Verbal Descriptions of Cue Direction Affect Object Desirability
Bibliographic record
Abstract
Approach-avoidance behaviours are observed across a broad range of species. For humans, we tend move toward things we like, and away from things we dislike. Previous research tested whether repeatedly shifting visuo-spatial attention toward an object in response to eye gaze cues can increase liking for that object. Here, we tested whether a gaze-liking effect can occur for verbal descriptions of looking behavior without shifts of attention. Also, we tested the gaze specificity hypothesis – that the liking effect is specific to gaze cues – by comparing the effect of different types of cue (pointing gestures and arrow cues). In Experiment 1, participants (N = 205) were split into 5 groups according to the type of cue that was described as directed either toward or away from an object. The results show that 1) attention is not necessary; the liking effect was recorded for verbal descriptions of looking 2) the effect also occurs for descriptions of pointing and arrows and 3) the liking effect is enhanced for gaze cues compared to arrows, consistent with the gaze specificity hypothesis. Results from a further experiment suggest that the effect is not due to demand compliance. We conclude that the gaze-liking effect occurs for verbal descriptions of eye gaze. Indeed, because our method bypasses altogether the use of visual cues, objects, and shifts in visual selective attention, our paradigm appears to be more sensitive at tapping into the fundamental approach-avoidance response that mediate the implicit liking effect. As such, it offers new opportunities for research investigations in the future.
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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 | 0.009 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".