Long-term implicit memory for sequential auditory patterns in humans

To understand auditory scenes, listeners track and retain the statistics of sensory inputs as they unfold over time. We combined behavioural manipulation and modelling to investigate how sequence statistics are encoded into long-term memory and used to interpret incoming sensory signals. In a series of experiments, participants detected the emergence of regularly repeating patterns in novel rapid sound sequences. Unbeknownst to them, a few regular patterns reoccurred sparsely (every ∼3 minutes). Reoccurring sequences showed a rapidly growing detection time advantage over novel sequences. This effect was implicit, robust to interference, and persisted up to 7 weeks. Human performance was reproduced by a memory-constrained probabilistic model, where sequences are stored as n-grams and are subject to memory decay. Results suggest that similar psychological mechanisms may underlie integration processes over different-time scales in memory formation and flexible retrieval.

[1]  Christopher M. Conway,et al.  Neurocognitive Basis of Implicit Learning of Sequential Structure and Its Relation to Language Processing , 2008, Annals of the New York Academy of Sciences.

[2]  Geraint A. Wiggins,et al.  EXPECTATION IN MELODY: THE INFLUENCE OF CONTEXT AND LEARNING , 2006 .

[3]  Josh H McDermott,et al.  Schema learning for the cocktail party problem , 2018, Proceedings of the National Academy of Sciences.

[4]  Trevor R. Agus,et al.  The detection of repetitions in noise before and after perceptual learning. , 2013, The Journal of the Acoustical Society of America.

[5]  Gustavo Deco,et al.  Oscillations, Phase-of-Firing Coding, and Spike Timing-Dependent Plasticity: An Efficient Learning Scheme , 2009, The Journal of Neuroscience.

[6]  Andrei Gorea,et al.  Time dilates more with apparent than with physical speed. , 2015, Journal of vision.

[7]  Daniel Schmitt,et al.  Whole Body Mechanics of Stealthy Walking in Cats , 2008, PloS one.

[8]  Suzanne Bunton,et al.  Semantically Motivated Improvements for PPM Variants , 1997, Comput. J..

[9]  Andrew P. Yonelinas,et al.  Detecting Changes in Scenes: The Hippocampus Is Critical for Strength-Based Perception , 2013, Neuron.

[10]  Dimitris K. Tasoulis,et al.  Nonparametric Monitoring of Data Streams for Changes in Location and Scale , 2011, Technometrics.

[11]  M. Botvinick,et al.  The hippocampus as a predictive map , 2016 .

[12]  Josh H McDermott,et al.  Recovering sound sources from embedded repetition , 2011, Proceedings of the National Academy of Sciences.

[13]  M. Chait,et al.  Enhanced deviant responses in patterned relative to random sound sequences , 2017, Cortex.

[14]  Daniel Pressnitzer,et al.  Rapid Formation of Robust Auditory Memories: Insights from Noise , 2010, Neuron.

[15]  Ian H. Witten,et al.  Multiple viewpoint systems for music prediction , 1995 .

[16]  Peter M C Harrison,et al.  Uncertainty and Surprise Jointly Predict Musical Pleasure and Amygdala, Hippocampus, and Auditory Cortex Activity , 2019, Current Biology.

[17]  Eleanor A. Maguire,et al.  Representations of specific acoustic patterns in the auditory cortex and hippocampus , 2014, Proceedings of the Royal Society B: Biological Sciences.

[18]  Aaron R. Seitz,et al.  Testing assumptions of statistical learning: Is it long-term and implicit? , 2009, Neuroscience Letters.

[19]  A. Yonelinas The hippocampus supports high-resolution binding in the service of perception, working memory and long-term memory , 2013, Behavioural Brain Research.

[20]  Morten H. Christiansen,et al.  Modality-constrained statistical learning of tactile, visual, and auditory sequences. , 2005, Journal of experimental psychology. Learning, memory, and cognition.

[21]  B. Shinn-Cunningham,et al.  Transformation of temporal sequences in the zebra finch auditory system , 2016, eLife.

[22]  Elizabeth K. Johnson,et al.  Statistical learning of tone sequences by human infants and adults , 1999, Cognition.

[23]  Florent Meyniel,et al.  The Neural Representation of Sequences: From Transition Probabilities to Algebraic Patterns and Linguistic Trees , 2015, Neuron.

[24]  P. Perruchet,et al.  Implicit learning and statistical learning: one phenomenon, two approaches , 2006, Trends in Cognitive Sciences.

[25]  Stefan Köhler,et al.  The neurobiological foundation of memory retrieval , 2019, Nature Neuroscience.

[26]  Jason M. Gold,et al.  Memory and incidental learning for visual frozen noise sequences , 2014, Vision Research.

[27]  N. Wasserman,et al.  Iddi(n)-Sîn, King of Simurrum: A New Rock-Relief Inscription and a Reverential Seal , 2003 .

[28]  Karl J. Friston,et al.  Predictive Processes and the Peculiar Case of Music , 2019, Trends in Cognitive Sciences.

[29]  Geraint A. Wiggins,et al.  Probabilistic models of expectation violation predict psychophysiological emotional responses to live concert music , 2013, Cognitive, Affective, & Behavioral Neuroscience.

[30]  Morten H. Christiansen,et al.  The long road of statistical learning research: past, present and future , 2017, Philosophical Transactions of the Royal Society B: Biological Sciences.

[31]  Daphna Shohamy,et al.  Retroactive and graded prioritization of memory by reward , 2018, Nature Communications.

[32]  Yuhong Jiang,et al.  High-capacity spatial contextual memory , 2005, Psychonomic bulletin & review.

[33]  H. Markram,et al.  Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs , 1997, Science.

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

[35]  M. Pearce Statistical learning and probabilistic prediction in music cognition: mechanisms of stylistic enculturation , 2018, Annals of the New York Academy of Sciences.

[36]  David M. W. Powers,et al.  Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation , 2011, ArXiv.

[37]  Morten H. Christiansen,et al.  Statistical learning research: A critical review and possible new directions. , 2019, Psychological bulletin.

[38]  Roberta Bianco,et al.  PPM-Decay: A computational model of auditory prediction with memory decay , 2020, bioRxiv.

[39]  David S Vicario,et al.  Statistical learning of recurring sound patterns encodes auditory objects in songbird forebrain , 2014, Proceedings of the National Academy of Sciences.

[40]  Michael M Merzenich,et al.  Perceptual Learning Directs Auditory Cortical Map Reorganization through Top-Down Influences , 2006, The Journal of Neuroscience.

[41]  J. Saffran,et al.  Infant Statistical Learning , 2018, Annual review of psychology.

[42]  María Herrojo Ruiz,et al.  Unsupervised statistical learning underpins computational, behavioural, and neural manifestations of musical expectation , 2010, NeuroImage.

[43]  Hubert R. Dinse,et al.  Learning without Training , 2013, Current Biology.

[44]  Haley R Pipkins,et al.  Polyamine transporter potABCD is required for virulence of encapsulated but not nonencapsulated Streptococcus pneumoniae , 2017, PloS one.

[45]  D. Poeppel,et al.  Neural Response Phase Tracks How Listeners Learn New Acoustic Representations , 2013, Current Biology.

[46]  Karl J. Friston,et al.  Brain responses in humans reveal ideal observer-like sensitivity to complex acoustic patterns , 2016, Proceedings of the National Academy of Sciences.

[47]  S. Thorpe,et al.  Spike Timing Dependent Plasticity Finds the Start of Repeating Patterns in Continuous Spike Trains , 2008, PloS one.

[48]  Robert Sekuler,et al.  Memory and learning with rapid audiovisual sequences. , 2015, Journal of vision.

[49]  Lucy S. Petro,et al.  The Significance of Memory in Sensory Cortex , 2017, Trends in Neurosciences.

[50]  Peter M C Harrison,et al.  Dissociating sensory and cognitive theories of harmony perception through computational modeling , 2018 .

[51]  Marcus T. Pearce,et al.  Information-Theoretic Properties of Auditory Sequences Dynamically Influence Expectation and Memory , 2018, Cogn. Sci..

[52]  Karl J. Friston,et al.  Is predictability salient? A study of attentional capture by auditory patterns , 2017, Philosophical Transactions of the Royal Society B: Biological Sciences.

[53]  Marijn C. W. Kroes,et al.  Action boosts episodic memory encoding in humans via engagement of a noradrenergic system , 2017, Nature Communications.

[54]  I. Nelken,et al.  Modeling the auditory scene: predictive regularity representations and perceptual objects , 2009, Trends in Cognitive Sciences.

[55]  Trevor R. Agus,et al.  Perceptual Learning of Acoustic Noise Generates Memory-Evoked Potentials , 2015, Current Biology.

[56]  M. Tervaniemi,et al.  Pitch discrimination accuracy in musicians vs nonmusicians: an event-related potential and behavioral study , 2005, Experimental Brain Research.

[57]  N. Cowan Visual and auditory working memory capacity , 1998, Trends in Cognitive Sciences.

[58]  Eero P. Simoncelli,et al.  Summary statistics in auditory perception , 2013, Nature Neuroscience.

[59]  Erik D. Thiessen,et al.  What's statistical about learning? Insights from modelling statistical learning as a set of memory processes , 2017, Philosophical Transactions of the Royal Society B: Biological Sciences.

[60]  M. Chun,et al.  Contextual cueing of visual attention , 2022 .

[61]  Ian H. Witten,et al.  Data Compression Using Adaptive Coding and Partial String Matching , 1984, IEEE Trans. Commun..

[62]  Marcus T. Pearce,et al.  The construction and evaluation of statistical models of melodic structure in music perception and composition , 2005 .

[63]  Ernest Mas-Herrero,et al.  Predictability and Uncertainty in the Pleasure of Music: A Reward for Learning? , 2019, The Journal of Neuroscience.

[64]  Geraint A. Wiggins,et al.  Improved Methods for Statistical Modelling of Monophonic Music , 2004 .

[65]  Miguel Maravall,et al.  Learning and recognition of tactile temporal sequences by mice and humans , 2017, bioRxiv.

[66]  Joanne Arciuli,et al.  Statistical Learning Is Related to Reading Ability in Children and Adults , 2012, Cogn. Sci..

[67]  Richard E. Ladner,et al.  On-line stochastic processes in data compression , 1996 .

[68]  Florence Rémy,et al.  Long Term Memory for Noise: Evidence of Robust Encoding of Very Short Temporal Acoustic Patterns , 2016, Front. Neurosci..

[69]  R N Aslin,et al.  Statistical Learning by 8-Month-Old Infants , 1996, Science.

[70]  D. Powers Evaluation: From Precision, Recall and F-Factor to ROC, Informedness, Markedness & Correlation , 2008 .

[71]  David S. Lorberbaum,et al.  Genetic evidence that Nkx2.2 acts primarily downstream of Neurog3 in pancreatic endocrine lineage development , 2017, eLife.

[72]  Sabri Boughorbel,et al.  Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric , 2017, PloS one.

[73]  I. Winkler,et al.  From sensory to long-term memory: evidence from auditory memory reactivation studies. , 2005, Experimental psychology.

[74]  Daniel Pressnitzer,et al.  Auditory memory for random time patterns. , 2017, The Journal of the Acoustical Society of America.

[75]  P. Dayan,et al.  Pupil-linked phasic arousal evoked by violation but not emergence of regularity within rapid sound sequences , 2019, Nature Communications.