A Computational Model of the Motivation-learning Interface

A Computational Model of the Motivation-learning Interface Manish Saggar (mishu@cs.utexas.edu) Department of Computer Science, University of Texas at Austin, Austin, TX 78705 USA Arthur B Markman (markman@psy.utexas.edu) W Todd Maddox (maddox@psy.utexas.edu) Department of Psychology, University of Texas at Austin, Austin, TX 78705 USA Risto Miikkulainen (risto@cs.utexas.edu) Department of Computer Science, University of Texas at Austin, Austin, TX 78705 USA computational models (Markman, Maddox and Baldwin, This paper presents a computational model for perceptual classification that incorporates motivation. The model is based on simple perceptron learning (Rosenblatt, 1958) with modifications that instantiate the influence of motivation. The paper first describes theories of motivation, and then presents the details of the computational model. Followed by presentation of results from model simulations and evaluation of how accurately the model fits to human data. Next, a new phenomenon predicted by the model has been demonstrated and tested against the human data, followed by analyses of model parameters. Finally, future directions for research are outlined. Abstract This paper presents a computational model of how motivation influences learning, elaborating on the empirical study of Markman, Baldwin and Maddox (2005). In a decision criterion learning task with unequal payoffs, the subjects were more likely to maximize the reward when their motivation was in line with the reward structure (i.e. when they were in a regulatory fit), whereas they were more likely to maximize accuracy when their motivation did not match the reward structure (i.e. when they were in a regulatory mismatch). The model accurately replicates this pattern of results, and also accounts for the individual subject's behavior. In addition, the model makes the novel prediction that regulatory-fit subjects who are near the reward threshold will shift their strategy toward maximizing accuracy, whereas regulatory-mismatch subjects who are far from the reward threshold will shift their strategy toward maximizing reward. When the original data was reanalyzed, this model-driven prediction was confirmed. These results constitute a first computational step towards understanding how motivation influences learning and cognition. Background This section reviews Regulatory Focus Theory (Higgins, 2000), and a framework developed for investigating the influence of motivation on behavior (Maddox, et al. 2005). Keywords: Computational modeling, Motivation, Learning, Regulatory focus. Regulatory Focus Theory Introduction Many of the tasks in our day-to-day life require us to choose one option over another. Knowingly or unknowingly we make decisions at every step and choose our behaviors from a large repertoire of possibilities. Cognition plays an important role in selection of these behaviors, but our motivational state to approach positive outcomes or avoid negative outcomes also affects which behaviors we select (Maddox, Markman and Baldwin, 2005). Cognitive research has focused on information processing and its effects on learning and behavior with little attention paid to motivation. Recent research in social cognition and cognitive science has begun to bridge this gap (see Maddox, Markman and Baldwin, 2005, for a review). Although there have been advances in the interface between motivation and learning, this work has been driven mostly by experimental techniques that explore the operation of motivational systems indirectly, rather than by The motivation literature distinguishes between approach and avoidance goals (Carver & Scheier, 1998; Lewin, 1935; Markman & Brendl, 2000). Goals with positive states that one wishes to achieve are called approach goals, while goals with negative states that one wishes to avoid are called avoidance goals. Higgins (1987, 1997) extended this idea by proposing regulatory focus theory, which suggests that - orthogonal to approach and avoidance goals - there are psychological states of readiness or sensitivity for potential gains/non-gains or losses/non-losses that tune the sensitivity of the motivational system. On this theory, a promotion focus is a state that focuses the motivational system on the presence or absence of gains in the environment, and a prevention focus is a state that focuses the motivational system on the presence or absence of losses. Higgins and colleagues (Higgins, 1987, 1997; Higgins, et al. 1994) outlined three aspects of regulatory focus. First, chronic focus, is an a priori predisposition toward a promotion or prevention focus. Higgins (1987) suggested that a promotion focus is associated with a person’s desire