Sequential Sampling Models Representing a Unifying Framework of Human Decision Making

Sequential sampling models have been applied for describing the cognitive processes underlying various psychological processes such as memory, perception, or value-based decision making. They share the common assuming that people accumulate information stochastically over time and once the accumulated information passes a decision boundary a response is made. Depending on the cognitive domain the models differ in the specific nature of the accumulation process. In particular, they differ in their assumption about what type of information is processed, how the information is represented, and how the boundary is defined. In this symposium we will illustrate how sequential sampling models successfully explain human behavior for various cognitive domains. For the decision making domain we show how people construct their preferences by accumulating options’ attributes values over time once a decision threshold is reached. For the perceptual domain we show how gradual accumulation of sensory evidence over time explains people’s perceptual decisions. Furthermore, we illustrate that sequential sampling models do not only predict the final outcome of a cognitive process but also present a description of the cognitive process itself. As such the symposium will illustrate how the analyses of response time distributions provide evidence for the dynamic accumulation process. Furthermore, the neurological basis of the dynamic accumulation process has recently also been explored. In sum, the symposium will illustrate the strength of sequential sampling model as a unifying framework for explain human cognition and behavior across many domains. Accumulation of Information with Attention Shifting Across Attributes Adele Diederich* & Jerome Busemeyer*