Electrophysiological signatures of the effect of context on exploration: Greater attentional and learning signals when exploration is costly
Bibliographic record
Abstract
Humans are excellent at modifying our behaviour depending on context. For example, we will change how we explore when losses are possible compared to when losses are not possible. However, it remains unclear what specific cognitive and neural processes are modulated when exploring in different contexts. Here, we had participants learn within two different contexts: in one the participants could lose points while in the other the participants could not. Our goal was to determine how the inclusion of losses impacted human exploratory behaviour (experiment one), and whether we could explain the neural basis of these effects using EEG (experiment two). In experiment one, we found that participants preferred less-variable choices and explored less often when losses were possible. In addition, computational modelling revealed that participants engaged in less random exploration, had a lower rate of learning, and showed lower choice stickiness when losses were possible. In experiment two, we replicated these effects while examining a series of neural signals involved in exploration. During exploration, signals tied to working memory and learning (P3b), attention orienting (P3a) and motivation (late positive potential; an exploratory analysis) were enhanced when losses were possible. These neural differences contribute to why exploratory behaviour is changed by different learning contexts and can be explained by the theoretical claim that losses recruit attention and lead to increased task focus. These results provide insight into the cognitive processes that underlie exploration, and how exploratory behaviour changes across contexts.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".