Preference Inferences from Eye-Related Cues in Sales-Consumer Settings: ERP Timing and Localization in Relation to Inferring Performance and Oxytocin Receptor (OXTR) Gene Polymorphisms
Bibliographic record
Abstract
Past physiological evidence, indicates that inferences on the mind of another person (i.e., goals, intentions, beliefs), is a well-defined brain process characterized by specific temporal and spatial properties. This study investigated brain responses during passive viewing (consumers’ role) of branded products (i.e., chocolates, chips, non alcoholic beverages) and preference inferences (sales consultants’ role) from eye-related information. Using EEG methods, event-related potentials (ERPs) were recorded while participants passively viewed pictures of branded products versus when they tried to infer others’ product preferences from eye-related information. ERP amplitudes were examined in two time windows, corresponding to the P3 component and the late positive potential (LPP). Dissimilar brain responses were found for preference inferences compared to passive viewing for the P3 and LPP components. P3 and LPP amplitudes were greater for preference inferences compared to passive viewing. In addition, enhanced P3 and LPP amplitudes were found for preference inferences compared to passive viewing for the High Inferring Performance (HI) as opposed to the Low Inferring Performance (LI) group. Finally, enhanced posterior P3 and LPP amplitudes were found for preference inferences compared to passive viewing for the GG as opposed to the A-allele carrier individuals of oxytocin receptor (OXTR) gene. Taken together, the results suggest that posterior P3 and LPP amplitude during preference inferences from eye-related cues as opposed to passive viewing of branded products reflects increased socially motivated attention allocation required for the social inferring task, for the GG compared to A-allele carrier individuals.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".