Computational Models of Decision Making

Busemeyer, Jerome R.; Johnson, Jacqueline

doi:10.1002/9780470752937.ch7

Cited by 109 publications

(72 citation statements)

References 75 publications

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“…The main improvement that our approach offers over these two models is in neurological realism, as reflected by modeled characteristics of individual processing units ('neurons') and the mapping of proposed computations onto specific brain regions and interactions supported by empirical findings. The models of Grossberg and Gutowski (1987) and Busemeyer and Johnson (2004) are not comparable in this respect to either ANDREA or the previously mentioned works of Deco and Rolls (2005) and Wagar and Thagard (2004). This of course is not a criticism of such methods of modeling.…”

Section: Discussionmentioning

confidence: 77%

“…Task modeling of this sort is important for exploring basic details of neural mechanisms for specific phenomena, but examining brain processes on a larger scale is required for explaining more complex and wide-ranging psychological findings, such as prospect theory and decision affect theory. Busemeyer and Johnson (2004) describe a connectionist model that they apply to a range of behaviors as diverse as those explored by ANDREA, including preference reversal effects and loss aversion. The network model called affective balance theory (Grossberg & Gutowski, 1987) also explores a wide range of risky decision phenomena in a mathematically sophisticated fashion, and proposes effects of emotional context on cognitive processing that are largely consistent with those implemented in ANDREA.…”

Section: Discussionmentioning

confidence: 99%

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Neural affective decision theory: Choices, brains, and emotions

Litt

Eliasmith

Thagard

2008

Cognitive Systems Research

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We present a theory and neurocomputational model of how specific brain operations produce complex decision and preference phenomena, including those explored in prospect theory and decision affect theory. We propose that valuation and decision making are emotional processes, involving interacting brain areas that include two expectation-discrepancy subsystems: a dopamine-encoded system for positive events and a serotonin-encoded system for negative ones.The model provides a rigorous account of loss aversion and the shape of the value function from prospect theory. It also suggests multiple distinct neurological mechanisms by which information framing may affect choices, including ones involving anticipated pleasure. It further offers a neural basis for the interactions among affect, prior expectations and counterfactual comparisons explored in decision affect theory. Along with predicting the effects of particular brain disturbances and damage, the model suggests specific neurological explanations for individual differences observed in choice and valuation behaviors.Keywords: computational neuroscience; decision making; emotion; framing; prospect theory. what kind of career to pursue? In traditional economics, the standard answer is that people decide by maximizing expected utility, but psychologists have found many problems with this kind of decision theory as a description of human behavior (e.g., Camerer, 2000;Kahneman & Tversky, 2000;Koehler, Brenner, & Tversky, 1997;Rottenstreich & Hsee, 2001;Tversky & Kahneman, 1991). Economists commonly take preferences as given, but from a psychological point of view it should be possible to explain how preferences arise from cognitive and affective processes.Work in this spirit has made tremendous progress in revealing key features and dynamics missed by theories disconnected from the study of cognitive, emotional and socially motivated phenomena, such as a common hypersensitivity to losses over equivalent gains (Kahneman & Tversky, 1979) and the affective influence of prior expectations and counterfactual comparisons on preference judgments (Mellers, 2000). Moreover, with the rise of cognitive and affective neuroscience, it should be possible to identify precise neural mechanisms underlying these behavioral-level explanations of why people make the choices that they do.We propose neural affective decision theory as a psychologically and neurologically realistic account of specific brain mechanisms underlying human preference and decision. The theory consists of four principles, which we shall list here and describe in detail later:1. Affect. Decision making is a cognitive-affective process, crucially dependent on emotional evaluation of potential actions. Brain.Decision making is a neural process driven by coordinated dynamic interactions among multiple brain areas, including parts of prefrontal cortex as well as major subcortical systems.3. Valuation. The brain forms preferences via interacting but distinct mechanisms for positive and negative outcomes, encode...

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Section: Discussionmentioning

confidence: 77%

Section: Discussionmentioning

confidence: 99%

Neural affective decision theory: Choices, brains, and emotions

Litt

Eliasmith

Thagard

2008

Cognitive Systems Research

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“…Although it bears a conceptual relationship to many other models used to generate decisions such as the drift diffusion model (DDM) (Fig. S5) (53)(54)(55), the predictions of the CDM and the DDM model are in fact very different (see SI Text for more details). Indeed, while the CDM model provides a good account for the comparison signal we observed in ACC, the DDM model fails to capture such an output.…”

Section: Discussionmentioning

confidence: 99%

Neural computations underlying action-based decision making in the human brain

Wunderlich

Rangel

O’Doherty

2009

Proc. Natl. Acad. Sci. U.S.A.

266

214

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Action-based decision making involves choices between different physical actions to obtain rewards. To make such decisions the brain needs to assign a value to each action and then compare them to make a choice. Using fMRI in human subjects, we found evidence for action-value signals in supplementary motor cortex. Separate brain regions, most prominently ventromedial prefrontal cortex, were involved in encoding the expected value of the action that was ultimately taken. These findings differentiate two main forms of value signals in the human brain: those relating to the value of each available action, likely reflecting signals that are a precursor of choice, and those corresponding to the expected value of the action that is subsequently chosen, and therefore reflecting the consequence of the decision process. Furthermore, we also found signals in the dorsomedial frontal cortex that resemble the output of a decision comparator, which implicates this region in the computation of the decision itself.acc ͉ action value ͉ reinforcement learning ͉ sma ͉ vmpfc C onsider a goalkeeper trying to stop a soccer ball during a penalty kick. Within a brief amount of time he needs to choose between jumping to the left or right goal posts. Repeated play against the same opponents allows him to learn about their scoring tendencies, which can be used to compute the values of a left and a right jump before making a decision. It is a long-established view in economics, psychology, and computational neuroscience that the brain makes choices among actions by first computing a value for each possible action, and then selecting one of them on the basis of those values (1-3). This raises two fundamental questions in decision neuroscience: (1) where in the brain are the values of different types of actions encoded? and (2) how and where does the brain compare those values to generate a choice?An emerging theme in decision neuroscience is that organisms need to make a number of value-related computations to make even simple choices (4). Consider the case of action-based choice exemplified by the goalkeeper's problem. First, he needs to assign a value to each action under consideration. These signals, known as action values, encode the value of each action before choice and regardless of whether it is subsequently chosen or not, which allows them to serve as inputs into the decision-making process (5-7). Second, these action values are compared to generate a choice.

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“…MDFT can be interpreted as a connectionist network (Roe et al, 2001;Busemeyer & Johnson, 2004), as illustrated in Figure 2. At each moment in time, attention can be allocated to attribute P (e.g., price) or attribute Q (e.g., quality), as illustrated in the first layer.…”

Section: Precursors To the Mlba Modelmentioning

confidence: 99%