Acquiring competence from both extrinsic and intrinsic rewards
Bibliographic record
Abstract
Structured abstract Background The distinction between extrinsic and intrinsic rewards and their related motivations has been a major concern in educational psychology for decades. Although both types of rewards are related to the dopamine-fueled activation of the reward circuitry, neuroscientific studies now support the view that their processing also involves independent brain mechanisms. Aims We show that these mechanisms also are already present in birds and nonhuman mammals, as they track cues and extrinsic rewards in their environment (such as food and shelter), and we discuss a number of intrinsically rewarded activities (such as information seeking and play). The two categories of motivated behaviors evolved to perform distinct functions and are both crucial for the species survival. Conclusion We assume that a human-animal comparison is appropriate, and suggest that both extrinsic and intrinsic rewards in humans are necessary to acquire competence, and optimally manage real-life settings, including school environments. More specifically, we argue that intrinsic and extrinsic motivations are additive rather than conflicting processes, and that intrinsic motivation is characterized by exploratory behavior and is associated with benefits for an individual; it is a step to apprehend and exploit the knowledge acquired by means of extrinsic sources of reward.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".