Research
Physical effort and arousal reshape perceptual decisions—especially when capacity is limited, as in aging and among older adults. The dissertation asks how those changes appear in behavior, in pupillometry, and in sequential sampling parameters that separate evidence quality from caution. The backbone is a handgrip dual-task paradigm (isometric force concurrent with cognitive and perceptual tasks), scaled from younger-adult validation to older-adult modeling. Dissertation chapters are not journal articles; titles and status are listed on Publications.
This project was funded by the National Institutes of Health for research on cognitive aging.
Current Research Program
The dissertation is one integrated program in three stages.
Stage 1: Younger-adult proof of concept
Under concurrent physical effort and cognitive or perceptual load, pupil-linked arousal often shifted more clearly than summary behavioral outcomes. That pattern supports the arousal measurement strategy and validates the handgrip dual-task paradigm for later stages—without over-reading weak or null behavioral contrasts.
Stage 2: Older-adult psychometric chapter
Auditory and visual same–different discrimination is analyzed with psychometric function modeling and pupillometry, under missingness, artifacts, and QC constraints typical of eye-tracking in aging. Where appropriate, analyses use continuous intensity and graded manipulations rather than coarse binning alone.
Stage 3: Older-adult mechanistic chapter
A hierarchical Bayesian Wiener drift-diffusion model (DDM) decomposes choices and response times. Physical effort costs align most clearly with drift rate (evidence accumulation). Boundary separation does not show comparably strong compensatory support. Non-decision time is constrained by the task design and is not treated as a free explanation of effort. Links between pupillometry and trial-level diffusion parameters are exploratory and inconclusive in the current specification.
Program overview: dual-task case study. Methods and computation: Skills.
Adjacent work in XR / HCI
Outside the dissertation, an arXiv preprint (case study) reports hand versus gaze pointing in a web-based, XR-relevant interaction task. Modality-specific failure modes match the intended contrast: gaze—slips and Midas Touch–type errors; hand—misses and spatial targeting failures. Throughput and Fitts-style analyses summarize speed–accuracy tradeoffs.
Adaptive policies must be read narrowly: gaze declutter was the only policy that executed and was evaluable in this dataset (timeouts down modestly; slip-dominated gaze errors remained dominant). Hand target-width inflation was not evaluable because rendered targets did not update (UI integration bug). Linear ballistic accumulator (LBA) modeling there applies to verification-phase RTs only—see Skills for stack and scope; it does not replace the dissertation Wiener DDM. This line supports HCI, human factors, and interaction breadth; the dissertation remains the primary training identity.
Core questions
- How does concurrent physical effort change perceptual decision-making across the lifespan, and where do effects appear—in behavior, in pupillometry, or in latent parameters?
- How does psychometric function modeling complement behavior and pupillometry when eye-tracking data are incomplete or noisy?
- Can a hierarchical Bayesian Wiener DDM separate drift-linked costs from caution, while treating non-decision time and pupil–parameter links with appropriate limits?
- How do individual differences (for example cognitive reserve) interact with load beyond group averages?
Approach
The progression is behavioral performance → psychometric function modeling → hierarchical Bayesian Wiener DDM, with pupillometry at stages where it informs arousal and physical effort without substituting for behavior. The aim is models you can read and stress-test, not one summary score that hides where the costs live. Implementation details: Skills.
Why this work matters
This work connects psychology, cognitive neuroscience, human factors, and interface-relevant inference about state under load—without claiming clinical translation, production deployment, or pupillometry as a diagnostic biomarker.
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