Non-monotonic Temporal-Weighting Indicates a Dynamically Modulated Evidence-Integration Mechanism

Perceptual decisions are thought to be mediated by a mechanism of sequential sampling and integration of noisy evidence whose temporal weighting profile affects the decision quality. To examine temporal weighting, participants were presented with two brightness-fluctuating disks for 1, 2 or 3 seconds and were requested to choose the overall brighter disk at the end of each trial. By employing a signal-perturbation method, which deploys across trials a set of systematically controlled temporal dispersions of the same overall signal, we were able to quantify the participants’ temporal weighting profile. Results indicate that, for intervals of 1 or 2 sec, participants exhibit a primacy-bias. However, for longer stimuli (3-sec) the temporal weighting profile is non-monotonic, with concurrent primacy and recency, which is inconsistent with the predictions of previously suggested computational models of perceptual decision-making (drift-diffusion and Ornstein-Uhlenbeck processes). We propose a novel, dynamic variant of the leaky-competing accumulator model as a potential account for this finding, and we discuss potential neural mechanisms.

[1]  Casimir J. H. Ludwig,et al.  Estimating the growth of internal evidence guiding perceptual decisions , 2011, Cognitive Psychology.

[2]  Henry Markram,et al.  Spike frequency adaptation and neocortical rhythms. , 2002, Journal of neurophysiology.

[3]  M. Usher,et al.  Parallel Activation of Memories in an Oscillatory Neural Network , 1991, Neural Computation.

[4]  Denis Cousineau,et al.  Confidence intervals in within-subject designs: A simpler solution to Loftus and Masson's method , 2005 .

[5]  N. Anderson.  Foundations of information integration theory , 1981 .

[6]  Philip L. Smith,et al.  Psychology and neurobiology of simple decisions , 2004, Trends in Neurosciences.

[7]  J. Gold,et al.  Neural computations that underlie decisions about sensory stimuli , 2001, Trends in Cognitive Sciences.

[8]  Casimir J. H. Ludwig,et al.  Temporal integration of sensory evidence for saccade target selection , 2009, Vision Research.

[9]  Marius Usher,et al.  Extending a biologically inspired model of choice: multi-alternatives, nonlinearity and value-based multidimensional choice , 2007, Philosophical Transactions of the Royal Society B: Biological Sciences.

[10]  C. Peterson,et al.  A primacy effect in subjective probability revision. , 1967, Journal of experimental psychology.

[11]  H. Markram,et al.  The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[12]  R. Hogarth,et al.  Order effects in belief updating: The belief-adjustment model , 1992, Cognitive Psychology.

[13]  N. Pennington,et al.  Explanation-based decision making: effects of memory structure on judgment , 1988 .

[14]  Samuel W Cheadle,et al.  Adaptive Gain Control during Human Perceptual Choice , 2014, Neuron.

[15]  Paul Cisek,et al.  Decision making by urgency gating: theory and experimental support. , 2012, Journal of neurophysiology.

[16]  D. Wolpert,et al.  Changing your mind: a computational mechanism of vacillation , 2009, Nature.

[17]  G. Pitz,et al.  Sequential effects in the revision of subjective probabilities. , 1967, Canadian journal of psychology.

[18]  Rafal Bogacz,et al.  Bounded Ornstein–Uhlenbeck models for two-choice time controlled tasks , 2010 .

[19]  Casimir J. H. Ludwig,et al.  The Temporal Impulse Response Underlying Saccadic Decisions , 2005, The Journal of Neuroscience.

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

[21]  A. Watson.  Probability summation over time , 1979, Vision Research.

[22]  Marius Usher,et al.  A neural model of the dynamic activation of memory , 1993, Biological Cybernetics.

[23]  L. Maffei,et al.  Neural Correlate of Perceptual Adaptation to Gratings , 1973, Science.

[24]  Jonathan D. Cohen,et al.  The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks. , 2006, Psychological review.

[25]  Christopher Summerfield,et al.  Adaptive gain control during human perceptual choice , 2014 .

[26]  P. Cisek,et al.  Decisions in Changing Conditions: The Urgency-Gating Model , 2009, The Journal of Neuroscience.

[27]  Marius Usher,et al.  The Timescale of Perceptual Evidence Integration Can Be Adapted to the Environment , 2013, Current Biology.

[28]  Jerome R. Busemeyer,et al.  Comparison of Decision Learning Models Using the Generalization Criterion Method , 2008, Cogn. Sci..

[29]  Xiang Zhou,et al.  Time-Varying Perturbations Can Distinguish Among Integrate-to-Threshold Models for Perceptual Decision Making in Reaction Time Tasks , 2009, Neural Computation.

[30]  Scott D. Brown,et al.  Revisiting the Evidence for Collapsing Boundaries and Urgency Signals in Perceptual Decision-Making , 2015, The Journal of Neuroscience.

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

[32]  J. Busemeyer,et al.  Model Comparisons and Model Selections Based on Generalization Criterion Methodology. , 2000, Journal of mathematical psychology.

[33]  M. Carandini,et al.  Visual cortex: Fatigue and adaptation , 2000, Current Biology.

[34]  D. Vickers,et al.  Evidence for an accumulator model of psychophysical discrimination. , 1970, Ergonomics.

[35]  James L. McClelland,et al.  The time course of perceptual choice: the leaky, competing accumulator model. , 2001, Psychological review.

[36]  Bingni W. Brunton,et al.  Rats and Humans Can Optimally Accumulate Evidence for Decision-Making , 2013, Science.

[37]  M. Usher,et al.  The demise of short-term memory revisited: empirical and computational investigations of recency effects. , 2005, Psychological review.

[38]  Scott D. Brown,et al.  The simplest complete model of choice response time: Linear ballistic accumulation , 2008, Cognitive Psychology.

[39]  Timothy D. Hanks,et al.  Bounded Integration in Parietal Cortex Underlies Decisions Even When Viewing Duration Is Dictated by the Environment , 2008, The Journal of Neuroscience.

[40]  I. Erev,et al.  On Surprise, Change, and the Effect of Recent Outcomes , 2011, Front. Psychology.

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

[42]  M. Shadlen,et al.  Choice Certainty Is Informed by Both Evidence and Decision Time , 2014, Neuron.

[43]  Marius Usher,et al.  Testing Multi-Alternative Decision Models with Non-Stationary Evidence , 2011, Front. Neurosci..

[44]  Jeffrey N. Rouder,et al.  Modeling Response Times for Two-Choice Decisions , 1998 .

[45]  Philip L. Smith.  Bloch's law predictions from diffusion process models of detection , 1998 .

[46]  Marius Usher,et al.  Decisions reduce sensitivity to subsequent information , 2015, Proceedings of the Royal Society B: Biological Sciences.

[47]  Nick Chater,et al.  Salience driven value integration explains decision biases and preference reversal , 2012, Proceedings of the National Academy of Sciences.

[48]  Max C. Keuken,et al.  Early evidence affects later decisions: Why evidence accumulation is required to explain response time data , 2014, Psychonomic bulletin & review.

[49]  Marius Usher,et al.  Adaptive Spontaneous Transitions between Two Mechanisms of Numerical Averaging , 2015, Scientific Reports.

[50]  M. Hasselmo,et al.  High acetylcholine levels set circuit dynamics for attention and encoding and low acetylcholine levels set dynamics for consolidation. , 2004, Progress in brain research.

[51]  A. Gorea,et al.  New look at Bloch's law for contrast. , 1986, Journal of the Optical Society of America. A, Optics and image science.

[52]  C. Summerfield,et al.  Robust averaging during perceptual judgment , 2011, Proceedings of the National Academy of Sciences.

[53]  Adam Kepecs,et al.  Seeing at a glance, smelling in a whiff: rapid forms of perceptual decision making , 2006, Nature Reviews Neuroscience.

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

[55]  Francis Tuerlinckx,et al.  The Ising Decision Maker: a binary stochastic network for choice response time. , 2014, Psychological review.

[56]  L. Kaufman,et al.  Handbook of perception and human performance , 1986 .

[57]  Philip L. Smith.  Psychophysically principled models of visual simple reaction time. , 1995 .

[58]  M. Shadlen,et al.  A role for neural integrators in perceptual decision making. , 2003, Cerebral cortex.

[59]  Marius Usher,et al.  Disentangling decision models: from independence to competition. , 2013, Psychological review.

[60]  Kinneret Teodorescu,et al.  On the Decision to Explore New Alternatives: The Coexistence of Under‐ and Over‐exploration , 2014 .

[61]  Philip L. Smith,et al.  A comparison of sequential sampling models for two-choice reaction time. , 2004, Psychological review.

[62]  R. Hertwig,et al.  The description–experience gap in risky choice , 2009, Trends in Cognitive Sciences.

[63]  Marius Usher,et al.  Using Time-Varying Evidence to Test Models of Decision Dynamics: Bounded Diffusion vs. the Leaky Competing Accumulator Model , 2012, Front. Neurosci..

[64]  L. Beach,et al.  Intuitive estimation of means , 1966 .

[65]  Richard C. Atkinson,et al.  Human Memory: A Proposed System and its Control Processes , 1968, Psychology of Learning and Motivation.