Unreliable feedback deteriorates information processing in primary visual cortex

It is well-established that increased sensory uncertainty impairs perceptual decision-making and leads to degraded neural stimulus representations. Recently, we also showed that providing unreliable feedback to choices leads to changes in perceptual decision-making similar to those of increased stimulus noise: A deterioration in objective task performance, a decrease in subjective confidence and a lower reliance on sensory information for perceptual inference. To investigate the neural basis of such feedback-based changes in perceptual decision-making, in the present study, two groups of healthy human participants (n = 15 each) performed a challenging visual orientation discrimination task while undergoing functional magnetic resonance imaging (fMRI). Critically, one group received reliable feedback regarding their task performance in an intervention phase, whereas the other group correspondingly received unreliable feedback - thereby keeping stimulus information constant. The effects of feedback reliability on performance and stimulus representation in the primary visual cortex (V1) were studied by comparing the pre- and post-intervention test phases between the groups. Compared to participants who received reliable feedback, those receiving unreliable feedback showed a decline in task performance that was paralleled by reduced distinctness of fMRI response patterns in V1. These results show that environmental uncertainty can affect perceptual inference at the earliest cortical processing stages.

[1]  T. Hendler,et al.  Contrast sensitivity in human visual areas and its relationship to object recognition. , 2002, Journal of neurophysiology.

[2]  Philipp Sterzer,et al.  Category-selective processing in the two visual pathways as a function of stimulus degradation by noise , 2019, NeuroImage.

[3]  S. Jbabdi,et al.  How can a Bayesian approach inform neuroscience? , 2012, The European journal of neuroscience.

[4]  C. Summerfield,et al.  Attention Sharpens the Distinction between Expected and Unexpected Percepts in the Visual Brain , 2013, The Journal of Neuroscience.

[5]  P. Petrovic,et al.  Believing is seeing: expectations alter visual awareness , 2008, Current Biology.

[6]  Janneke F. M. Jehee,et al.  Perceptual learning increases orientation sampling efficiency. , 2016, Journal of vision.

[7]  G. Rees,et al.  Predicting the orientation of invisible stimuli from activity in human primary visual cortex , 2005, Nature Neuroscience.

[8]  D. Heeger,et al.  Neuronal basis of contrast discrimination , 1999, Vision Research.

[9]  J. Durlak How to select, calculate, and interpret effect sizes. , 2009, Journal of pediatric psychology.

[10]  Karl J. Friston,et al.  On Hyperpriors and Hypopriors: Comment on Pellicano and Burr , 2022 .

[11]  Karl J. Friston,et al.  Cerebral hierarchies: predictive processing, precision and the pulvinar , 2015, Philosophical Transactions of the Royal Society B: Biological Sciences.

[12]  Jakob Heinzle,et al.  Feature-specific prediction errors for visual mismatch , 2019, NeuroImage.

[13]  F. Tong,et al.  Decoding the visual and subjective contents of the human brain , 2005, Nature Neuroscience.

[14]  Leslie G. Ungerleider,et al.  Increased Activity in Human Visual Cortex during Directed Attention in the Absence of Visual Stimulation , 1999, Neuron.

[15]  Wen-Ming Luh,et al.  The effect of spatial smoothing on fMRI decoding of columnar-level organization with linear support vector machine , 2013, Journal of Neuroscience Methods.

[16]  Hans P. Op de Beeck,et al.  Against hyperacuity in brain reading: Spatial smoothing does not hurt multivariate fMRI analyses? , 2010, NeuroImage.

[17]  Miguel P Eckstein,et al.  Template changes with perceptual learning are driven by feature informativeness. , 2014, Journal of vision.

[18]  Norbert Kathmann,et al.  Differential modulation of visual object processing in dorsal and ventral stream by stimulus visibility , 2016, Cortex.

[19]  J. Haynes,et al.  Perceptual Learning and Decision-Making in Human Medial Frontal Cortex , 2011, Neuron.

[20]  Pascal Mamassian,et al.  Visual Confidence. , 2016, Annual review of vision science.

[21]  Aaron R. Seitz,et al.  Learning what to expect (in visual perception) , 2013, Front. Hum. Neurosci..

[22]  Simon B. Eickhoff,et al.  A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data , 2005, NeuroImage.

[23]  Jordy Thielen,et al.  Evidence for confounding eye movements under attempted fixation and active viewing in cognitive neuroscience , 2019, Scientific Reports.

[24]  Barbara A Dosher,et al.  Perceptual learning retunes the perceptual template in foveal orientation identification. , 2004, Journal of vision.

[25]  Jim M. Monti,et al.  Neural repetition suppression reflects fulfilled perceptual expectations , 2008, Nature Neuroscience.

[26]  Leslie G. Ungerleider,et al.  Mechanisms of directed attention in the human extrastriate cortex as revealed by functional MRI. , 1998, Science.

[27]  Haynes John-Dylan,et al.  Perceptual learning and decision making in human medial frontal cortex , 2012 .

[28]  Barbara A Dosher,et al.  Augmented Hebbian reweighting: interactions between feedback and training accuracy in perceptual learning. , 2010, Journal of vision.

[29]  Nicky Daniels,et al.  The Effect of Spatial Smoothing on Representational Similarity in a Simple Motor Paradigm , 2017, Front. Neurol..

[30]  I. Peretz,et al.  Random Feedback Makes Listeners Tone-Deaf , 2018, Scientific Reports.

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

[32]  C. Mathys,et al.  Hierarchical Prediction Errors in Midbrain and Basal Forebrain during Sensory Learning , 2013, Neuron.

[33]  Frank Tong,et al.  Relationship between BOLD amplitude and pattern classification of orientation-selective activity in the human visual cortex , 2012, NeuroImage.

[34]  Giancarlo Valente,et al.  The effect of spatial resolution on decoding accuracy in fMRI multivariate pattern analysis , 2016, NeuroImage.

[35]  Guillaume A. Rousselet,et al.  Robust Correlation Analyses: False Positive and Power Validation Using a New Open Source Matlab Toolbox , 2012, Front. Psychology.

[36]  Martin N. Hebart,et al.  Human visual and parietal cortex encode visual choices independent of motor plans , 2012, NeuroImage.

[37]  John-Dylan Haynes,et al.  Searchlight-based multi-voxel pattern analysis of fMRI by cross-validated MANOVA , 2014, NeuroImage.

[38]  Karl J. Friston,et al.  A theory of cortical responses , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[39]  F. D. de Lange,et al.  Prior Expectations Bias Sensory Representations in Visual Cortex , 2013, The Journal of Neuroscience.

[40]  R. S. Varrier,et al.  Sustained effects of corrupted feedback on perceptual inference , 2019, Scientific Reports.

[41]  M. Fahle,et al.  Effects of biased feedback on learning and deciding in a vernier discrimination task , 1999, Vision Research.

[42]  M. Fahle,et al.  The role of feedback in learning a vernier discrimination task , 1997, Vision Research.

[43]  D. Knill,et al.  The Bayesian brain: the role of uncertainty in neural coding and computation , 2004, Trends in Neurosciences.

[44]  Karl J. Friston,et al.  The Computational Anatomy of Psychosis , 2013, Front. Psychiatry.