A Normative Theory of Decision Making from Multiple Stimuli: The Contextual Diffusion Decision Model
arXiv:2603.28600v1 Announce Type: new Abstract: The dynamics of simple two-alternative forced-choice (2AFC) decisions are well-modeled by a class of random walk models (e.g. Laming, 1968; Ratcliff, 1978; Usher & McClelland, 2001; Bogacz et al., 2006). However, in real-life, even simple decisions involve dynamically changing influence of additional information. In this work, we describe a computational theory of decision making from multiple sources of information, grounded in Bayesian inference and consistent with a simple neural network. This Contextual Diffusion Decision Model (CDDM) is a formal generalization of the Diffusion Decision Model (DDM), a popular existing model of fixed-context decision making (Ratcliff, 1978), and shares with it both a mechanistic and a probabilistic motivat
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Abstract:The dynamics of simple two-alternative forced-choice (2AFC) decisions are well-modeled by a class of random walk models (e.g. Laming, 1968; Ratcliff, 1978; Usher & McClelland, 2001; Bogacz et al., 2006). However, in real-life, even simple decisions involve dynamically changing influence of additional information. In this work, we describe a computational theory of decision making from multiple sources of information, grounded in Bayesian inference and consistent with a simple neural network. This Contextual Diffusion Decision Model (CDDM) is a formal generalization of the Diffusion Decision Model (DDM), a popular existing model of fixed-context decision making (Ratcliff, 1978), and shares with it both a mechanistic and a probabilistic motivation. Just as the DDM is a model for a variety of simple two-alternative forced-choice (2AFC) decision making tasks, we demonstrate that the CDDM supports a variety of simple context-dependent tasks of longstanding interest in psychology, including the Flanker (Eriksen & Eriksen, 1974), AX-CPT (Servan-Schreiber et al., 1996), Stop-Signal (Logan & Cowan, 1984), Cueing (Posner, 1980), and Prospective Memory paradigms (Einstein & McDaniel, 2005). Further, we use the CDDM to perform a number of normative rational analyses exploring optimal response and memory allocation policies. Finally, we show how the use of a consistent model across tasks allows us to recover consistent qualitative data patterns in multiple tasks, using the same model parameters.
Subjects:
Neurons and Cognition (q-bio.NC)
Cite as: arXiv:2603.28600 [q-bio.NC]
(or arXiv:2603.28600v1 [q-bio.NC] for this version)
https://doi.org/10.48550/arXiv.2603.28600
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Michael Shvartsman [view email] [v1] Mon, 30 Mar 2026 15:49:01 UTC (1,651 KB)
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