MétaCan
Menu
Back to cohort
Record W3042772102 · doi:10.1080/23311975.2020.1791444

Motivational congruence effect: How reward salience and choice influence motivation and performance

2020· article· en· W3042772102 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCogent Business & Management · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsUniversity of Calgary
FundersUniversity of Calgary
KeywordsPsychologySalience (neuroscience)SalientSocial psychologyCongruence (geometry)AttributionCognitive psychology

Abstract

fetched live from OpenAlex

The effect of performance-contingent reward and choice on motivation and performance continues to be debated. Studies in economics and behavioral psychology consider performance-contingent rewards and choice as two separate motivational mechanisms that reinforce motivation and performance. However, theories on self-determination and motivational crowding predict that performance-contingent rewards negatively interact with choice, reducing its positive effect on motivation. The conceptual and methodological differences between these streams suggest a more nuanced approach that considers factors including reward salience and task type. Building upon attribution theory, we designed and conducted an experiment to test the effect of choice (choice vs. no-choice) and reward (salient, non-salient, and no reward) on overall motivation and performance. Non-salient reward and choice interacted in a positive way, resulting in motivation and performance improvement, what we describe as a "Motivational Congruence Effect." Similarly, salient reward in a no-choice condition had a positive effect on motivation and performance.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.624

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.027
GPT teacher head0.272
Teacher spread0.245 · 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