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http://nus.edu.sg/sotl-asia/events.htm

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http://cdtl.nus.edu.sg/scholarship-research.htm
Geertsema, Johan, Chng Huang Hoon, Mark Gan & Alan Soong. Teaching excellence and the rise of education-focused employment tracks. (2018). In Broughan, Christine, Graham Steventon & Lynn Clouder (eds.). Global Perspectives on Teaching Excellence: A new era for higher education. London: Routledge.
http://www.chronicle.com/article/A-Tenure-Track-for-Teachers-/240015
https://www.heacademy.ac.uk/system/files/resources/shifting_academic_careers_final.pdf

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centre for development of teaching and learning

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The Centre for Development of Teaching and Learning (CDTL) seeks to enhance the quality of teaching and learning at NUS and aspires to establish itself as a leading centre in higher education.

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https://www.cetl.hku.hk/wp-content/uploads/2016/07/Speech_June24_CETL_v5.pdf

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Working with individuals and institutions in higher education (HE) to provide students with an excellent learning experience is core to what we do at the HEA. This is why we manage and lead the development of the UK Professional Standards Framework (UKPSF), a nationally-recognised framework for benchmarking success within HE teaching and learning support. We believe that the UKPSF is essential to driving improvement in, and raising the profile of,
learning and teaching in HE.

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UK Professional Standards Framework (UKPSF)

# data[].attributes.attachment_link.description
Working with individuals and institutions in higher education (HE) to provide students with an excellent learning experience is core to what we do at the HEA. This is why we manage and lead the development of the UK Professional Standards Framework (UKPSF), a nationally-recognised framework for benchmarking success within HE teaching and learning support.

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This book offers a range of approaches and specific examples of how a sample of internationally leading research-intensive universities, from a variety of regions around the world, work to improve teaching and learning. It describes and analyzes broad university initiatives and approaches that have the potential of driving institution-wide change processes in teaching and learning, thus providing a link between strategic ambitions and cultural transformation in the universities. Globally, research-intensive universities are increasingly pressured to increase their performance in both research and education. However, while much focus internationally has been devoted to how universities are working to boost their research performance, less is known about how internationally leading universities are working to improve teaching and learning. Through comparative cases drawn from universities in Europe, Asia and the US, key practices and lessons are identified and showcased providing a unique insight into the ways internationally leading research universities work to support and enhance staff engagement in teaching and learning. It will be essential reading for researchers and advanced students working in Higher Education and Sociology, particularly those with an interest in comparative studies.

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Strengthening Teaching and Learning in Research Universities

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This book offers a range of approaches and specific examples of how a sample of internationally leading research-intensive universities, from a variety of regions around the world, work to improve teaching and learning.

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Abstract

Noun/verb dissociations in the literature defy interpretation due to the confound between lexical category and semantic meaning; nouns and verbs typically describe concrete objects and actions. Abstract words, pertaining to neither, are a critical test case: dissociations along lexical-grammatical lines would support models purporting lexical category as the principle governing brain organisation, whilst semantic models predict dissociation between concrete words but not abstract items. During fMRI scanning, participants read orthogonalised word categories of nouns and verbs, with or without concrete, sensorimotor meaning. Analysis of inferior frontal/insula, precentral and central areas revealed an interaction between lexical class and semantic factors with clear category differences between concrete nouns and verbs but not abstract ones. Though the brain stores the combinatorial and lexical-grammatical properties of words, our data show that topographical differences in brain activation, especially in the motor system and inferior frontal cortex, are driven by semantics and not by lexical class.

Keywords: Embodied meaning, fMRI, Grammatical category, Lexical category, Semantics

1. Introduction

The neurobiological basis of noun and verb processing has been elucidated by cognitive neuroscience research. A range of neuropsychological ( Damasio & Tranel, 1993; Daniele, Giustolisi, Silveri, Colosimo, & Gainotti 1994; Kemmerer, Rudrauf, Manzel, & Tranel 2012; Miceli, Silveri, Villa, & Caramazza 1984; Neininger & Pulvermueller, 2001; Neininger & Pulvermüller, 2003) and brain imaging studies ( Bedny, Caramazza, Grossman, Pascual-Leone, & Saxe 2008; Perani et al. 1999; Price, Wise, & Frackowiak 1996; Pulvermüller, Lutzenberger, & Preissl 1999) have linked these word classes to specific parts of the human brain, with verb processing associated with inferior frontal and middle temporal cortex and noun processing related to other temporal and parietal areas. Differences in grammatical or lexical class may not, however, be the principle factor in the neural differentiation between nouns and verbs. As one variable of interest, word meaning, or semantics, has frequently been discussed as an underlying determinant of noun/verb dissociations ( Pulvermüller, Lutzenberger et al. 1999; Shallice, 1988; Vigliocco, Vinson, Druks, Barber, & Cappa 2011; Warrington & Shallice, 1984). An essential confound exists in the literature as most verbs are undeniably words used to speak about actions whereas most nouns refer to objects, so it is hardly possible to match and control for relevant semantic differences between the lexical classes; furthermore, were one to succeed in precisely matching sets of nouns and verbs for factors such as the concreteness of their object reference and intensity of their action relationship, one might, from a linguistic perspective, still argue that such selections would certainly be far from representing typical specimens from the lexical groups. Given this seemingly hopeless confound of lexical class with semantics, it is therefore unsurprising that many scholars have tried to trace the "lexical" differences to their semantic origins, at least as far as putative word class specific brain activation patterns are concerned.

Ingenious attempts have been made to clarify this issue by varying semantic properties within the lexical classes so that consistent noun/verb differences in brain activation - for example in the middle-temporal cortex ( Bedny et al. 2008) - reveal more genuine lexical class differences. In addition, many authors have attempted to strip words of their semantics by contrasting homophonous pseudowords in noun and verb context (to wug vs. the wug), thus providing a tool for ascertaining differential representation of lexical categories ( Cappelletti, Fregni, Shapiro, Pascual-Leone, & Caramazza, 2008; Laiacona & Caramazza, 2004; Shapiro & Caramazza, 2003; Shapiro, Moo, & Caramazza 2006; Shapiro, Pascual-Leone, Mottaghy, Gangitano, & Caramazza 2001). However, taking on the role of an advocatus diaboli, one might still argue that the phrase "to wug" suggests an action (e.g., whacking) whereas the context "the wug" is more compatible with an object (a rug) interpretation and, therefore, these pseudowords were not truly stripped of semantic associations, but were, in fact, semantically biased by the contexts in which they were presented: as the authors did not explore this possibility empirically, this interpretation (which has earlier been suggested and supported by Pulvermüller, Kherif, Hauk, Mohr, and Nimmo-Smith (2009) and Vigliocco et al. (2011)) cannot be ruled out at this point. Further evidence for representation of lexical categories in the brain comes from differential brain activity in response to homophonous noun and verb affixes presented in noun and verb contexts ( Pulvermüller & Shtyrov, 2009), which persist even after the contributions of the noun/verb stems are subtracted. Differential neural responses to acoustically identical affixes of nouns and verbs would appear to strongly support genuine differentiation between lexical categories at the inflectional level. However, since these affixes most typically appear in the context of action verbs and object nouns, respectively, it is entirely probable that their neuronal circuits bind with the semantic knowledge attached to their companion words. Even such surprising noun/verb distinctions in brain activation patterns may, therefore, be traced to a semantic origin.

Indeed, whilst the bulk of evidence regarding noun and verb processing fails to replicate clear brain activation differences between these lexical categories and is frequently confounded by semantics (see Vigliocco et al. 2011, for review), there is unambiguous evidence that semantic associations alone, when disentangled from and unconfounded by lexical category differences, differentially activate cortical areas. This has been concisely addressed by the exploration of different semantic categories within the same lexical class. Action words (verbs) semantically related to the different effectors of the body have been robustly shown to produce differential somatotopic activity in motor systems ( Aziz-Zadeh & Damasio, 2008; Boulenger, Hauk, & Pulvermüller 2009; Cappa and Pulvermüller, 2012; Hauk, Johnsrude, & Pulvermüller 2004; Hauk, Shtyrov, & Pulvermüller 2008; Kemmerer, Castillo, Talavage, Patterson, & Wiley, 2008; Pulvermüller, Härle, & Hummel 2001), and likewise, nouns with strong gustatory, olfactory or auditory associations have been shown to differentially activate these respective sensory brain regions ( Barrós-Loscertales et al. 2012; González et al., 2006; Kiefer, Sim, Herrnberger, Grothe, & Hoenig 2008). These sensorimotor activations specific to the semantic category of linguistic symbols (words) occur in conjunction with left-perisylvian area activations generally seen during language processing. These semantic activation topographies support a model of language processing based on Hebbian cell assemblies that bind together distributed semantic category-specific sensorimotor and left-hemispheric perisylvian language circuits ( Pulvermüller, 1999; Pulvermüller, 2002; Pulvermüller, 2012; Pulvermüller, 2013). The functional relevance of sensorimotor activation for language processing has been demonstrated by causal effects of sensorimotor cortex activation on the processing of specific semantic types of symbols ( Boulenger et al. 2006; Devlin & Watkins, 2007; Glenberg & Kaschak, 2002; Glenberg, Sato, & Cattaneo 2008a; Pulvermüller, Hauk, Nikulin, & Ilmoniemi 2005) and by a range of patient studies ( Bak, O'Donovan, Xuereb, Boniface, & Hodges, 2001; Pulvermüller et al. 2010; for discussion, see Kemmerer et al., 2012). It therefore appears that differences in meaning between linguistic symbols are manifest in neuronal circuits with specific brain topographies.

Whilst neural differentiation between semantic categories is relatively well-supported, the influence of lexical categories in modulating brain activity is, for the previously mentioned reasons, still undetermined. The optimal design for comparing lexical (noun/verb) and semantic (action/object relationship) differences in brain activation would vary these factors independently. Earlier work explored this strategy using EEG ( Brown & Lehmann, 1979; Kellenbach, Wijers, Hovius, Mulder, & Mulder, 2002; Pulvermüller, Mohr, & Schleichert, 1999) and fMRI ( Vigliocco et al. 2006) but, especially in the fMRI studies, it was not always possible to control all relevant confounds in an optimal fashion. For example, Vigliocco et al. (2006) compared Italian nouns and verbs with sensory or motor features and found a semantic-topographical but not a lexical class difference. However, a shortcoming of this study was that their Italian noun/verb stimuli shared stems but differed in their affixes (e.g. noun "arrivo" [-O] and verb "arrivare" [-ARE]) and no stimulus matching for word length, word frequency or other lexical variables was reported. This study, as many earlier ones, did not exclude important psycholinguistic confounds which might have led differences in brain activation between nouns and verbs to be overlooked. On the other hand, the fact that "sensory words were judged as less familiar, acquired later, and less imageable than motor words" ( Vigliocco et al., 2006, p. 1791) leaves it open whether the observed differences in brain activation between word types were due to their sensorimotor semantics or to other psycholinguistic features. It is therefore of the essence to properly address the issue of putative lexical-grammatical class differences in brain activation with these pitfalls avoided, and in particular to examine the relationship of lexical class differences to the semantic differences in brain activation reported by the aforementioned authors.

The debate concerning lexical vs. semantic differences as the primary factor for neural differentiation might be addressed with the exploration of well-matched word categories orthogonalised for semantic and lexical factors, such that the contribution of these factors to brain activation in specific cortical areas can be clarified. Whilst nouns and verbs have generally been investigated in the context of concrete items which refer respectively to objects and actions in the world (e.g. "door" and "speak"), they are also highly typical as abstract items generally used to speak about abstract concepts or feelings (e.g. "despair" and "suffer", "idea" and "think") and therefore possessing few, if any, sensorimotor associations. Using typical nouns and verbs of a concrete or abstract semantic nature, we here tested predictions of theories of lexical and semantic category representation in the human brain. The lexical-grammatical approach to category-specific local brain processes postulates that the differences in word-elicited cortical activation landscapes are best described in terms of the lexical (or grammatical) categories of nouns and verbs ( Daniele et al. 1994; Miceli, Silveri, Nocentini, & Caramazza, 1988; Miceli et al., 1984; Shapiro, Shelton, & Caramazza 2000; Shapiro et al. 2001; Caramazza and Shelton, 1998; Bedny, Caramazza, Pascual-Leone, & Saxe, 2012; Bedny et al. 2008; Laiacona & Caramazza, 2004; Shapiro & Caramazza, 2003). This position implies that the same differences are present for concrete and abstract members of these lexical categories.

In contrast, a semantic approach postulates a difference in brain activation topographies only for concrete nouns and verbs semantically related to objects and actions respectively, but not for abstract nouns and verbs, which lack such clear differences in semantic links with action and perception information. The grounded semantics position views semantic representations as circuits tying together symbolic word form information with action and perception schemas ( Barsalou, 1999; Lakoff, 1987). In this perspective, neuronal circuits in motor systems (the neural substrate of action schemas) contribute to semantic knowledge about action-related verbs, whereas meaning knowledge related to object words, typically concrete nouns, is underpinned by neuronal assemblies reaching into inferior-temporal cortex of the ventral-visual "what" stream of object processing ( Barsalou, 2008; Gallese & Lakoff, 2005; Martin, 2007; Pulvermüller, 1999; Pulvermüller & Fadiga, 2010). Cortical areas associated with movement or object perception, in middle temporal and inferior temporal/fusiform gyrus respectively, may house additional perceptual schemas related to actions and objects. Abstract words which belong to the noun and verb categories, but which cannot be differentiated from each other based on action- or perception-related semantic features, are hypothesised to evoke similar topographical patterns of brain activation. Previous studies of abstract language processing have implicated a wide range of brain regions, including multimodal dorsolateral prefrontal ( Binder, Westbury, McKiernan, Possing, & Medler 2005; Boulenger et al., 2009; Moseley, Carota, Hauk, Mohr, & Pulvermüller 2012), anterior temporal ( Patterson, Nestor, & Rogers 2007) and superior parietal cortex ( Binder et al. 2005). As a number of studies on abstract word processing have previously found activation in premotor and prefrontal cortex ( Moseley et al., 2012; Pexman, Hargreaves, Edwards, Henry, & Goodyear, 2007; Pulvermüller & Hauk, 2006), it seems to be reasonable to predict such activation for our present abstract items, without any further prediction about differences between abstract nouns and verbs.

With tight matching of stimuli and the use of event-related functional resonance imaging (fMRI), we here address the debate around the question as to whether brain activation topographies elicited by words are driven by lexical or semantic factors, or by both. In an orthogonalised design, we presented participants with concrete nouns, concrete verbs, abstract nouns and abstract verbs, hypothesising that differential brain activation between concrete items but not between abstract ones would support a view of semantics driving brain differences between nouns and verbs (reflected in an interaction effect of lexical category and abstractness). Divisions between nouns and verbs but not between abstract and concrete items of the same lexical category, reflected in a main effect of lexical category, would imply that the differential topographies for nouns and verbs are driven by the grammatical categories that these items belong to, rather than their varying semantic associations.

2. Materials and methods

2.1. Subjects

Participants ( n = 18) were right-handed, monolingual native speakers of English all of whom had no history of psychiatric or neurological illness and were free of psychotropic medication. They had normal or corrected-to-normal vision as suitable for a task within the visual modality. The mean age of participants was 29 (SE = 2.8), all were strongly right-handed (mean laterality quotient = 90, SE = 3.1, Oldfield, 1971), and the group had a mean IQ slightly above average (mean = 110, SE = 3.0) as tested using Form A of the Cattell Culture Fair test ( Cattell & Cattell, 1960). Ethical approval was obtained from the Cambridge Psychology Research Ethics Committee (CPREC 2008.64): after receiving written and verbal briefing concerning the full nature of the experiment, participants gave written consent and were all remunerated for their time.

2.2. Stimuli

In order to disentangle the effects of lexical category from semantic-abstractness, four word categories of 40 words each were employed (please see Appendix A). Abstract nouns (such as 'clue', 'jape', 'truce') were contrasted with concrete nouns ('mouse', 'cheese', 'spade'), abstract verbs ('faze', 'bide', 'glean') and concrete verbs ('peel', 'chomp', 'skate'). Prior to the fMRI study, 10 native speakers of English were recruited to provide ratings for a large word corpus on a range of semantic variables. These covered aspects of sensorimotor features, such as imageability, concreteness, visual-relatedness, form-relatedness, colour-relatedness and action-relatedness, and affective-emotional features such as arousal and valence ( Bradley & Lang, 1994; Osgood, Suci, & Tannenhaus 1975). Details of the behavioural procedures are described elsewhere ( Pulvermüller, Lutzenberger, et al., 1999a, Pulvermüller, Mohr, et al., 1999b). The psycholinguistic properties of words were obtained from the CELEX database ( Baayen, Piepenbrock, & van Rijn, 1993), and stimulus groups were consequently matched on length, bigram and trigram frequency, logarithmic lemma frequency, and number of orthographic neighbours (see Appendix B for psycholinguistic and statistical properties of the stimuli). Our study included lexically unambiguous nouns and verbs; lexically ambiguous noun/verbs (such as "the/to walk") were allowed if their lemma frequencies indicated a dominant usage as either verb or noun. Statistically, the noun lemma frequencies of items in the noun word category by far outnumbered their verb lemma frequencies (abstract nouns: t(39) = 4.574, p < .000 l; concrete nouns: t(39) = 7.891, p < .0001), and the reverse was true for the verbs (concrete verbs: t(39) = −10.950, p < .0001; abstract verbs: t(39) = −24.240, p < .0001). As can be seen in Appendix B, there were no main effects (or indeed interactions) of lexical category or semantic-abstractness on psycholinguistic properties of stimuli. This being the case, we were confident that brain activation in contrasts focusing on lexical category and semantic-abstractness were free of ulterior confounding effects.

The experimental word categories were dispersed among 200 filler words during presentation, with which they were matched in length ( F(1, 359) = 1.006, p > .436), bigram ( F(1, 359) = 1.679, p > .084) and trigram frequency ( F(1, 359) = .868, p > .560). 120 hash marks, matched to word stimuli in length, acted as a low level visual baseline in contrasts.

2.3. Procedure and experimental design

Adopting a paradigm previously employed for investigating lexicosemantic processing (e.g., Hauk et al. 2004; for review, see Pulvermüller et al. 2009), words written in lowercase letters were presented tachistoscopically while haemodynamic responses were recorded using event-related fMRI. This passive reading paradigm was chosen to be unbiased towards semantic or grammatical processing. Despite no overt instructions for semantic processing, it is reliably known to evoke early differential activations that reflect a word's semantic category (see Hauk et al., 2008, for review), strongly implying that reading automatically evokes semantic processing of word stimuli in typical participants. Subjects were instructed to attend to and carefully read all stimulus words silently, without moving their lips or tongue. The passive reading task was delivered in three blocks of approximately 7 min each. A short presentation time of 150 ms ensured that saccades were discouraged and that participants had to continuously attend to the screen in order to perform the task. A central fixation cross was displayed between stimuli for an average 2350 ms, with a jitter of ±250 ms, resulting in SOAs between 2250 and 2750 ms (average 2500 ms). The order of stimuli was pseudo-randomised (restriction: not more than two items of the same category in direct succession) with two lists, counter-balanced across subjects.

Following the scan, our participants were requested to complete a short unheralded word recognition test outside the scanner. In the recognition test, they were presented with a list of experimental stimuli and novel words and had to rate each word on a scale from 1 to 7, indicating how certain they were that a given item had appeared in the fMRI experiment. For evaluation, ratings were converted into percentage correct/incorrect responses. The test contained a combination of 50 experimental and 25 novel distracter words, and above chance performance was thus taken to confirm that subjects had engaged with the task.

2.4. Imaging methods and data analysis

A Siemens 3T Tim Trio (Siemens, Erlangen, Germany) with a head coil attached was employed during data collection. The functional scans consisted of 32 slices which covered the whole brain in descending order (slice thickness: 3 mm, in-plane resolution: 3 × 3 mm, inter-slice distance: 0.75 mm), and echo-planar sequence parameters were TR = 2000 ms TE = 30 ms and flip angle = 78 degrees.

SPM5 (Wellcome Department of Imaging Neuroscience, London, UK) was employed for all processing stages. Images were corrected for slice timing and re-aligned to the first image using sinc interpolation. The EPI images were co-registered to the structural T1 images, which were normalised to the 152-subject T1 template of the Montreal Neurological Institute (MNI), and the resulting transformation parameters applied to the co-registered EPI images. During this pre-processing, images were resampled with a spatial resolution of 2 × 2 × 2 mm and spatially smoothed with an 8-mm full-width half-maximum Gaussian kernel. Single-subject and second level statistical contrasts were computed using the canonical Haemodynamic Response Function (HRF) of the general linear model, a measure for the amplitude of brain response. Low-frequency noise was removed by applying a high-pass filter of 128s. Onset times for each stimulus were extracted from Eprime output files and integrated into a model for each block in which each stimulus group was modelled as a separate event. Group data were then analysed with a random-effects analysis. Activation to each of the experimental word categories was compared statistically against baseline (the hash mark condition) and subsequently between critical stimulus conditions (nouns vs. verbs and abstract vs. concrete words, see below). Stereotaxic coordinates for voxels are reported in the Montreal Neurological Institute (MNI) standard space.

In addition to whole brain analysis, a regions of interest (ROI) analysis was undertaken in which 2 mm-radius regions were defined using the MarsBar function of SPM5 ( Brett, Anton, Valabregue, & Poline 2002). This analysis employed both an apriori (theory-led) and a data-driven approach. In the former, a number of coordinates were identified and taken from previous literature concerning category-specific effects for concrete objects in frontotemporal cortex ( Chao, Haxby, & Martin 1999; Martin & Chao, 2001; Martin & Weisberg, 2003; Martin, Wiggs, Ungerleider, & Haxby, 1996). Regions were also examined from the recent work of Bedny et al. (2008), who used a motor localiser to identify areas activated by biological motion (left and right area MT+, left and right superior temporal sulcus respectively) and a semantic decision task to identify areas activated by the contrast of action verbs vs. animal nouns (left tempero-parietal junction, left and right anterior superior temporal sulcus).

In a similar fashion, in our data-driven approach, we extracted the regions where clearest evidence for activation (in terms of error probabilities/ t-values) was found in the contrast of all experimental words pooled together against the baseline, plotted at an FDR-corrected significance level of p < .05. The criteria for ROI selection was based on t/ Z-values-obtained in this contrast: we selected the three "most significant voxels", i.e. those with the highest t/ Z values for the words vs. baseline contrast. As this comparison (words vs. baseline) is orthogonal to both of the variables investigated (lexical category, abstractness), the strategy applied for selecting ROIs follows recent recommendations to avoid "double dipping" ( Kriegeskorte, Simmons, Bellgowan & Baker, 2009). In this data-driven analysis, average activation values within each of these 2 mm-radius spheres for each subject and each of the four word categories were entered into a repeated-measures ANOVA with the factors ROI x lexical category (2) × semantics/abstractness (2).

Note that, because 2 × 2 × 2 mm voxels, 8 mm smoothing kernel and 2 mm ROI radius were chosen, the half maximum width of each ROI was 12 mm. This allowed us to keep overlap between ROIs to a minimum while at the same time compensating for some of the spatial variance caused by the projection of individual brains to the averaged MNI template. Where appropriate, Huynh-Feldt correction was applied to correct for sphericity violations. In this case, epsilon values and corrected p values are reported throughout.

3. Results

3.1. Behavioural results

Whereas psycholinguistic properties were matched between word groups (see Methods, Appendix B), results of the semantic rating study executed prior to the fMRI experiment revealed significant differences in the semantic variables of imageability, arousal, action-relatedness, concreteness, visual-relatedness, colour-relatedness and form-relatedness (see Appendix B). For all of these features, 2-way ANOVAs revealed significant interaction effects and, in most cases, additional main effects. The interactions of all object-related features, including concreteness, imageability, form- and visual-relatedness, showed, as expected, highest values for concrete nouns towering over all other word groups. For arousal and action-relatedness, which both reflect semantic action features, concrete verbs achieved the highest ratings and concrete nouns the lowest. In addition, object-related semantic ratings were higher for nouns than for verbs and higher for concrete items than for abstract ones; with regard to action-relatedness, verbs dominated over nouns and, again, concrete over abstract items.

Statistical tests for word groups, including interactions and main effects, are displayed in Appendix B. Pairwise comparisons between stimulus groups showed that the abstract noun category was indeed significantly less imageable ( t(78) = −14.028, p < .001), less concrete ( t(78) = −16.812, p < .001), less related to visual objects ( t(78) = −15.145, p < .001), and less form/shape-related ( t(78) = −10.443, p < .001) than concrete nouns. Likewise, abstract verbs were significantly less imageable ( t(78) = −8.613, p < .001), less concrete ( t(78), and less action-related ( t(78) = −3.018, p < .005) than concrete verbs. There were no significant differences between the two abstract categories in imageability ( t(78) = .809, p > .421), visual- ( t(78) = 1.364, p > .175) or form-relatedness ( t(78) = 8.54, p > .395), though abstract verbs were significantly less concrete than abstract nouns ( t(78) = 2.206, p < .031). As expected, the most highly imageable category, concrete nouns, significantly outperformed concrete verbs in imageability ( t(78) = 8.988, p < .001), concreteness ( t(78) = 18.307, p < .001), and visual- ( t(78) = 9.814, p < .001) and form-relatedness ( t(78) = 4.861, p < .001).

On the surprise word recognition test performed after scanning, performance was above chance (average hit rate: 76.2% (SE = 4.2%), false positive rate: 56.8% (SE = 5.2%), d'prime rate: 0.53). Although these results only document moderate recognition of stimulus words, possibly due to the large number of the stimuli presented and the long delay between experiment and later testing outside the scanner (∼23 min average), they document that subjects had been attentive during passive reading. In order to check that concrete items were not processed any more thoroughly than abstract ones, d'prime values were calculated for each word category. The average d'primes for each category were as follows: concrete nouns = .024; concrete verbs = 0.59; abstract nouns = 0.52; abstract verbs = 0.56. One-sample t-tests revealed that the d'prime of each word category was significantly above 0 (concrete nouns: t[17] = 2.092, p < .05; concrete verbs: t[17] = 4.135, p < .002; abstract nouns: t[17] = 3.324, p < .005; abstract verbs: t[17] = 3.669, p < .003). A two-way ANOVA (lexical category × concreteness) revealed no significant main effects or interactions between the d'primes of different word categories, such that there was no behavioural evidence for processing differences between word categories.

3.2. fMRI results

Examination of the contrast of all experimental words against the hashmark baseline, presented at an FDR-corrected significance level of p < .05 in part A, revealed activation typical of that generally seen in visual language processing tasks ( Bookheimer, 2002; Kronbichler et al. 2004). A very large left-hemispheric cluster extended from inferior frontal gyrus (pars orbitalis (BA 47), pars triangularis (BA 45) and pars opercularis (BA 44)) over precentral and postcentral gyrus to supramarginal gyrus, down over superior, middle and inferior temporal and fusiform gyrus, and even back to inferior occipital cortex. Other left-hemispheric clusters included the middle cingulate, parietal and superior occipital cortex and the cerebellum. Activation was also observed in the right hemisphere, with large clusters located at right middle frontal cortex, precentral gyrus and the right cerebellum (close to fusiform gyrus), and a smaller cluster at right supramarginal gyrus. Activation maxima for this contrast are displayed in Appendix C.

3.3. Data-driven ROI analyses

Using a data-driven approach, we examined stimulus-induced activation dynamics in ROIs where clearest word-related activation was present. In the contrast of all words vs. baseline, three left frontocentral activation clusters stood out with regard to their low p- and high t-values ( t > 6.5; see , and Appendix C) (see also Methods). Activation evoked by the four word categories at these three foci, located in inferior frontal cortex and insula (−32, 18, −2), on the precentral gyrus (−42, −8, 46) and across the central sulcus (−54, −16, 42), was entered into a 3 (ROI: inferior frontal, precentral, central) by 2 (Lexical category: noun/verb) by 2 (Semantics: concrete/abstract) ANOVA. Crucially, a significant interaction of all three factors, ROI, Lexical category and Semantics ( F(2, 34) = 4.002, p < .028), demonstrated that the four word categories evoked significantly different topographic activation patterns across these three frontocentral regions. (B).

To further investigate this complex interaction, separate analyses of variance were carried out for concrete and abstract words (design: ROI × Lexical category [nouns vs. verbs]). For concrete nouns and verbs, there was a significant interaction of the ROI factor with Lexical category ( F(2, 34) = 4.38, p < 0.020). Planned comparison tests failed to reveal a category difference in the inferior frontal and precentral ROIs, but documented stronger haemodynamic activity in central motor cortex for concrete action-related verbs than for object-related nouns ( F(1, 17) = 5.66, p < 0.029) and a tendency in the opposite direction for the inferior frontal ROI ( F(1, 17) = 2.227, p > .15). When grouping together premotor and motor ROIs (i.e. precentral and central), significantly stronger responses to concrete verbs than to concrete nouns were re-confirmed ( F(1, 17) = 5.74, p < 0.028). The same two-way analysis of variance carried out for abstract nouns and verbs failed to reveal a significance interaction effect of the ROI and Lexical category factors ( F(2,34) = 0.79, p > 0.46, n.s.). There was no indication of word category differences in motor, premotor or prefrontal areas of interest. This pattern of results shows that only concrete action-/object-related nouns and verbs, but not abstract ones, activate the frontocentral areas differentially.

Further inspection of activation patterns to abstract and concrete nouns and verbs in the three ROIs suggested that, over and above the statistically confirmed category-difference for concrete but not abstract items, the abstract items seemed to group with action verbs. Pooling haemodynamic responses to abstract words with those to concrete action verbs did indeed confirm significantly greater activity in the central motor ROI than that evoked by concrete nouns ( t [17] = 2.285, p < .04). The precentral region indicated the same trend but without reaching significance. The inferior frontal ROI showed a trend towards stronger responses to concrete nouns compared with the other three categories, though this did not reach significance ( t(17) = 1.351, p > .195).

3.4. A-priori defined ROI analyses

In a second set of analyses, we investigated the word-type related activation in regions highlighted in previous research. ROIs were therefore taken from the literature and effects of lexical category or semantics were investigated by two-way ANOVAs. Previous work targeting both lexical category differences (noun-verb) and semantic dissociations (living-nonliving, animals-tools, etc.) was exploited in defining ROIs ( Bedny et al. 2008; Chao et al. 1999; Martin & Chao, 2001; Martin & Weisberg, 2003; see Vigliocco et al. 2011). Bedny et al. (2008) reported a significant effect of lexical category but NOT of the only semantic variable they focused on, motion-related semantic word properties. This lexical category effect was seen in three ROIs, where verbs evoked greater activity than nouns: left temperoparietal junction (TPJ: coordinates −58, −48, 22), superior temporal sulcus (STS: −57, −55, 12) and anterior superior temporal sulcus (aSTS: −57, −41, −1). However, using the same ROIs to scrutinise the present data set, we could not observe any concordant significant effect, and, more generally, not any main effect or interaction of either Lexical category or Semantics (all F-values <0.5). Their left STS ROI revealed a trend towards a lexical category difference which, though weak and far below significance ( F(1, 17) = .422, p > .525), somewhat resembled that reported by Bedny, with numerically greater activity for verbs (see , Part A). No significant effect of lexical category appeared in either the left temperoparietal junction ROI ( F(1, 17) = .400, p > .536) or the left anterior superior temporal sulcus ROI ( F(1, 17) = .105, p > .750); in these cases, any numerical differences in favour of verbs were also absent in our data, in favour of a numerical contrast in the opposite direction. The combination of all three Bedny et al. regions (TPJ, STS and aSTS) also failed to reveal a significant effect of lexical category or semantics. Although, in our present analysis, activation maxima did not arise in the left STS in the contrast of all experimental words against baseline, we chose two coordinates located in the cluster of STS activation which were closest to Bedny et al.'s original anterior and posterior STS regions (see B). These coordinates, too, failed to replicate the verb advantage reported by Bedny and colleagues in left STS and showed no effect of lexical category. The present study was therefore unable to replicate the noun/verb difference in haemodynamic responses previously reported in left middle-temporal cortex.

So far, analysis of all ROIs from the previous literature failed to reveal effects of lexical category. We did, however, observe a main effect of lexical category in analysis of two left frontal-insula regions (one more anterior at MNI coordinates −27, 33, 11, the other more posterior at −27, −3, 23) suggested by Martin et al.'s (1996) results of a positron emission tomography (PET) experiment investigating the naming of visually depicted animals and tools ( F(1, 17) = 6.280, p < .025). Of more important note, though, was the significant interaction between this lexical category factor and the semantics variable ( F(1, 17) = 9.319, p = .007). This interaction was driven by significantly greater activation for concrete nouns (see ) compared with concrete verbs in both the more anterior first ( t(17) = 2.301, p < .035) and posterior ( t(17) = 3.046, p < .01) frontal regions. Whilst nouns generally evoked greater average activation than verbs in these regions, the difference between abstract nouns and verbs did not reach significance in the present study. Comparison of brain responses to concrete nouns to the pooled response to all three other word types confirmed the relatively enhanced signal to the former in the anterior ROI ( t(17) = 2.611, p = .018) and a trend in this direction in the posterior ( t(17) = 1.672, p = .113). Note, furthermore, the similarity between the activation advantage for concrete nouns in this ROI defined by Martin et al. (1996) and the data-driven IFG/insular ROI found in the present study. Martin et al. had investigated animal and tool naming and these ROIs showed strongest responses in animal naming; in our study, which used words in a passive reading task, most of the concrete nouns were also animal names. The inferior frontal region thus appears particularly engaged in animal name processing, regardless whether this occurs during naming or passive reading.

4. Discussion

In a study of abstract and concrete noun and verb processing, we found a significant interaction effect of orthogonalized semantic (abstract vs. concrete) and lexical (noun vs. verb) factors in the frontocentral motor system. In central and precentral motor cortex, activation to concrete verbs was generally enhanced compared with concrete nouns and, crucially, a similar difference for abstract word groups was absent. Inferior frontal regions suggested the opposite contrast, activation greater for concrete nouns than for concrete verbs, but, once again, the contrast of nouns vs. verbs was not significant for abstract items. As statistically significant effects of lexical category appeared in interaction with semantic differences between abstract and concrete words, our results argue against a distinction between topographical patterns of brain activation in terms of the lexical categories of nouns and verbs. Rather, our data show that brain activation patterns to nouns and verbs depend on the semantic nature of these items. The most prominent brain distinctions include enhanced activity in central motor cortex to verbs typically used to speak about actions relative to object-related nouns, and relatively stronger activation in inferior frontal cortex to object nouns as compared with action verbs.

Our neurometabolic data reveals a pattern of activation across frontal and temporal cortices typical of that generally seen in visual word processing ( Bookheimer, 2002; Kronbichler et al., 2004). It is also consistent with the aforementioned model of distributed word-related cell assemblies, showing activation spread out across left perisylvian language cortex and incorporating semantic circuits in extrasylvian regions, especially motor and visual areas ( Pulvermüller, 1999; Pulvermüller, 2002; Pulvermüller, 2012; Pulvermüller, 2013). In an investigation of three frontal regions (in IFG-insula, precentral and central gyrus) most significantly active during processing of experimental words, a region (3) by semantic abstractness (2) by lexical category (2) ANOVA revealed a significant interaction of all three factors. Further investigation confirmed the lexical category difference in brain activation patterns for concrete but not for abstract items. These results show that noun/verb differences in brain activation patterns are specific to concrete items and therefore depend on semantics.

A search for effects of lexical category in temporal regions implicated in previous literature was unfruitful, though a lexical category effect did appear in two frontal regions previously implicated by Martin et al. (1996) in the processing of animal pictures. This effect was driven by a particular strength for concrete nouns, which were indeed mainly animal words, as consistent with this and other previous studies reporting substantial activation overlap in this area for animal concepts across modalities ( Martin, 2007; Martin & Chao, 2001). Considering the theoretical models previously discussed, our findings demonstrate greater support for a semantic than a lexical interpretation of focal neurometabolic noun/verb differences, but demand a more complex discussion of the impact of lexical category and semantics on the brain.

4.1. Lexical categories in the brain

The proposition that lexical (grammatical) categories are differentially represented in the brain would seem plausible given that nouns and verbs are suggested by many to be linguistic universals ( Vigliocco et al., 2011), even present in American Sign Language (ASL: Supalla and Newport, 1978), pidgin and creole languages ( Slobin, 1975). Exceptions do exist ( Broschart, 1997; Foley, 1998; Langacker, 1987; Robins, 1952), however, such that linguists now query whether these categories are truly shared cross-culturally across languages ( Croft, 2001; Kemmerer & Eggleston, 2010). Nouns and verbs are defined combinatorially and due to the extreme diversity of language systems (some which lack inflectional categories and function word types, for example), it is clear that the combinatorial criteria for inclusion in the noun/verb categories must differ between languages. At present, the brain-imaging work on nouns and verbs assume that these categories are valid in the Western population (speakers of English or European languages such as Italian and German) and that, therefore, it is possible that these categories have a shared and specific basis in the brain. It is this claim that we investigate here; the wider notion that all language-speaking individuals have inborn brain representational systems for these grammatical categories cannot be ascertained, as it is uncertain whether these conceptual categories can be applied to all languages in the same manner. The semantic feature that words are used to speak about actions or objects seems to be shared by many, if not all, languages and therefore would provide a solid basis for a cross-linguistic distinction.

Based on previous evidence from neuropsychological, neurophysiological and neurometabolic investigation, a range of authors have suggested that the lexical/grammatical category of words might be the primary dimension by which neural segregation is driven ( Shapiro et al. 2000; Shapiro et al. 2001; Caramazza and Shelton, 1998; Bedny et al. 2008; Cappelletti et al., 2008; Laiacona & Caramazza, 2004; Mahon & Caramazza, 2008; Shapiro et al., 2006; but see also Damasio & Tranel, 1993; Daniele et al., 1994; Gainotti, 2000; Luzzatti et al. 2002). This idea is founded on noun and verb dissociations in patient studies ( Bak, O'Donovan, Xuereb, Boniface, & Hodges 2001; Bak et al. 2006; Boulenger et al., 2008; Cappa et al., 1998; Cotelli et al. 2006; Damasio et al. 2001; Daniele et al. 1994; Miceli et al., 1984; Miceli et al., 1988; Shapiro & Caramazza, 2003), electrophysiological studies ( Brown, Lehmann, & Marsh 1980; Dehaene, 1995; Preissl, Pulvermüller, Lutzenberger, & Birbaumer 1995; Pulvermüller, Lutzenberger et al., 1999; Pulvermüller, Mohr et al., 1999; Pulvermüller, Preissl, Lutzenberger, & Birbaumer 1996) and metabolic imaging studies ( Perani et al. 1999; Warburton et al., 1996). As such, some authors, such as Bedny et al. (2012), suggest that language processing and conceptual representation is amodal and functionally separate from perceptual and action systems of the brain. This view has a rich tradition in approaches to cognitive science ( Anderson, 2003; Fodor, 1985; Machery, 2007), viewing the manipulation of abstract amodal symbols as a core component of mental functions. The amodal symbolic system would interface with sensorimotor systems only for receiving its input or passing on its output, but otherwise maintain functional separation from those brain systems concerned with action and perception (cf., for example, Bedny et al., 2012; Mahon & Caramazza, 2008; Pylyshyn, 1984). Therefore, this position interprets the noun/verb dissociations found in clinical and neurofunctional studies in the sense of a lexical category difference unrelated to semantics.

Problematically, as mentioned in the introduction, nouns and verbs differ on a range of dimensions uncontrolled for in many of the studies mentioned in the previous paragraph. These features are either semantic in nature (as many nouns relate to objects whereas most verbs are used to speak about actions) or immanent to psycholinguistics measures (for example word frequency) or more general linguistic features (for example to the degree to which combinatorial grammatical information is linked to classes of lexical items) (see, for example, Bird, Howard, & Franklin 2001; Bird, Lambon Ralph, Patterson, & Hodges 2000; Gainotti, 2000; Pulvermüller, Lutzenberger et al. 1999). Therefore, although noun/verb dissociations in patient populations and differential brain activation to these categories have been reported in the studies above, it is unclear to what degree such dissociation depends on linguistic and semantic features of these word groups.

In an attempt to take these confounds into consideration, Bedny et al. (2008) focused on nouns and verbs varying in semantic features, especially in their semantic relationship to motion perception. We would like to consider these findings in detail as, despite a similar design, Bedny and colleagues' stimulus selection along with their results dramatically differ from those reported here. Contrary to previous studies ( Martin et al. 1996), these authors reported that activity in middle temporal regions close to motion-sensitive areas "responded preferentially to verbs relative to nouns, even when the nouns have higher visual-motion properties" (than verbs) (p. 11352) and hence suggested that "concepts... are organised according to conceptual (lexical) properties" (p. 11347). In their attempt to tease apart lexical and semantic factors, these authors controlled semantic aspects related to visually perceived motion, grouping together animal nouns and action verbs as "high motion" items in spite of their fundamental differences with regard to a range of semantic dimensions. This neglect and lack of control for semantic aspects of verb and noun stimuli is a major shortcoming, as previous work has documented brain activation differences related to semantic action- vs. object-relatedness, manipulability of referent objects of nouns, or action-relatedness of verbs (see next section; Brambati et al. 2006; Damasio et al. 2001; Martin & Weisberg, 2003; Pulvermüller et al. 2009; Tranel, Martin, Damasio, Grabowski, & Hichwa 2005). Bedny's comparison of "high-motion" noun and verb categories, namely animal names and action verbs (such as "sheep" vs. "grasp"), is problematic, as we have demonstrated in previous work that many animal words lack action-semantic links and, correspondingly, fail to elicit action-related brain activity, whereas action verbs, which represent the prototype of action-related lexical materials, activate cortical motor systems along with middle-temporal cortex ( Moseley et al., 2012). It has indeed been suggested that the middle-temporal activation might reflect visual motion processing, but there is so far no firm proof for this hypothesis and general action-relatedness provides at least one alternative cognitive-semantic feature that may be reflected ( Kiefer et al. 2012). Because likely semantic determinants of their middle-temporal activations were not sufficiently documented, the noun/verb difference in brain activation observed by Bedny et al. cannot be seen as unrelated to semantics.

With greater control of semantic stimulus properties related to action and perception, our present findings as summarised in indeed failed to support the hypothesis brought forward by Bedny et al. that noun/verb differences might be sufficient for differential middle-temporal activation. This was true in spite of the care taken to replicate the exact regions of interest where Bedny and colleagues found their effects, and we even explored adjacent regions where activation maxima were observed in our present data set. Any significant main effects of lexical class were absent both in Bedny et al.'s left STS and temperoparietal ROIs and in adjacent ROIs defined in a data-driven manner. Although there was a weak tendency in the previously reported STS ROI towards higher activity for verbs, the opposite trend emerged from both TPJ and aSTS regions. Therefore the present data fail to confirm the conclusions drawn by Bedny et al. A recent review concludes that, after exclusion of linguistic and semantic confounds, any possible differences between the grammatical categories of nouns and verbs are weak if present at all ( Vigliocco et al. 2011). Our work leads us to concur that there is, to date, no unambiguous evidence for lexical category differences in middle temporal cortex. More generally, our present results seem to discourage the idea that lexical differences per se are reflected at brain-level by different areas for either "nouns" or "verbs".

4.2. Semantic categories in the brain

Whilst our findings belie local dissociation between words on the sole basis of lexical category, they are consistent with a semantic approach postulating that the meaning of words is reflected in differential brain activation topographies elicited when these words are recognised and understood. Any topographical difference in brain activation to concrete nouns and verbs, or neuropsychological dissociations between the same, would, accordingly, be a consequence of the fact that these items are typically used to speak about objects and actions respectively ( Gainotti, 2000; Pulvermüller & Fadiga, 2010; Pulvermüller, Lutzenberger et al. 1999; Pulvermüller, Mohr et al. 1999; Shallice, 1988).

The modulation of frontocentral brain activity by semantic features of stimulus words in the present study, especially the stronger activation seen in the central motor region to concrete action verbs compared with concrete object nouns, is consistent with a wealth of literature showing semantically-driven differences in word-elicited brain activation ( Aziz-Zadeh and Damasio, 2008; Barrós-Loscertales et al., 2012; Boulenger et al., 2009. Gainotti, 2000; González et al., 2006; Hauk et al., 2004; Kemmerer et al., 2008; Kiefer et al., 2008; Pulvermüller et al. 2001; Tettamanti et al. 2005; Willems, Hagoort, & Casasanto 2010). The appearance of dissociations within grammatical categories, for example between face-, arm- and leg-related verbs ( Hauk et al., 2004) and between action- and sound-related nouns ( Kiefer et al., 2012; Trumpp, Traub, & Kiefer, 2013) is strong evidence for semantic modulation of neural response to words which is independent of lexical category.

Patient studies, too, would appear to support this interpretation. Deficits for processing tool concepts and words are associated with frontoparietal sensorimotor systems ( Gainotti, 2004; Gainotti, Silveri, Daniel, & Giustolisi 1995) and deficits for animals with occipitotemporal regions ( Hart & Gordon, 1992; Tranel, Damasio, & Damasio 1997). These dissociations appear to be underpinned by the dissociation between action- and perception-related knowledge, with manipulability and other action-features most relevant for tools, and visual-features such as colour and form most relevant for animals. More recent work with stringent psycholinguistic matching has revealed relative impairments for action-word processing in a range of neurological diseases and disorders characterised by motor impairment ( Bak et al., 2001; Bak et al. 2006; Boulenger et al., 2008; Cappa et al., 1998; Cotelli et al., 2006; Moseley et al., 2013). Importantly, deficits in processing action language, associated with lesions to inferior frontal and motor systems, are accompanied by concordant deficits in semantic processing of actions in nonverbal tasks ( Bak et al. 2006). This pattern of deficits provides further evidence for a semantic rather than grammatical basis of category-specific semantic and conceptual disorders, a position reached by two recent reviews of the literature ( Kemmerer et al., 2012; Kiefer & Pulvermüller, 2012).

The conclusions drawn in the present paper are consistent with previous works but avoid some of the methodological pitfalls evident in the same. Vigliocco et al. (2006), as in the current paper, reported brain dissociations between sensory and motor words but no distinctions on the basis of lexical category. Problematically, this study used Italian nouns and verbs sharing the same stem but differing in their affixes, which immediately inform the reader of the word's lexical category. The co-occurrence of verb affixes with verb stems (used to speak about actions) and the co-presence of noun affixes with nouns (related to objects) appears to indirectly load the neuronal circuit of affixes with semantic links ( Pulvermüller & Shtyrov, 2009). The study also suffered from poor stimulus matching, such that apparent dissociation between motor and sensory words might also be explained by differences in familiarity, imageability and age of acquisition (see, for example, Hauk et al., 2008). Other electrocortical dissociations on the basis of both lexical and semantic distinctions were reported by Kellenbach et al. (2002) and Barber, Kousta, Otten, and Vigliocco (2010). Whilst these could not be localised to specific brain regions in the former, the latter argued that, as both differences showed the same N400 topography, they might both best be explained in terms of a semantic effect ( Barber et al., 2010). Whilst we too would support primary dissociations between word types at the semantic level, the N400 can reflect a range of different psycholinguistic features (e.g., Hauk, Davis, Ford, Pulvermüller, & Marslen-Wilson, 2006) so that a strong conclusion on semantics being the only relevant variable required more support from an experiment avoiding major psycholinguistic confounds.

In light of these flaws in pre-existing research, our present study using well-matched stimulus materials, spatially precise event-related fMRI and a fully orthogonal design crossing the effects of lexical category and semantic type now provides strong support that action- and object-related referential semantics but not lexical categories (noun/verb) are reflected at brain-level by a topographical distinction between motor systems and inferior-temporal activations. The current work can therefore corroborate some of the statements made by studies above which, due to their methodological flaws, could not be strongly defended the findings reported here suggest that previously reported noun/verb differences in the brain were driven by semantics. This position seems consistent with an EEG study, where Pulvermüller, Mohr et al. (1999) reported neurophysiological dissociations between action verbs and object nouns, which were closely paralleled by the contrast between action and object nouns, but no evidence for neurophysiological dissociations between action nouns and verbs.

A lack of neurophysiological and neurometabolic differences in brain activation patterns elicited by the lexical categories might lead some to suggest that lexical categories are illusory, lacking a brain basis - an argument that would of course be flawed. Apart from their semantic differences, nouns and verbs are distinct in their combinatorial properties: English nouns combine with articles and adjectives, and verbs combine with nouns, pronouns and specific prepositions or complementizers. It is necessary to neurally represent the different combinatorial properties of these words in the brain, and the imprinting of different combinatorial patterns of nouns and verbs in a neurocomputational model induces fine-grained connection differences at the neuronal circuit level which provide a neuromechanistic correlate of combinatorial lexical categories ( Buzsáki, 2010; Pulvermüller, 2010; Pulvermüller & Knoblauch, 2009). However, such differences at the micro-circuit level, related to the combinatorial properties of nouns and verbs, may be too fine-grained to become manifest as differential brain activations revealed by standard neuroimaging techniques (fMRI, EEG or MEG). As such, with the data available at present, these topographical differences between word types are best explained in semantic terms, as outlined in the following section.

4.3. Neuronal dissociations between concrete nouns and verbs as explained by the cell assembly model

Differential activation was found for concrete nouns and verbs, whereby the latter activated motor and premotor areas more strongly than the former and the opposite contrast was significant in inferior frontal cortex. These results are consistent with and explained by a neuromechanistic model rooted in Hebb's concept of distributed cell assemblies. Because knowledge about the form and meaning of a word are normally active together such that neuronal connections between the respective neuronal circuits are strengthened, these meaning- and form-related circuits are joined together into one higher-order semantic network - to the degree that one circuit part typically does not activate without the other becoming active too. There is room for flexibility in this mechanism, especially if attentional resources are limited, overt motor action is being prepared for, or context puts a focus on grammatical processing ( Angrilli, Dobel, Rockstroh, Stegagno, & Elbert 2000; Chen, Davis, Pulvermüller, & Hauk 2013; Hoenig, Sim, Bochev, Herrnberger, & Kiefer 2008; Pulvermüller, Cook, & Hauk 2012; Rueschemeyer, Lindemann, Van Elk, & Bekkering 2009; van Elk, van Schie, Zwaan, & Bekkering 2010). However, for typical passive tasks (reading, listening), action-related verbs activate semantic circuits involving motor and action schemas stored in motor and premotor cortex, and a wealth of neuroimaging and neuropsychological work indicates that this activation is functionally important for action word processing ( Buccino et al. 2005; D'Ausilio et al. 2009; Devlin & Watkins, 2007; Glenberg & Kaschak, 2002; Moseley et al., 2013; Pulvermüller et al. 2005; Shebani & Pulvermüller, 2011).

For object-related nouns, visual knowledge about objects stored in inferior-temporal areas is of special relevance. Previous research ( Kiefer et al., 2008; Kiefer et al., 2012; Martin et al., 1996; Pulvermüller & Hauk, 2006) has documented focal differences between fine-grained word types in temporal cortex. This was not replicated in our dataset, possibly because our concrete noun category lacked semantic uniformity, including nouns from several different semantic categories which may have led to a mix of temporal region activations and weighed against semantic dissociations. For example, the concrete noun category was predominantly dominated by animal names, which were rated as strongly semantically-related to form knowledge ( Appendix B). Pre-existing work reported that form-related words activate inferior frontal areas (−46 28 10; Pulvermüller & Hauk, 2006), such that the current activation advantage for concrete nouns in more anterior inferior frontal cortex (−27 33 11) may be hypothesised to reflect form knowledge immanent to animal concepts. In this context, it is important to recall that our inferior frontal ROIs, where concrete nouns activated more strongly than concrete verbs, were motivated by previous work by Martin and colleagues, who reported stronger activation during animal naming compared with tool naming in these regions ( Chao et al. 1999; Martin & Chao, 2001; Martin et al. 1996). However, as other concrete nouns were also part of this lexico-semantic subcategory, it is not surprising that any inferior-frontal effect potentially related to form-semantics did not yield clear significant results.

4.4. Abstract nouns and verbs and the motor system

It may come as a surprise that abstract nouns and verbs activated the motor systems to a similar degree as action verbs. There are however theoretical arguments for involvement of motor systems in abstract meaning processing. For abstract words typically used to speak about emotions and internal states of the body, semantic theory postulates that these are learnt when word form and state-/emotion-expressing actions are linked with each other ( Baker & Hacker, 2009; Wittgenstein, 1953) - a prediction consistent with motor activity evoked by emotion-related words ( Moseley et al. 2012). (Note that abstract emotion words may be both nouns and verbs (e.g. (the) fear), and, therefore, a degree of motor activation to the nouns and verbs in this study can be explained). Abstract metaphors, idioms and other types of abstract concept, including numbers, have also been suggested to be intrinsically linked with visually-observable behaviours and actions ( Boulenger, Shtyrov, & Pulvermüller 2012; Boulenger et al., 2009; Glenberg et al. 2008b; Tschentscher, Hauk, Fischer, & Pulvermüller 2012) or arrangements/relationships in space ( Casasanto, 2009; Lakoff & Johnson, 1980) that represent typical instantiations of their abstract meaning. In this view, knowledge about actions and perceptions and corresponding processes in sensorimotor areas of cortex play a role in abstract concept and meaning processing ( Barsalou, 1999; Gallese & Lakoff, 2005; Kiefer and Pulvermüller, 2012; Lakoff & Núñez, 2000; Wilson-Mendenhall, Barrett, Simmons, & Barsalou, 2011).

Abstract nouns and verbs can, of course, differ semantically both between and within their lexical categories, and in order to obtain a representative sample of abstract items from each lexical category, it was not possible to focus on specific semantic subclasses of abstract terms in this present work. Our results are therefore consistent with a fundamental role of motor systems in abstract word and concept processing, as suggested above. On theoretical grounds, the cell assembly model predicts comparably weak sensorimotor links for some abstract terms (e.g., "beauty" and "justice"), because their semantic manifestation in action and perception is quite variable and therefore correlation learning predicts relatively weak links between sign and concept. We did not find a general difference in activation between our strongly action-related verbs and the abstract categories here, but, as mentioned, this may be due to the stimulus selection, especially a low proportion of abstract terms with variable semantics in the present stimulus set. In this context, it is noteworthy that Pexman et al. (2007) also found sensorimotor activation for both abstract and concrete concepts but in their study activation to the former was weaker than that to the latter, which is consistent with somewhat weaker sensorimotor semantic links in cell assemblies for abstract semantics.

5. Conclusions

This study investigated whether topographical patterns of brain activation to nouns and verbs are driven by lexical (grammatical) class or rather by semantics and word meaning. We found that concrete nouns and verbs activate frontocentral cortex to different degrees. Whereas motor and premotor areas are relatively more strongly activated by action verbs, concrete nouns activated more anterior prefrontal areas. At the cognitive level, these differential activations appear to relate to the processing of action schemas that are part of the semantic representation of action verbs and of form knowledge semantically linked to object words. Abstract nouns and verbs fail to elicit similar activation differences, thus calling into question previous claims about genuine brain loci for the major lexical categories. Systematic investigation of other areas, especially temporal cortex, also failed to reveal a genuine distinction between noun and verb processing loci. We suggest that topographical brain activation differences elicited by words are driven by semantic factors and that the lexical category distinction is mechanistically implemented at a level beyond the grain size of neurometabolic imaging.

Funding

This work was supported by the MRC (MC_US_A060_0034, U1055.04.003.00001.01 to F.P., MRC studentship to R.M.), EPSRC and BBSRC (BABEL grant), DFG (Center of Excellence "Languages of Emotion") and Freie Universität Berlin.

Acknowledgments

We would like to thank Clare Cook, Olaf Hauk, Bettina Mohr, Yury Shtyrov and Francesca Carota for their help at different stages of this work.

Appendix A

Experimental word stimuli (presented in lowercase).

Appendix B

Psycholinguistic and semantic properties of word stimuli. Statistical analysis of variance (ANOVA) between word groups is displayed in F values, as are possible interactions and main effects.

Appendix C

MNI coordinates evoked by the contrast of all experimental words against a low-level visual baseline (###), calculated at an FDR-corrected significance level of p < .05.The maxima from the most highly significant cluster, which were the focus of our analysis, are highlighted in bold.

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Nouns, verbs, objects, actions, and abstractions: Local fMRI activity indexes semantics, not lexical categories

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Noun/verb dissociations in the literature defy interpretation due to the confound between lexical category and semantic meaning; nouns and verbs typically describe concrete objects and actions.

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The Geography of Thought has 1,798 ratings and 208 reviews. Matthew said: While this book certainly sheds a lot of light on the different approaches in t...

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https://www.google.com.sg/search?q=yang+liu+artist+the+boss&rlz=1CDGOYI_enSG721SG721&hl=en-GB&prmd=ivn&source=lnms&tbm=isch&sa=X&ved=0ahUKEwjY76LK0bfWAhWMN48KHaInAlUQ_AUIEigB&biw=414&bih=660&dpr=3#imgrc=XBn_pJbWhySzTM:https://www.google.com.sg/search?q=yang+liu+artist+the+boss&rlz=1CDGOYI_enSG721SG721&hl=en-GB&prmd=ivn&source=lnms&tbm=isch&sa=X&ved=0ahUKEwjY76LK0bfWAhWMN48KHaInAlUQ_AUIEigB&biw=414&bih=660&dpr=3#imgrc=XBn_pJbWhySzTM:

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yang liu artist the boss - Google Search

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SoTL - translation - transfer
Generalize
Evidence - other contexts
Theory - background- underlying- theory building

https://www.jstor.org/stable/j.ctt16gh5wx

http://digitalcommons.georgiasouthern.edu/cgi/viewcontent.cgi?article=1013&context=ij-sotl

https://en.m.wikipedia.org/wiki/Thick_description

Moderate essentialism

NATURE and NURTURE

Biology and Culture

World views - Investigator bias and lens AND communication with others

Localise “all ... is local “

Generalise

SoRL research needs THICK DESCRIPTION which is LEGIBLE to others

KNOW THE FIELD
KNOW YOUR AUDIENCE
KNOW YOUR INTENT

Data and evidence of quality of T and L
Role of SoTL
Benchmarks
Thick description
Quality assurance
Promotion

BIOLOGY and LEARNING- TRAINING EXPERIENCE and ENVIRONMENT s

How to recognise and evaluate excellence (teaching or professional)

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First in a three-part series: Mary Huber, Ph.D., introduces us to SoTL, the scholarship of teaching and learning, its motivation and history.

We are posting this work, "What is the Scholarship of Teaching and Learning?" as a series of blog posts with permission from the original author.

About Mary Huber
Mary Taylor Huber is senior scholar emerita and consulting scholar the Carnegie Foundation for the Advancement of Teaching, and serves as a content specialist to Stanford's CTL on the scholarship of teaching and learning. Since joining the Carnegie Foundation in 1985, Huber has directed projects on Cultures of Teaching in Higher Education; led Carnegie's roles in the Integrative Learning Project and the U.S. Professors of the Year Award; and worked closely with the Carnegie Academy for the Scholarship of Teaching and Learning (CASTL). She speaks, consults, and writes on the scholarship of teaching and learning, integrative learning, and faculty roles and rewards.
Part I: What is the Scholarship of Teaching and Learning?

"...to improve as teachers, faculty need reliable information about how well students are learning in their classes. They also benefit from opportunities to collaborate and to learn from professional colleagues" ( The Study of Undergraduate Education at Stanford University, 2012, p. 86).

"Ultimately, investigative work into teaching and learning will not be an intriguing aside, or add-on, but an essential facet of good teaching-built into the expected repertoire of scholarly practice" (Shulman, 2000, p. 105).

The scholarship of teaching and learning (SoTL) is an approach to college and university teaching that views classrooms (and other learning spaces) as sites for inquiry, innovation, and knowledge-building.

In SoTL, you, the educator, look closely and critically at your students' learning to improve your own courses and programs. You also go public with insights, experiences and results that colleagues can evaluate and build on.

By doing SoTL, you advance the profession of teaching in higher education by joining pedagogical conversations in and across institutions and fields.

Why Engage in SoTL?

This is an extraordinary time for teaching and learning in higher education. On the one hand, there's been bad news about "limited learning" in college from the large longitudinal studies conducted by Richard Arum and Josipa Roksa (2011) and by the Wabash National Study of Liberal Arts Education (Pascarella, Blaich, Marin, and Hanson, 2011). On the other hand, new approaches to creating more engaging learning environments are exciting. New developments in technology have gained attention most recently, but other pedagogies and programs encouraging more active and collaborative learning have also invited experimentation and show promise (see Kuh, Kinzie, Schuh, and White, 2005).

People in virtually every discipline are paying more attention to undergraduate education. And, while some differ within and among these communities, they all agree that they should base pedagogical innovation on deep understanding of, and evidence about, student learning.

Educators interested in teaching practices and their relation to student learning are making lessons from the learning sciences more accessible, formally evaluating various teaching techniques, and doing robust and rigorous inquiry-often called "the scholarship of teaching and learning" or "SoTL."

History

Introduced into the vocabulary of higher education as "the scholarship of teaching," in Scholarship Reconsidered by Ernest L. Boyer (1990), the idea gained attention for giving college and university teaching a place within a broader vision of scholarship that also included the discovery, integration, and application of knowledge.

In later years, under the guidance of Stanford Professor Emeritus Lee Shulman, who succeeded Boyer as President of the Carnegie Foundation for the Advancement of Teaching, the work became more widely known as "the scholarship of teaching and learning" (See Shulman, 2004, 2011).

Since then, the term has been widely adopted in higher education both in the US and internationally (at least in Anglophone countries) to refer to an inquiry-driven approach to teaching in which educators look carefully and critically at their students' learning, use the results to improve their teaching, and share what they have found with their colleagues in their institutions and fields.

SoTL has become a valuable addition to the wider "teaching commons," defined as "an emergent conceptual space for exchange and community among faculty, students, and all others committed to learning as an essential activity of life in contemporary democratic society" (Huber and Hutchings, 2005, p.1). This larger commons includes as well the work of researchers in education and the learning sciences, scholars engaged in discipline-based education research (National Research Council, 2012), professional development staff, and public policy experts, to name a few.

SoTL brings the experience of students and faculty into this larger conversation, giving practitioners a seat at the table and enabling them to speak with a voice that can be heard (Huber and Hutchings, 2005; Hutchings, Huber, and Ciccone, 2011).

Next up: Doing SoTL. Read the full text of Mary's paper "What is the Scholarship of Teaching and Learning?", which includes an extensive list of resources.
Mary Huber is Senior Scholar Emerita and Consulting Scholar at The Carnegie Foundation for the Advancement of Teaching. Involved in research at the Carnegie Foundation since 1985, Huber has directed projects on Cultures of Teaching in Higher Education; led Carnegie's roles in the Integrative Learning Project and the U.S. Professors of the Year Award; and worked closely with the Carnegie Academy for the Scholarship of Teaching and Learning (CASTL). She speaks, consults, and writes on the scholarship of teaching and learning, integrative learning, and faculty roles and rewards.
References Cited

Arum, R., and Roksa, J. 2011. Academically Adrift: Limited Learning on College Campuses. Chicago: University of Chicago Press. Preview of first two chapters available at: http://www.amazon.com/Academically-Adrift-Limited-Learning-Campuses/dp/0226028569

Bernstein, D., and Bass, R. 2005. The Scholarship of Teaching and Learning. Academe 91(4): 37-43. Available through Academe's J-Stor site: http://www.stanford.edu/dept/undergrad/sues/SUES_Report.pdf

Boyer, E. 1990. Scholarship Reconsidered: Priorities of the Professoriate. Princeton, NJ: The Carnegie Foundation for the Advancement of Teaching. PDF available at: http://www.hadinur.com/paper/BoyerScholarshipReconsidered.pdf

Huber, M., and Hutchings, P. 2005. The Advancement of Learning: Building the Teaching Commons. San Francisco: Jossey-Bass. PDF of first chapter available at: http://www.carnegiefoundation.org/publications/advancement-learning-building-teaching-commons

Hutchings, P. 2000. "Approaching the Scholarship of Teaching and Learning." Introduction to P. Hutchings (ed.). Opening Lines: Approaches to the Scholarship of Teaching and Learning. Menlo Park, CA: The Carnegie Foundation for the Advancement of Teaching. PDF available at: http://www.carnegiefoundation.org/publications/opening-lines-approaches-scholarship-teaching-and-learning

Hutchings, P, ed.. 2002. Ethics of Inquiry: Issues in the Scholarship of Teaching and Learning. Menlo Park, CA: The Carnegie Foundation for the Advancement of Teaching. PDF of Foreword available at: http://www.carnegiefoundation.org/publications/ethics-inquiry-issues-scholarship-teaching-and-learning

Hutchings, P., Huber, M., and Ciccone, A. 2011. The Scholarship of Teaching and Learning Reconsidered: Institutional Integration and Impact. San Francisco: Jossey-Bass. PDF of first chapter available at: http://www.carnegiefoundation.org/publications/the-scholarship-teaching-and-learning-reconsidered-institutional-integration-and-impact

Indiana University's History Learning Project. http://www.iub.edu/~hlp/

Linkon, S. L. 2011. Literary Learning: Teaching the English Major. Bloomington, IN: Indiana University Press.

National Research Council. 2012. Discipline-Based Education Research: Understanding and Improving Learning in Undergraduate Science and Engineering. Singer, S.R., Nielsen, N.R., and Schweingruber, H.A. (Eds). Committee on the Status, Contributions, and Future Direction of Discipline-Based Education Research. Board on Science Education. Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press. PDF can be downloaded free at: http://www.nap.edu/catalog.php?record_id=13362

Pace, D., and Middendorf, J. 2004. Decoding the Disciplines: Helping Students Learn Disciplinary Ways of Thinking. (New Directions for Teaching and Learning, 98). San Francisco, CA: Jossey-Bass. Summary available at History Learning Project site: http://www.iub.edu/~hlp/ Full PDF available at: http://www.iub.edu/~tchsotl/part3/Decoding Middendorf.pdf

Pascarella, E., Blaich, C. , Marin, G.L., and Hanson, J. M. 2011. "How Robust are the Findings of Academically Adrift?" Change 43 (3): pp. 20-24. Abstract available at: http://www.changemag.org/Archives/Back%20Issues/2011/May-June%202011/academically-adrift-abstract.html Full PDF available at: http://www.tandfonline.com/doi/full/10.1080/00091383.2011.568898

Salvatori, M. R., and Donahue, P. 2004. The Elements (and Pleasures) of Difficulty. New York: Pearson/Longman.

Shulman, L. S. 2000. "Inventing the Future." Conclusion to P. Hutchings (ed.), Opening Lines: Approaches to the Scholarship of Teaching and Learning. Menlo Park, CA: The Carnegie Foundation for the Advancement of Teaching. PDF available at: http://www.carnegiefoundation.org/publications/opening-lines-approaches-scholarship-teaching-and-learning

Shulman, L.S. 2004. Teaching as Community Property: Essays on Higher Education. San Francisco: Jossey-Bass. Introduction available at: http://www.carnegiefoundation.org/publications/teaching-community-property-essays-higher-education

Shulman, L. S. 2011. "The Scholarship of Teaching and Learning: A Personal Account and Reflection." IJSOTL (International Journal of the Scholarship of Teaching and Learning) 5 (1). http://academics.georgiasouthern.edu/ijsotl/v5n1/featured_essay/PDFs/_Shulman.pdf

Stanford University. 2012. The Study of Undergraduate Education at Stanford University. Stanford, CA: Stanford University. http://www.stanford.edu/dept/undergrad/sues/SUES_Report.pdf

Werder, C., and Otis, M. M. 2010. Engaging Student Voices in the Study of Teaching and Learning. Sterling, VA: Stylus Publishing, LLC.

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What is the Scholarship of Teaching and Learning? with Mary Huber

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First in a three-part series: Mary Huber, Ph.D., introduces us to SoTL, the scholarship of teaching and learning, its motivation and history. We are posting this work, "What is the Scholarship of Teaching and Learning?" as a series of blog posts with permission from the original author.

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Dong C, Goh PS. Twelve tips for the effective use of videos in medical education. Med Teach. 2015 Feb; 37(2):140-5.

http://www.ncbi.nlm.nih.gov/pubmed/25110154

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Message to participants:

"Thank you for signing up for the "Use of videos in medical education" workshop at CenMED, NUS. We strongly encourage you to complete the pre-workshop reading and assignment below before the workshop. This will give you background knowledge, and a foundation to build on during the workshop, and enable you to engage in the workshop discussion and interactive activities to a greater degree. The idea is to start to build a theoretical understanding before the workshop, to enable you to use your time in the workshop to clarify and reinforce basic concepts, and to develop further skill in thinking about, evaluating and making use videos in medical education.

Your participation in the workshop activities will embody and illustrate many of the ideas and educational principles underlying the flipped classroom practice. Specifically, you should plan to undertake approximately 3 hours of preparation at home the workshop. We encourage you to begin you workshop preparation early. This will include reading the few recommended articles (see section below after the Padlet wall), before summarising the take home points and posting this, together with a teaching project where you hope to use videos in medical education, on the workshop Padlet wall below, before the workshop (this is an interactive digital wall, which is on a private space on Padlet.com, and is not searchable on Google - you are able to write, edit and delete your own posts, without needing to login (by just double clicking or double tapping on the Padlet wall, or clicking the circled red + button bottom right of the Padlet wall), as long as you use the same mobile or desktop device; even though this platform works with all mobile and desktop devices, you will probably find using a laptop/workstation or tablet the easiest way to write and edit your posts on this website. You might also op to use initials, and omit specifying your institutional affiliation, in order to further anonymise your Padlet posts, as this workshop blog is available for open access).

By undertaking pre-workshop preparation, actively writing down your reflection on this pre-workshop preparation, as well as starting to think about and write down a draft proposal on how you intend or plan to use videos in your educational activities, and posting this on the Padlet wall before the workshop, will put you in a much better position to take advantage of the activities during the workshop to add to your thinking, and develop your educational plans and proposals further during, and after the workshop. There is also a better opportunity for specific feedback to be given to you.

Please bring along to the workshop the same WiFi enabled mobile device, tablet or laptop that you have used to post on the Padlet wall. This will enable you to continue to refine, and add to your post on the Padlet wall. I have organised the Padlet posts so that the latest entry is always at the top. This will enable you to find your earlier post by scrolling down. You can interact with the Padlet website directly on the blog, or by going to the Padlet link immediately below the embedded Padlet webpage below.

May we also request that you complete the short anonymised Survey Monkey survey embedded on the blog below, before the workshop, to give us an idea of your teaching background and what you hope to get out of the workshop."

(Please read [above] the articles above on using videos in medical education. Please write a short 250 word maximum personal reaction, and post this on the Padlet wall above, together with a brief description of your teaching experience, current teaching responsibilities, and a short 250 word maximum proposal of how you intend to use videos in your teaching setting. You should complete this assignment
http://padlet.com/dnrgohps/UseOfVideosMedEd
(you can choose to post directly on the embedded Padlet wall, or open the Padlet wall as a separate website by clicking on the link above)

Pre-reading and pre-workshop assignment:
To Play or Not To Play: Leveraging Video in Medical Education (J Grad Med Educ, 2013)
Use of online clinical videos for clinical skills training for medical students: benefits and challenges (BMC Medical Education, 2014)
before you come to the workshop. We will be developing your proposal further during the workshop.
Please feel free to explore the other online resources on this workshop blog. This blog will be the main platform for presenting and reviewing the educational content we will be covering during the workshop.)

as an exercise, compare viewing the video above, with reading the transcript

then consider what might be the "best" way to blend or combine video with a transcript, with traditional "teaching"

Narrated text of workshop presentation:

Welcome to all of you, and thank you for signing up for this Use of Videos in Medical Education workshop organised by CenMED.

My name is Poh Sun, and may I also introduce my fellow faculty, Terry.

To start, can I ask all of you to recall, and reflect on the engagement process undertaken by CenMED, starting from the email all of you received, the reminder emails, sharing with all of you the workshop blog, which incorporates instructions for pre-workshop reading, a pre-workshop assignment and an embedded online bulletin board or Padlet wall for you to post, and share your reflections on the pre-reading, and your educational background and educational setting.

May I ask those of you who were able to both do the pre-reading and post on the Padlet wall to raise your hands? Did you have any difficulty with either task?

How many of you were able to do some or all of the pre-reading, but did not post on the Padlet wall?

Could some of you share why was this so?

For those of you who were not able to do the pre-reading, could you share with us why your were not able to do so?

Let's now examine some of the key ideas related to using videos in medical education.

Can I invite those of you who have done the pre-reading to share with us why you feel that videos should be used in medical education?

1. Why should one consider using videos in medical education?

What do we teach with? Why do we teach?

What do we hope our students learn? Know and are are able to do?

- telling → illustrating - showing, demonstrating → practice with feedback

(words → pictures → video)

"The impact of visual representation on recall and retention of taught material has been demonstrated. One study of teaching methods found that telling alone, showing material visually alone, and combined telling and showing each resulted in 70% or greater recall at 3 hours (85% for combined). However, retention 3 days later was 10% for telling alone and 20% for showing alone, compared with 65% retention for combined telling and showing."

Dwyer FM. Strategies for Improving Visual Learning. State College, Pa: Learning Services; 1978. pp. 1-20.

A Picture is Worth a Thousand Words: Practical Use of Videotape in Teaching

Linda E Pinsky and Joyce E Wipf

Below from Google search for

"It is important not to fall into the trap of considering that the use of technology or media is going to be the "silver bullet" that will make students learn or be more motivated. The learning activities that students perform with videos are a critical part of the learning outcomes and motivations (Boyle, 1997). That is, simply presenting information in a stimulating digital video format will not automatically nor necessarily lead to in-depth learning (Karppinen, 2005). Rather it is the pedagogy, the well crafted message, the whole approach, and design that are the critical elements, not the media".

above quote from website below

Applying the science of learning to medical education

MEDICAL EDUCATION 2010; 44: 543-549

"In this paper, we discuss creative applications of videotapes in three major categories: presenting information, triggering discussion, and as a tool for direct self-observation and feedback."

A Picture is Worth a Thousand Words: Practical Use of Videotape in Teaching

Linda E Pinsky and Joyce E Wipf

J Gen Intern Medv.15(11); 2000 NovPMC1495618

"On the shift toward video, Mendelsohn said: "The best way to tell stories in this world- where so much information is coming at us-actually is video. It commands so much information in a much quicker period so actually the trend helps us digest more of the information in a quicker way."

2. How could one start to do use videos?

What are some of the key tips and ideas one should be aware of?

3. How could you blend videos with your current teaching?

e.g. the purpose of this video ..... (provide link) .... is to illustrate, or to demonstrate the following .... for the following purpose ...... before or during class ..... in order for the students to understand / to know how to / to start a discussion on ......

4. As you go through this workshop, please take notes, refine your educational project, and post this on the Padlet wall elaborating on your original pre-workshop post further

At the end of the workshop, soon after the workshop - please go back to your original Padlet post, and

1. Write down the key messages/ideas that you feel resonate with you, and that you might find useful

2. Write down why you feel these messages and ideas might be helpful in your educational setting

3. Write down how you intend to use/apply these ideas

(What you take away from an educational session, what you are able to recall, and apply is ultimately an indicator of successful individual learning).

1. Use existing videos available online - curate and index useful videos, to improve access /accessibility

2. Work with professional videographers and instructional designers (existing institutional)

3. Observe best practices, and incorporate these into your own practice

4. Try out, experiment, get feedback, gather evidence, reflect on practice

5. Start slowly, and progressively and cumulatively gain experience, expertise and confidence

6. Always consider your learning objectives, and apply best practices in instructional design and educational pedagogy

(eLearning, eRepositories and eScholarship)

"Based on these ideas, it is possible to sketch in a few thoughts about what teachers need to do to support learning. They have to make sure that the point of the learning is clearly communicated to everybody involved and that there is shared understanding of what it means to get there. Educators need to watch for the actions that show that people are starting to move toward the aims of the learning and support those actions. Learners need to have material structured in a continuum from easier ideas and actions toward more challenging ones, so when they have mastered simpler processes they can tackle the more complex ones that build on them (this is referred to as "scaffolding"). The drive in learning is toward understanding and mastery, meaning that educators have to ensure that learners finish the course feeling that they have achieved these two elements.

In the introduction I suggested that responsible teaching recognized the following principles:

Encourage active learning. People need to learn by doing wherever possible, rather than simply hearing or reading about how to do.

Allow people to have some control over their own learning.

Build connections between what is being learned and the experiences of learners, moving over time toward more complex ideas.

Encourage collaboration and conversation between learners."

above from blog post below

7. Ultimately, our aim is to blend and combine video with other instructional formats, to give our students choices, to move from a teacher centric to learner centric paradigm.

Case studies

(table below from earlier working draft of above article)

(useful resources and how to do it tips from CDTL, NUS)

Pre-workshop SurveyMonkey responses:

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12 Tips for Using Video in Medical Education

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Message to participants: "Thank you for signing up for the "Use of videos in medical education" workshop at CenMED, NUS. We strongly encourage you to complete the pre-workshop reading and assignment below before the workshop.

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from
https://www.slideshare.net/dnrgohps/applied-learning-analytics-for-modular-content-updated-79912376

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Applied learning analytics for modular content (updated)

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illustration from
https://www.instagram.com/p/BPLQ61ThCWR/?taken-by=gohpohsun&hl=en
and see also
https://www.slideshare.net/dnrgohps/reflect-on-how-blooms-taxonomy-and-millers-pyramid-might-apply-to-learning-continuum-map

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View this post on Instagram

A post shared by Goh Poh Sun 吳 宝 山 (@gohpohsun)

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illustration from
https://www.instagram.com/p/BPV65EIBxHY/?taken-by=gohpohsun&hl=en

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View this post on Instagram

A post shared by Goh Poh Sun 吳 宝 山 (@gohpohsun)

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illustration from
https://www.instagram.com/p/BP0tO-ClotF/?taken-by=gohpohsun&hl=en

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note taking promotes active learning (see blog post)
http://www.e-ir.info/2015/10/17/strategies-for-making-large-lectures-more-interactive/

1. Start the lecture with an activity so students feel that they have permission to interact with the material, the lecturer and other students.

2. Give students 2-3 minutes to summarise a section or key idea before you move on.

3. Stop and give students 3 minutes to write down their understanding of a particular concept.

4. Give students a few minutes to write down things they don’t yet understand or were not sure about which they can then discuss with the person sitting next to them or direct questions to the lecturer.

5. Ask a question but give everyone a minute or two to write an answer before asking for responses.

6. At the end of the lecture give students two minutes to write down the main point of the lecture and any questions they still have for discussion in tutorials.

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Active learning is what we would like students to be doing with the taught material: thinking about and engaging with the content, rather than mindlessly copying slides or thinking about something else altogether. In a previous post I wrote some thoughts on active learning as a concept - this week is all about strategies. Early experiences in lecturing are often focused on surviving the lecture (for you, not for the students) but moving beyond that we can think more about what the students are doing during the lecture and how to promote active engagement with the content.

A simple way to do this is to discuss note taking with students. It might seem basic, but if you provide students with PowerPoint slides (so they don't feel like they have to write everything) and give them cues on what to take notes of then you can open up more space for thinking about the material. This could be as simple as encouraging students to write down questions that arise while listening, concepts they don't yet understand, or connections they can see between lectures or examples. I also discuss with students how they take notes physically, as recent research suggests that people have better recall when writing notes by hand than they do from typing notes (I am not prescriptive on this - for me it's about giving students information to help them decide what is right for them). Giving students cues on what to take notes on can help them to recognise which sections are most important and can help them to prioritise.

After trawling through articles in Education and International Relations I've pulled together a list of strategies for encouraging active learning in lectures. One way of promoting active learning is via multi-week simulations - which are great for some topics - but I focused here on simple strategies that could easily be integrated into any lecture topic. The suitability of these strategies will obviously depend on the material being taught, the year level of students, and the size of the class. In collecting these ideas I particularly envisaged large first-year lectures. But part of my aim in collecting a big list of ideas was to have a range of different ideas to use, depending on the situation and content. I also find it helpful when introducing an activity to explain to students the rationale of the activity - whether it links to a certain type of assessment or to a particular skill like critical thinking. Being aware of the purpose of activities can also help students to recognise - and articulate - the transferable skills they are developing.

These are not 'activities for the sake of activities' but will need to be tailored according to what you want students to learn. The timing of activities will also vary depending on your goals; i.e. a quiz at the start of a lecture to check last week's learning; a quiz mid-way through a lecture to break up material and refocus; or a quiz at the end of a lecture to recap material. The ideas in this list are not specific activities to meet specific learning objectives, rather they are ideas that can be applied to the content you are teaching. The general premise here is to have some diversity not only in what students are doing, but also what they are seeing and hearing. This is also a way to support diverse learning styles, as different activities will appeal to different students.

1. Start the lecture with an activity so students feel that they have permission to interact with the material, the lecturer and other students.

2. Problem solving exercises in pairs or groups - applying a concept.

3. Video clips, with specific instructions on what to look for.

4. Questions listed on slides.

5. Make connections between lectures to recap and to build on prior knowledge.

6. A vote on opposing perspectives.

7. Encourage students to ask questions - both during the class as well as at the end.

8. Give students 2-3 minutes to summarise a section or key idea before you move on.

9. Ask students to make a judgement on a situation.

10. Ask students to list and prioritise concepts presented in the lecture.

11. Stop and give students 3 minutes to write down their understanding of a particular concept.

12. Give students a few minutes to write down things they don't yet understand or were not sure about which they can then discuss with the person sitting next to them or direct questions to the lecturer.

13. Ask students to brainstorm possible exam questions on a given topic and then give feedback on whether they were about the right degree of difficulty.

14. Ask a question but give everyone a minute or two to write an answer before asking for responses.

15. Ask students to explain their understanding of a concept to the person sitting next to them - teaching is an effective means of learning.

16. A quiz with show of hands.

17. Give students a break to recharge - especially in long lectures.

18. Change the visual stimulus from text to images, or video.

19. Ask students to apply a concept individually or in pairs.

20. Use examples that are relevant to student knowledge and experience.

21. Vary student engagement between note taking, listening, and active participation.

22. Draw connections between experience and taught concepts e.g. to introduce globalization ask students to look at where their shoes were made and then collect responses on post-its to put on a map (or digital equivalent).

23. Read aloud a key excerpt from a book to break up the monotony of PowerPoint.

24. Break up PowerPoint slides with images, graphs, websites, primary sources or news items.

25. Ask students to identify a quote, image or date displayed on PowerPoint. Asking students to identify what happened on a series of relevant dates in world history can be a nice way to engage prior knowledge at the start of a topic.

26. Create crosswords for students (using free online tools) - I haven't looked into this but it sounds like fun - clues could be designed to require students to apply their knowledge rather than simply match definitions.

27. Provide four images and ask which one is the odd-one-out (linked to the concept under discussion).

28. Buzz groups i.e. small-group discussion for brainstorming the answer to a specific question.

29. Semi-structured debates on contentious issues. This is harder with large groups, but you can split students into two opposing sides and divide each side into smaller discussion groups - with time to prepare their argument - before drawing on some groups to participate in a wider debate. It can be a good way to practice developing an argument.

30. Give students activities to focus their weekly readings, such as writing a list of pros and cons, or matching a theoretical concept to a news item.

31. Put students into the role of diplomats e.g. ask how they would advise political leaders in a given situation.

32. Create opportunities for students to apply theoretical frameworks to a given scenario and develop arguments on the prescriptions different positions would advocate.

33. Use guest lectures (in person or via Skype) to give students access to people who are employed in an aspect of international relations (outside of academia).

34. Use regular multiple-choice questions as open-book activities where the questions are structured to require application of concepts to a problem or issue, rather than simple recall.

35. Bring critical thinking into activities by asking students to apply their knowledge. For example; apply International Relations theory to a current news item; give instruction on how a specific country should vote on a UN Security Council resolution; or a take-home examination to encourage deep learning rather than surface learning.

36. At the end of the lecture give students two minutes to write down the main point of the lecture and any questions they still have for discussion in tutorials.

This list is not intended to be comprehensive, rather it's an exercise in brainstorming. What other strategies can you think of?

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Strategies for Making Large Lectures More Interactive

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Active learning is what we would like students to be doing with the taught material: thinking about and engaging with the content, rather than mindlessly copying slides or thinking about something else altogether. In a previous post I wrote some thoughts on active learning as a concept - this week is all about strategies.

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"Simply put, active learning is the process of learning via engaging with the content. It means students are interacting with the material in any way that can promote active thought, via ‘activities’ for learning or via re-framing the note-taking process to encouraging thinking about the material rather than transcribing the content."
quote from blog below
http://www.e-ir.info/2015/10/08/what-is-active-learning-and-why-is-it-important/

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We have all sat through (and perhaps given) lectures where the lecturer read a pre-prepared script with little or no scope for interaction. These 'delivery mode' lectures are draining for the lecturer and demotivating for students. The education literature commonly quotes studies showing that when material is delivered using a single method (i.e. students are passively listening) their concentration limit is between 10 and 20 minutes, a small fraction of a lecture. Passively listening to a lecture can be useful at promoting learning at the lower end of a taxonomy of learning such as - to 'remember' and 'understand' - but is not as good at promoting higher-level skills like 'apply', 'analyse' and 'evaluate'. While all of these types of learning are important, and build on each other, higher-level critical thinking skills are integral to the study of International Relations. 'Delivery mode' lectures, where students listen rather than interact, are not good at promoting higher-level learning and skills.

How can we make lectures more interactive and more geared towards higher-level learning? 'Active learning' approaches form a key alternative. Simply put, active learning is the process of learning via engaging with the content. It means students are interacting with the material in any way that can promote active thought, via 'activities' for learning or via re-framing the note-taking process to encouraging thinking about the material rather than transcribing the content. While definitions of active learning vary, they share common priorities: students are doing more than simply listening; the aim is skills-development rather than just conveying information; students engage in activities (e.g. discussion, debate, application of principles) aimed to promote higher-order thinking (such as critical thinking, analysis etc). Simulations are becomingly increasingly used in International Relations as tools for active learning, and are well suited to particular topics, however here I wanted to think more broadly about the ways in which active learning could be integrated into any and all lecture topics. Next week I will post a list of strategies for active learning that could be easily integrated into large lectures, including a wide variety of ideas for varying what students are doing, seeing and hearing.

Promoting active learning in lectures has many benefits for student learning. The drop-off in concentration can be limited by using a different approach to learning each 15 minutes (which means changing the way students are engaged, rather than changing topics). Active learning promotes recall and deeper understanding of material, as students are engaging with the content rather than simply listening to it. There are also equity benefits that flow from active learning, as lower-performing students have greater benefits from active learning than students who are already achieving high grades. Another equity outcome from active learning is that using different modes of delivery supports students who have different learning styles. There are clear ethical as well as pedagogical benefits to the use of active learning techniques.

Interacting with content through active learning has some compelling advantages over 'delivery mode' lectures. It helps to maintain student concentration and deepens learning towards the higher-level skills like critical thinking. It also helps to engage students who might otherwise struggle. This does not mean doing away with spoken lectures, rather it means integrating different ways of engaging with the material at regular intervals throughout the lecture. Next week I will offer a range of possible strategies for making lectures more interactive. Students report that active learning can enhance learning, be more fun, and can help maintain concentration.

Further Reading on E-International Relations

Jess Gifkins is a Research Fellow at the Asia-Pacific Centre for the Responsibility to Protect. Her research focuses on decision-making practices in the United Nations Security Council and international responses to mass atrocity crimes. Her work has been published in Cooperation and Conflict, the Australian Journal of International Affairs, Global Responsibility to Protect, among other outlets. She is Associate Editor of Critical Military Studies.

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What Is 'Active Learning' and Why Is It Important?

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We have all sat through (and perhaps given) lectures where the lecturer read a pre-prepared script with little or no scope for interaction. These 'delivery mode' lectures are draining for the lecturer and demotivating for students. The education literature commonly quotes studies showing that when material is delivered using a single method (i.e.

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Key Characteristics of the Scholarship of Teaching and Learning

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Boyer believed the fourth area, now better known as SoTL, is a vital area of scholarship besides the traditional research endeavours. Today, SoTL is recognised by worldwide universities and practised by their academics as systematic, deliberate inquiry into how to improve teaching and students' learning in the specific disciplines that they were trained in as well as to share the scholarship with others.

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Course Catalog, Posters and Flyers of KyotoUx Courses｜Resources | CONNECT

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Course Catalog, Posters and Flyers of KyotoUx Courses

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Tangible products
From
Hangouts and Hanging out

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teaching and learning cycle model - Google Search

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Medical Education Peer Reviewed Publications (selected)

The intention of writing a recent series of reflection pieces (article 5, 6, 7, 9 and 11), as well the other articles below, was to set down in print as many of the useful ideas / and pointers I could think of, and which I have found useful and currently use, on the subject of TeL and implementing this, distilled from my reading, practice, and scholarly inquiry since 2002, as well as from the last 6 years of faculty development presentations, symposia and workshops I have been involved in designing and have participated in at NUS, at APMEC and AMEE; and have presented as invited speaker to conferences in Colombo, Sri Lanka; Kaohsiung, Taiwan; Jakarta, Indonesia; and as visiting professor in Almaty, Kazakhstan.

(articles in Medical Teacher and MedEdPublish have been viewed over 10,500 times in 2015, 2016 and 2017, as of 12 September 2017, and papers 2, 4, 5 and 6 are amongst the top rated papers in Oct, Nov, Dec 2016, Jan 2017 and July 2017 - see below)

2. Goh, P.S., Sandars, J. Insights from the culinary arts for medical educators. MedEdPublish. 2017 Jan; 6(1), Paper No:10. Epub 2017 Jan 18.

3. Goh, P.S. A proposal for a grading and ranking method as the first step toward developing a scoring system to measure the value and impact of viewership of online material in medical education - going beyond "clicks" and views toward learning. MedEdPublish. 2016 Oct; 5(3), Paper No:62. Epub 2016 Dec 9.

4. Goh, P.S. Presenting the outline of a proposal for a 5 part program of medical education research using eLearning or Technology enhanced learning to support Learning through the continuum of Undergraduate, through Postgraduate to Lifelong learning settings. MedEdPublish. 2016 Oct; 5(3), Paper No:55. Epub 2016 Dec 7.

5. Goh, P.S. The value and impact of eLearning or Technology enhanced learning from one perspective of a Digital Scholar. MedEdPublish. 2016 Oct; 5(3), Paper No:31. Epub 2016 Oct 18.

6. Goh, P.S. A series of reflections on eLearning, traditional and blended learning. MedEdPublish. 2016 Oct; 5(3), Paper No:19. Epub 2016 Oct 14.

7. Goh, P.S. Technology enhanced learning in Medical Education: What's new, what's useful, and some important considerations. MedEdPublish. 2016 Oct; 5(3), Paper No:16. Epub 2016 Oct 12.

8. Sandars, J., Goh, P.S. Is there a need for a specific educational scholarship for using e-learning in medical education? Med Teach. 2016 Oct;38(10):1070-1071. Epub 2016 April 19.

9. Goh, P.S. eLearning or Technology enhanced learning in medical education - Hope, not Hype. Med Teach. 2016 Sep; 38(9): 957-958, Epub 2016 Mar 16

10. Goh, P.S., Sandars, J. An innovative approach to digitally flip the classroom by using an online "graffiti wall" with a blog. Med Teach. 2016 Aug;38(8):858. Epub 2016 Jul 14.

11. Goh, P.S. Using a blog as an integrated eLearning tool and platform. Med Teach. 2016 Jun;38(6):628-9. Epub 2015 Nov 11.

12. Sandars J, Patel RS, Goh PS, Kokatailo PK, Lafferty N. The importance of educational theories for facilitating learning when using technology in medical education. Med Teach. 2015 Mar 17:1-4.

13. Dong C, Goh PS. Twelve tips for the effective use of videos in medical education. Med Teach. 2015 Feb; 37(2):140-5.

14. Liaw SY, Wong LF, Chan SW, Ho JT, Mordiffi SZ, Ang SB, Goh PS, Ang EN. Designing and evaluating an interactive multimedia Web-based simulation for developing nurses' competencies in acute nursing care: randomized controlled trial. J Med Internet Res. 2015 Jan 12;17(1):e5.

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Digital Scholarship - The Scholarship of Teaching and Learning (SOTL) / SOTL using TEL (Technology Enhanced Learning)

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Medical Education Peer Reviewed Publications (selected) The intention of writing a recent series of reflection pieces (article 5, 6, 7, 9 and 11), as well the other articles below, was to set down in print as many of the useful ideas / and pointers I could think of, and which

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Designing Courses for Online Learning | Centre for Teaching Excellence

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Substitution Augmentation Modification Redefinition (SAMR) model describes the impact of technology on learning, from low impact (learning is enhanced) to high impact (learning is transformed). The model lists the lower impact on learning, first as directly substituting the current task with no functional change; and second as augmenting the current task with functional improvements.

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Notes and reflections by Poh-Sun during meeting activities 

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Notes and reflections by Poh-Sun during meeting activities 

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Why
Purpose-Intent
Audience-Market-Value
Quality
Whom
Where-Context
Stakeholders-Engagement-How/When/Where
Support-Alignment-Network-Group-Initiative-Community-Scaling-Translation-Movement-Adoption
Passions-Incentives-Alignment-Benefits-Costs
Faculty Development-Culture
Value-Valued-Impact
Interest-
Principles-Values-underpinning SoTL
Joining Conversation-Showing Values/Impact
Body of work-adds to theory-informs practice
Recognises and supports scholarship
Showcase-Show value-Show valued
Toolbox-not just Tool
Intention BEFORE Toolbox/ Tool use-selection-development
Intermediate AUTHENTIC practice - FOR TRANSFER
REALISTIC and RELATABLE EXAMPLES and SIMULATIONS
Conceptual vs PRACTICE
Low vs HIGH FIDELITY
Real FINANCIAL simulation also SIMULATE emotional pain of REAL LOSSES
Online screen .. physical real world real life evaluation
Use and Adoption!!! = Useful and effective by BOTH FACULTY AND STUDENTS
Knowledge vs Practice transfer
It takes TIME and effort on ALL PARTIES to
Awareness before Understanding or Knowledge before Simulated Practice before transfer to practice before habitual change in practice
(Modified Miller triangle)
Dinner conversation with parents
What did u learn today?
What can u do?

Value for money
Value for efforts BY ALL PARTIES
SHARE THE RECIPE !!! = SOTL
First decide
WHO ARE YOUR DINERs
Then chose recipes
What would u like to eat?
What are u hungry for?
What is GOOD FOR YOU??