Decoding subjective decisions from orbitofrontal cortex

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Abstract

When making a subjective choice, the brain must compute a value for each option and compare those values to make a decision. The orbitofrontal cortex (OFC) is critically involved in this process, but the neural mechanisms remain obscure, in part due to limitations in our ability to measure and control the internal deliberations that can alter the dynamics of the decision process. Here, we tracked the dynamics by recovering temporally precise neural states from multi-dimensional data in OFC. During individual choices, OFC alternated between states associated with the value of two available options, with dynamics that predicted whether a subject would decide quickly or vacillate between the two alternatives. Ensembles of value-encoding neurons contributed to these states, with individual neurons shifting activity patterns as the network evaluated each option. Thus, the mechanism of subjective decision-making involves the dynamic activation of OFC states associated with each choice alternative.

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The time course of perceptual choice: the leaky, competing accumulator model.

M. Usher, J L McClelland (2001)

The time course of perceptual choice is discussed in a model of gradual, leaky, stochastic, and competitive information accumulation in nonlinear decision units. Special cases of the model match a classical diffusion process, but leakage and competition work together to address several challenges to existing diffusion, random walk, and accumulator models. The model accounts for data from choice tasks using both time-controlled (e.g., response signal) and standard reaction time paradigms and its adequacy compares favorably with other approaches. A new paradigm that controls the time of arrival of information supporting different choice alternatives provides further support. The model captures choice behavior regardless of the number of alternatives, accounting for the log-linear relation between reaction time and number of alternatives (Hick's law) and explains a complex pattern of visual and contextual priming in visual word identification.

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Neurons in the orbitofrontal cortex encode economic value.

Camillo Padoa-Schioppa, John Assad (2006)

Economic choice is the behaviour observed when individuals select one among many available options. There is no intrinsically 'correct' answer: economic choice depends on subjective preferences. This behaviour is traditionally the object of economic analysis and is also of primary interest in psychology. However, the underlying mental processes and neuronal mechanisms are not well understood. Theories of human and animal choice have a cornerstone in the concept of 'value'. Consider, for example, a monkey offered one raisin versus one piece of apple: behavioural evidence suggests that the animal chooses by assigning values to the two options. But where and how values are represented in the brain is unclear. Here we show that, during economic choice, neurons in the orbitofrontal cortex (OFC) encode the value of offered and chosen goods. Notably, OFC neurons encode value independently of visuospatial factors and motor responses. If a monkey chooses between A and B, neurons in the OFC encode the value of the two goods independently of whether A is presented on the right and B on the left, or vice versa. This trait distinguishes the OFC from other brain areas in which value modulates activity related to sensory or motor processes. Our results have broad implications for possible psychological models, suggesting that economic choice is essentially choice between goods rather than choice between actions. In this framework, neurons in the OFC seem to be a good candidate network for value assignment underlying economic choice.

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Probabilistic decision making by slow reverberation in cortical circuits.

Xiao-Jing Wang (2002)

Recent physiological studies of alert primates have revealed cortical neural correlates of key steps in a perceptual decision-making process. To elucidate synaptic mechanisms of decision making, I investigated a biophysically realistic cortical network model for a visual discrimination experiment. In the model, slow recurrent excitation and feedback inhibition produce attractor dynamics that amplify the difference between conflicting inputs and generates a binary choice. The model is shown to account for salient characteristics of the observed decision-correlated neural activity, as well as the animal's psychometric function and reaction times. These results suggest that recurrent excitation mediated by NMDA receptors provides a candidate cellular mechanism for the slow time integration of sensory stimuli and the formation of categorical choices in a decision-making neocortical network.