News & Events
Jan244:00pmMetcalf Research Building
Michael S. Goodman ’74 Memorial Colloquium Series. Speaker: Peter Kvam, The Ohio State University. Title: Connecting decision data through cognitive models. Abstract: Cognitive models and the theories they embody allow us to connect behavioral, self-report, and even neural data to sets of underlying cognitive processes. Although these models are frequently applied to a single task or type of observation, data can be also connected across tasks and measures by tying model parameters to a common set of latent traits or processes. To do so, I examine a model structure where performance on two different decision measures is linked using a “joint” model that accounts for individual differences and group-level behavior across multiple sources of data. This joint modeling approach is applied to two clinically diagnostic behavioral measures, the delay discounting and Cambridge gambling tasks, in order to determine whether they measure the same propensities for impulsive decision making. Using a model comparison method based on Bayes factors that allows us to obtain evidence for the “null” hypothesis, I show that the two tasks measure unique dimensions of impulsivity that are related to substance use and addiction. I conclude by examining additional applications of the joint modeling approach as well as other future research directions.
Jan253:00pm - 5:00pmFriedman Hall
The intertwined roles of 3D vision and sensorimotor adaptation in reach-to-grasp movements
Jan304:00pmMetcalf Research Building
Michael S. Goodman ’74 Memorial Colloquium Series. Speaker: Elizabeth Gunderson, Temple University. Title: The development of mathematical cognition and motivation. Abstract: Mathematical skills are critical for academic and career success, and it is therefore vital to understand how cognitive and motivational processes work together to influence children’s developmental trajectories in math. In this talk, I will examine cognitive and motivational processes that lead to individual differences in mathematical development, using a combination of longitudinal and experimental methods in preschool and early elementary school students. Focusing first on cognitive processes, I ask how and why spatial skills relate to numerical development. I provide evidence that number line estimation is a key mediator of this relation, and in a 2-year longitudinal study, show that mental transformation skill is a specific predictor of growth in number line estimation skill, over and above other spatial skills. Further, in the challenging domain of fraction learning, I use experimental training studies to show that number line estimation is not only correlated with children’s fraction magnitude knowledge, but causes improvements in fraction magnitude knowledge even on an untrained transfer task. Focusing on motivational processes, I show that young children already perceive math as involving more fixed ability than verbal skills, making math a special challenge in terms of academic motivation. Finally, in a 6-month longitudinal study, I provide evidence that math achievement, math anxiety, and growth mindsets are reciprocally related over time, demonstrating early-developing relations between mathematical cognition and motivation. Together, these results elucidate specific processes that lead to individual differences in early mathematical development, and highlight the importance of improving both cognitive skills and motivation for setting children onto a positive trajectory in math.
Feb44:00pmMetcalf Research Building
Michael S. Goodman ’74 Memorial Seminar Series. Speaker: Derek Powell, Stanford University. Title: Modeling intuitive theories and belief revision. Abstract: Intuitive theories, or mental representations of the causal and logical structure of the world, are essential to humans’ ability to explain the past, predict the future, and decide how to act in the present. Yet these theories are often imperfect, and identifying where they contain gaps or misconceptions is essential for developing educational messages that fill in those gaps and convince people to revise their beliefs. Here, I illustrate these issues using vaccine skepticism as a case study: First, I show how developing even a basic understanding of people’s intuitive theories can support effective pro-vaccine education. Then, I introduce theoretical and methodological tools for modeling intuitive theories and belief revision in more sophisticated ways. My colleagues and I empirically uncovered the structure of people’s intuitive theories about vaccination using Bayesian network structure learning algorithms. We collected ratings on 14 beliefs from a large sample of participants (N = 1130) and used these data to uncover a model of the intuitive theories surrounding participants’ vaccination decisions. The resulting model captures the relationships among participants’ beliefs in this domain and how those beliefs shifted in response to evidence. I consider how this approach can be used to develop more effective educational interventions, and to understand intuitive theories across domains, groups, and development. Finally, I discuss how this approach can help adjudicate between competing psychological theories of belief revision.
Feb64:00pmMetcalf Research Building
CLPS - Michael S. Goodman ’74 Memorial Colloquium Series. Speaker: Daphna Buchsbaum, University of Toronto. Title: Why did you do that? Integrating social learning and causal inference. Abstract: Imagine watching as a fellow traveler approaches a ticket machine and then rubs a dollar bill carefully on the machine’s side before inserting it and retrieving a ticket. When it’s your turn, what do you do? Inserting a dollar seems important, but perhaps you would rub it first too – after all, the other traveler must have had a reason to do so. Our causal learning takes place in a rich social context, where other people’s goal-directed actions lead to many of the causal outcomes we observe. In this talk, I will look at how social interaction informs and influences our judgments about the causal nature of the world. I will first present studies exploring how sensitivity to social cues and to physical causal knowledge influence which actions learners of different ages (and species) imitate, and suggest that differences in imitation behaviour can at least in part be explained by broad differences in understanding of other’s knowledge and intentions—especially the intention to teach. I will then present work examining how children and adults reconcile direct observations of probabilistic data with predictions made by social informants, including how they reconcile conflicting opinions amongst multiple informants. Finally, I will briefly discuss ongoing comparative work exploring cognitive prerequisites to social and causal inference, such as intuitive physics and statistical reasoning. Throughout this work, I use computational probabilistic models to evaluate what learners with differing social assumptions should infer from the evidence they receive.
Apr33:00pm - 5:00pmFriedman Hall
Title TBA, Location 190 Thayer Street, Friedman Auditorium