A Normative Theory of Decision Making from Multiple Stimuli: The Contextual Diffusion Decision Model
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866910085703270400 |
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| author | Shvartsman, Michael Srivastava, Vaibhav Sundaram, Narayanan Cohen, Jonathan D. |
| author_facet | Shvartsman, Michael Srivastava, Vaibhav Sundaram, Narayanan Cohen, Jonathan D. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_28600 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Normative Theory of Decision Making from Multiple Stimuli: The Contextual Diffusion Decision Model Shvartsman, Michael Srivastava, Vaibhav Sundaram, Narayanan Cohen, Jonathan D. Neurons and Cognition 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. |
| title | A Normative Theory of Decision Making from Multiple Stimuli: The Contextual Diffusion Decision Model |
| topic | Neurons and Cognition |
| url | https://arxiv.org/abs/2603.28600 |