Do emotional ensembles shape behavior? Investigating the role of average emotional expression in approach-avoidance decisions
Bibliographic record
Abstract
The visual system efficiently processes multiple sources of information by leveraging ensemble encoding, the ability to extract statistical summaries (e.g., the average size of circles) from sets of similar objects. This also occurs for high-level stimuli such as faces, and summary statistics such as the average expression in a crowd of faces provides critical cues about the intentions of others. Emotional expressions are pervasive drivers of decision-making, and similarly, affective images, such as the valence of photographs, have been shown to significantly influence approach-avoidance decisions. While ensemble processing has been extensively studied in visual perceptual tasks, its influence on approach-avoidance decisions, a fundamental aspect of human behavior, remains insufficiently explored. To address this, we examined how ensemble processing influences approach-avoidance decisions in real-world scenarios, namely, when deciding whether or not to watch a movie. To investigate this, we examined the relationship between implicit ensemble processing (i.e., passive viewing of ensemble stimuli) and approach-avoidance behavior. Participants viewed ensembles of six faces expressing a positive, negative, or neutral average emotion. Following the presentation of each face ensemble, participants completed an approach-avoidance task using a social decision-making paradigm. Specifically, participants were presented with a positive, negative or neutral movie poster and then quickly decided whether or not they would prefer to watch the depicted film. We found that average expression did not influence approach-avoidance behaviors towards affective movie posters. Instead, viewing decisions depended only on the affective content of the movie poster, independent of the face ensemble it was paired with. Critically, this research deepens our understanding of the intersection between ensemble processing and decision-making. As a next step, we will test whether explicit ensemble processing influences approach-avoidance decisions, as explicit judgments of an average feature are known to create more precise ensemble representations compared with implicit processing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".