Food, taste, and the role of the cortical laminar in action and imagination
Exploring the neural landscape of imagination and abstract spaces
External cues imbued with significance can enhance the motivational state of an organism, trigger related memories and influence future planning and goal directed behavior. At the same time, internal thought and imaginings can moderate and counteract the impact of external motivational cues. The neural underpinnings of imagination have been largely opaque, due to the inherent inaccessibility of mental actions. The talk will describe studies utilizing imagination and tracking how its neural correlates bidirectionally interact with external motivational cues. Stimulus-response associative learning is only one form of memory organization. A more comprehensive and efficient organizational principal is the cognitive map. In the last part of the talk we will examine this concept in the case of abstract memories and social space. Social encounters provide opportunities to become intimate or estranged from others and to gain or lose power over them. The locations of others on the axes of power and affiliation can serve as reference points for our own position in the social space. Research is beginning to uncover the spatial-like neural representation of these social coordinates. We will discuss recent and growing evidence on utilizing the principals of the cognitive map across multiple domains, providing a systematic way of organizing memories to navigate life.
Mental Simulation, Imagination, and Model-Based Deep RL
Mental simulation—the capacity to imagine what will or what could be—is a salient feature of human cognition, playing a key role in a wide range of cognitive abilities. In artificial intelligence, the last few years have seen the development of methods which are analogous to mental models and mental simulation. In this talk, I will discuss recent methods in deep learning for constructing such models from data and learning to use them via reinforcement learning, and compare such approaches to human mental simulation. While a number of challenges remain in matching the capacity of human mental simulation, I will highlight some recent progress on developing more compositional and efficient model-based algorithms through the use of graph neural networks and tree search.