Humans treat unreliable filled-in percepts as more real than veridical ones

Humans often evaluate sensory signals according to their reliability for optimal decision-making. However, how do we evaluate percepts generated in the absence of direct input that are, therefore, completely unreliable? Here, we utilize the phenomenon of filling-in occurring at the physiological blind-spots to compare partially inferred and veridical percepts. Subjects chose between stimuli that elicit filling-in, and perceptually equivalent ones presented outside the blind-spots, looking for a Gabor stimulus without a small orthogonal inset. In ambiguous conditions, when the stimuli were physically identical and the inset was absent in both, subjects behaved opposite to optimal, preferring the blind-spot stimulus as the better example of a collinear stimulus, even though no relevant veridical information was available. Thus, a percept that is partially inferred is paradoxically considered more reliable than a percept based on external input. In other words: Humans treat filled-in inferred percepts as more real than veridical ones. DOI: http://dx.doi.org/10.7554/eLife.21761.001

[1]  C. Midgley,et al.  Binocular interactions in human vision. , 1998 .

[2]  Jiqiang Guo,et al.  Stan: A Probabilistic Programming Language. , 2017, Journal of statistical software.

[3]  Éva M. Bankó,et al.  Dissociating the Effect of Noise on Sensory Processing and Overall Decision Difficulty , 2011, The Journal of Neuroscience.

[4]  Thom Carney,et al.  Orientation discrimination as a function of stimulus eccentricity and size: Nasal/temporal retinal asymmetry , 1988, Vision Research.

[5]  Frans W Cornelissen,et al.  The Eyelink Toolbox: Eye tracking with MATLAB and the Psychophysics Toolbox , 2002, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[6]  Karl J. Friston,et al.  A free energy principle for the brain , 2006, Journal of Physiology-Paris.

[7]  P. König,et al.  Vision and Haptics Share Spatial Attentional Resources and Visuotactile Integration Is Not Affected by High Attentional Load. , 2015, Multisensory research.

[8]  T. Sakurai.  The role of orexin in motivated behaviours , 2014, Nature Reviews Neuroscience.

[9]  Christopher Summerfield,et al.  Near-optimal Integration of Magnitude in the Human Parietal Cortex , 2016, Journal of Cognitive Neuroscience.

[10]  Michael S Landy,et al.  Combining Priors and Noisy Visual Cues in a Rapid Pointing Task , 2006, The Journal of Neuroscience.

[11]  Peter König,et al.  Audition and vision share spatial attentional resources, yet attentional load does not disrupt audiovisual integration , 2015, Front. Psychol..

[12]  Karl J. Friston,et al.  Perceptions as Hypotheses: Saccades as Experiments , 2012, Front. Psychology.

[13]  Andreas K. Engel,et al.  Crossmodal Integration Improves Sensory Detection Thresholds in the Ferret , 2015, bioRxiv.

[14]  R. Held,et al.  Residual Visual Function after Brain Wounds involving the Central Visual Pathways in Man , 1973, Nature.

[15]  Konrad Paul Kording,et al.  Bayesian integration in sensorimotor learning , 2004, Nature.

[16]  Konrad Paul Kording,et al.  Causal Inference in Multisensory Perception , 2007, PloS one.

[17]  J Rovamo,et al.  Resolution of gratings oriented along and across meridians in peripheral vision. , 1982, Investigative ophthalmology & visual science.

[18]  M. Fahle,et al.  Naso-temporal asymmetry of visual perception and of the visual cortex , 1988, Vision Research.

[19]  A. Hendrickson,et al.  Human photoreceptor topography , 1990, The Journal of comparative neurology.

[20]  A. J. Morandi,et al.  Retinal sensitivity in the region of the blind spot. , 1962, Journal of the Optical Society of America.

[21]  W. Newsome,et al.  Neural basis of a perceptual decision in the parietal cortex (area LIP) of the rhesus monkey. , 2001, Journal of neurophysiology.

[22]  E. Erdfelder,et al.  Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses , 2009, Behavior research methods.

[23]  Peter König,et al.  Visual stimulus locking of EEG is modulated by temporal congruency of auditory stimuli , 2009, Experimental Brain Research.

[24]  Shravan Vasishth,et al.  Bayesian linear mixed models using Stan: A tutorial for psychologists, linguists, and cognitive scientists , 2015, 1506.06201.

[25]  P. König,et al.  Audio-visual integration during overt visual attention , 2008 .

[26]  Leslie G. Ungerleider,et al.  A general mechanism for perceptual decision-making in the human brain , 2004, Nature.

[27]  Michael D Crossland,et al.  Spatial alignment over retinal scotomas. , 2009, Investigative ophthalmology & visual science.

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

[29]  Peter König,et al.  Predictions of Visual Content across Eye Movements and Their Modulation by Inferred Information , 2015, The Journal of Neuroscience.

[30]  John K Kruschke,et al.  Bayesian data analysis. , 2010, Wiley interdisciplinary reviews. Cognitive science.

[31]  M. Ernst,et al.  Humans integrate visual and haptic information in a statistically optimal fashion , 2002, Nature.

[32]  G. DeAngelis,et al.  Neural correlates of multisensory cue integration in macaque MSTd , 2008, Nature Neuroscience.

[33]  Julia Kastner,et al.  Introduction to Robust Estimation and Hypothesis Testing , 2005 .

[34]  D. Burr,et al.  The Ventriloquist Effect Results from Near-Optimal Bimodal Integration , 2004, Current Biology.

[35]  C. Summerfield,et al.  Expectation in perceptual decision making: neural and computational mechanisms , 2014, Nature Reviews Neuroscience.

[36]  Peter König,et al.  Attentional Resource Allocation in Visuotactile Processing Depends on the Task, But Optimal Visuotactile Integration Does Not Depend on Attentional Resources , 2016, Front. Integr. Neurosci..

[37]  J. Movshon,et al.  A computational analysis of the relationship between neuronal and behavioral responses to visual motion , 1996, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[38]  Christopher R Fetsch,et al.  Neural correlates of reliability-based cue weighting during multisensory integration , 2011, Nature Neuroscience.

[39]  Jess R. Kerlin,et al.  Cortical representation of space around the blind spot. , 2005, Journal of neurophysiology.

[40]  Aki Vehtari,et al.  Understanding predictive information criteria for Bayesian models , 2013, Statistics and Computing.