Auditory hindsight bias: Fluency misattribution versus memory reconstruction.
Bibliographic record
Abstract
We report 4 experiments investigating auditory hindsight bias-the tendency to overestimate the intelligibility of distorted auditory stimuli after learning their identity. An associative priming manipulation was used to vary the amount of processing fluency independently of prior target knowledge. For hypothetical designs, in which hindsight judgments are made for peers in foresight, we predicted that judgments would be based on processing fluency and that hindsight bias would be greater in the unrelated- compared to related-prime context (differential-fluency hypothesis). Conversely, for memory designs, in which foresight judgments are remembered in hindsight, we predicted that judgments would be based on memory reconstruction and that there would be independent effects of prime relatedness and prior target knowledge (recollection hypothesis). These predictions were confirmed. Specifically, we found support for the differential-fluency hypothesis when a hypothetical design was used in Experiments 1 and 2 (hypothetical group). Conversely, when a memory design was used in Experiments 2 (memory group), 3A, and 3B, we found support for the recollection hypothesis. Together, the results suggest that qualitatively different mechanisms create hindsight bias in the 2 designs. The results are discussed in terms of fluency misattributions, memory reconstruction, anchoring-and-adjustment, sense making, and a multicomponent model of hindsight bias. (PsycINFO Database Record
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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 | 0.004 | 0.026 |
| 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 | 0.001 |
| Research integrity | 0.001 | 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".