Learning affordances through action: Evidence from visual search
Bibliographic record
Abstract
It has long been thought that objects are processed according to affordances they offer. Much of the evidence for this conclusion, however, comes from studies that used images of tools that participants may or may not have previous experience interacting with. Moreover, many tools are spatially asymmetric, adding a further potential confound. In the current study, we eliminated these confounds by using simple geometric stimuli and having participants learn that certain color-shape combinations afforded successfully finishing a task whereas others did not. The learning trials began with a small circle (the 'agent') surrounded by two circles and two squares that were blue or yellow and were contained with a '+' shaped structure. The participant's task was to move the agent, using the arrow keys, past the shapes, out of the structure. Importantly, two of these color-shape combinations allowed the agent to pass (doors) while the other two stopped the agent (walls). To measure whether doors were preferentially processed after affordances were learned, the test trials had participants search for a 'T' among 'L's that were presented on the same color-shape combinations. Evidence for affordance processing would be found if responses times were shorter for targets appearing on doors than targets on walls. The data supported this hypothesis, indicating that not only do affordances guide object processing, but also that affordances can be learned and assigned to otherwise arbitrary stimuli. The response time benefit may reflect a search bias with the attentional system prioritizing the processing of previously action relevant stimuli. Meeting abstract presented at VSS 2017
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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 | 0.010 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".