The neural signature of information regularity in temporally extended event sequences

Statistical regularities exist at different timescales in temporally unfolding event sequences. Recent studies have identified brain regions that are sensitive to the levels of regularity in sensory inputs, enabling the brain to construct a representation of environmental structure and adaptively generate actions or predictions. However, the temporal specificity of the statistical regularity to which the brain responds remains largely unknown. This uncertainty applies to the regularities of sensory inputs as well as instrumental actions. Here, we used fMRI to investigate the neural correlates of regularity in sequences of task events and action selections in a visuomotor choice task. We quantified timescale-dependent regularity measures by calculating Shannon's entropy and surprise from a sliding-window of consecutive task events and actions. Activity in the frontopolar cortex negatively correlated with the entropy in action selection, while activity in the temporoparietal junction, the striatum, and the cerebellum negatively correlated with the entropy in stimulus events at longer timescales. In contrast, activity in the supplementary motor area, the superior frontal gyrus, and the superior parietal lobule was positively correlated with the surprise of each stimulus across different timescales. The results suggest a spatial distribution of regions sensitive to various information regularities according to a temporal hierarchy, which may play a central role in concurrently monitoring the regularity in previous and current events over different timescales to optimize behavioral control in a dynamic environment.

[1]  B. Scholl,et al.  The Automaticity of Visual Statistical Learning Statistical Learning , 2005 .

[2]  Randall D. Beer,et al.  The brain has a body: adaptive behavior emerges from interactions of nervous system, body and environment , 1997, Trends in Neurosciences.

[3]  David Badre,et al.  Cognitive control, hierarchy, and the rostro–caudal organization of the frontal lobes , 2008, Trends in Cognitive Sciences.

[4]  M. Bar The proactive brain: using analogies and associations to generate predictions , 2007, Trends in Cognitive Sciences.

[5]  R. Aslin,et al.  Unsupervised Statistical Learning of Higher-Order Spatial Structures from Visual Scenes , 2001, Psychological science.

[6]  R. Passingham,et al.  Reading Hidden Intentions in the Human Brain , 2007, Current Biology.

[7]  Richard N. Aslin,et al.  The Goldilocks Effect: Human Infants Allocate Attention to Visual Sequences That Are Neither Too Simple Nor Too Complex , 2012, PloS one.

[8]  Alexander M. Harner,et al.  Evidence for effector independent and dependent representations and their differential time course of acquisition during motor sequence learning , 2000, Experimental Brain Research.

[9]  W. Geisler Sequential ideal-observer analysis of visual discriminations. , 1989 .

[10]  J. Duncan Disorganisation of behaviour after frontal lobe damage , 1986 .

[11]  W. Prinz,et al.  An online neural substrate for sense of agency. , 2013, Cerebral cortex.

[12]  Michael J. Constantino,et al.  Neural repetition suppression reflects fulfilled perceptual expectations , 2008 .

[13]  E. N. Sokolov,et al.  Perception and the Conditioned Reflex , 1965 .

[14]  Karen A. Daniels,et al.  Conscious and Unconscious Processing of Nonverbal Predictability in Wernicke's Area , 2000, The Journal of Neuroscience.

[15]  Gary H. Glover,et al.  Contributions of the hippocampus and the striatum to simple association and frequency-based learning , 2005, NeuroImage.

[16]  W. Penny,et al.  Time Scales of Representation in the Human Brain: Weighing Past Information to Predict Future Events , 2011, Front. Hum. Neurosci..

[17]  J. Fuster Upper processing stages of the perception–action cycle , 2004, Trends in Cognitive Sciences.

[18]  Randall K. Jamieson,et al.  Applying an exemplar model to the serial reaction-time task: Anticipating from experience , 2009, Quarterly journal of experimental psychology.

[19]  Karl J. Friston The free-energy principle: a unified brain theory? , 2010, Nature Reviews Neuroscience.

[20]  Karl J. Friston,et al.  Dissociable Roles of Ventral and Dorsal Striatum in Instrumental Conditioning , 2004, Science.

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

[22]  D Mumford,et al.  On the computational architecture of the neocortex. II. The role of cortico-cortical loops. , 1992, Biological cybernetics.

[23]  P. Haggard Human volition: towards a neuroscience of will , 2008, Nature Reviews Neuroscience.

[24]  Roger A. Barker,et al.  Perseveration and Choice in Parkinson's Disease: The Impact of Progressive Frontostriatal Dysfunction on Action Decisions , 2012, Cerebral cortex.

[25]  David J. Turk,et al.  The angular gyrus computes action awareness representations. , 2008, Cerebral cortex.

[26]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[27]  Stefan J. Kiebel,et al.  Evidence for neural encoding of Bayesian surprise in human somatosensation , 2012, NeuroImage.

[28]  Uri Hasson,et al.  Multiple sensitivity profiles to diversity and transition structure in non-stationary input , 2012, NeuroImage.

[29]  R. Näätänen,et al.  Early selective-attention effect on evoked potential reinterpreted. , 1978, Acta psychologica.

[30]  Gregory S. Berns,et al.  The Context of Uncertainty Modulates the Subcortical Response to Predictability , 2001, Journal of Cognitive Neuroscience.

[31]  Richard I. Ivry,et al.  Attention and structure in sequence learning. , 1990 .

[32]  Tim Curran,et al.  Attentional and Nonattentional Forms of Sequence Learning , 1993 .

[33]  H. Yin,et al.  The role of the basal ganglia in habit formation , 2006, Nature Reviews Neuroscience.

[34]  James B. Rowe,et al.  Selection and inhibition mechanisms for human voluntary action decisions , 2012, NeuroImage.

[35]  Bruce R. Rosen,et al.  Activity in Ventrolateral and Mid-Dorsolateral Prefrontal Cortex during Nonspatial Visual Working Memory Processing: Evidence from Functional Magnetic Resonance Imaging , 2000, NeuroImage.

[36]  M. Goldsmith,et al.  Statistical Learning by 8-Month-Old Infants , 1996 .

[37]  M. Brass,et al.  Unconscious determinants of free decisions in the human brain , 2008, Nature Neuroscience.

[38]  Kazutoshi Gohara,et al.  Continuous hitting movements modeled from the perspective of dynamical systems with temporal input , 2000 .

[39]  Scott T Grafton,et al.  Repetition suppression for performed hand gestures revealed by fMRI , 2009, Human brain mapping.

[40]  B. Love,et al.  Striatal and hippocampal entropy and recognition signals in category learning: simultaneous processes revealed by model-based fMRI. , 2012, Journal of experimental psychology. Learning, memory, and cognition.

[41]  Ivan Toni,et al.  Movement-Specific Repetition Suppression in Ventral and Dorsal Premotor Cortex during Action Observation , 2009, Cerebral cortex.

[42]  R. Gómez Variability and Detection of Invariant Structure , 2002, Psychological science.

[43]  Marcia K. Johnson,et al.  Implicit Perceptual Anticipation Triggered by Statistical Learning , 2010, The Journal of Neuroscience.

[44]  Karl J. Friston,et al.  Estimating efficiency a priori: a comparison of blocked and randomized designs , 2003, NeuroImage.

[45]  Marvin M. Chun,et al.  Neural Evidence of Statistical Learning: Efficient Detection of Visual Regularities Without Awareness , 2009, Journal of Cognitive Neuroscience.

[46]  M. Walton,et al.  Interactions between decision making and performance monitoring within prefrontal cortex , 2004, Nature Neuroscience.

[47]  C. Kennard,et al.  The anatomy of visual neglect , 2003 .

[48]  R. Näätänen,et al.  The mismatch negativity (MMN) in basic research of central auditory processing: A review , 2007, Clinical Neurophysiology.

[49]  Richard N Aslin,et al.  Bayesian learning of visual chunks by human observers , 2008, Proceedings of the National Academy of Sciences.

[50]  Ricarda I. Schubotz,et al.  Dissociating dynamic probability and predictability in observed actions—an fMRI study , 2014, Front. Hum. Neurosci..

[51]  J. O'Doherty,et al.  Encoding Predictive Reward Value in Human Amygdala and Orbitofrontal Cortex , 2003, Science.

[52]  E. Koechlin,et al.  Motivation and cognitive control in the human prefrontal cortex , 2009, Nature Neuroscience.

[53]  Uri Hasson,et al.  Uncertainty in visual and auditory series is coded by modality‐general and modality‐specific neural systems , 2014, Human brain mapping.

[54]  R. Schubotz Opinion TRENDS in Cognitive Sciences Vol.11 No.5 Prediction , 2022 .

[55]  D. Mumford On the computational architecture of the neocortex , 2004, Biological Cybernetics.

[56]  Tim Shallice,et al.  Bizarre Responses, Rule Detection and Frontal Lobe Lesions , 1996, Cortex.

[57]  G. McCarthy,et al.  Perceiving patterns in random series: dynamic processing of sequence in prefrontal cortex , 2002, Nature Neuroscience.

[58]  Karl J. Friston,et al.  Action and behavior: a free-energy formulation , 2010, Biological Cybernetics.

[59]  Uri Hasson,et al.  Neural systems mediating recognition of changes in statistical regularities , 2012, NeuroImage.

[60]  A. Graybiel Habits, rituals, and the evaluative brain. , 2008, Annual review of neuroscience.

[61]  Karl J. Friston,et al.  False discovery rate revisited: FDR and topological inference using Gaussian random fields , 2009, NeuroImage.

[62]  Luca Passamonti,et al.  Changes in “Top-Down” Connectivity Underlie Repetition Suppression in the Ventral Visual Pathway , 2011, The Journal of Neuroscience.

[63]  Karl J. Friston,et al.  Encoding uncertainty in the hippocampus , 2006, Neural Networks.

[64]  E. Koechlin,et al.  Anterior Prefrontal Function and the Limits of Human Decision-Making , 2007, Science.

[65]  E. Koechlin,et al.  The Architecture of Cognitive Control in the Human Prefrontal Cortex , 2003, Science.

[66]  Joseph D Skufca,et al.  Control entropy: a complexity measure for nonstationary signals. , 2008, Mathematical biosciences and engineering : MBE.

[67]  Marshall M. Haith,et al.  Young Infants' Visual Expectations for Symmetric and Asymmetric Stimulus Sequences. , 1991 .

[68]  S. Debener,et al.  Trial-by-Trial Fluctuations in the Event-Related Electroencephalogram Reflect Dynamic Changes in the Degree of Surprise , 2008, The Journal of Neuroscience.

[69]  K. Grill-Spector,et al.  Repetition and the brain: neural models of stimulus-specific effects , 2006, Trends in Cognitive Sciences.

[70]  B. Libet,et al.  Time of conscious intention to act in relation to onset of cerebral activity (readiness-potential). The unconscious initiation of a freely voluntary act. , 1983, Brain : a journal of neurology.

[71]  James B. Rowe,et al.  Action selection: A race model for selected and non-selected actions distinguishes the contribution of premotor and prefrontal areas , 2010, NeuroImage.

[72]  Karl J. Friston,et al.  A Hierarchy of Time-Scales and the Brain , 2008, PLoS Comput. Biol..

[73]  David A. Caulton,et al.  On the Modularity of Sequence Representation , 1995 .

[74]  O Josephs,et al.  Event-related functional magnetic resonance imaging: modelling, inference and optimization. , 1999, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[75]  A. Borst Seeing smells: imaging olfactory learning in bees , 1999, Nature Neuroscience.

[76]  C. Summerfield,et al.  Expectation (and attention) in visual cognition , 2009, Trends in Cognitive Sciences.

[77]  M. Brass,et al.  Voluntary Selection of Task Sets Revealed by Functional Magnetic Resonance Imaging , 2006 .

[78]  E. Amenedo,et al.  MMN in the visual modality: a review , 2003, Biological Psychology.

[79]  D. Eckstein,et al.  Rule-Selection and Action-Selection have a Shared Neuroanatomical Basis in the Human Prefrontal and Parietal Cortex , 2008, Cerebral cortex.

[80]  Raymond J. Dolan,et al.  Information theory, novelty and hippocampal responses: unpredicted or unpredictable? , 2005, Neural Networks.

[81]  M. Botvinick Hierarchical models of behavior and prefrontal function , 2008, Trends in Cognitive Sciences.

[82]  Karl J. Friston,et al.  Reinforcement Learning or Active Inference? , 2009, PloS one.

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

[84]  Karl J. Friston,et al.  Influence of Uncertainty and Surprise on Human Corticospinal Excitability during Preparation for Action , 2008, Current Biology.

[85]  William A. Cunningham,et al.  Type I and Type II error concerns in fMRI research: re-balancing the scale. , 2009, Social cognitive and affective neuroscience.

[86]  Karl J. Friston,et al.  A Bayesian account of ‘hysteria’ , 2012, Brain : a journal of neurology.

[87]  D. Willingham,et al.  Implicit motor sequence learning is represented in response locations , 2000, Memory & cognition.