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Record W2473121210 · doi:10.3389/fpsyg.2016.01010

Is Moving More Memorable than Proving? Effects of Embodiment and Imagined Enactment on Verb Memory

2016· article· en· W2473121210 on OpenAlexafffund
David M. Sidhu, Penny M. Pexman

Bibliographic record

VenueFrontiers in Psychology · 2016
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesAlberta Innovates - Health Solutions
KeywordsPsychologyVerbCognitive psychologyLinguisticsCognitive scienceCommunication

Abstract

fetched live from OpenAlex

Theories of embodied cognition propose that sensorimotor information is simulated during language processing (e.g., Barsalou, 1999). Previous studies have demonstrated that differences in simulation can have implications for word processing; for instance, lexical processing is facilitated for verbs that have relatively more embodied meanings (e.g., Sidhu et al., 2014). Here we examined the effects of these differences on memory for verbs. We observed higher rates of recognition (Experiments 1a-2a) and recall accuracy (Experiments 2b-3b) for verbs with a greater amount of associated bodily information (i.e., an embodiment effect). We also examined how this interacted with the imagined enactment effect: a memory benefit for actions that one imagines performing (e.g., Ditman et al., 2010). We found that these two effects did not interact (Experiment 3b), suggesting that the memory benefits of automatic simulation (i.e., the embodiment effect) and deliberate simulation (i.e., the imagined enactment effect) are distinct. These results provide evidence for the role of simulation in language processing, and its effects on memory.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.697
Threshold uncertainty score0.714

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.287
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations26
Published2016
Admission routes2
Has abstractyes

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