Boosting Brain Signal Variability Underlies Liberal Shifts in Decision Bias

Strategically adopting decision biases allows organisms to tailor their choices to environmental demands. For example, a liberal response strategy pays off when target detection is crucial, whereas a conservative strategy is optimal for avoiding false alarms. Using conventional time-frequency analysis of human electroencephalographic (EEG) activity, we previously showed that bias setting entails adjustment of evidence accumulation in sensory regions (Kloosterman et al., 2019), but the presumed prefrontal signature of a strategic conservative-to-liberal bias shift has remained elusive. Here, we show that a liberal bias shift relies on frontal regions adopting a more unconstrained neural regime (boosted entropy) that is suited to the detection of unpredictable events. Overall EEG variation, spectral power and event-related potentials could not explain this relationship, highlighting the unique contribution of moment-to-moment neural variability to bias shifts. Neural variability modulation through prefrontal cortex appears instrumental for permitting an organism to tailor its decision bias to environmental demands. Impact statement Moment-to-moment variability is a prominent feature of neural activity. Rather than representing mere noise, this variability might enable us to flexibly adapt our decision biases to the environment.

[1]  Samira M. Epp,et al.  Higher performers upregulate brain signal variability in response to more feature-rich visual input , 2020, NeuroImage.

[2]  G. Woodman,et al.  Event-related potential studies of attention , 2000, Trends in Cognitive Sciences.

[3]  S. Jones When brain rhythms aren't ‘rhythmic’: implication for their mechanisms and meaning , 2016, Current Opinion in Neurobiology.

[4]  Gustavo Deco,et al.  Stochastic dynamics as a principle of brain function , 2009, Progress in Neurobiology.

[5]  C. Grady,et al.  The Importance of Being Variable , 2011, The Journal of Neuroscience.

[6]  I. Dinstein,et al.  The Relationship between Trial-by-Trial Variability and Oscillations of Cortical Population Activity , 2019, Scientific Reports.

[7]  Niels A Kloosterman,et al.  Standard multiscale entropy reflects neural dynamics at mismatched temporal scales: What’s signal irregularity got to do with it? , 2020, PLoS Comput. Biol..

[8]  Natasa Kovacevic,et al.  Increased Brain Signal Variability Accompanies Lower Behavioral Variability in Development , 2008, PLoS Comput. Biol..

[9]  D. M. Green,et al.  Signal detection theory and psychophysics , 1966 .

[10]  Nelson J. Trujillo-Barreto,et al.  Induced gamma band responses in human EEG after the control of miniature saccadic artifacts , 2011, NeuroImage.

[11]  Christopher Summerfield,et al.  Metacognition in human decision-making: confidence and error monitoring , 2012, Philosophical Transactions of the Royal Society B: Biological Sciences.

[12]  D. Altman,et al.  Statistics notes: Calculating correlation coefficients with repeated observations: Part 1—correlation within subjects , 1995 .

[13]  J. Obleser,et al.  Local cortical desynchronization and pupil-linked arousal differentially shape brain states for optimal sensory performance , 2019, eLife.

[14]  Henrik Walter,et al.  The left inferior frontal gyrus is involved in adjusting response bias during a perceptual decision-making task , 2014, Brain and behavior.

[15]  Rufin VanRullen,et al.  Four Common Conceptual Fallacies in Mapping the Time Course of Recognition , 2011, Front. Psychology.

[16]  Irene E. Nagel,et al.  Amphetamine modulates brain signal variability and working memory in younger and older adults , 2015, Proceedings of the National Academy of Sciences.

[17]  A. Compte,et al.  Bump attractor dynamics in prefrontal cortex explains behavioral precision in spatial working memory , 2014, Nature Neuroscience.

[18]  David J. Heeger,et al.  Neural variability: friend or foe? , 2015, Trends in Cognitive Sciences.

[19]  Niels A. Kloosterman,et al.  Dynamic modulation of decision biases by brainstem arousal systems , 2017, eLife.

[20]  Anne E. Urai,et al.  Confirmation Bias through Selective Overweighting of Choice-Consistent Evidence , 2018, Current Biology.

[21]  Andrew M. Clark,et al.  Stimulus onset quenches neural variability: a widespread cortical phenomenon , 2010, Nature Neuroscience.

[22]  Niels A. Kloosterman,et al.  Humans strategically shift decision bias by flexibly adjusting sensory evidence accumulation in visual cortex , 2018 .

[23]  Johannes J. Fahrenfort,et al.  Masking Disrupts Reentrant Processing in Human Visual Cortex , 2007, Journal of Cognitive Neuroscience.

[24]  Aggelos K. Katsaggelos,et al.  Signal Processing and Communications , 2001 .

[25]  R. Romo,et al.  The role of fluctuations in perception , 2008, Trends in Neurosciences.

[26]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[27]  S. MacDonald,et al.  Neuroscience and Biobehavioral Reviews Review Moment-to-moment Brain Signal Variability: a next Frontier in Human Brain Mapping? , 2022 .

[28]  Nitzan Censor,et al.  Neural Variability Quenching Predicts Individual Perceptual Abilities , 2017, The Journal of Neuroscience.

[29]  R. Fisher FREQUENCY DISTRIBUTION OF THE VALUES OF THE CORRELATION COEFFIENTS IN SAMPLES FROM AN INDEFINITELY LARGE POPU;ATION , 1915 .

[30]  Hauke R. Heekeren,et al.  Age-Related Decline in Brain Resources Modulates Genetic Effects on Cognitive Functioning , 2008, Front. Neurosci..

[31]  W. T. Maddox,et al.  Multiple brain networks contribute to the acquisition of bias in perceptual decision-making , 2015, Front. Neurosci..

[32]  Ann M Hermundstad,et al.  Adaptive coding for dynamic sensory inference , 2017, bioRxiv.

[33]  M Doppelmayr,et al.  A method for the calculation of induced band power: implications for the significance of brain oscillations. , 1998, Electroencephalography and clinical neurophysiology.

[34]  C. Grady,et al.  The modulation of BOLD variability between cognitive states varies by age and processing speed. , 2013, Cerebral cortex.

[35]  Markus Werkle-Bergner,et al.  On the estimation of brain signal entropy from sparse neuroimaging data , 2016, Scientific Reports.

[36]  Viktor K. Jirsa,et al.  Noise during Rest Enables the Exploration of the Brain's Dynamic Repertoire , 2008, PLoS Comput. Biol..

[37]  P. Grassberger,et al.  Characterization of experimental (noisy) strange attractors , 1984 .

[38]  Niels A. Kloosterman,et al.  Top-down modulation in human visual cortex predicts the stability of a perceptual illusion. , 2015, Journal of neurophysiology.

[39]  Joshua I. Gold,et al.  Pupil Size as a Window on Neural Substrates of Cognition , 2020, Trends in Cognitive Sciences.

[40]  R. Dolan,et al.  The neural basis of metacognitive ability , 2012, Philosophical Transactions of the Royal Society B: Biological Sciences.

[41]  S. Cole,et al.  Brain Oscillations and the Importance of Waveform Shape , 2017, Trends in Cognitive Sciences.

[42]  Ulman Lindenberger,et al.  Humans strategically shift decision bias by flexibly adjusting sensory evidence accumulation , 2019, eLife.

[43]  Jack W. Tsao,et al.  Observed brain dynamics, P.P. Mitra, H. Bokil. Oxford University Press (2008), ISBN-13: 978-0-19-517808-1, 381 pages, $65.00 , 2009 .

[44]  Douglas D. Garrett,et al.  Standard multiscale entropy reflects spectral power at mismatched temporal scales: What’s signal irregularity got to do with it? , 2019, bioRxiv.

[45]  T. Yarkoni Big Correlations in Little Studies: Inflated fMRI Correlations Reflect Low Statistical Power—Commentary on Vul et al. (2009) , 2009, Perspectives on psychological science : a journal of the Association for Psychological Science.

[46]  T. Brismar,et al.  Comment on "Multiscale entropy analysis of complex physiologic time series". , 2004, Physical review letters.

[47]  A. McIntosh,et al.  Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy , 2013, Journal of visualized experiments : JoVE.

[48]  Sarah Marzen,et al.  The evolution of lossy compression , 2015, Journal of The Royal Society Interface.

[49]  F. Perrin,et al.  Spherical splines for scalp potential and current density mapping. , 1989, Electroencephalography and clinical neurophysiology.

[50]  Thad A. Polk,et al.  Higher performers upregulate brain signal variability in response to more feature-rich visual input , 2018, NeuroImage.

[51]  Anne E. Urai,et al.  Choice history biases subsequent evidence accumulation , 2019, eLife.

[52]  Roger Ratcliff,et al.  The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks , 2008, Neural Computation.

[53]  Pere Caminal,et al.  Refined Multiscale Entropy: Application to 24-h Holter Recordings of Heart Period Variability in Healthy and Aortic Stenosis Subjects , 2009, IEEE Transactions on Biomedical Engineering.

[54]  L. Pinneo On noise in the nervous system. , 1966, Psychological review.

[55]  Stephen M Fleming,et al.  Overcoming status quo bias in the human brain , 2010, Proceedings of the National Academy of Sciences.

[56]  Margot J. Taylor,et al.  Brain noise is task dependent and region specific. , 2010, Journal of neurophysiology.

[57]  Stanislas Dehaene,et al.  Cortical activity is more stable when sensory stimuli are consciously perceived , 2015, Proceedings of the National Academy of Sciences.

[58]  M. D’Esposito,et al.  Causal evidence for frontal cortex organization for perceptual decision making , 2016, Proceedings of the National Academy of Sciences.

[59]  A. Verghese The Importance Of Being. , 2016, Health affairs.

[60]  J. Rinzel,et al.  Noise-induced alternations in an attractor network model of perceptual bistability. , 2007, Journal of neurophysiology.

[61]  Xiao-Jing Wang,et al.  Probabilistic Decision Making by Slow Reverberation in Cortical Circuits , 2002, Neuron.

[62]  Rishidev Chaudhuri,et al.  The intrinsic attractor manifold and population dynamics of a canonical cognitive circuit across waking and sleep , 2019, Nature Neuroscience.

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

[64]  K. Linkenkaer-Hansen,et al.  Long-Range Temporal Correlations and Scaling Behavior in Human Brain Oscillations , 2001, The Journal of Neuroscience.

[65]  Sandro Romani,et al.  Discrete attractor dynamics underlies persistent activity in the frontal cortex , 2019, Nature.

[66]  Michael X Cohen,et al.  Analyzing Neural Time Series Data: Theory and Practice , 2014 .

[67]  J. Gold,et al.  Relationships between Pupil Diameter and Neuronal Activity in the Locus Coeruleus, Colliculi, and Cingulate Cortex , 2016, Neuron.

[68]  T. Knapen,et al.  Decision-related pupil dilation reflects upcoming choice and individual bias , 2014, Proceedings of the National Academy of Sciences.

[69]  Ning Xinbao Multiscale entropy analysis of complex physiologic time series , 2007 .

[70]  Marta Kutas,et al.  Cognitive and neural mechanisms of decision biases in recognition memory. , 2002, Cerebral cortex.

[71]  Viktor K. Jirsa,et al.  The multiscale entropy: Guidelines for use and interpretation in brain signal analysis , 2016, Journal of Neuroscience Methods.

[72]  J. Bakdash,et al.  Repeated Measures Correlation , 2017, Front. Psychol..

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

[74]  Sung-joo Lim,et al.  Dopaminergic modulation of hemodynamic signal variability and the functional connectome during cognitive performance , 2017, bioRxiv.

[75]  C. Cassanello,et al.  Frontal Eye Field Neurons Signal Changes in Decision Criteria , 2009, Nature Neuroscience.