Towards a more complete understanding of decision-making: Constraining theories through modelling additional sources of data

Evidence accumulation models (EAMs) have been the dominant models of speeded decision-making over the last several decades, proposing that evidence accumulates for each alternative until some threshold level is reached for one alternative, triggering a decision. Although models with these general properties have provided a good description of response time distributions across a range of decision-making paradigms, providing broad insight into how the decision-making process may operate, little insight has been gained into the more specific properties of the decision-making process.

Here, Nathan Evans will discuss the role that additional data -- such as measurements from the decision process at different points in time, or data from other tasks/measures -- can play in furthering our understanding of decision-making by constraining models to simultaneously account for multiple sources of data.

Specifically, he will show how data from more than just the standard response time distributions can provide a more specific understanding of the decision-making process, both in terms of the dynamics of the process over time, and the relationship between decision-making components and other constructs in different fields. More generally, these principles are in no way restricted to decision-making research or response time models, and attempting to harness additional data through model-based approaches may prove useful in gaining more a specific understanding of a variety of cognitive processes.

Sandwich lunch available from 12.30pm, seminar starts at 1.00pm.