Decision Dynamics and Decision States in the Leaky Competing Accumulator Model

Keynote Address
James L. (Jay) McClelland 
Lucie Stern Professor & Chair 
Dept. of Psychology, Stanford University
Classical theory starting with Signal Detection Theory and the Sequential Probability Ratio Test has provided a foundation for
considering the optimal policy for decision making as observers accumulate uncertain information. Certain features of human data run counter to the predictions of the classical models in their purest forms and have motivated exploration of mechanistic computational formulations that can be related both to issues of optimality and to principles of information processing dynamics.

I will describe ongoing collaborative work on these issues. The focus will be on the leaky competing accumulator model of the accumulation of uncertain stimulus information under externally-imposed limits in the time allowed to reach a decision. I will present relevant behavioral data as well as mathematical analysis of the model and computer simulations. The balanced role of analytic approximation and computer simulation in this work will be emphasized.

We are exploring a version of the model in which mutual inhibition between accumulators corresponding to choice alternatives outweighs the leakage or decay of activation. This version of the model provides a way of understanding an extensive body of recent data, and introduces a hybrid type of decision state, which has features of continuous as well as discrete decision state representations.