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      Neural mediators of changes of mind about perceptual decisions

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          Abstract

          Changing one’s mind on the basis of new evidence is a hallmark of cognitive flexibility. To revise our confidence in a previous decision, new evidence should be used to update beliefs about choice accuracy, but how this process unfolds in the human brain remains unknown. Here we manipulated whether additional sensory evidence supports or negates a previous motion direction discrimination judgment while recording markers of neural activity in the human brain using fMRI. A signature of post-decision evidence (change in log-odds correct) was selectively observed in the activity of posterior medial frontal cortex (pMFC). In contrast, distinct activity profiles in anterior prefrontal cortex (aPFC) mediated the impact of post-decision evidence on subjective confidence, independently of changes in decision value. Together our findings reveal candidate neural mediators of post-decisional changes of mind in the human brain, and indicate possible targets for ameliorating deficits in cognitive flexibility.

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          Most cited references 35

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          Can parametric statistical methods be trusted for fMRI based group studies?

          The most widely used task fMRI analyses use parametric methods that depend on a variety of assumptions. While individual aspects of these fMRI models have been evaluated, they have not been evaluated in a comprehensive manner with empirical data. In this work, a total of 2 million random task fMRI group analyses have been performed using resting state fMRI data, to compute empirical familywise error rates for the software packages SPM, FSL and AFNI, as well as a standard non-parametric permutation method. While there is some variation, for a nominal familywise error rate of 5% the parametric statistical methods are shown to be conservative for voxel-wise inference and invalid for cluster-wise inference; in particular, cluster size inference with a cluster defining threshold of p = 0.01 generates familywise error rates up to 60%. We conduct a number of follow up analyses and investigations that suggest the cause of the invalid cluster inferences is spatial auto correlation functions that do not follow the assumed Gaussian shape. By comparison, the non-parametric permutation test, which is based on a small number of assumptions, is found to produce valid results for voxel as well as cluster wise inference. Using real task data, we compare the results between one parametric method and the permutation test, and find stark differences in the conclusions drawn between the two using cluster inference. These findings speak to the need of validating the statistical methods being used in the neuroimaging field.
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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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              The trouble with overconfidence.

              The authors present a reconciliation of 3 distinct ways in which the research literature has defined overconfidence: (a) overestimation of one's actual performance, (b) overplacement of one's performance relative to others, and (c) excessive precision in one's beliefs. Experimental evidence shows that reversals of the first 2 (apparent underconfidence), when they occur, tend to be on different types of tasks. On difficult tasks, people overestimate their actual performances but also mistakenly believe that they are worse than others; on easy tasks, people underestimate their actual performances but mistakenly believe they are better than others. The authors offer a straightforward theory that can explain these inconsistencies. Overprecision appears to be more persistent than either of the other 2 types of overconfidence, but its presence reduces the magnitude of both overestimation and overplacement.
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                Author and article information

                Journal
                9809671
                21092
                Nat Neurosci
                Nat. Neurosci.
                Nature neuroscience
                1097-6256
                1546-1726
                21 February 2018
                12 March 2018
                April 2018
                12 September 2018
                : 21
                : 4
                : 617-624
                Affiliations
                [1 ]Wellcome Centre for Human Neuroimaging, University College London, London, UK
                [2 ]Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK
                [3 ]Amsterdam Brain and Cognition Center, University of Amsterdam, Netherlands
                [4 ]Princeton Neuroscience Institute and Department of Psychology, Princeton University, New Jersey, USA
                Author notes
                Correspondence: Stephen M. Fleming, Wellcome Centre for Human Neuroimaging, University College London, 12 Queen Square, London, WC1N 3BG, stephen.fleming@ 123456ucl.ac.uk
                Article
                EMS76047
                10.1038/s41593-018-0104-6
                5878683
                29531361

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                Neurosciences

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