Disentangling Reversal-learning Impairments in Frontotemporal Dementia and Alzheimer Disease
Bibliographic record
Abstract
BACKGROUND: Individuals with frontotemporal dementia (FTD) often present with poor decision-making, which can affect both their financial and social situations. Delineation of the specific cognitive impairments giving rise to impaired decision-making in individuals with FTD may inform treatment strategies, as different neurotransmitter systems have been associated with distinct patterns of altered decision-making. OBJECTIVE: To use a reversal-learning paradigm to identify the specific cognitive components of reversal learning that are most impaired in individuals with FTD and those with Alzheimer disease (AD) in order to inform future approaches to treatment for symptoms related to poor decision-making and behavioral inflexibility. METHOD: We gave 30 individuals with either the behavioral variant of FTD or AD and 18 healthy controls a stimulus-discrimination reversal-learning task to complete. We then compared performance in each phase between the groups. RESULTS: The FTD group demonstrated impairments in initial stimulus-association learning, though to a lesser degree than the AD group. The FTD group also performed poorly in classic reversal learning, with the greatest impairments being observed in individuals with frontal-predominant atrophy during trials requiring inhibition of a previously advantageous response. CONCLUSION: Taken together, these results and the reversal-learning paradigm used in this study may inform the development and screening of behavioral, neurostimulatory, or pharmacologic interventions aiming to address behavioral symptoms related to stimulus-reinforcement learning and response inhibition impairments in individuals with FTD.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".