MétaCan
Menu
Back to cohort
Record W2143234284 · doi:10.1177/0956797614533969

The Value of Numbers in Economic Rewards

2014· article· en· W2143234284 on OpenAlexaff
Frank J. Kanayet, John E. Opfer, William A. Cunningham

Bibliographic record

VenuePsychological Science · 2014
Typearticle
Languageen
FieldMathematics
TopicCognitive and developmental aspects of mathematical skills
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIntraparietal sulcusPsychologyValue (mathematics)Numerical cognitionCognitionCognitive psychologyMagnitude (astronomy)Construct (python library)Orbitofrontal cortexSocial psychologyNeuroscienceStatisticsComputer sciencePosterior parietal cortexMathematicsPrefrontal cortex

Abstract

fetched live from OpenAlex

Previous work has identified a distributed network of neural systems involved in appraising the value of rewards, such as when winning $100 versus $1. These studies, however, confounded monetary value and the number used to represent it, which leads to the possibility that some elements in the network may be specialized for processing numeric rather than monetary value. To test this hypothesis, we manipulated numeric magnitude and units to construct a range of economic rewards for simple decisions (e.g., 1¢, $1, 100¢, $100). Consistent with previous research in numerical cognition, results showed that blood-oxygen-level-dependent (BOLD) activity in intraparietal sulcus was correlated with changes in numeric magnitude, independent of monetary value, whereas activity in orbitofrontal cortex was correlated with monetary value, independent of numeric magnitude. Finally, region-of-interest analyses revealed that the BOLD response to numeric magnitude, but not monetary value, described a compressive function. Together, these findings highlight the importance of numerical cognition for understanding how the brain processes monetary rewards.

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)0.002
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.174
Threshold uncertainty score0.276

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.034
GPT teacher head0.377
Teacher spread0.343 · 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 designTheoretical or conceptual
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

Citations19
Published2014
Admission routes1
Has abstractyes

Explore more

Same venuePsychological ScienceSame topicCognitive and developmental aspects of mathematical skillsFrench-language works237,207