Subjective time is predicted by local and early visual processing

Time is as pervasive as it is elusive to study, and how the brain keeps track of millisecond time is still unclear. Here we studied the mechanisms underlying duration perception by looking for a neural signature of subjective time distortion induced by motion adaptation. We recorded electroencephalographic signals in human participants while they were asked to discriminate the duration of visual stimuli after translational motion adaptation. Our results show that distortions of subjective time can be predicted by the amplitude of the N200 event-related potential and by the activity in the Beta band frequency spectrum. Both effects were observed from occipital electrodes contralateral to the adapted stimulus. Finally, a multivariate decoding analysis highlights the impact of motion adaptation throughout the visual stream. Overall, our findings show the crucial involvement of local and low-level perceptual processes in generating a subjective sense of time.

[1]  Richard Ragot,et al.  Processes involved in tempo perception: a CNV analysis. , 2003, Psychophysiology.

[2]  Michele Fornaciai,et al.  Early Numerosity Encoding in Visual Cortex Is Not Sufficient for the Representation of Numerical Magnitude , 2018, Journal of Cognitive Neuroscience.

[3]  Timothy J. Andrews,et al.  fMR-adaptation reveals a view-invariant representation for familiar faces in the fusiform face area , 2010 .

[4]  Scott W. Brown.  Time, change, and motion: The effects of stimulus movement on temporal perception , 1995, Perception & psychophysics.

[5]  M Concetta Morrone,et al.  Neural mechanisms for timing visual events are spatially selective in real-world coordinates , 2007, Nature Neuroscience.

[6]  W. Maass,et al.  State-dependent computations: spatiotemporal processing in cortical networks , 2009, Nature Reviews Neuroscience.

[7]  William Curran,et al.  Direction-contingent duration compression is primarily retinotopic , 2014, Vision Research.

[8]  Alan Johnston,et al.  Motion-direction specificity for adaptation-induced duration compression depends on temporal frequency. , 2013, Journal of vision.

[9]  J. Tiihonen,et al.  Amygdala-orbitofrontal structural and functional connectivity in females with anxiety disorders, with and without a history of conduct disorder , 2018, Scientific Reports.

[10]  R. Barry,et al.  EOG correction: a comparison of four methods. , 2005, Psychophysiology.

[11]  Ethan Meyers,et al.  The neural decoding toolbox , 2013, Front. Neuroinform..

[12]  A. Engel,et al.  Beta-band oscillations—signalling the status quo? , 2010, Current Opinion in Neurobiology.

[13]  A. Bompas,et al.  Systematic biases in adult color perception persist despite lifelong information sufficient to calibrate them. , 2013, Journal of vision.

[14]  Darren E. Koenig,et al.  The absolute threshold of cone vision. , 2011, Journal of vision.

[15]  M. Treisman,et al.  The Internal Clock: Evidence for a Temporal Oscillator Underlying Time Perception with Some Estimates of its Characteristic Frequency , 1990, Perception.

[16]  Richard B Ivry,et al.  Evaluating dedicated and intrinsic models of temporal encoding by varying context , 2009, Philosophical Transactions of the Royal Society B: Biological Sciences.

[17]  William Curran,et al.  The many directions of time , 2011, Cognition.

[18]  I. Peretz,et al.  Individual Differences in Rhythmic Cortical Entrainment Correlate with Predictive Behavior in Sensorimotor Synchronization , 2016, Scientific Reports.

[19]  D. Burr,et al.  Transient spatiotopic integration across saccadic eye movements mediates visual stability. , 2013, Journal of neurophysiology.

[20]  Michele Fornaciai,et al.  Neural Dynamics of Serial Dependence in Numerosity Perception , 2020, Journal of Cognitive Neuroscience.

[21]  Baolin Li,et al.  Duration adaptation modulates EEG correlates of subsequent temporal encoding , 2017, NeuroImage.

[22]  Peter Brown,et al.  Phasic increases in cortical beta activity are associated with alterations in sensory processing in the human , 2006, Experimental Brain Research.

[23]  Leonardo Chelazzi,et al.  Gains and losses adaptively adjust attentional deployment towards specific objects , 2010 .

[24]  Joonkoo Park,et al.  Attractive Serial Dependence in the Absence of an Explicit Task , 2018, Psychological science.

[25]  Roberto Arrighi,et al.  Spatiotopic selectivity of adaptation-based compression of event duration. , 2011, Journal of vision.

[26]  C A Grimbergen,et al.  High-quality recording of bioelectric events , 1991, Medical and Biological Engineering and Computing.

[27]  W. Paulus,et al.  Identification of the visual motion area (area V5) in the human brain by dipole source analysis , 2004, Experimental Brain Research.

[28]  Inci Ayhan,et al.  Retinotopic adaptation-based visual duration compression. , 2010, Journal of vision.

[29]  U. Karmarkar,et al.  Timing in the Absence of Clocks: Encoding Time in Neural Network States , 2007, Neuron.

[30]  Michael Bach,et al.  Directional tuning of human motion adaptation as reflected by the motion VEP , 2001, Vision Research.

[31]  Satu Palva,et al.  The role of cortical beta oscillations in time estimation , 2016, Human brain mapping.

[32]  S. Dehaene,et al.  Characterizing the dynamics of mental representations: the temporal generalization method , 2014, Trends in Cognitive Sciences.

[33]  Hedderik van Rijn,et al.  Single trial beta oscillations index time estimation , 2015, Neuropsychologia.

[34]  W. Meck,et al.  Dissecting the Brain's Internal Clock: How Frontal–Striatal Circuitry Keeps Time and Shifts Attention , 2002, Brain and Cognition.

[35]  David C. Burr,et al.  Adaptation-Induced Compression of Event Time Occurs Only for Translational Motion , 2016, Scientific Reports.

[36]  Martin Wiener,et al.  Multiple Mechanisms for Temporal Processing , 2011, Front. Integr. Neurosci..

[37]  Sheng He,et al.  Larger stimuli are judged to last longer. , 2007, Journal of vision.

[38]  Denis G. Pelli,et al.  ECVP '07 Abstracts , 2007, Perception.

[39]  Clarisse Aichelburg,et al.  When Time and Numerosity Interfere: The Longer the More, and the More the Longer , 2012, PloS one.

[40]  J. Knott,et al.  Event related potentials of brain (CNV and P300) in a paired associate learning paradigm. , 1977, Psychophysiology.

[41]  Massimiliano Di Luca,et al.  Modality-specific temporal constraints for state-dependent interval timing , 2018, Scientific Reports.

[42]  T Elbert,et al.  The processing of temporal intervals reflected by CNV-like brain potentials. , 1991, Psychophysiology.

[43]  E. Macaluso,et al.  Neural basis for priming of pop-out during visual search revealed with fMRI. , 2007, Cerebral cortex.

[44]  Heinrich H Bülthoff,et al.  An advantage for detecting dynamic targets in natural scenes. , 2006, Journal of vision.

[45]  Derek H. Arnold,et al.  Spatially Localized Distortions of Event Time , 2006, Current Biology.

[46]  Tadeusz W Kononowicz,et al.  The contingent negative variation (CNV): timing isn’t everything , 2016, Current Opinion in Behavioral Sciences.

[47]  Shin'ya Nishida,et al.  The spatial tuning of adaptation-based time compression. , 2009, Journal of vision.

[48]  G. Cicchini,et al.  Multiple channels of visual time perception , 2016, Current Opinion in Behavioral Sciences.

[49]  Warren H. Meck,et al.  Contingent negative variation and its relation to time estimation: a theoretical evaluation , 2011, Front. Integr. Neurosci..

[50]  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.

[51]  Masato Yumoto,et al.  Perceptual change in response to a bistable picture increases neuromagnetic beta-band activities , 2008, Neuroscience Research.