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      Event-Related Potentials Index Prediction Error Signalling During Perceptual Processing of Emotional Facial Expressions

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          Abstract

          Humans use socially relevant stimuli to guide perceptual processing of the surrounding environment, with emotional stimuli receiving preferential attention due to their social importance. Predictive coding theory asserts this cognitive process occurs efficiently by combining predictions about what is to be perceived with incoming sensory information, generating prediction errors that are then used to update future predictions. Recent evidence has identified differing neural activity that demonstrates how spatial and feature-based attention may interact with prediction, yet how emotion-guided attention may influence this relationship remains unknown. In the present study, participants viewed a display of two faces in which attention, prediction, and emotion were manipulated, and responded to a face expressing a specific emotion (anger or happiness). The N170 was found to be enhanced by unpredictable as opposed to predictable stimuli, indicating that it indexes general prediction error signalling processes. The N300 amplitudes were also enhanced by unpredictable stimuli, but they were also affected by the attentional status of angry but not happy faces, suggesting that there are differences in prediction error processes indexed by the N170 and N300. Overall, the findings suggest that the N170 and N300 both index violations of expectation for spatial manipulations of stimuli in accordance with prediction error responding processes.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s10548-023-00951-2.

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          Most cited references55

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          The free-energy principle: a unified brain theory?

          A free-energy principle has been proposed recently that accounts for action, perception and learning. This Review looks at some key brain theories in the biological (for example, neural Darwinism) and physical (for example, information theory and optimal control theory) sciences from the free-energy perspective. Crucially, one key theme runs through each of these theories - optimization. Furthermore, if we look closely at what is optimized, the same quantity keeps emerging, namely value (expected reward, expected utility) or its complement, surprise (prediction error, expected cost). This is the quantity that is optimized under the free-energy principle, which suggests that several global brain theories might be unified within a free-energy framework.
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            Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects.

            We describe a model of visual processing in which feedback connections from a higher- to a lower-order visual cortical area carry predictions of lower-level neural activities, whereas the feedforward connections carry the residual errors between the predictions and the actual lower-level activities. When exposed to natural images, a hierarchical network of model neurons implementing such a model developed simple-cell-like receptive fields. A subset of neurons responsible for carrying the residual errors showed endstopping and other extra-classical receptive-field effects. These results suggest that rather than being exclusively feedforward phenomena, nonclassical surround effects in the visual cortex may also result from cortico-cortical feedback as a consequence of the visual system using an efficient hierarchical strategy for encoding natural images.
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              A theory of cortical responses.

              This article concerns the nature of evoked brain responses and the principles underlying their generation. We start with the premise that the sensory brain has evolved to represent or infer the causes of changes in its sensory inputs. The problem of inference is well formulated in statistical terms. The statistical fundaments of inference may therefore afford important constraints on neuronal implementation. By formulating the original ideas of Helmholtz on perception, in terms of modern-day statistical theories, one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts.It turns out that the problems of inferring the causes of sensory input (perceptual inference) and learning the relationship between input and cause (perceptual learning) can be resolved using exactly the same principle. Specifically, both inference and learning rest on minimizing the brain's free energy, as defined in statistical physics. Furthermore, inference and learning can proceed in a biologically plausible fashion. Cortical responses can be seen as the brain's attempt to minimize the free energy induced by a stimulus and thereby encode the most likely cause of that stimulus. Similarly, learning emerges from changes in synaptic efficacy that minimize the free energy, averaged over all stimuli encountered. The underlying scheme rests on empirical Bayes and hierarchical models of how sensory input is caused. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of cortical organization and responses. The aim of this article is to encompass many apparently unrelated anatomical, physiological and psychophysical attributes of the brain within a single theoretical perspective. In terms of cortical architectures, the theoretical treatment predicts that sensory cortex should be arranged hierarchically, that connections should be reciprocal and that forward and backward connections should show a functional asymmetry (forward connections are driving, whereas backward connections are both driving and modulatory). In terms of synaptic physiology, it predicts associative plasticity and, for dynamic models, spike-timing-dependent plasticity. In terms of electrophysiology, it accounts for classical and extra classical receptive field effects and long-latency or endogenous components of evoked cortical responses. It predicts the attenuation of responses encoding prediction error with perceptual learning and explains many phenomena such as repetition suppression, mismatch negativity (MMN) and the P300 in electroencephalography. In psychophysical terms, it accounts for the behavioural correlates of these physiological phenomena, for example, priming and global precedence. The final focus of this article is on perceptual learning as measured with the MMN and the implications for empirical studies of coupling among cortical areas using evoked sensory responses.
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                Author and article information

                Contributors
                a.pegna@uq.edu.au
                Journal
                Brain Topogr
                Brain Topogr
                Brain Topography
                Springer US (New York )
                0896-0267
                1573-6792
                14 March 2023
                14 March 2023
                2023
                : 36
                : 3
                : 419-432
                Affiliations
                [1 ]GRID grid.1024.7, ISNI 0000000089150953, Faculty of Health, School of Psychology and Counselling, , Queensland University of Technology (QUT), ; Brisbane, QLD Australia
                [2 ]GRID grid.431245.5, ISNI 0000 0004 0385 5290, Defence Science and Technology Group, Information Sciences Division, ; Eagle Farm, QLD Australia
                [3 ]GRID grid.1024.7, ISNI 0000000089150953, Centre for Vision and Eye Research, , Queensland University of Technology (QUT), ; Brisbane, QLD Australia
                [4 ]GRID grid.1003.2, ISNI 0000 0000 9320 7537, Laboratory of Cognitive and Experimental Neuropsychology, Faculty of Health and Behavioural Sciences, School of Psychology, , The University of Queensland, ; Brisbane, QLD Australia
                Author notes

                Handling Editor: Micah M. Murray

                Article
                951
                10.1007/s10548-023-00951-2
                10164013
                36917320
                161bdff5-b4e8-41e8-80ae-3d3f78db3237
                © The Author(s) 2023

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 25 February 2022
                : 22 February 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001793, Queensland University of Technology;
                Award ID: Queensland University of Technology
                Funded by: The University of Queensland
                Categories
                Original Article
                Custom metadata
                © Springer Science+Business Media, LLC, part of Springer Nature 2023

                Neurology
                attention,emotion,prediction,n170,n300
                Neurology
                attention, emotion, prediction, n170, n300

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