Visuospatial bias due to stimulus valence requires conceptual processing
Bibliographic record
Abstract
A central proposal of embodied cognition is that perceptual features constitute an essential part of the conceptual representation. Indeed, experimental studies of everyday metaphors such as ‘happy is up’ and ‘sad is down’ have revealed how processing concepts can generate spatial biases toward locations compatible with the concepts’ meaning. An important question that has remained unanswered is whether spatial association between positive and negative valence (e.g., happy/sad) is in fact conceptual in nature. That is, the observed visuospatial biases may have been simply due to perceived stimulus valence and, as such, non-conceptual. To disentangle conceptual and perceptual processing, we tested the ability of conceptual (affect words) and perceptual (affective faces) stimulus valence in causing visuospatial bias above/below fixation. In each trial, observers were presented with a valenced (or neutral control) stimulus, which was categorized either based on valence (positive vs. negative) or a perceptual feature (upright vs. upside down), followed by a visual target above or below fixation, and made a speeded keypress response to the peripheral target. First, whereas spatial biases were always observed following the conceptual stimuli, with perceptual stimuli, spatial biases were observed only when subjects categorized the faces based on valence. Second, inverting the perceptual (face) stimuli, which induced conceptual ambiguity along the vertical axis (e.g., above fixation is down to an inverted face), reversed the direction of spatial bias, again consistent with the conceptual nature of the effect. Finally, we varied gaze direction of the same face stimuli and found no interaction between the valence-induced bias and the effect of upward/downward gaze cues, suggesting that the mechanism underlying the valence-based spatial bias differs from those responsible for covert shifts of spatial attention. Thus, the perception of positive and negative valence alone cannot generate spatial bias and that conceptual processing is, indeed, necessary for visuospatial biases. Meeting abstract presented at VSS 2012
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".