Exploring the neuro-computational mechanisms underlying age-related changes in complex decision-making
Bibliographic record
Abstract
Over the last decade, research in decision-making has made remarkable advancements in understanding how the relative engagement in model-based and model-free decision-making changes with healthy aging. While we are beginning to understand the factors that affect older adults’ shift away from model-based decision-making, the exact mechanisms at play are still poorly understood. This dissertation presents findings as well as a novel theory which aims to advance our understanding of these neuro-computational mechanisms. Chapter 2 demonstrates \nthat, in contrast to younger adults, older adults do not benefit from more distinct probabilistic transitions between stages in a two-step decision-making task. By examining trial-by-trial neurocomputational dynamics, this first empirical paper provides evidence for age-related deficits in the ability to represent probabilistic transitions, and predict the value of upcoming choice options. Chapter 3 presents a novel theory: the diminished state space theory of human aging. This theoretical contribution proposes that older adults’ deficits in model-based learning \nare due to their underlying difficulties in representing state spaces. Chapter 4 examines one of the computational explanations brought forward in this theoretical paper. Namely, that older adults’ diminished state spaces may be explained (at least in part) by their difficulties updating their internal task representation. In line with this hypothesis, results demonstrate that in contrast to younger adults, older adults show difficulties identifying outcomes that signal the need to update their internal model. Together, these findings suggest that older adults’ deficits in model-based decision-making can be explained by their diminished state space representations, which in turn may in part result from their difficulty updating their internal model during cognitive tasks. Ultimately, \nthis dissertation provides important insights regarding older adults’ deficits, and opens future directions for the study of age-related changes in representational abilities.
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 | 0.006 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".