Dynamic representation of time in brain states

The ability to process time on the scale of milliseconds and seconds is essential for behaviour. A growing number of studies have started to focus on brain dynamics as a mechanism for temporal encoding. Although there is growing evidence in favour of this view from computational and in vitro studies, there is still a lack of results from experiments in humans. We show that high-dimensional brain states revealed by multivariate pattern analysis of human EEG are correlated to temporal judgements. First, we show that, as participants estimate temporal intervals, the spatiotemporal dynamics of their brain activity are consistent across trials. Second, we present evidence that these dynamics exhibit properties of temporal perception, such as scale invariance. Lastly, we show that it is possible to predict temporal judgements based on brain states. These results show how scalp recordings can reveal the spatiotemporal dynamics of human brain activity related to temporal processing.

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

[2]  F A Wichmann,et al.  Ning for Helpful Comments and Suggestions. This Paper Benefited Con- Siderably from Conscientious Peer Review, and We Thank Our Reviewers the Psychometric Function: I. Fitting, Sampling, and Goodness of Fit , 2001 .

[3]  Christian K. Machens,et al.  Striatal dynamics explain duration judgments , 2015, bioRxiv.

[4]  D. Buonomano,et al.  The neural basis of temporal processing. , 2004, Annual review of neuroscience.

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

[6]  Jeffrey N. Rouder,et al.  Bayes factor approaches for testing interval null hypotheses. , 2011, Psychological methods.

[7]  J. Gibbon.  Scalar expectancy theory and Weber's law in animal timing. , 1977 .

[8]  Michael J. Wolff,et al.  Decoding Rich Spatial Information with High Temporal Resolution , 2015, Trends in Cognitive Sciences.

[9]  H. Eichenbaum,et al.  Hippocampal “Time Cells” Bridge the Gap in Memory for Discontiguous Events , 2011, Neuron.

[10]  F. Vidal,et al.  Event-Related Potentials as Indices of Time Processing: A Review , 2004 .

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

[12]  R. Oostenveld,et al.  Nonparametric statistical testing of EEG- and MEG-data , 2007, Journal of Neuroscience Methods.

[13]  J. Staddon.  Interval timing: memory, not a clock , 2005, Trends in Cognitive Sciences.

[14]  Hedderik van Rijn,et al.  Decoupling Interval Timing and Climbing Neural Activity: A Dissociation between CNV and N1P2 Amplitudes , 2014, The Journal of Neuroscience.

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

[16]  D. Buonomano,et al.  Neural dynamics of in vitro cortical networks reflects experienced temporal patterns , 2010, Nature Neuroscience.

[17]  Robert A. Hyde,et al.  Mnemonic representations of transient stimuli and temporal sequences in the rodent hippocampus in vitro , 2012, Nature Neuroscience.

[18]  N. Sigala,et al.  Dynamic Coding for Cognitive Control in Prefrontal Cortex , 2013, Neuron.

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

[20]  David M. Groppe,et al.  Mass univariate analysis of event-related brain potentials/fields I: a critical tutorial review. , 2011, Psychophysiology.

[21]  Dimitrios Pantazis,et al.  Can visual information encoded in cortical columns be decoded from magnetoencephalography data in humans? , 2015, NeuroImage.

[22]  D. Durstewitz,et al.  Contextual encoding by ensembles of medial prefrontal cortex neurons , 2012, Proceedings of the National Academy of Sciences.

[23]  R. Church,et al.  Determinants of temporal or stimulus control in humans and rats , 2015 .

[24]  Nicholas E. Myers,et al.  Revealing hidden states in visual working memory using electroencephalography , 2015, Front. Syst. Neurosci..

[25]  P. Mahalanobis.  On the generalized distance in statistics , 1936 .

[26]  Rainer Goebel,et al.  Information-based functional brain mapping. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[27]  Dean V. Buonomano,et al.  Timing as an intrinsic property of neural networks: evidence from in vivo and in vitro experiments , 2014, Philosophical Transactions of the Royal Society B: Biological Sciences.

[28]  R. Church,et al.  The effect of stimulus discriminability on strategies for learning multiple temporal discriminations , 2010, Behavioural Processes.

[29]  Dean V Buonomano,et al.  Neurocomputational models of interval and pattern timing , 2016, Current Opinion in Behavioral Sciences.

[30]  R. Church,et al.  A modular theory of learning and performance , 2007, Psychonomic bulletin & review.

[31]  Peter A. Tass,et al.  Augmented brain function by coordinated reset stimulation with slowly varying sequences , 2015, Front. Syst. Neurosci..

[32]  Robert Oostenveld,et al.  FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data , 2010, Comput. Intell. Neurosci..

[33]  Daniel Durstewitz,et al.  Time at the center, or time at the side? Assessing current models of time perception , 2016, Current Opinion in Behavioral Sciences.

[34]  F. Vidal,et al.  Activation of the supplementary motor area and of attentional networks during temporal processing , 2002, Experimental Brain Research.

[35]  Catalin V. Buhusi,et al.  What makes us tick? Functional and neural mechanisms of interval timing , 2005, Nature Reviews Neuroscience.

[36]  Moritz Grosse-Wentrup,et al.  Multisubject Learning for Common Spatial Patterns in Motor-Imagery BCI , 2011, Comput. Intell. Neurosci..

[37]  R. Miall,et al.  Distinct systems for automatic and cognitively controlled time measurement: evidence from neuroimaging , 2003, Current Opinion in Neurobiology.

[38]  Jeffrey N. Rouder,et al.  Bayesian t tests for accepting and rejecting the null hypothesis , 2009, Psychonomic bulletin & review.

[39]  Gustavo Rohenkohl,et al.  Testing sensory evidence against mnemonic templates , 2015, eLife.

[40]  Karl J. Friston,et al.  EEG and MEG Data Analysis in SPM8 , 2011, Comput. Intell. Neurosci..