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Record W2338894993 · doi:10.1097/wnr.0000000000000575

The scarcity heuristic impacts reward processing within the medial-frontal cortex

2016· article· en· W2338894993 on OpenAlexafffund
Chad C. Williams, Boaz Y. Saffer, Robert B. McCulloch, Olave E. Krigolson

Bibliographic record

VenueNeuroreport · 2016
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsUniversity of British ColumbiaUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyPerceptionScarcityCognitive psychologyNeuroscienceFrontal cortexEconomics

Abstract

fetched live from OpenAlex

Objects that are rare are often perceived to be inherently more valuable than objects that are abundant - a bias brought about in part by the scarcity heuristic. In the present study, we sought to test whether perception of rarity impacted reward evaluation within the human medial-frontal cortex. Here, participants played a gambling game in which they flipped rare and abundant 'cards' on a computer screen to win financial rewards while electroencephalographic data were recorded. Unbeknownst to participants, reward outcome and frequency was random and equivalent for both rare and abundant cards; thus, only a perception of scarcity was true. Analysis of the electroencephalographic data indicated that the P300 component of the event-related brain potential differed in amplitude for wins and losses following the selection of rare cards, but not following the selection of abundant cards. Importantly, then, we found that the perception of card rarity impacted reward processing even though reward feedback was independent of and subsequent to card selection. Our data indicate a top-down influence of the scarcity heuristic on reward evaluation, and specifically the processing of reward magnitude, within the human medial-frontal cortex.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.611
Threshold uncertainty score0.788

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.112
GPT teacher head0.364
Teacher spread0.252 · 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 designBench or experimental
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

Citations9
Published2016
Admission routes2
Has abstractyes

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