Costs of attentional set-shifting during dynamic foraging, controlled by a novel Unity3D-based integrative experimental toolkit
Bibliographic record
Abstract
Introduction: There is an increasing demand for experiments in which participants are presented with realistic stimuli, complex tasks, and meaningful actions. The Unified Suite for Experiments (USE) is a complete hardware and software suite for the design and control of dynamic, game-like behavioral neuroscience experiments, with support for human, nonhuman, and AI agents. We present USE along with an example feature-based learning experiment coded in the suite. Methods: USE extends the game engine Unity3D with a hierarchical, modular state-based architecture that supports tasks of any complexity. The hardware, based around an Arduino Mega2560 board, governs communication between the experimental computer and any experimental hardware. Participants in our task had their eyes tracked as they navigated via joystick through a virtual arena, choosing between two objects on each trial, only one of which was rewarded. Objects were composed of multiple features, each with two possible values. Each context, signaled by the pattern of the floor, had a single rewarded feature value (e.g. red objects might be rewarded on a grass floor, pyramidal objects might be rewarded on a marble one). Results: USE’s hardware enables the synchronization of all data streams with precision and accuracy well under 1 ms. Gaze was classified into behaviors (e.g. fixations/saccades) which displayed appropriate characteristics (e.g. velocities/magnitudes), and demonstrated ecologically meaningful characteristics when re-presented over task videos. Rule learning was all-or nothing, moving from chance to near-perfect performance in one or two trials. Participants displayed standard effects of set switching, including worse performance when contexts differed from the previous trial, and when rules involved an extra-dimensional shift from the previous block. Conclusions USE enables the creation and temporally-precise reconstruction of highly complex tasks in dynamic environments. Our example task shows that costs associated with attentional set-switching generalize to such dynamic tasks.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".