Stimulus reliability automatically biases temporal integration of discrete perceptual targets
Bibliographic record
Abstract
Abstract Many decisions, from crossing a busy street to choosing a profession, require integration of discrete sensory events. Previous studies have shown that integrative decision-making favours more reliable stimuli, mimicking statistically optimal integration. It remains unclear, however, whether reliability biases are automatic or strategic. To address this issue, we asked observers to reproduce the average motion direction of two suprathreshold coherent motion signals, presented successively and varying in reliability. Although unbiased responses were both optimal and possible by virtue of task rules and suprathreshold motion coherence, we found robust behavioural biases favouring the more reliable stimulus. Using population-tuning modelling of brain activity recorded using electroencephalography, we characterised tuning to the average motion direction. In keeping with the behavioural biases, the tuning profiles also exhibited reliability biases. Taken together, our findings reveal that temporal integration of discrete sensory events is automatically and sub-optimally weighted according to stimulus reliability.
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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 | 0.007 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".