The Kennedy lab is recruiting for multiple funded postdoctoral positions in theoretical and computational neuroscience, following our recent lab move to Scripps Research in San Diego, CA! Ongoing projects in the lab span topics in: reservoir computing with heterogeneous cell types, reinforcement learning/control theory analysis of complex behavior, neuromechanical whole-organism modeling, diffusion models for imitation learning/forecasting of mouse social interactions, joint analysis/modeling of effects of internal states on neural + vocalization + behavior data. With additional NIH and foundation funding for: characterizing progression of behavioral phenotypes in Parkinson’s, modeling cellular/circuit mechanisms underlying internal state-dependent changes in neural population dynamics, characterizing neural correlates of social relationships across species. Projects are flexible and can be tailored to applicants’ research and training goals, and there are abundant opportunities for new collaboration with local experimental groups. San Diego has a fantastic research community and very high quality of life. Our campus is located at the Pacific coast, at the northern edge of UCSD and not far from the Salk Institute. Postdoctoral stipends are well above NIH guidelines and include a relocation bonus, with research professorship positions available for qualified applicants.

Developing agents capable of modeling complex environments and human behaviors within them is a key goal of artificial intelligence research. Progress towards this goal has exciting potential for applications in video games, from new tools that empower game developers to realize new creative visions, to enabling new kinds of immersive player experiences. This talk focuses on recent advances of my team at Microsoft Research towards scalable machine learning architectures that effectively capture human gameplay data. In the first part of my talk, I will focus on diffusion models as generative models of human behavior. Previously shown to have impressive image generation capabilities, I present insights that unlock applications to imitation learning for sequential decision making. In the second part of my talk, I discuss a recent project taking ideas from language modeling to build a generative sequence model of an Xbox game.

Developing agents capable of modeling complex environments and human behaviors within them is a key goal of artificial intelligence research. Progress towards this goal has exciting potential for applications in video games, from new tools that empower game developers to realize new creative visions, to enabling new kinds of immersive player experiences. This talk focuses on recent advances of my team at Microsoft Research towards scalable machine learning architectures that effectively capture human gameplay data. In the first part of my talk, I will focus on diffusion models as generative models of human behavior. Previously shown to have impressive image generation capabilities, I present insights that unlock applications to imitation learning for sequential decision making. In the second part of my talk, I discuss a recent project taking ideas from language modeling to build a generative sequence model of an Xbox game.

Decision-making in noisy environments with constant sensory evidence involves integrating sequentially-sampled evidence, a strategy formalized by diffusion models which is supported by decades behavioral and neural findings. By contrast, it is unknown whether this strategy is also used during decision-making when the underlying sensory evidence is expected to change. Here, we trained monkeys to identify the dominant color of a dynamically refreshed checkerboard pattern that doesn't become informative until after a variable delay. Animals' behavioral responses were briefly suppressed after an abrupt change in evidence, and many neurons in the frontal eye field displayed a corresponding dip in activity at this time, similar to the dip frequently observed after stimulus onset. Generalized drift-diffusion models revealed that behavior and neural activity were consistent with a brief suppression of motor output without a change in evidence accumulation itself, in contrast to the popular belief that evidence accumulation is paused or reset. These results suggest that a brief interruption in motor preparation is an important strategy for dealing with changing evidence during perceptual decision making.

The assumed data generating models (response distributions) of experimental or observational data in cognitive science have become increasingly complex over the past decades. This trend follows a revolution in model estimation methods and a drastic increase in computing power available to researchers. Today, higher-level cognitive functions can well be captured by and understood through computational cognitive models, a common example being drift diffusion models for decision processes. Such models are often expressed as the combination of two modeling layers. The first layer is the response distribution with corresponding distributional parameters tailored to the cognitive process under investigation. The second layer are latent models of the distributional parameters that capture how those parameters vary as a function of design, stimulus, or person characteristics, often in an additive manner. Such cognitive models can thus be understood as special cases of distributional regression models where multiple distributional parameters, rather than just a single centrality parameter, are predicted by additive models. Because of their complexity, distributional models are quite complicated to estimate, but recent advances in Bayesian estimation methods and corresponding software make them increasingly more feasible. In this talk, I will speak about the specification, estimation, and post-processing of Bayesian distributional regression models and how they can help to better understand cognitive processes.