The Annual Convention of the Society for Mathematical Psychology ("MathPsych 2009") was held in Amsterdam on August 1-4, 2009. We stress the highlights for the purpose of our blog, Social Cognition.
On August 2, Joe Johnson (Miami University) illustrated, via a simple mathematical model, the ability for predicting decision behavior based solely on perceptual data. Johnson uses an evidence accumulation model with a ratio choice rule to predict athletes´intuitive, initial choice in a realistic game situation. Markus Raab (German Sport University in Cologne) investigates the preference for intuitive decisions in contrast to deliberative ones. He applies a mathematical choice model based in team handball attack situations. Bayesian models also are applied and so Tom Lodewyckx (University of Leuven) and others design a Bayesian state-space model for affectivity. They use Markov chain Monte Carlo methods to estimate the model parameters. This framework is used to high resolution psychophysiological and behavioral data obtained during adolescent-parent interactions expressing dynamical emotions.
Vanpaemel (Umiversity of Leuven) and Michael Lee (University of California, Irvine) advocate the advantages of hierarchical Bayesian modeling in providing one way of specifying theoretically-based priors for competing models of category learning. In a similar line, the group of Rich Shiffrin (Indiana University) spoke about information integration in perceptual decision making. According to the authors, researchers studying judgment and decision making have shown that people employ sub-optimal strategies when integrating information fron multiple sources. But another group of researchers has had success using Bayesian optimal models to explain information integration in fields such as perception, memory and categorization. Shiffrin and colaborators design a decision making experiment to test the range of this difference.
Amy Perfors and Daniel Navarro (University of Adelaide) consider the situation in which a reasoner must induce the rule that explains an observed sequence of data, but the hypothesis space of possible rules is not explicitly enumerated or identified. They present mathematical optimality results showing that as long the hypotheses tend to be sparse (that is, tend to be true only for a small proportion of entities in the world), then confirmation bias is a near-optimal strategy. The authors propose, in a very interesting manner, to chose queries that one knows will lead to an affirmative response for at least some hypotheses (hypotheses being considered). This positive-test strategy is closely related to the confirmation bias.
The Meeting finished on August 4 hoping to achieve the same successful objectives for coming events.
