The deciding hand: How an analysis of human reach movements reveals choice biases
Bibliographic record
Abstract
Decision making, or the resolution of competition, is one of the most central components of human cognition: from low-level, brief, sensory events that compete for cortical activation and amplification, to high level symbols and complex objects that compete first for recognition then later for influence over decisions. Despite its centrality to understanding human thought, the science of decision making is usually restricted only to an analysis of what decisions people make. This approach overlooks the very important component of how people execute their decisions. Here, I will show results from a variety of studies demonstrating that an analysis of the physical reach movements people make to indicate a decision, and careful manipulation of the timing of decision stimuli, can be used to reveal subtle aspects of decision making and the precise timelines over which they operate. I will present evidence from studies where decisions are driven by low level visual properties (e.g. luminance), where decisions are influenced by arbitrary, more cognitively driven properties (e.g. reward associations) and finally, where decisions are made based completely on participant driven properties separate from any specific stimuli features (e.g. personal preference). The analysis of the resulting spatial reach trajectories as participants physically interact with the choice options reveals decision biases including: an initial bias toward high luminance that decays with time, a bias toward gain and a delayed bias away from loss, and reaches that reflect an individual's decision difficulty. Notably, these biases would have been invisible using conventional research methods.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".