Habitual scanning bias in complex problem solving
Bibliographic record
Abstract
Habitual scanning direction biases performance on perceptual tasks such as characterizing the shading of an object (Smith et al. 2015) or the symmetry of a scene (Afsari 2016). But how does this spatial bias affect complex problem solving? More generally, how do perceptual patterns affect cognitive outcomes? We employ Ravens-like puzzles as a new experimental paradigm to reflect problem solving with directional bias. Based on work of Matzen et al. (2010), we consistently vary complexity and puzzle attributes, including direction of change. In a preliminary eye-tracking study, we find that scanning patterns for our Ravens-like puzzles are predominantly horizontal, but also vary by directionality of puzzle attributes. Do participants solve puzzles more correctly when puzzle directionality corresponds to habitual scanning direction? If so, this suggests an extension of models for predicting Ravens-like puzzle performance to include directional factors (Hayes et al. 2011). If scanning direction does not affect correctness, this suggests a disjoint between habitual behaviour and top-down attention in spatial problem solving. We conduct a behavioural experiment to isolate the effects of habitual scanning patterns on puzzle-solving performance. We analyze correctness for two groups of Ravens-like puzzles: puzzles with counter-habitual bias, or attribute directionalities which elicit more vertical scanning, and puzzles without counter-habitual bias. To ensure that variation in other puzzle attributes is identical between these two groups, we generate a set of transposed puzzles where each puzzle and its transpose have opposite directional biases. We then compare performance on all puzzles with and without counter-habitual bias. We find that puzzles with counter-habitual bias correspond with significantly lower correctness. This finding indicates that people perform better on complex visual problems where bottom-up directional bias matches habitual trends of perception. More broadly, it suggests continuity between perceptual patterns and cognitive outcomes in a complex task setting.
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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 | 0.000 |
| 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".