Inverse decision modeling infers latent properties of decision processes from observed behavior, but existing formulations rely primarily on action trajectories. In verbalized cognitive tasks, task execution also produces response dynamics that action-only formulations leave unmodeled, such as verbal production, interaction, and hesitation. We propose a factorized inverse decision model (FIDM) that decomposes each individual's task-execution likelihood into an action factor and an effort factor, governed by separate individual-specific parameters. From raw verbal transcripts, a language model produces structured task-execution traces for factorized inference. On data from 400 older adults performing a grocery-shopping dialog task for cognitive screening, controlled recovery shows selective estimation of the intended factors, while matched semi-synthetic conditions show that FIDM preserves action-execution distinctions even when aggregate behavioral summaries are matched. Action evidence further localizes task-defined deviations across participants. In cognitive-status classification, FIDM provides information complementary to clinical scores, trajectory summaries, and frozen language representations, with consistent gains across all evaluated baselines in the binary setting.
Large language models are increasingly used to simulate human participants in social and behavioral studies, yet static persona prompting typically maps a participant profile and an experimental scenario directly to a response, entangling stable dispositions with situation-specific interpretations. To address this limi...
Zhi-Bo Deng, Dong-Yuan Li, Shu-Wen Ge et al.· 0 citations
Online metacognitive processes support learning and decision-making by monitoring and integrating heuristic cues, such as evidence variability and conflict, throughout deliberation. Certain computational approaches, such as Markov and Quantum random-walk models (MRM and QRM), describe these metacognitive processes as...
Ritesh K. Malaiya, R. Golden, Michael J. Lundie et al.· Computational Brain & Be...· 0 citations
Chain-of-Thought (CoT) reasoning is increasingly incorporated into Vision-Language-Action (VLA) models, yet it can degrade the performance of stronger embodied agents. We investigate this capability-dependent effect by distinguishing explicit reasoning from latent decision computation, i.e., perception-grounded computa...
Yuan-Peng Lin, Zi-Yu Zhou, Jin-Long Zhao et al.· 0 citations
Human decision-making in dynamic household environments depends on memory retrieval processes that shape action selection and timing. This paper presents an instruction-following LLM agent as an executable human performance model for simulating memory retrieval behavior in household tasks. The proposed framework integr...
Jong-Geun Pyeon, F. Tehranchi· Proceedings of the Human Fac...· 0 citations
Adaptive behaviour depends on the interplay between bottom-up processing of environmental inputs and top-down adjustments of internal representations. However, how reward uncertainty (as a constraint on sensory evidence) and goal setting (as a top-down constraint on policy precision) jointly shape this hierarchical org...
Arthur Traon, M. Abidi, Emmanuel Schneider et al.· Biological Psychology· 0 citations
The component that dominates a latent world model's prediction error need not be the one whose repair most improves action selection. We show this by comparing action sequences from identical physical starts and separating endpoint error into a candidate-pool center and action-relative responses. Across four model fami...
Rui Min, Xian-Yao Li, Fang Xu et al.· 0 citations
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