Accumulation of Evidence during Sequential Decision Making: The Importance of Top–Down Factors

In the last decade, great progress has been made in characterizing the accumulation of neural information during simple unitary perceptual decisions. However, much less is known about how sequentially presented evidence is integrated over time for successful decision making. The aim of this study was to study the mechanisms of sequential decision making in humans. In a magnetoencephalography (MEG) study, we presented healthy volunteers with sequences of centrally presented arrows. Sequence length varied between one and five arrows, and the accumulated directions of the arrows informed the subject about which hand to use for a button press at the end of the sequence (e.g., LRLRR should result in a right-hand press). Mathematical modeling suggested that nonlinear accumulation was the rational strategy for performing this task in the presence of no or little noise, whereas quasilinear accumulation was optimal in the presence of substantial noise. MEG recordings showed a correlate of evidence integration over parietal and central cortex that was inversely related to the amount of accumulated evidence (i.e., when more evidence was accumulated, neural activity for new stimuli was attenuated). This modulation of activity likely reflects a top–down influence on sensory processing, effectively constraining the influence of sensory information on the decision variable over time. The results indicate that, when making decisions on the basis of sequential information, the human nervous system integrates evidence in a nonlinear manner, using the amount of previously accumulated information to constrain the accumulation of additional evidence.

[1]  J. Bullier,et al.  Visual latencies in areas V1 and V2 of the macaque monkey , 1995, Visual Neuroscience.

[2]  B. Cumming,et al.  Decision-related activity in sensory neurons reflects more than a neuron’s causal effect , 2009, Nature.

[3]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[4]  Leslie G. Ungerleider,et al.  A general mechanism for perceptual decision-making in the human brain , 2004, Nature.

[5]  R. Oostenveld,et al.  Theta and Gamma Oscillations Predict Encoding and Retrieval of Declarative Memory , 2006, The Journal of Neuroscience.

[6]  W. Newsome,et al.  Neural basis of a perceptual decision in the parietal cortex (area LIP) of the rhesus monkey. , 2001, Journal of neurophysiology.

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

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

[9]  Thomas E. Nichols,et al.  Nonparametric permutation tests for functional neuroimaging: A primer with examples , 2002, Human brain mapping.

[10]  R. Oostenveld,et al.  Tactile Spatial Attention Enhances Gamma-Band Activity in Somatosensory Cortex and Reduces Low-Frequency Activity in Parieto-Occipital Areas , 2006, The Journal of Neuroscience.

[11]  J. Gold,et al.  The neural basis of decision making. , 2007, Annual review of neuroscience.

[12]  S. Dehaene,et al.  Dynamics of prefrontal and cingulate activity during a reward-based logical deduction task. , 2006, Cerebral cortex.

[13]  Thomas R Knösche,et al.  Tangential derivative mapping of axial MEG applied to event-related desynchronization research , 2000, Clinical Neurophysiology.

[14]  Paul Schrater,et al.  Shape perception reduces activity in human primary visual cortex , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[15]  Michael N. Shadlen,et al.  Probabilistic reasoning by neurons , 2007, Nature.

[16]  J. Gold,et al.  Representation of a perceptual decision in developing oculomotor commands , 2000, Nature.

[17]  T. Sejnowski,et al.  Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects , 2000, Clinical Neurophysiology.

[18]  J. Bullier,et al.  Parallel versus serial processing: new vistas on the distributed organization of the visual system , 1995, Current Opinion in Neurobiology.

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

[20]  M. Rushworth,et al.  General Mechanisms for Making Decisions? This Review Comes from a Themed Issue on Cognitive Neuroscience Edited the Representation of Value and Reward Expectations in Frontal Cortex Reward Prediction Errors and Learning Rates Other Types of Prediction Error , 2022 .

[21]  P. Sajda,et al.  Temporal characterization of the neural correlates of perceptual decision making in the human brain. , 2006, Cerebral cortex.

[22]  Jeffrey D. Schall,et al.  Neural basis of deciding, choosing and acting , 2001, Nature Reviews Neuroscience.

[23]  Michael L. Platt,et al.  Neural correlates of decision variables in parietal cortex , 1999, Nature.

[24]  Leslie G. Ungerleider,et al.  Involvement of human left dorsolateral prefrontal cortex in perceptual decision making is independent of response modality , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[25]  Werner Lutzenberger,et al.  Dynamics of oscillatory activity during auditory decision making. , 2007, Cerebral cortex.

[26]  R. Ilmoniemi,et al.  Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain , 1993 .

[27]  C. Summerfield,et al.  A Neural Representation of Prior Information during Perceptual Inference , 2008, Neuron.

[28]  I. Toni,et al.  Oscillations , 2018, Physics to a Degree.

[29]  J. Kalaska,et al.  Neural Correlates of Reaching Decisions in Dorsal Premotor Cortex: Specification of Multiple Direction Choices and Final Selection of Action , 2005, Neuron.