Visual search strategies in a change detection task
Bibliographic record
Abstract
Visual Search Strategies in a Change Detection Task Purpose: Serial visual search for a target amongst distractor items is widely believed to be a linear process (Treisman and Gelade, 1980). Visual search for change in simple orientation stimuli using a change detection paradigm also yields search slopes that reflect a linear process (Rensink, 2000). The present study investigates whether or not detecting change in more complex stimuli is also linear. Method: 2 trained observers were shown an original and modified display presented in an alternating sequence, with each display shown for 1250ms separated by a 250ms blank screen. The stimuli were happy and sad schematic faces, with set sizes ranging from 2 to 10 items. Different ranges of set sizes were used in different experiments. A change in the facial expression of one of the faces occurred on half of the trials; reaction times to detect the change were measured. Results: Change-absent search slopes increased by a factor of two at set size 4 or 6 (depending on the individual observer). In addition, reaction times for set sizes of 6 and 8 differed depended on the range of set sizes used in testing (eg. 2,4,6 versus 6,8,10), with reaction times for higher ranges of set sizes being longer. Conclusion: These results suggest that observers use search strategies that depend on two factors: the context, or range in which the set size appears, and the capacity of visual short-term memory (vSTM). First, it appears that a display is searched more extensively when it is the smallest in a range of set sizes than when it is the largest. Secondly, the increase in slope at 4 or 6 items can be explained in terms of the capacity of vSTM, which is about 5 items (Rensink, 2000; Pashler, 1988): it may be that when this capacity is exceeded, search becomes less efficient.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".