Plinko: A spatial probability task to measure learning and updating.
Bibliographic record
Abstract
Research has demonstrated that humans efficiently learn the statistics of their visual environment (e.g., Fiser & Aslin, 2001). Typical studies present participants with series of events and ask them to predict which event will occur on specific trials. Responses are then aggregated over bins of trials to represent a probability distribution of participant predictions. Although informative, these tasks provide limited information about how participant expectations evolve over the course of a task. We present a novel spatial probability task that attempts to overcome this limitation. Based on the game Plinko (the modern incarnation of Galtons Bean Machine), participants view balls that drop through pegs and land in slots. On every trial, participants are asked to estimate how likely a ball will fall in each slot. Participants adjust a cup or bars under the slots to represent their likelihood estimations. We exposed participants to four distinct distributions of ball drops and measured how accurately they could represent each distribution (Experiment 1) and shift from one distribution to the next (Experiment 2). Rather than representing participant expectations by building probability distributions over multiple trials, our measures provide a probability distribution on each trial of the task. Participants managed to use the cup to accurately track the mean and variance of each distribution by adjusting the cups center position and width throughout the task. Participants were also efficient at using the bars, matching the computers distributions with an average accuracy of 80%. Participants also managed to effectively shift from one distribution to the next using either the cup or bars, and this without being made explicitly aware that any changes would occur. These results suggest that our task provides an effective measure of spatial probability learning while also providing a rich representation of changes in participant predictions over the course of the task. Meeting abstract presented at VSS 2014
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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 | 0.002 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".