How do we select the tools we use
Bibliographic record
Abstract
How do we decide to use tools? Is the decision grounded on the idea of affordances proposed by Gibson (1977,1979) and elegantly followed by others (e.g., Tucker & Ellis, 1998)? One major focus of affordances is based on the idea that the handle of an object will ultimately play a key role on the effector used (e.g., left or right hand) and how to pick it up. However, what if the area of the tool that makes contact with the object upon which it acts (e.g., flat surface of the hammer) plays a critical role as well? We tested if the handle or the contact area of the tools ultimately affect the decision of the observer in a series of studies, in which different tools [1 designed for the action such as driving a nail (e.g., hammer), 1 with similar graspability (e.g., wrench), 1 with similar functionality (e.g., rock), and 1 nonsense tool (e.g., comb)] were presented. In the first study, we did not constrain the amount of time the participants had to decide on which tool to select and use. In this condition participants selected the tool designed for the action first (88%), and to use the tool with similar functionality second (46%). However, once we constrained participants to choose as quickly as possible, there tended to be an even split between tools that looked similar (50% split) when chosen first. Another interest was that the actions performed were chosen by the participants, not instructed; however most participants chose to perform very similar actions regardless of tool selected. This leads us to the next question: What is driving this decision making? The tool or the object?
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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 | 0.013 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.012 |
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".