Age Affects How Task Difficulty and Complexity Modulate Perceptual Decision-Making
Bibliographic record
Abstract
Decisions differ in difficulty and rely on perceptual information that varies in richness (complexity); aging affects cognitive function including decision-making, and yet, the interaction between difficulty and perceptual complexity have rarely been addressed in aging. Using a parametric fMRI modulation analysis and psychophysics, we address how task difficulty affects decision-making when controlling for the complexity of the perceptual context in which decisions are made. Perceptual complexity was varied in a factorial design while participants made perceptual judgments on the spatial frequency of two patches that either shared the same orientation (simple condition) or were orthogonal in orientation (complex condition). Psychophysical thresholds were measured for each participant in each condition and served to set individualized levels of difficulty during scanning. Findings indicate that discriminability interacts with complexity, to influence decisional difficulty. Modulation as a function of difficulty is maintained with age, as indicated by coupling between increased activation in fronto-parietal regions and suppression in the lateral hubs, however, age has a specific effect in the ventral anterior cingulate cortex (ACC), driven by performance at near-threshold (difficult) levels for the simpler stimulus combination condition, but not the more complex one. Taken together, our findings suggest that the context of difficulty, or what is perceived as important, changes with age, and that decisions that would seem neutral to younger participants, may carry more emphasis with age.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".