Optimal Decision Making on the Basis of Evidence Represented in Spike Trains

2010 ◽  
Vol 22 (5) ◽  
pp. 1113-1148 ◽  
Author(s):  
Jiaxiang Zhang ◽  
Rafal Bogacz
Keyword(s):  
Decision Making ◽  
Spike Trains ◽  
Decision Procedures ◽  
Optimal Decision ◽  
Ratio Test ◽  
Perceptual Decision Making ◽  
Sensory Evidence ◽  
Optimal Decision Making ◽  
Alternative Choice ◽  
Choice Tasks

Experimental data indicate that perceptual decision making involves integration of sensory evidence in certain cortical areas. Theoretical studies have proposed that the computation in neural decision circuits approximates statistically optimal decision procedures (e.g., sequential probability ratio test) that maximize the reward rate in sequential choice tasks. However, these previous studies assumed that the sensory evidence was represented by continuous values from gaussian distributions with the same variance across alternatives. In this article, we make a more realistic assumption that sensory evidence is represented in spike trains described by the Poisson processes, which naturally satisfy the mean-variance relationship observed in sensory neurons. We show that for such a representation, the neural circuits involving cortical integrators and basal ganglia can approximate the optimal decision procedures for two and multiple alternative choice tasks.

The physics of optimal decision making: A formal analysis of models of performance in two-alternative forced-choice tasks.

2006 ◽  
Vol 113 (4) ◽  
pp. 700-765 ◽  
Author(s):  
Rafal Bogacz ◽  
Eric Brown ◽  
Jeff Moehlis ◽  
Philip Holmes ◽  
Jonathan D. Cohen
Keyword(s):  
Decision Making ◽  
Formal Analysis ◽  
Forced Choice ◽  
Optimal Decision ◽  
Optimal Decision Making ◽  
Forced Choice Tasks ◽  
Choice Tasks

The Basal Ganglia and Cortex Implement Optimal Decision Making Between Alternative Actions

2007 ◽  
Vol 19 (2) ◽  
pp. 442-477 ◽  
Author(s):  
Rafal Bogacz ◽  
Kevin Gurney
Keyword(s):  
Decision Making ◽  
Basal Ganglia ◽  
Brain Regions ◽  
Optimal Decision ◽  
Detailed Knowledge ◽  
Ratio Test ◽  
Anatomy And Physiology ◽  
Neurophysiological Studies ◽  
Sequential Probability ◽  
Optimal Decision Making

Neurophysiological studies have identified a number of brain regions critically involved in solving the problem of action selection or decision making. In the case of highly practiced tasks, these regions include cortical areas hypothesized to integrate evidence supporting alternative actions and the basal ganglia, hypothesized to act as a central switch in gating behavioral requests. However, despite our relatively detailed knowledge of basal ganglia biology and its connectivity with the cortex and numerical simulation studies demonstrating selective function, no formal theoretical framework exists that supplies an algorithmic description of these circuits. This article shows how many aspects of the anatomy and physiology of the circuit involving the cortex and basal ganglia are exactly those required to implement the computation defined by an asymptotically optimal statistical test for decision making: the multihypothesis sequential probability ratio test (MSPRT). The resulting model of basal ganglia provides a new framework for understanding the computation in the basal ganglia during decision making in highly practiced tasks. The predictions of the theory concerning the properties of particular neuronal populations are validated in existing experimental data. Further, we show that this neurobiologically grounded implementation of MSPRT outperforms other candidates for neural decision making, that it is structurally and parametrically robust, and that it can accommodate cortical mechanisms for decision making in a way that complements those in basal ganglia.

scholarly journals The interplay between multisensory integration and perceptual decision making

2019 ◽  
Author(s):  
Manuel R. Mercier ◽  
Celine Cappe
Keyword(s):  
Decision Making ◽  
Multisensory Integration ◽  
Multisensory Processing ◽  
Optimal Decision ◽  
Perceptual Decision Making ◽  
Perceptual Decision ◽  
Human Participants ◽  
Sensory Encoding ◽  
Optimal Decision Making ◽  
The Brain

AbstractFacing perceptual uncertainty, the brain combines information from different senses to shape optimal decision making and to guide behavior. Despite overlapping neural networks underlying multisensory integration and perceptual decision making, the process chain of decision formation has been studied mostly in unimodal contexts and is thought to be supramodal. To reveal whether and how multisensory processing interplay with perceptual decision making, we devised a paradigm mimicking naturalistic situations where human participants were exposed to continuous cacophonous audiovisual inputs containing an unpredictable relevant signal cue in one or two modalities. Using multivariate pattern analysis on concurrently recorded EEG, we decoded the neural signatures of sensory encoding and decision formation stages. Generalization analyses across conditions and time revealed that multisensory signal cues were processed faster during both processing stages. We further established that acceleration of neural dynamics was directly linked to two distinct multisensory integration processes and associated with multisensory benefit. Our results, substantiated in both detection and categorization tasks, provide evidence that the brain integrates signals from different modalities at both the sensory encoding and the decision formation stages.

scholarly journals Distinct mechanisms mediate speed-accuracy adjustments in cortico-subthalamic networks

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Damian M Herz ◽  
Huiling Tan ◽  
John-Stuart Brittain ◽  
Petra Fischer ◽  
Binith Cheeran ◽  
...  
Keyword(s):  
Decision Making ◽  
Spectral Characteristics ◽  
Low Frequency ◽  
Optimal Decision ◽  
Beta Activity ◽  
Perceptual Decision Making ◽  
Beta Power ◽  
Precise Relationship ◽  
Optimal Decision Making ◽  
Speed Accuracy

Optimal decision-making requires balancing fast but error-prone and more accurate but slower decisions through adjustments of decision thresholds. Here, we demonstrate two distinct correlates of such speed-accuracy adjustments by recording subthalamic nucleus (STN) activity and electroencephalography in 11 Parkinson’s disease patients during a perceptual decision-making task; STN low-frequency oscillatory (LFO) activity (2–8 Hz), coupled to activity at prefrontal electrode Fz, and STN beta activity (13–30 Hz) coupled to electrodes C3/C4 close to motor cortex. These two correlates differed not only in their cortical topography and spectral characteristics but also in the relative timing of recruitment and in their precise relationship with decision thresholds. Increases of STN LFO power preceding the response predicted increased thresholds only after accuracy instructions, while cue-induced reductions of STN beta power decreased thresholds irrespective of instructions. These findings indicate that distinct neural mechanisms determine whether a decision will be made in haste or with caution.

Integration of Reinforcement Learning and Optimal Decision-Making Theories of the Basal Ganglia

2011 ◽  
Vol 23 (4) ◽  
pp. 817-851 ◽  
Author(s):  
Rafal Bogacz ◽  
Tobias Larsen
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Basal Ganglia ◽  
Reaction Times ◽  
Learning Theories ◽  
Optimal Decision ◽  
Neural Responses ◽  
Sensory Evidence ◽  
Optimal Decision Making ◽  
Reinforcement Learning Models

This article seeks to integrate two sets of theories describing action selection in the basal ganglia: reinforcement learning theories describing learning which actions to select to maximize reward and decision-making theories proposing that the basal ganglia selects actions on the basis of sensory evidence accumulated in the cortex. In particular, we present a model that integrates the actor-critic model of reinforcement learning and a model assuming that the cortico-basal-ganglia circuit implements a statistically optimal decision-making procedure. The values of corico-striatal weights required for optimal decision making in our model differ from those provided by standard reinforcement learning models. Nevertheless, we show that an actor-critic model converges to the weights required for optimal decision making when biologically realistic limits on synaptic weights are introduced. We also describe the model's predictions concerning reaction times and neural responses during learning, and we discuss directions required for further integration of reinforcement learning and optimal decision-making theories.

scholarly journals GEAR: On Optimal Decision Making with Auxiliary Data

Stat ◽  
2021 ◽  
Author(s):  
Hengrui Cai ◽  
Rui Song ◽  
Wenbin Lu
Keyword(s):  
Decision Making ◽  
Optimal Decision ◽  
Auxiliary Data ◽  
Optimal Decision Making

scholarly journals Flexible categorization in perceptual decision making

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Genís Prat-Ortega ◽  
Klaus Wimmer ◽  
Alex Roxin ◽  
Jaime de la Rocha
Keyword(s):  
Decision Making ◽  
Network Models ◽  
Drift Diffusion Model ◽  
Perceptual Decision Making ◽  
Drift Diffusion ◽  
Perceptual Decision ◽  
Attractor Dynamics ◽  
Attractor Model ◽  
Sensory Evidence ◽  
Winner Take All

AbstractPerceptual decisions rely on accumulating sensory evidence. This computation has been studied using either drift diffusion models or neurobiological network models exhibiting winner-take-all attractor dynamics. Although both models can account for a large amount of data, it remains unclear whether their dynamics are qualitatively equivalent. Here we show that in the attractor model, but not in the drift diffusion model, an increase in the stimulus fluctuations or the stimulus duration promotes transitions between decision states. The increase in the number of transitions leads to a crossover between weighting mostly early evidence (primacy) to weighting late evidence (recency), a prediction we validate with psychophysical data. Between these two limiting cases, we found a novel flexible categorization regime, in which fluctuations can reverse initially-incorrect categorizations. This reversal asymmetry results in a non-monotonic psychometric curve, a distinctive feature of the attractor model. Our findings point to correcting decision reversals as an important feature of perceptual decision making.

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