Investigating the neuropsychological features of hoarding disorder using a novel virtual reality paradigm
Bibliographic record
Abstract
Introduction Hoarding disorder represents a considerable health concern that warrants further investigation of its associated neuropsychological components. The present study examined a key aspect of the cognitive–behavioural model of hoarding, information processing (memory, attention, decision making, categorisation). Mixed findings in the literature on the presence of cognitive deficits may be attributable to the use of assessment tools with low ecological validity. Thus, novel virtual reality (VR) environments were developed to examine the information-processing components with improved ecological validity.Methods Two groups (i.e., with hoarding disorder, n = 36; without hoarding disorder, n = 40) similar in age and gender were recruited from the community to complete a series of standardised and novel VR memory and decision-making tasks, and to complete a categorisation task for objects in a messy VR home office.Results Higher attentional difficulties related to ADHD symptoms, poorer category efficiency, and poorer trait, but not state, memory confidence, were reported in the hoarding group. There was no evidence of memory and decision-making impairments specific to the hoarding group.Conclusions Results from this research advance our understanding of the cognitive–behavioural components of hoarding and offer implications for future treatment and VR research initiatives.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".