Arbitrary and explicit prediction biases perceptual categorization
Bibliographic record
Abstract
Predictions influence perception by biasing the percept in line with the expectation (Kok et al., 2012; 2018). However, this prediction-induced bias has primarily been observed by eliciting predictions through learned statistical regularities. Therefore, it is not clear whether arbitrary and explicit predictions would also bias our perception. To test this, we had participants perform a categorization task after making an arbitrary and explicit prediction on what they were about to see. More precisely, on each trial, participants first made an explicit binary prediction as to whether they would see one of the two objects (e.g., “Dog” or “Boar”) by clicking either a top or bottom buttons (e.g., “Dog” and “Boar” buttons) displayed on the computer screen. Subsequently, a morph of the two objects was briefly presented (50ms), and participants categorized the object as one of the two objects by clicking either the left or right buttons (e.g., “Dog” and “Boar” buttons) displayed on the computer screen. To avoid motor priming, the response options for predictions and categorizations were orthogonalized and fully counter-balanced on a trial-by-trial basis. Here, we found that participants’ categorization was indeed biased in the direction of their predictions. For instance, 50% morph objects (i.e., a morph of 50% Dog and 50% Boar) were more likely to be categorized as the predicted object than the counterpart, and we replicated this finding in two other object pairs (i.e., “Face and Tree” and “Gecko and Branch”, Stöttinger et al., 2015). Our results extend the prediction-induced biases to arbitrary and explicit predictions, and the results of an ongoing EEG study examining the origin of prediction-induced biases (i.e., do predictions bias perception or categorization decisions?) will be discussed.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".