The development of chasing detection: Do 4-year-olds show evidence of a pop-out effect for chasing stimuli?
Bibliographic record
Abstract
Introduction. Humans attend selectively to animate stimuli (New, Cosmides & Tooby,2007) and interpret biological motion as goal-directed (Heider & Simmel, 1944; Tremoulet & Feldman, 2000). Meyerhoff, Schwan & Huff (2014) reported evidence of a pop-out effect for chasing displays: a chasing pair of circles was shown among a varying number of distracters, and reaction times for identifying the chaser did not increase proportionately to the number of distracters. Both animacy perception and chasing detection develop early in life (Rochat, Striano & Morgan, 2004; Frankenhuis, House, Barrett & Johnson, 2013). The purpose of this study was to test whether attention to chasing, as evidenced by the pop-out effect, has developed by the age of 4. Method. Participants were adults and 4-year-olds. We adapted Meyerhoff et al.'s (2014) procedure for use with 4-year-olds, by using a decorated touch screen to display stimuli and record responses and adding a child-engaging cover story. The stimuli set consisted of black circles presented on a green background. The chaser, chasee and distracters were identical in appearance. On each trial, the chasing pair was presented among a varying number of distracters (2,4,6,8,10). The chasee and distracters moved around the screen in a randomly determined pattern while the chaser pursued the chasee in a heat-seeking fashion. Participants were tasked with identifying the chaser by touching it on the screen. Results and Discussion. We hypothesized that we would find a pop-out effect for chasing stimuli among non-chasing distracters for both adults and 4-year- olds. Our independent variable was number of distracters and our dependent variable was reaction time. The number of distracters did not predict reaction time for adults (F(1, 136) = 0.026, p< .05) or 4-year- olds (F(1, 49) = 1.892, p< .05), which is consistent with a pop-out effect for chase stimuli. Meeting abstract presented at VSS 2017
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".