Dynamic Decision Making in Complex Task Environments: Principles and Neural Mechanisms Annual Workshop Introduction August, 2008

FY07 MURI BAA Topic 15 Building Bridges between Neuroscience, Cognition, and Human Decision Making Objective: The general goal is to form a complete and thorough understanding of basic human decision processes … by building a lattice of theoretical models with bridges that span across fields …. The main effort of this work is intended to be in the direction of new integrative theoretical developments … using mathematical and/or computation modeling … accompanied and supported by rigorous empirical models tests and empirical model comparisons ….. From BAA , Topic 15 Slide from Zhang 2007

Our MURI Grant Builds on past neurophysiological and theoretical investigations of the dynamics of decision making in humans and non-human primates. Extends the empirical effort by employing fMRI, EEG, and MEG convergently to understand the distributed brain systems involved in decision making. Extends both the theory and experimental investigations to successively more complex decision making environments as the project continues. Bridges to investigations concerned with decision making processes in real-life situations (e.g. those faced by air-traffic controllers and pilots).

Aims of the Grant Aim 1: Investigate dynamics of decision making in classical tasks via –Theory and Modeling –Primate Neurophysiology –Human Cognitive Neuroscience Fundamental tenets of the research: –Decision making occurs through a real-time dynamic process that depends upon neural activity distributed across a wide range of participating brain areas, each shaping the decision making process in its own way. –An effort to understand decision making as an optimization problem is useful because They allow us to understand how closely behavioral and neural processes can approximate optimality They allow us to understand how simple neural mechanisms can lead to optimal performance.

Aim 2: Extending the theory to decision making in continuous time and space Detection of targets in noisy backgrounds when time of onset and possible location of targets is uncertain. –Optimality analysis, role of leaky integration, threshold tuning, and adjustment of integration rate in achieving or approximating optimality. Locating targets in a continuous space. –How is optimization achieved and regulated in response to different demands for speed and precision? –How does the neural representation of a continuous value (e.g. location in space) evolve over time during processing?

Aim 3: Extensions to Real-World Situations Distraction, vigilance, and divided attention. –Extension of neurocognitive models to address such phenomena. –Examination of the neural basis of the Central Bottleneck: Competition among neural populations representing stimuli/responses associated with different tasks?

Goals for this workshop Review progress on Aim 1 –Primate behavior and neurophysiology Experiment Optimality analysis Relationship between neural activity and behavior –Human experiments and model tests –Further cognitive neuroscience investigations Brainstorm on wrapping up Aim 1, and moving forward to Aims 2 and 3.

Wald (1947) “Sequential Probability Ratio Test (SPRT)” “Sequential Methods” in Statistics Armitage (1950): N(N-1)/2 pair-wise likelihood ratio processes Baum and Veeravalli (1994): Bayesian analysis on posterior probability of N hypotheses; Dragalin et al, (1999, 2000): asymptotic optimality of MSPRT Multiple hypotheses setting Page (1954): CUSUM procedure Shiryayev (1963): Bayesian scheme with geometric prior Roberts (1966): modifying Shiryayev to non-Bayesian version Change-point detection setting Slide from Zhang 2007

The Drift Diffusion Model Continuous version of the SPRT At each time step a small random step is taken. Mean direction of steps is +m for one direction, –m for the other. When criterion is reached, respond. Alternatively, in ‘time controlled’ tasks, respond when signal is given.

Two Problems with the DDM Accuracy should gradually improve toward ceiling levels, even for very hard discriminations, but this is not what is observed in human data. The model predicts correct and incorrect RT’s will have the same distribution, but incorrect RT’s are generally slower than correct RT’s. Hard -> Easy RT Errors Correct Responses Prob. Correct Hard Easy

Usher and McClelland (2001) Leaky Competing Accumulator Model Addresses the process of deciding between two alternatives based on external input ( 1 +  2 = 1) with leakage, self-excitation, mutual inhibition, and noise: dy 1 /dt =  1 -(y 1 )+f(y 1 )–f(y 2 )+ 1 dy 2 /dt =  2 -(y 2 )+f(y 2 )–f(y 1 )+ 2

Wong & Wang (2006) ~Usher & McClelland (2001)

Contributions from Princeton Holmes et al: –Mathematical analysis of dynamical models of decision making. –Investigations of optimality and deviations from optimality. –Relations between models and levels of description Cohen et al: –Neural basis of executive function and cognitive control. –Functional brain imaging and neurally grounded models in many areas of cognitive neuroscience.

Comparative Model Analysis (Bogacz et al, 2006)

Physiology of Decision and Value Neural basis of decision making based on uncertain sensory information, recording from individual neurons in primates. How do neurons represent (and update our representation of) the value of a choice alternative?

Other Participants Urban lab: –Biophysical processes that allow neurons to oscillate and synchronize their activity –Roles of oscillation and synchrony in information processing in neural circuits –Urban-McClelland collaboration: Use of MEG to investigate functional synchronization of neural populations across brain areas. –Will extend to decision making in concert with ongoing EEG investigations. Johnston / Lachter: –Processing limitations affecting throughput, accuracy, and timely responding in human operators. –Attentional limitations and the central bottleneck revealed in dual task situations. –MURI work: investigating decision dynamics using continuous response measures.