Bias or Reality? A Psychoacoustic Inquiry into the Audibility of Audio Cables
Bibliographic record
Abstract
This study investigates the impact of audio cables on perceived sound quality in high-fidelity (hi-fi) audio systems, focusing on psychoacoustic perceptions and cognitive bias. The research addresses whether listeners can distinguish between an entry-level RCA cable and a high-quality RCA cable under blind test conditions, and how prior knowledge about the cables affects their perceptions under open test conditions. Results from blind tests indicate no significant differences in perceived sound quality between the cables, suggesting that the physical properties of audio cables do not affect the perception of sound quality under blind test conditions. Conversely, results from open tests reveal that prior knowledge, induced by informing participants about which cable is used, significantly influences judgments of sound quality. This discrepancy underscores the role of cognitive mechanisms in evaluation of audio equipment such as cables, suggesting that subjective assessments of sound quality are heavily influenced by cognitive bias. These findings provide valuable insights for consumers and manufacturers in the hi-fi audio domain, emphasizing the tangible effects of cognitive bias on sound quality perception, and the importance of blind testing when aiming to mitigate such effects. In extension, this study serves to benefit scientific disciplines concerned with psychology and cognition by providing an effective example of how biases, such as expectation and confirmation biases, operate within auditory perception.
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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 | 0.008 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".