Perceptual illusions in the auditory domain
Bibliographic record
Abstract
cognitive illusions have provided an essential basis for understanding cognitive processes across a range of contexts.Such sources of error in the performance of cognitive tasks have been fundamental in instructing researchers about mechanisms underlying low-lever perceptuar experience, remembering, and judgment and decision-making' Essential for illuminating inefficiencies in human cognitive abilities, such research has also provided clues about basic cognitive mechanisms.specifi calry, one important outcome of adopting a research focus that emphasizes cognitive illusions is that human cognitive processing does not directly make contact with either the sensory environment or representations of prior experience.Instead, at least in part, all aspects of cognitive processing reflect an imperfect construction of reality.In the case of auditory perceptual experience, an appreciation of this role of constructive processes must complement the more traditional approach of treating the perception of simple sounds and sound sequences as guided mainry by low-lever processing of sensory input.My research balances the more typicar emphasis on bottom_up processes of auditory perception by providing an investigation of top-down sources of error in perception of the spatial location, temporal duration and intensity of auditory events.In a series of experiments' I demonstrated that the perceived duration and intensity of auditory events can depend on the nature and degree of frequency change within a sound.The results of the current study provide one example as to how one feature of a sound can be used as an erroneous basis for generating judgments about some other dimension.The broad implication is that pattems of errors observed within the laboratory provide a usefi.llbasis for discovering the essential knowledge that everyday experience provides.Although AesrRAcr PrncspruerlrrusroNs ii pnnc¡pruarlrrusro¡¡s iii they may occasionally lead to error, as Gigerenzer and Todd (2000) have proposed, it is people's tendency to rely on fast heuristics that usually lead to accurate decisions that is at the root of human intellectual superiority.
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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 | 0.011 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".