Spring 2014March 31: 4:00 in PY 101, Title TBA, Dennis Proffitt, Dept. of Psychology, University of Virginia
April 7: 4:00 in PY 101, Title TBA, Thomas Palmeri, Psychological Sciences, Vanderbilt University
April 21: 4:00 in PY 101, Title TBA, Arthur Glenberg, Dept. of Psychology, Arizona State University
Fall 2013September 9: 4:00 in PY 101
Being social as rooted in practical situations of interaction
Communication and Science Studies
University of California, San Diego
This talk will report on a long-term and ongoing observational study of activities around a research project in social robotics. The roboticists whose work I observe design educational robots geared toward children between 18 and 24 months of age. To do so, they work on the computational architecture, while continually updating the robot's current version by immersing it in a preschool setting. In this talk I will neither inform on the robot's computational architecture nor provide suggestions for the future improvements of the robot's design. Instead, my interest is in social agency and how it is instantiated at the preschool. That social robots provide an opportunity to specify what it means to be an agent in design terms is one of their major appeals. I will show videotaped excerpts from everyday interactions at the preschool to suggest a somewhat different take. Grounded in local, embodied practices that those who engage with the robot and each other recognize and understand, I will discuss the robot's agency as a local, multiparty achievement that is sensitive to situational particularities and interactional contingencies. (Background Paper)
October 21: 4:00 in PY 101
Self-organization in human crowds: From individual to collective behavior
Dept. of Cognitive, Linguistic and Psychological Sciences
The collective behavior of human crowds is thought to emerge from local interactions between pedestrians. There are many agent-based models of such "flocking" behavior in animals and humans, but precious little experimental data. I will describe an empirically-grounded model of pedestrian and crowd dynamics, following what Sumpter, et al. (2012) call the modeling cycle. First, taking a local-to-global approach, we use experiments in VR to create a model of individual locomotor behavior and neighbor interactions. This pedestrian model is then implemented in multi-agent simulations to test whether it can generate global patterns of crowd behavior. Conversely, taking a global-to-local approach, we collect motion-capture data on human crowds (N=20) to test the simulations. Patterns of crowd behavior are analyzed to estimate the local coupling between neighbors and refine the model. I will report simulations of several crowd scenarios, including (a) Grand Central, in which individuals criss-cross the room, (b) swarm, in which participants walk about while staying together as a group, and (c) counterflow, in which two groups pass through each other. Each global pattern can be simulated with a few model components. Analysis of the swarm data suggests a strong but fairly local coupling (1-2 m). The results support the view that crowd dynamics emerge from a few simple pedestrian interactions, and may be accounted for by principles of self-organization. (Background Paper 1, Background Paper 2)
December 2: 4:00 in PY 101
Statistical learning in the mind and brain
Dept. of Psychology
The environment is highly stable over time. Not only do we repeatedly encounter the same people, places, and objects, but they also tend to appear in regular sequences and configurations. These regularities are extracted rapidly and without effort or awareness, resulting in higher-order knowledge of words, events, and scenes. With behavioral studies, I will suggest that this kind of statistical learning has widespread consequences for the mind, including showing that it commands attention to locations and features with structure. With neuroimaging and patient work, I will then suggest that the medial temporal lobe memory system, and hippocampus in particular, implements this kind of statistical learning in the brain, including showing that this region learns by shaping the representational space for objects to mirror the structure of the environment. These studies reveal the ubiquity and power of statistical learning, and they highlight the inherently reciprocal connections between perception, attention, and memory. (Background Paper)
Spring 2013March 18: Amy Needham, Department of Psychological Sciences, Vanderbilt University, "Perceptual-Motor Learning in Infancy"
April 15: , Melanie Mitchell, Computer Science Department, Portland State University, "Using Analogy to Discover the Meaning of Images"
Fall 2012September 24: Scott Makeig, Swartz Center for Computational Neuroscience, University of California, San Diego, "Mining Event-Related Brain Dynamics"
October 22: Antonio Rangel, Economics and Neuroscience, Caltech, "The Neuroeconomics of Simple Choice"
Spring 2012February 6: Brian Scassellati, Department of Computer Science, Yale University, "Using Human-Robot Interactions to Study Human-Human Social Behavior"
March 19: John Spencer, Department of Psychology, University of Iowa, "The Integration Challenge in Cognitive Science and the Promise of Embodied Neural Dynamics"
Fall 2011October 24: Jeffrey Krichmar, Dept. of Cognitive Sciences and Dept. of Computer Science, University of California at Irvine, "Neuromodulation as a Brain-Inspired Strategy for Controlling Autonomous Robots and a Means to Investigate Social Cognition during Human-Robot Interactions"
November 28: Anthony Chemero, Psychology, Franklin & Marshall College, "Interaction-Dominant Dynamics, Phenomenology, and Extended Cognition"
Spring 2011January 24: Hod Lipson, Mechanical and Aerospace Engineering, Cornell University, "Self-reflective machines"
March 21: Lawrence Barsalou, Department of Psychology, Emory University, "Grounding Knowledge in the Brain's Modal Systems"
April 4: Susan Goldin-Meadow, Department of Psychology, University of Chicago, "How Our Hands Help Us Think"
Fall 2010September 13: Michael Richardson, Center for Cognition, Action and Perception, Psychology Department, University of Cincinnati, "Affording structured coordination: An ecological approach to self-organized social action"
October 18: Ennio Mingolla, Department of Cognitive and Neural Systems, Department of Psychology, Boston University, "Neural models of visually-guided steering, obstacle avoidance, and route selection"
Spring 2010February 22: Mary Hayhoe, Dept. of Psychology, The University of Texas at Austin, "Adaptive Control of Attention and Gaze in the Natural World".
February 24: Dana Ballard, Dept. of Computer Sciences, The University of Texas at Austin, "Modular Reinforcement Learning as a Model of Embodied Cognition".
April 5: Asif Ghazanfar, Dept. of Psychology, Princeton University, "Vocal Communication Through Coupled Oscillations: Substrates for the Evolution of Speech".