Validation of a computerized visual search game for cognitive assessment
Bibliographic record
Abstract
There is the demand in the field of neuropsychology and cognitive aging for better tools for assessing cognitive decline - more realistic (ecological validity), more complex (combined cognitive domains) and more sensitive (earlier detection). Sensitivity in tools also incorporates the consideration of cognitive reserve and individual differences: that people with differing stimulating experiences can also present a different cognitive decline and risk for dementia in older age (Stern, 2012). Thus, by using a complex visual search paradigm (foraging) and technology (virtual reality) we created a computerized game which we hope can be useful to fill some of these gaps. We decided on a complex visual search task as it incorporates the main cognitive domains that have been theorized to be crucial in the formulation of cognitive reserve: arousal, attention, working memory and learning (Robertson, 2013; 2014). With this study we aim to validate the task and examine it's feasibility and usability to detect cognitive performance in a wide array of cognitive domains in older people of differing levels of cognitive reserve. The task is a desktop virtual reality game where people forage in different rooms for common household items (e.g. mugs). Participants can (restrictively) navigate in the rooms. The task includes 5 blocks: 4 blocks in which participants forage for one type of item (e.g. mugs) and 1 block in which participants forage for multiple types of items (e.g. mugs, socks, toys, and cans). There are 2 difficulty conditions based on target-distractor similarity: in easy trials target-distractor similarity is low, and in hard trials target-distractor similarity is high. There is also a memory task at the end where memory for object-location learning of the objects in the rooms is examined. In order to validate the task, participants will also complete various traditional neuropsychological tests. The neuropsychological tests being used are: Trail Making Test A and B, Letter-Digit Substitution test, Corsi Block-tapping task, Modified-Wisconsin Card Sorting test and Location Learning test. We are also using the Montreal Cognitive Assessment task and the Nederlandse Leestest voor Volwassenen (Dutch version of NART) as measures of global cognition. Lastly we are using 3 questionnaires on trait curiosity [Epistemic Curiosity (Litman, & Spielberger, 2003), Perceptual Curiosity (Collins, Litman, & Spielberger, 2004), and CEI-II (Kashdan et al., 2004)], the Cognitive Failure Questionnaire for subjective cognitive complaints and the Cognitive Reserve Index questionnaire (CRIq) as a measure of cognitive reserve. We also ask about previous computer use and gaming experience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".