Categorization links Perceptual Fluency and Aesthetic Pleasure
Bibliographic record
Abstract
Perceptual fluency is the ease with which incoming stimuli are processed. Fluency facilitates categorization decisions (Miles et al, 2012; Torralbo et al, 2013) and increases subjective feelings of aesthetic pleasure (Reber et al 2004). Prototypical members of a semantic category are also processed with higher fluency and aesthetic pleasure. We here examine the relationship of categorization behavior with perceptual fluency and aesthetic pleasure. Participants were asked to categorize briefly presented scene images into one of six categories. Images were immediately followed by a perceptual mask. Images were presented at five different durations by setting the stimulus onset asynchrony (SOA) to five different values, ranging from 13.3 to 106.6 milliseconds. Reaction time and categorization accuracy were recorded at each SOA for each image. In a separate online experiment, participants on Prolific were presented with these images and asked to rate them based on two questions: “How easy is this image to perceive?” and “How much do you enjoy viewing this image?”. Responses were collected on a 5-point Likert scale. We found that a change in reaction time as a function of stimulus presentation during categorization tasks provided an objective index for feelings of fluency, whereas categorization accuracy predicted aesthetic pleasure responses. We also found a significant correlation between fluency and aesthetic pleasure and no association between categorization accuracy and fluency. Our results indicate a dissociation between fluency and aesthetic pleasure. For fluently experienced stimuli, categorization speed increases more quickly with longer SOAs, whereas categorization accuracy is higher for more enjoyable stimuli. Different aspects of categorization behavior can be used to understand the relationships between prototypicality, fluent processing and aesthetic pleasure. Reward processing mechanisms may underlie the neural basis of this triad.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".