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Record W3082085696 · doi:10.1101/2020.09.01.278168

Gains and Losses Affect Learning Differentially at Low and High Attentional Load

2020· preprint· en· W3082085696 on OpenAlexaff
Kianoush Banaie Boroujeni, Marcus R. Watson, Thilo Womelsdorf

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsYork University
FundersNational Institute of Mental HealthNational Institutes of Health
KeywordsStimulus (psychology)Cognitive psychologyCognitionAffect (linguistics)PsychologyCognitive loadTask (project management)Learning effectAttentional controlNeuroscienceCommunication

Abstract

fetched live from OpenAlex

Abstract Prospective gains and losses modulate cognitive processing, but it is unresolved whether gains and losses can facilitate flexible learning in changing environments. The prospect of gains might enhance flexible learning through prioritized processing of reward-predicting stimuli but is unclear how far this learning benefit extends when task demands increase. Similarly, experiencing losses might facilitate learning when they trigger attentional re-orienting away from loss-inducing stimuli, but losses may also impair learning by reducing the precise encoding of loss-inducing stimuli. To clarify these divergent views, we tested how varying magnitudes of gains and losses affect the flexible learning of object values in environments that varied attentional load by increasing the number of interfering object features during learning. With this task design we found that larger prospective gains improved learning efficacy and learning speed, but only when attentional load was low. In contrast, expecting losses generally impaired learning efficacy and this impairment was larger at higher attentional load. These findings functionally dissociate the contributions of prospective gains and losses on flexible learning, suggesting they operate via separate control mechanisms. One process is triggered by experiencing loss and seems to disrupt the encoding of specific loss-inducing features which leads to less efficient exploration during learning. The second process is triggered by experiencing gains which enhances learning through a more efficient prioritizing of reward-predicting stimulus features as long as the interference of distracting information is limited. These results demonstrate strengths and limitations of motivational regulation of learning efficacy in multidimensional environments having variable attentional loads. Significance statement Increasing the prospective gains is assumed to enhance flexible learning, but there is no consensus on whether imposing losses enhances or impairs flexible learning. We show that anticipating loss of already attained assets generally reduced learning changes in the relevance of visual objects and that this learning impediment is more pronounced when learning demands higher attentional control of interference from distracting object features. Moreover, we show that increasing the prospective gains indeed facilitates learning, but only when the learning problem has intermediate or low attentional demands. These findings document that the beneficial effects of gains hit a limit when task demands increase, and that prospective losses reduce cognitive flexibility already at low task demands which is exacerbated when task demands increase. These findings provide novel insight into the strengths and limitations of gains and of losses to support flexible learning in multidimensional environments imposing variable attentional loads.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.066
GPT teacher head0.293
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 source (direct Gemma or distilled Codex), 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

Citations4
Published2020
Admission routes1
Has abstractyes

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