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Record W4252045136 · doi:10.31234/osf.io/j2agk

Reward sensitivity following boredom and cognitive effort: A high-powered neurophysiological investigation

2018· preprint· en· W4252045136 on OpenAlexaff
Marina Milyavskaya, Michael Inzlicht, Travis S. Johnson, Michael J. Larson

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicMind wandering and attention
Canadian institutionsUniversity of TorontoCarleton University
Fundersnot available
KeywordsBoredomCognitionPsychologyEgo depletionMoodCognitive psychologyControl (management)Task (project management)Developmental psychologySelf-controlSocial psychologyComputer scienceNeuroscience

Abstract

fetched live from OpenAlex

What do people feel like doing after they have exerted cognitive effort or are bored? Here, we empirically test whether people are drawn to rewards (at the neural level) following cognitive effort and boredom. This elucidates the experiences and consequences of engaging in cognitive effort, and compares it to the consequences of experiencing boredom, an affective state with predicted similar motivational consequences. Event-related potentials were recorded after participants (N=243) were randomized into one of three conditions – boredom (passively observing strings of numbers), cognitive effort (adding 3 to each digit of a four-digit number), or control. In the subsequent task, we focused on the feedback negativity (FN) to assess the brain’s immediate response to the presence or absence of reward. Phenomenologically, participants in the boredom condition reported more fatigue than those in the cognitive effort condition, despite reporting exerting less effort. Results suggest participants in the boredom condition exhibited larger FN amplitude than participants in the control condition, while the cognitive effort condition was neither different from boredom nor control. The neural and methodological implications for ego depletion research, including issues of replicability, are discussed.

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 categoriesMeta-epidemiology (narrow)
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 score0.357
Threshold uncertainty score1.000

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 science0.0000.001
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 head0.042
GPT teacher head0.274
Teacher spread0.232 · 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.

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

Citations17
Published2018
Admission routes1
Has abstractyes

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