Cueing effects emerge when humans (Homo sapiens) view images of mammals (mammalia) and birds (aves).
Bibliographic record
Abstract
Humans use eye- and head-gaze cues to facilitate social interactions among members of their own species. Research examining nonhuman animal-to-human cueing effects has received little attention, but may provide valuable insight into the mechanisms that have enabled species to coexist and thrive in shared environments. The objective of the current studies was to determine how gaze cues influence the attention and target detection of humans when they view images of mammals (human, orangutan, and dog; Experiment 1) and aves (owl, macaw parrot, and duck; Experiment 2). Participants were presented with an image of a forward-facing head that was suddenly replaced with an image of the head facing to the left or right, creating an apparent head rotation and change of orientation. A target appeared randomly on the left or right side of the head-gaze cue after 1 of 4 stimulus-onset asynchronies (SOAs; 100, 300, 600, or 1,000 ms). Participants were asked to indicate the location of the target by pressing a spatially corresponding key. The analysis of response times (RTs) revealed facilitatory cueing effects (RTs to cued targets were shorter than to uncued targets) across all SOAs in Experiment 1 (images of mammals). Such facilitatory cueing effects were only present at short SOAs (i.e., 100 and 300 ms) in Experiment 2 (images of aves). These findings provide initial evidence that the processing of gaze cues observed during human-to-human interactions is similar to that observed during mammal-to-human interactions, but is different in aves-to-human interactions. Alternative interpretations of the data are discussed. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
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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 | 0.001 |
| 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".