Mechanism of duration perception in artificial brains suggests new model of attentional entrainment

While cognitive theory has advanced several candidate frameworks to explain attentional entrainment, the neural basis for the temporal allocation of attention is unknown. Here we present a new model of attentional entrainment that is guided by empirical evidence obtained using a cohort of 50 artificial brains. These brains were evolved in silico to perform a duration judgement task similar to one where human subjects perform duration judgements in auditory oddball paradigms1. We found that the artificial brains display psychometric characteristics remarkably similar to those of human listeners, and also exhibit similar patterns of distortions of perception when presented with out-of-rhythm oddballs. A detailed analysis of mechanisms behind the duration distortion in the artificial brains suggests that their attention peaks at the end of the tone, which is inconsistent with previous attentional entrainment models. Instead, our extended model of entrainment emphasises increased attention to those aspects of the stimulus that the brain expects to be highly informative.

[1]  Neil A. Macmillan,et al.  Detection Theory , 2021 .

[2]  Anthony M. Zador,et al.  A critique of pure learning and what artificial neural networks can learn from animal brains , 2019, Nature Communications.

[3]  Nikolaus Kriegeskorte,et al.  Cognitive computational neuroscience , 2018, Nature Neuroscience.

[4]  Ali Tehrani-Saleh,et al.  Evolution leads to a diversity of motion-detection neuronal circuits , 2018, ALIFE.

[5]  G. La Camera,et al.  Expectation-induced modulation of metastable activity underlies faster coding of sensory stimuli , 2017, bioRxiv.

[6]  Randal S. Olson,et al.  Markov Brains: A Technical Introduction , 2017, ArXiv.

[7]  Joshua J. LaRocque,et al.  The neural correlates of dreaming , 2014, Nature Neuroscience.

[8]  C. Koch,et al.  Integrated information theory: from consciousness to its physical substrate , 2016, Nature Reviews Neuroscience.

[9]  W. Meck,et al.  Temporal cognition: Connecting subjective time to perception, attention, and memory. , 2016, Psychological bulletin.

[10]  Erin L. Rich,et al.  Decoding subjective decisions from orbitofrontal cortex , 2016, Nature Neuroscience.

[11]  Caspar Addyman,et al.  Computational models of interval timing , 2016, Current Opinion in Behavioral Sciences.

[12]  Ali Tehrani-Saleh,et al.  Flies as Ship Captains? Digital Evolution Unravels Selective Pressures to Avoid Collision in Drosophila , 2016, ALIFE.

[13]  Arend Hintze,et al.  Computational evolution of decision-making strategies , 2015, CogSci.

[14]  C. Koch,et al.  Consciousness: here, there and everywhere? , 2015, Philosophical Transactions of the Royal Society B: Biological Sciences.

[15]  J. Devin McAuley,et al.  Attentional entrainment and perceived event duration , 2014, Philosophical Transactions of the Royal Society B: Biological Sciences.

[16]  Adam Binch,et al.  Perception as Bayesian Inference , 2014 .

[17]  Randal S. Olson,et al.  Exploring the evolution of a trade-off between vigilance and foraging in group-living organisms , 2014, Royal Society Open Science.

[18]  Stefan Habenschuss,et al.  Stochastic Computations in Cortical Microcircuit Models , 2013, PLoS Comput. Biol..

[19]  N. Kriegeskorte,et al.  Author ' s personal copy Representational geometry : integrating cognition , computation , and the brain , 2013 .

[20]  Laura A. Carlson,et al.  When What You Hear Influences When You See , 2013, Psychological science.

[21]  Arend Hintze,et al.  Predator confusion is sufficient to evolve swarming behaviour , 2012, Journal of The Royal Society Interface.

[22]  Arend Hintze,et al.  The Evolution of Representation in Simple Cognitive Networks , 2012, Neural Computation.

[23]  Arend Hintze,et al.  Integrated Information Increases with Fitness in the Evolution of Animats , 2011, PLoS Comput. Biol..

[24]  A. Zador,et al.  Auditory cortex mediates the perceptual effects of acoustic temporal expectation , 2010, Nature Neuroscience.

[25]  Karl J. Friston,et al.  Attention, Uncertainty, and Free-Energy , 2010, Front. Hum. Neurosci..

[26]  W. Richards,et al.  Perception as Bayesian Inference , 2008 .

[27]  R. Lenski,et al.  Historical contingency and the evolution of a key innovation in an experimental population of Escherichia coli , 2008 .

[28]  Nikolaus Kriegeskorte,et al.  Frontiers in Systems Neuroscience Systems Neuroscience , 2022 .

[29]  A. Nobre,et al.  The hazards of time , 2007, Current Opinion in Neurobiology.

[30]  C. Gilbert,et al.  Brain States: Top-Down Influences in Sensory Processing , 2007, Neuron.

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

[32]  Hod Lipson,et al.  Resilient Machines Through Continuous Self-Modeling , 2006, Science.

[33]  Christoph Adami,et al.  What Do Robots Dream Of? , 2006, Science.

[34]  Nathaniel S. Miller,et al.  The time of our lives: life span development of timing and event tracking. , 2006, Journal of experimental psychology. General.

[35]  Michael T. Lippert,et al.  Mechanisms for Allocating Auditory Attention: An Auditory Saliency Map , 2005, Current Biology.

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

[37]  C. Koch,et al.  Attention-driven discrete sampling of motion perception. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[38]  W. Meck,et al.  Cortico-striatal circuits and interval timing: coincidence detection of oscillatory processes. , 2004, Brain research. Cognitive brain research.

[39]  P. Tse,et al.  Attention and the subjective expansion of time , 2004, Perception & psychophysics.

[40]  F. Vidal,et al.  Functional Anatomy of the Attentional Modulation of Time Estimation , 2004, Science.

[41]  Randall D. Beer,et al.  The Dynamics of Active Categorical Perception in an Evolved Model Agent , 2003, Adapt. Behav..

[42]  J. Devin McAuley,et al.  Modeling effects of rhythmic context on perceived duration: a comparison of interval and entrainment approaches to short-interval timing. , 2003, Journal of experimental psychology. Human perception and performance.

[43]  Daniel Durstewitz,et al.  Self-Organizing Neural Integrator Predicts Interval Times through Climbing Activity , 2003, The Journal of Neuroscience.

[44]  C. Koch,et al.  Is perception discrete or continuous? , 2003, Trends in Cognitive Sciences.

[45]  M. Jones,et al.  Temporal Aspects of Stimulus-Driven Attending in Dynamic Arrays , 2002, Psychological science.

[46]  C. Koch,et al.  Computational modelling of visual attention , 2001, Nature Reviews Neuroscience.

[47]  Dean V. Buonomano,et al.  Neural Network Model of the Cerebellum: Temporal Discrimination and the Timing of Motor Responses , 1999, Neural Computation.

[48]  Stefan Treue,et al.  Feature-based attention influences motion processing gain in macaque visual cortex , 1999, Nature.

[49]  J. Devin McAuley,et al.  Effect of deviations from temporal expectations on tempo discrimination of isochronous tone sequences. , 1998, Journal of experimental psychology. Human perception and performance.

[50]  G. Fechner.  Elemente der Psychophysik , 1998 .

[51]  F. Macar,et al.  Effects of attention manipulation on judgments of duration and of intensity in the visual modality , 1997, Memory & cognition.

[52]  J. Devin McAuley.  Perception of time as phase: toward an adaptive-oscillator model of rhythmic pattern processing , 1996 .

[53]  William Bialek,et al.  Entropy and Information in Neural Spike Trains , 1996, cond-mat/9603127.

[54]  E. Seidemann,et al.  Simultaneously recorded single units in the frontal cortex go through sequences of discrete and stable states in monkeys performing a delayed localization task , 1996, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[55]  F. Macar,et al.  Controlled attention sharing influences time estimation , 1994, Memory & cognition.

[56]  Neil A. Macmillan,et al.  Detection Theory: A User's Guide , 1991 .

[57]  G. Milliken.  Nonlinear Regression Analysis and Its Applications , 1990 .

[58]  M. R. Jones,et al.  Dynamic attending and responses to time. , 1989, Psychological review.

[59]  D. G. Watts,et al.  Nonlinear Regression Analysis and Its Applications , 1988 .

[60]  R M Church,et al.  Scalar Timing in Memory , 1984, Annals of the New York Academy of Sciences.

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

[62]  M. R. Jones,et al.  Time, our lost dimension: toward a new theory of perception, attention, and memory. , 1976, Psychological review.

[63]  Ewart A. C. Thomas,et al.  Cognitive processing and time perception , 1975 .

[64]  T. Poggio,et al.  Memo No . 067 June 27 , 2017 Theory of Deep Learning III : Generalization Properties of SGD , 2017 .

[65]  Sabine Fenstermacher,et al.  Genetic Algorithms Data Structures Evolution Programs , 2016 .

[66]  Daniel Durstewitz,et al.  Neurocomputational models of time perception. , 2014, Advances in experimental medicine and biology.

[67]  Hugo Merchant,et al.  Neurobiology of Interval Timing , 2014, Advances in Experimental Medicine and Biology.

[68]  Léon Bottou,et al.  Large-Scale Machine Learning with Stochastic Gradient Descent , 2010, COMPSTAT.

[69]  G. Buzsáki.  Rhythms of the brain , 2006 .

[70]  J. Hawkins,et al.  On Intelligence , 2004 .

[71]  S. Grondin,et al.  From physical time to the first and second moments of psychological time. , 2001, Psychological bulletin.

[72]  E. Large,et al.  The dynamics of attending: How people track time-varying events. , 1999 .

[73]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[74]  A. Raftery.  Bayesian Model Selection in Social Research , 1995 .

[75]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[76]  S. Gould.  Wonderful Life: The Burgess Shale and the Nature of History , 1989 .

[77]  H. Lejeune,et al.  Time in animal behaviour , 1980 .

[78]  Jeffrey D. Ullman,et al.  Introduction to Automata Theory, Languages and Computation , 1979 .

[79]  David J. Getty,et al.  Discrimination of short temporal intervals: A comparison of two models , 1975 .

[80]  N Moray,et al.  Where is capacity limited? A survey and a model. , 1967, Acta psychologica.

[81]  M. Treisman.  Temporal discrimination and the indifference interval. Implications for a model of the "internal clock". , 1963, Psychological monographs.