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Record W2132863201 · doi:10.3389/fnagi.2011.00015

The Effects of Prior Exposure on Face Processing in Younger and Older Adults

2011· article· en· W2132863201 on OpenAlexafffund
Jennifer J. Heisz, Jennifer D. Ryan

Bibliographic record

VenueFrontiers in Aging Neuroscience · 2011
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsBaycrest HospitalUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsPsychologyVisual processingYoung adultFace (sociological concept)Developmental psychologyAudiologyCognitive psychologyMedicinePerceptionNeuroscience

Abstract

fetched live from OpenAlex

Older adults differ from their younger counterparts in the way they view faces. We assessed whether older adults can use past experience to mitigate these typical face-processing differences; that is, we examined whether there are age-related differences in the use of memory to support current processing. Eye movements of older and younger adults were monitored as they viewed faces that varied in the type/amount of prior exposure. Prior exposure was manipulated by including famous and novel faces, and by presenting faces up to five times. We expected that older adults may have difficulty quickly establishing new representations to aid in the processing of recently presented faces, but would be able to invoke face representations that have been stored in memory long ago to aid in the processing of famous faces. Indeed, younger adults displayed effects of recent exposure with a decrease in the total fixations to the faces and a gradual increase in the proportion of fixations to the eyes. These effects of recent exposure were largely absent in older adults. In contrast, the effect of fame, revealed by a subtle increase in fixations to the inner features of famous compared to non-famous faces, was similar for younger and older adults. Our results suggest that older adults' current processing can benefit from lifetime experience, however the full benefit of recent experience on face processing is not realized in older adults.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.734
Threshold uncertainty score0.335

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.021
GPT teacher head0.247
Teacher spread0.227 · 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

Citations40
Published2011
Admission routes2
Has abstractyes

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