Structure Learning in Human Sequential Decision-Making

Studies of sequential decision-making in humans frequently find suboptimal performance relative to an ideal actor that has perfect knowledge of the model of how rewards and events are generated in the environment. Rather than being suboptimal, we argue that the learning problem humans face is more complex, in that it also involves learning the structure of reward generation in the environment. We formulate the problem of structure learning in sequential decision tasks using Bayesian reinforcement learning, and show that learning the generative model for rewards qualitatively changes the behavior of an optimal learning agent. To test whether people exhibit structure learning, we performed experiments involving a mixture of one-armed and two-armed bandit reward models, where structure learning produces many of the qualitative behaviors deemed suboptimal in previous studies. Our results demonstrate humans can perform structure learning in a near-optimal manner

“Shake It Baby, Shake It”: Media Preferences, Sexual Attitudes and Gender Stereotypes Among Adolescents

In this study exposure to and preferences for three important youth media (TV, music styles/music TV, internet) were examined in relation to adolescents’ permissive sexual attitudes and gender stereotypes (i.e., views of men as sex-driven and tough, and of women as sex objects). Multivariate structural analysis of data from a school-based sample of 480 13 to 16-year-old Dutch students revealed that preferences, rather than exposure were associated with attitudes and stereotypes. For both girls and boys, preferences for hip-hop and hard-house music were associated positively with gender stereotypes and preference for classical music was negatively associated with gender stereotypes. Particularly for boys, using internet to find explicit sexual content emerged as a powerful indicator of all attitudes and stereotypes