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Record W4388928816 · doi:10.3389/fpsyg.2023.1253085

Memory representations in a cross-modal matching task: evidence for a verbal component

2023· article· en· W4388928816 on OpenAlexaff
Katherine Marie Estabrooks, Muhammad Tayyab Sohail, Young In Song, Geneviève Desmarais

Bibliographic record

VenueFrontiers in Psychology · 2023
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsMount Allison University
Fundersnot available
KeywordsPsychologyCognitive psychologyObject (grammar)DistractionTask (project management)CognitionMatching (statistics)Verbal memoryRepresentation (politics)Computer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

In everyday tasks, one often uses touch to find what has been seen. Recent research has identified that when individuals view or touch an object, they may create a verbal memory representation; however, this research involved object naming, which may have prompted the use of verbal strategies. Research has also identified variability in memory representations for objects, which may indicate individual differences. To investigate memory representations and their associations with individual differences in cognitive styles, we measured the cognitive styles of 127 participants and had them complete a non-verbal matching task without distractors, or with verbal or visual distractors. In the task, they viewed an object and then touched an object - or vice versa - and indicated whether the objects were the same or different. On trials where different objects were presented, participants responded consistently more slowly and made more matching errors for similar objects compared to distinct objects. Importantly, higher scores on the verbalizer cognitive style predicted faster reaction times on the matching task across all trial types and distraction conditions. Overall, this indicates that cross-modal object processing in short-term memory may be facilitated by a verbal code.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.371
Threshold uncertainty score0.653

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.107
GPT teacher head0.451
Teacher spread0.345 · 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 designObservational
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

Citations1
Published2023
Admission routes1
Has abstractyes

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