The Effect of Task on Object Processing revealed by EEG decoding

At which phase(s) does task demand affect object processing? Previous studies showed that task demand affects object representations in higher-level visual areas but not so much in earlier areas. There are, however, limitations in those studies concerning the relatively weak manipulation of task due to the use of familiar real-life objects, and/or the low temporal resolution in brain activation measures such as fMRI. In the current study, observers categorized images of artificial objects in one of two orthogonal dimensions, shape and texture. Electroencephalogram (EEG), a technique with higher temporal resolution, and multivariate pattern analysis (MVPA) were employed to reveal object processing across time under different task demands. Results showed that object processing along the task-relevant dimension was enhanced starting from a relatively late time (∼230ms after image onset), within the time range of the event-related potential (ERP) components N170 and N250. The findings are consistent with the view that task exerts an effect on object processing at the later phases of processing in the ventral visual pathway.

[1]  J. Haynes.  A Primer on Pattern-Based Approaches to fMRI: Principles, Pitfalls, and Perspectives , 2015, Neuron.

[2]  R. Itier,et al.  Effects of task demands on the early neural processing of fearful and happy facial expressions , 2017, Brain Research.

[3]  Radoslaw Martin Cichy,et al.  The representational dynamics of task and object processing in humans , 2018, eLife.

[4]  B. Scholl,et al.  Cognition does not affect perception: Evaluating the evidence for “top-down” effects , 2015, Behavioral and Brain Sciences.

[5]  Nicole M. Long,et al.  Bottom-Up and Top-Down Factors Differentially Influence Stimulus Representations Across Large-Scale Attentional Networks , 2018, The Journal of Neuroscience.

[6]  C. Teufel,et al.  How to (and how not to) think about top-down influences on visual perception , 2017, Consciousness and Cognition.

[7]  Berenice Valdés-Conroy,et al.  Following the time course of face gender and expression processing: a task-dependent ERP study. , 2014, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[8]  Simon van Gaal,et al.  No Evidence that Predictions and Attention Modulate the First Feedforward Sweep of Cortical Information Processing , 2018, bioRxiv.

[9]  J. Hohwy.  Priors in perception: Top-down modulation, Bayesian perceptual learning rate, and prediction error minimization , 2017, Consciousness and Cognition.

[10]  Geraint Rees,et al.  Pattern classification using functional magnetic resonance imaging. , 2011, Wiley interdisciplinary reviews. Cognitive science.

[11]  Jeffrey S. Johnson,et al.  The time course of encoding and maintenance of task-relevant versus irrelevant object features in working memory , 2019, Cortex.

[12]  Isabel Gauthier,et al.  An early electrophysiological response associated with expertise in letter perception , 2005, Cognitive, affective & behavioral neuroscience.

[13]  J. Hohwy.  The Predictive Mind , 2013 .

[14]  Karl J. Friston.  The free-energy principle: a unified brain theory? , 2010, Nature Reviews Neuroscience.

[15]  Thomas Serre,et al.  A feedforward architecture accounts for rapid categorization , 2007, Proceedings of the National Academy of Sciences.

[16]  Thomas Serre,et al.  A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex , 2005 .

[17]  Keiji Tanaka,et al.  Matching Categorical Object Representations in Inferior Temporal Cortex of Man and Monkey , 2008, Neuron.

[18]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[19]  Na Yeon Kim,et al.  Task influences pattern discriminability for faces and bodies in ventral occipitotemporal cortex , 2016, Social neuroscience.

[20]  Janneke F. M. Jehee,et al.  Attention Improves Encoding of Task-Relevant Features in the Human Visual Cortex , 2011, The Journal of Neuroscience.

[21]  James J. DiCarlo,et al.  How Does the Brain Solve Visual Object Recognition? , 2012, Neuron.

[22]  Janneke F. M. Jehee,et al.  Less Is More: Expectation Sharpens Representations in the Primary Visual Cortex , 2012, Neuron.

[23]  Yaoda Xu,et al.  Goal-Directed Visual Processing Differentially Impacts Human Ventral and Dorsal Visual Representations , 2017, The Journal of Neuroscience.

[24]  Steven J. Luck,et al.  ERPLAB: an open-source toolbox for the analysis of event-related potentials , 2014, Front. Hum. Neurosci..

[25]  H. P. Op de Beeck,et al.  Task Context Overrules Object- and Category-Related Representational Content in the Human Parietal Cortex , 2017, Cerebral cortex.

[26]  Anina N. Rich,et al.  Feature-selective Attention in Frontoparietal Cortex: Multivoxel Codes Adjust to Prioritize Task-relevant Information , 2017, Journal of Cognitive Neuroscience.

[27]  James M. Shine,et al.  Predictions penetrate perception: Converging insights from brain, behaviour and disorder , 2017, Consciousness and Cognition.

[28]  Dimitrios Pantazis,et al.  Multivariate pattern analysis of MEG and EEG: A comparison of representational structure in time and space , 2016, NeuroImage.

[29]  Susan G. Wardle,et al.  Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data , 2016, Journal of Cognitive Neuroscience.

[30]  D H Brainard,et al.  The Psychophysics Toolbox. , 1997, Spatial vision.

[31]  T. Allison,et al.  Electrophysiological Studies of Face Perception in Humans , 1996, Journal of Cognitive Neuroscience.

[32]  A. Newen,et al.  Why cognitive penetration of our perceptual experience is still the most plausible account , 2017, Consciousness and Cognition.

[33]  M. Tarr,et al.  Becoming a “Greeble” Expert: Exploring Mechanisms for Face Recognition , 1997, Vision Research.

[34]  T. Poggio,et al.  Neural mechanisms of object recognition , 2002, Current Opinion in Neurobiology.

[35]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[36]  D. Stokes,et al.  Noise, uncertainty, and interest: Predictive coding and cognitive penetration , 2017, Consciousness and Cognition.

[37]  Ann-Christine Ehlis,et al.  Source Localization of Early Stages of Face Processing , 2005, Brain Topography.

[38]  Bruno Rossion,et al.  ERP evidence for task modulations on face perceptual processing at different spatial scales , 2003, Cogn. Sci..

[39]  A. Raftopoulos.  The cognitive impenetrability of the content of early vision is a necessary and sufficient condition for purely nonconceptual content , 2014 .

[40]  Gabriel Curio,et al.  MEG/EEG sources of the 170-ms response to faces are co-localized in the fusiform gyrus , 2007, NeuroImage.

[41]  A. Clark.  Whatever next? Predictive brains, situated agents, and the future of cognitive science. , 2013, The Behavioral and brain sciences.

[42]  J. Tanaka,et al.  A Neural Basis for Expert Object Recognition , 2001, Psychological science.

[43]  Z. Pylyshyn.  Is vision continuous with cognition? The case for cognitive impenetrability of visual perception. , 1999, The Behavioral and brain sciences.

[44]  S. Fazelpour,et al.  Attention in the predictive mind , 2017, Consciousness and Cognition.

[45]  A. Burton,et al.  Human brain potential correlates of repetition priming in face and name recognition , 2002, Neuropsychologia.

[46]  Thomas E. Nichols,et al.  Nonparametric permutation tests for functional neuroimaging: A primer with examples , 2002, Human brain mapping.

[47]  Gary Lupyan,et al.  Cognitive Penetrability of Perception in the Age of Prediction: Predictive Systems are Penetrable Systems , 2015 .

[48]  Russell A. Epstein,et al.  Coding of Object Size and Object Category in Human Visual Cortex , 2016, Cerebral cortex.

[49]  Kathrin Cohen Kadosh,et al.  Task-dependent Activation of Face-sensitive Cortex: An fMRI Adaptation Study , 2010, Journal of Cognitive Neuroscience.

[50]  N. Kanwisher,et al.  The lateral occipital complex and its role in object recognition , 2001, Vision Research.

[51]  Dwight J. Kravitz,et al.  Task context impacts visual object processing differentially across the cortex , 2014, Proceedings of the National Academy of Sciences.

[52]  D G Pelli,et al.  The VideoToolbox software for visual psychophysics: transforming numbers into movies. , 1997, Spatial vision.

[53]  F. Macpherson.  The relationship between cognitive penetration and predictive coding , 2017, Consciousness and Cognition.

[54]  A. Burton,et al.  N250r: a face-selective brain response to stimulus repetitions , 2004, Neuroreport.

[55]  James W. Tanaka,et al.  A Reevaluation of the Electrophysiological Correlates of Expert Object Processing , 2006, Journal of Cognitive Neuroscience.