The Neural Basis of Decision Making

Department of Neuroscience, University of Pennsylvania, Philadelphia, Pennsylvania 19104-6074, USA.
Annual Review of Neuroscience (Impact Factor: 19.32). 02/2007; 30(1):535-74. DOI: 10.1146/annurev.neuro.29.051605.113038
Source: PubMed


The study of decision making spans such varied fields as neuroscience, psychology, economics, statistics, political science, and computer science. Despite this diversity of applications, most decisions share common elements including deliberation and commitment. Here we evaluate recent progress in understanding how these basic elements of decision formation are implemented in the brain. We focus on simple decisions that can be studied in the laboratory but emphasize general principles likely to extend to other settings.

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    • "g . , the value associated with making a left or right saccade ; Gold and Shadlen , 2007 ; Kable and Glimcher , 2009 ) . It is not clear whether they also represent the value associated with goods in addition to the action - value representations ( Padoa - Schioppa , 2011 ; but see Tobler et al . "
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    ABSTRACT: A lottery is a list of mutually exclusive outcomes together with their associated probabilities of occurrence. Decision making is often modeled as choices between lotteries and-in typical research on decision under risk-the probabilities are given to the subject explicitly in numerical form. In this study, we examined lottery decision task where the probabilities of receiving various rewards are contingent on the subjects' own visual performance in a random-dot-motion (RDM) discrimination task, a metacognitive or second order judgment. While there is a large literature concerning the RDM task and there is also a large literature on decision under risk, little is known about metacognitive decisions when the source of uncertainty is visual. Using fMRI with humans, we found distinct fronto-striatal and fronto-parietal networks representing subjects' estimates of his or her performance, reward value, and the expected value (EV) of the lotteries. The fronto-striatal network includes the dorsomedial prefrontal cortex and the ventral striatum, involved in reward processing and value-based decision-making. The fronto-parietal network includes the intraparietal sulcus and the ventrolateral prefrontal cortex, which was shown to be involved in the accumulation of sensory evidence during visual decision making and in metacognitive judgments on visual performance. These results demonstrate that-while valuation of performance-based lotteries involves a common fronto-striatal valuation network-an additional network unique to the estimation of task-related performance is recruited for the integration of probability and reward information when probability is inferred from visual performance.
    Frontiers in Neuroscience 09/2015; 9:314. DOI:10.3389/fnins.2015.00314 · 3.66 Impact Factor
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    • "Previous studies have demonstrated that single perceptual decisions may unfold over tens to hundreds of milliseconds (Gold and Shadlen, 2007; Romo and Salinas, 2003; Schall, 2001). The neuronal mechanisms for a single decision are well characterized by diffusion models that accumulate sensory evidence up to a bound, which signals the commitment to a choice (Drugowitsch et al., 2012, 2014; Huang et al., 2012; Ratcliff and McKoon, 2008). "
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    ABSTRACT: Intelligence relies on our ability to find appropriate sequences of decisions in complex problem spaces. The efficiency of a problem solver depends on the speed of its individual decisions and the number of decisions it can explore in parallel. It remains unknown whether the primate brain can consider multiple decisions at the same time. We therefore trained monkeys to navigate through a decision tree with stochastic sensory evidence at multiple branching points and recorded neuronal activity in visual cortical areas V1 and V4. We found a first phase of decision making in which neuronal activity increased in parallel along multiple branches of the decision tree. This was followed by an integration phase where the optimal overall strategy crystallized as the result of interactions between local decisions. The results reveal how sensory evidence is integrated efficiently for hierarchical decisions and contribute to our understanding of the brain mechanisms that implement complex mental programs.
    Neuron 09/2015; DOI:10.1016/j.neuron.2015.08.015 · 15.05 Impact Factor
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    • "Second, by using neutral and nonthreatening stimuli, decision accumulation is assessed without confounding OCD-relevant stimuli. Third, in studies manipulating dot motion-viewing time, decision accuracy improved as a function of motion-viewing duration (Gold and Shadlen, 2007), contrary to other perceptual tasks in which the two were unrelated (Uchida et al, 2006). Thus, RDMT reaction time is representative of decision-making. "
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    ABSTRACT: Objective The compulsive behaviour underlying Obsessive-Compulsive Disorder (OCD) may be related to abnormalities in decision-making. The inability to commit to ultimate decisions, e.g. patients unable to decide whether their hands are sufficiently clean, may reflect failures in accumulating sufficient evidence prior to a decision. Here we investigate the process of evidence accumulation in OCD in perceptual discrimination, hypothesizing enhanced evidence accumulation relative to healthy volunteers. Method Twenty-eight OCD patients and 35 healthy control subjects were tested with a low-level visual perceptual task (random dot motion task), whereby different coherent levels for motion were defined to measure high and low uncertainty, and two response conflict tasks as control tasks (flanker task and probabilistic selection task). Logistic regression analysis across all coherence levels (which accounted for visual detection threshold) and hierarchical drift diffusion modelling (HDDM) were used to characterize response strategies between patients with OCD and healthy controls in the random dot motion task. Results Patients required more evidence under high uncertainty perceptual contexts, as indexed by longer response time and higher decision boundaries. HDDM, which defines a decision when accumulated noisy evidence reaches a decision boundary, further showed slower drift rate towards the decision boundary reflecting poorer quality of evidence entering the decision process in patients under low uncertainty. With monetary incentives emphasizing speed, patients decreased the decision thresholds relative to controls, accumulating less evidence in low uncertainty. These findings were unrelated to visual perceptual deficits and response conflict. Conclusion This study provides evidence for impaired decision-formation processes in OCD, with a differential influence of high and low uncertainty contexts on evidence accumulation (decision threshold) and on the quality of evidence gathered (drift rates). It further emphasizes that OCD patients are sensitive to monetary incentives heightening speed in the speed-accuracy tradeoff, improving evidence accumulation and shifting away from pathological internal monitoring. These findings may have relevance for therapeutic approaches.
    Journal of Neurology Neurosurgery & Psychiatry 09/2015; 86(9). DOI:10.1136/jnnp-2015-311750.57 · 6.81 Impact Factor
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