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VERSION:2.0
PRODID:Linklings LLC
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TZID:America/Chicago
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DTSTART:19700308T020000
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DTSTART:19701101T020000
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BEGIN:VEVENT
DTSTAMP:20251016T135156Z
LOCATION:Grand A
DTSTART;TZID=America/Chicago:20251015T113000
DTEND;TZID=America/Chicago:20251015T115000
UID:HFESAM_ASPIRE 2025_sess218_LECT308@linklings.com
SUMMARY:Beyond Frequency and Recency: An Entropy-Based Extension for Model
 ing Memory Retrieval in Dynamic Environments
DESCRIPTION:Jongchan Pyeon, Duk Hee Ka, and Farnaz Tehranchi (Pennsylvania
  State University)\n\nThis study extends the ACT-R base-level learning equ
 ation by incorporating environmental uncertainty, observation duration, an
 d individual learning patterns to better model human memory retrieval in d
 ynamic, multi-object environments. We hypothesize that retrieval efficienc
 y depends not only on frequency and recency of interactions but also on sc
 ene entropy, time spent observing, and a learning pattern parameter (α). U
 sing a household task scenario in the AI2-THOR simulator, we analyzed sear
 ch times as proxies for memory access. Our modified equation integrates en
 tropy to reflect visual uncertainty, scales decay by observation duration,
  and includes α to capture task-specific learning dynamics. Results from e
 ight participants revealed distinct retrieval patterns (e.g., Increasing–D
 ecreasing vs. Decreasing–Increasing search times) related to prior experie
 nce. Compared to the original ACT-R model (MAE = 0.3135, correlation = 0.4
 568), our modified equation achieved a lower average MAE of 0.2629 and a h
 igher correlation of 0.6126 with observed behavior, demonstrating improved
  predictive accuracy. These findings offer a stronger foundation for model
 ing memory retrieval and have implications for adaptive user interfaces, t
 raining systems, and assistive agents that rely on understanding human mem
 ory performance in visually complex settings.\n\nTrack: Cognitive Engineer
 ing & Decision Making\n\nSession Chairs: Srijani Mukherjee (Member), Alexa
 ndra Watral (Mayo Clinic), and Jin Yong Kim (University of Michigan)\n\n
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