spontaneously and unconsciously react with their corresponding facial muscles. These reactions are automatic, very quick, and not always visible to the eye, but can be detected with facial electromyographic equipment. Second, these automatic reactions affect voluntary facial actions by facilitating congruent actions. For example, people that are exposed to a positive stimulus react quickly and automatically with their smile muscles even before they are consciously aware of the valence of the stimulus. Then, if they are instructed to voluntarily react with a smile and/or a frown to the positive stimulus, the voluntary smile will be formed faster and be more pronounced than the voluntary frown that requires more time and is less pronounced. The same is true for a negative stimulus, which facilitates a faster and stronger voluntary frown, as compared to a voluntary smile. Therefore, since these automatic reactions are present even when people are instructed to not move their facial muscles, one could question if it is possible at all to expose participants to a truly neutral muscle condition in an experiment that uses emotional facial stimuli, such as in the present study. That is, when we compared the difference in feedback effects between voluntary facial actions and neutral muscle conditions, automatic facial reactions similar to those described above were probably initially present in all conditions. It is therefore interesting that even though a congruent facial action is faster and stronger, as compared to an incongruent action (Dimberg et al. 2002), it was the latter actions that primarily modulated ongoing emotional experiences.

In the facial feedback literature there are no obvious explanations for the attenuating effect found in the present study. One potential explanation could perhaps be found in the above mentioned field of automatic facial reactions to emotional stimuli (e.g., Dimberg et al. 2000, 2002), where recent studies (e.g., Wood et al. 2016a, b) suggest that incongruent facial actions can disrupt the automatic mimicry of facial stimuli and that this lack of mimicry might lower the general impact of the stimulus. If this is the case it could offer an explanation for the attenuating effect of incongruent facial actions, but it would not explain the absence of an enhancing effect from congruent actions.

Another potential explanation, based on the idea that emotions are governed by different affect programs (Tomkins 1962), could be that one function of emotional facial actions is to initiate corresponding affect programs. That is, if no emotions were present a specific facial action would initiate a corresponding affect program and thus initiate an emotional experience. With an emotion already present, however, a congruent facial action would probably have no or little effect since the corresponding affect program already would have been initiated by, for example, an emotional stimulus. An incongruent facial action, on the other hand, would initiate a conflicting affect program, which might reduce the activation of the originally initiated program. Admittedly, while this explanation would account for the present results it remains hard to test empirically.

Finally, there is also the possibility that the present attenuation effect is the result of a ceiling effect in the intensity of the induced emotions. In such a case, the presented stimuli evoked the emotions to a maximum level and simply left no room for further enhancement, but plenty of room for an incongruent action to cause attenuation. This is unlikely though since the present stimuli, pictures of facial expressions, are not expected to induce strong emotions (e.g., Wangelin et al. 2012). A suggestion for future research could be to investigate if there is any difference in general feedback effects, and between enhancing and attenuating modulation, when stimuli of different intensity levels are used. It would be interesting to see if the feedback effects become stronger with stimuli that induce very strong emotions, or if the effects even become weaker (e.g., Baumeister et al. 2016). Moreover, in order to eliminate the possibility of a ceiling effect it could also be interesting to use stimuli that is expected to induce weaker emotions, for example pictures of mildly happy and mildly angry persons, which would allow congruent actions to enhance the evoked emotions in case of a ceiling effect.

The effort by Wagenmakers et al. ( 2016) to replicate the well-known feedback study by Strack et al. ( 1988) is the largest and most ambitious replication study of facial feedback to this day. As discussed earlier, they could not replicate the original results which questions the original study, but it should also be noted that some of the methodological choices made in the replication study have been questioned by Strack ( 2016). It is also important to note, as Wagenmakers et al. ( 2016) do as well, that the failed replication does not mean a general refutation of the facial feedback hypothesis. Indeed, as the results of the present study also suggest, facial feedback effects have been demonstrated in numerous studies that cover a wide range of different methods. For this reason there is a need for more replication efforts and a continued systematic evaluation of the facial feedback field, and the scope and ambition of the effort by Wagenmakers et al. ( 2016) has certainly set the standard for future such studies.

Finally, the present results may have potential implications for the clinical studies that investigate the effect of Botox in the treatment of depression (e.g., Finzi and Rosenthal 2014; Magid et al. 2014; Wollmer et al. 2012, 2014). If, as we found in the present study, facial feedback primarily attenuates present emotions, and further, if constant frowning contributes to the depressed state, then this frowning may act to specifically attenuate positive emotions. The paralysis of frown muscles with Botox could then block this attenuation, which may result in a general recovery of positive emotions, and consequently this effect could be one basis for the documented improvements of the patients. Future research could study how Botox affects the day-to-day emotional life of depressed patients in order to find out in which situations the improvements are experienced most frequently.

The second question of Experiment 2 was if smiling or frowning produces a stronger feedback effect than the other. The results indicated that there were no significant differences in effect between them, which suggest that a smile and a frown produce feedback effects that are roughly equal in strength. That is, in relation to the specific emotional stimuli used in these experiments, they produced equally strong feedback. One interesting question is if the smile and the frown actions might differ in how easy they are to activate and/or differ in the strength of the activation itself. This question has earlier been addressed in studies that also used the voluntary facial action technique (for a discussion see Dimberg and Söderkvist 2011). In that study it was noted that the strength of the muscle activity for smiles and frowns did not differ and further that the activity levels over repeated trials were not differently affected by for example fatigue. This suggests that the two different facial actions should be comparable to each other. Moreover, the stimulus material consisted of clearly positive and clearly negative pictures, and it is possible that the relative strength between smiling and frowning could be different with another type of stimuli. Future research could therefore use more neutral or ambiguous stimuli to further investigate this question, and if completely neutral stimuli were used it would allow for a comparison of the initiating ability of facial feedback between smiling and frowning.

The third question of Experiment 2 was if positive or negative emotions are easier to modulate with feedback. This question has been addressed in both Dimberg and Söderkvist ( 2011) and in the present study, but none of these studies have found any significant differences between positive and negative emotions. There simply seems to be no difference between them in this regard.

In the present study it was assumed that the participants followed the instructions correctly. They were not monitored during the experiments, but we have no indications that they found the instructions difficult to follow. The control questions afterward aimed to find out if they had realized the true purpose, but also aimed to verify that they had followed the instructions. The few participants that did not follow the instructions correctly were easily identified. Note also that if a participant should not follow the muscle instructions correctly it would in fact work against the facial feedback hypothesis and consequently reduce the general level of facial feedback effect in the present experiments.

As mentioned above, the purpose of the present study was to further detail the description of how facial feedback occurs. The facial feedback hypothesis, as discussed by Adelmann and Zajonc ( 1989), defines facial feedback as facial muscle actions that have the ability to modulate present emotions or, in the absence of emotions, to initiate emotions. Collectively then, we would like to add that the effect of facial feedback mainly occurs at the time when the facial action is performed, and that facial feedback primarily attenuates ongoing emotional experiences.

Acknowledgements

This project was supported by a grant to Ulf Dimberg from the Faculty of Social Sciences at Uppsala University. We thank Mats Fredrikson and two anonymous reviewers for valuable comments on the article.

Compliance with Ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

References

Strack, F. (2016). Reflection on the smiling registered replication report. Perspectives on Psychological Science, 1745691616674460. [PubMed]

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How the Experience of Emotion is Modulated by Facial Feedback

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Journal of Nonverbal Behavior The facial feedback hypothesis states that facial actions modulate subjective experiences of emotion. Using the voluntary facial action technique, in which the participants react with instruction induced smiles and frowns when exposed to positive and negative emotional pictures and then rate the pleasantness of these stimuli, four questions were addressed in the present study.