ERP Evidence for Rapid Hedonic Evaluation of Logos
Bibliographic record
Abstract
We know that human neurocognitive systems rapidly and implicitly evaluate emotionally charged stimuli. But what about more everyday, frequently encountered kinds of objects, such as computer desktop icons and business logos? Do we rapidly and implicitly evaluate these more prosaic visual images, attitude objects that might only engender a mild sense of liking or disliking, if at all? To address this question, we asked participants to view a set of unfamiliar commercial logos in the context of a target identification task as brain electrical responses to these objects were recorded via event-related potentials (ERPs). Following this task, participants individually identified those logos that were most liked or disliked, allowing us to then compare how ERP responses to logos varied as a function of hedonic evaluation-a procedure decoupling evaluative responses from any normative classification of the logos themselves. In Experiment 1, we found that visuocortical processing manifest a specific bias for disliked logos that emerged within the first 200 msec of stimulus onset. In Experiment 2, we replicated this effect while dissociating normative- and novelty-related influences. Taken together, our results provide direct electrophysiological evidence suggesting that we rapidly and implicitly evaluate commercial branding images at a hedonic level.
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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 | 0.003 |
| 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) | 0.003 | 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".