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      Pupil dilation signals surprise: evidence for noradrenaline’s role in decision making

       
      Frontiers in Neuroscience
      Frontiers Media SA

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          Abstract

          Our decisions are guided by the rewards we expect. These expectations are often based on incomplete knowledge and are thus subject to uncertainty. While the neurophysiology of expected rewards is well understood, less is known about the physiology of uncertainty. We hypothesize that uncertainty, or more specifically errors in judging uncertainty, are reflected in pupil dilation, a marker that has frequently been associated with decision making, but so far has remained largely elusive to quantitative models. To test this hypothesis, we measure pupil dilation while observers perform an auditory gambling task. This task dissociates two key decision variables – uncertainty and reward – and their errors from each other and from the act of the decision itself. We first demonstrate that the pupil does not signal expected reward or uncertainty per se, but instead signals surprise, that is, errors in judging uncertainty. While this general finding is independent of the precise quantification of these decision variables, we then analyze this effect with respect to a specific mathematical model of uncertainty and surprise, namely risk and risk prediction error. Using this quantification, we find that pupil dilation and risk prediction error are indeed highly correlated. Under the assumption of a tight link between noradrenaline (NA) and pupil size under constant illumination, our data may be interpreted as empirical evidence for the hypothesis that NA plays a similar role for uncertainty as dopamine does for reward, namely the encoding of error signals.

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          Most cited references25

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          An insular view of anxiety.

          We propose a general hypothesis that integrates affective and cognitive processing with neuroanatomy to explain anxiety pronenes. The premise is that individuals who are prone to anxiety show an altered interoceptive prediction signal, i.e., manifest augmented detection of the difference between the observed and expected body state. As a consequence, the increased prediction signal of a prospective aversive body state triggers an increase in anxious affect, worrisome thoughts and other avoidance behaviors. The anterior insula is proposed to play a key role in this process. Further testing of this model--which should include investigation of genetic and environmental influences--may lead to the development of novel treatments that attenuate this altered interoceptive prediction signal in patients with anxiety disorders.
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            A framework for mesencephalic dopamine systems based on predictive Hebbian learning.

            We develop a theoretical framework that shows how mesencephalic dopamine systems could distribute to their targets a signal that represents information about future expectations. In particular, we show how activity in the cerebral cortex can make predictions about future receipt of reward and how fluctuations in the activity levels of neurons in diffuse dopamine systems above and below baseline levels would represent errors in these predictions that are delivered to cortical and subcortical targets. We present a model for how such errors could be constructed in a real brain that is consistent with physiological results for a subset of dopaminergic neurons located in the ventral tegmental area and surrounding dopaminergic neurons. The theory also makes testable predictions about human choice behavior on a simple decision-making task. Furthermore, we show that, through a simple influence on synaptic plasticity, fluctuations in dopamine release can act to change the predictions in an appropriate manner.
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              Neural correlates, computation and behavioural impact of decision confidence.

              Humans and other animals must often make decisions on the basis of imperfect evidence. Statisticians use measures such as P values to assign degrees of confidence to propositions, but little is known about how the brain computes confidence estimates about decisions. We explored this issue using behavioural analysis and neural recordings in rats in combination with computational modelling. Subjects were trained to perform an odour categorization task that allowed decision confidence to be manipulated by varying the distance of the test stimulus to the category boundary. To understand how confidence could be computed along with the choice itself, using standard models of decision-making, we defined a simple measure that quantified the quality of the evidence contributing to a particular decision. Here we show that the firing rates of many single neurons in the orbitofrontal cortex match closely to the predictions of confidence models and cannot be readily explained by alternative mechanisms, such as learning stimulus-outcome associations. Moreover, when tested using a delayed reward version of the task, we found that rats' willingness to wait for rewards increased with confidence, as predicted by the theoretical model. These results indicate that confidence estimates, previously suggested to require 'metacognition' and conscious awareness are available even in the rodent brain, can be computed with relatively simple operations, and can drive adaptive behaviour. We suggest that confidence estimation may be a fundamental and ubiquitous component of decision-making.
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                Author and article information

                Journal
                Frontiers in Neuroscience
                Front. Neurosci.
                Frontiers Media SA
                1662453X
                2011
                2011
                : 5
                Article
                10.3389/fnins.2011.00115
                4c3b6051-5879-4d60-be6f-e3c58b3434be
                © 2011
                History

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