A Kiosk Station for the Assessment of Multiple Cognitive Domains and Cognitive Enrichment of Monkeys
Bibliographic record
Abstract
Nonhuman primates (NHP's) are self-motivated to perform cognitive tasks on touchscreens in their animal housing setting. To leverage this ability, fully integrated hardware and software solutions are needed that work within housing and husbandry routines while also spanning cognitive task constructs of the Research Domain Criteria (RDoC). Here, we detail such an integrated robust hardware and software solution for running cognitive tasks in cage-housed NHP's with a cage-mounted Kiosk Station (KS-1). KS-1 consists of a frame for mounting flexibly on housing cages, a touchscreen animal interface with mounts for receptables, reward pumps, and cameras, and a compact computer cabinet with an interface for controlling behavior. Behavioral control is achieved with a Unity3D program that is virtual-reality capable, allowing semi-naturalistic visual tasks to assess multiple cognitive domains.KS-1 is fully integrated into the regular housing routines of monkeys. A single person can operate multiple KS-1's. Monkeys engage with KS-1 at high motivation and cognitive performance levels at high intra-individual consistency. KS-1 is optimized for flexible mounting onto standard apartment cage systems and provides a new design variation complementing existing cage-mounted touchscreen systems. KS-1 has a robust animal interface with options for gaze/reach monitoring. It has an integrated user interface for controlling multiple cognitive tasks using a common naturalistic object space designed to enhance task engagement. All custom KS-1 components are open-sourced.In summary, KS-1 is a versatile new tool for cognitive profiling and cognitive enrichment of cage-housed monkeys. It reliably measures multiple cognitive domains which promises to advance our understanding of animal cognition, inter-individual differences, and underlying neurobiology in refined, ethologically meaningful behavioral foraging contexts.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".