Deciphering the Dynamics of the Unconscious Brain under General Anesthesia 
The neural computations underlying real-world social interaction perception Abstract: The measure of a machine learning algorithm is the difficulty of the tasks it can perform, and sufficiently difficult tasks are critical drivers of strong machine learning models. However, quantifying the generalization difficulty of machine learning benchmarks has remained challenging. We propose what is to our knowledge the first model agnostic measure of the inherent generalization difficulty of tasks. Our inductive bias complexity measure quantifies the total information required to generalize well on a task minus the information provided by the data. It does so by measuring the fractional volume occupied by hypotheses that generalize on a task given that they fit the training data. It scales exponentially with the intrinsic dimensionality of the space over which the model must generalize but only polynomially in resolution per dimension, showing that tasks which require generalizing over many dimensions are drastically more difficult than tasks involving more detail in fewer dimensions. Our measure can be applied to compute and compare supervised learning, reinforcement learning and meta-learning generalization difficulties against each other. We show that applied empirically, it formally quantifies intuitively expected trends, e.g. that in terms of required inductive bias, MNIST < CIFAR10 < Imagenet and fully observable Markov decision processes (MDPs) < partially observable MDPs. Further, we show that classification of complex images < few-shot meta-learning with simple images. Our measure provides a quantitative metric to guide the construction of more complex tasks requiring greater inductive bias, and thereby encourages the development of more sophisticated architectures and learning algorithms with more powerful generalization capabilities.
A Zoom link will be provided if requested no later than 24 hours before the event. Please email Kris Brewer (brew@mit.edu) with this request.
Organizer: Hector Penagos Organizer Email: cbmm-contact@mit.edu
Hippocampal memory, cognition, and the role of sleep - part 2 
Hippocampal memory, cognition, and the role of sleep - part 1 Leyla Isik is the Clare Boothe Luce Assistant Professor in the Department of Cognitive Science at Johns Hopkins University. Her research aims to answer the question of how humans extract complex information using a combination of human neuroimaging, intracranial recordings, machine learning, and behavioral techniques. Before joining Johns Hopkins, Isik was a postdoctoral researcher at MIT and Harvard in the Center for Brains, Minds, and Machines working with Nancy Kanwisher and Gabriel Kreiman. Isik completed her PhD at MIT where she was advised by Tomaso Poggio.
Abstract: Humans perceive the world in rich social detail. We effortlessly recognize not only objects and people in our environment, but also social interactions between people. The ability to perceive and understand social interactions is critical for functioning in our social world. We recently identified a brain region that selectively represents others’ social interactions in the posterior superior temporal sulcus (pSTS) across two diverse sets of controlled, animated videos. However, it is unclear how social interactions are processed in the real world where they co-vary with many other sensory and social features. In the first part of my talk, I will discuss new work using naturalistic fMRI movie paradigms and novel machine learning analyses to understand how humans process social interactions in real-world settings. We find that social interactions guide behavioral judgements and are selectively processed in the pSTS, even after controlling for the effects of other perceptual and social information, including faces, voices, and theory of mind. In the second part of my talk, I will discuss the computational implications of social interaction selectivity and present a novel graph neural network model, SocialGNN, that instantiates these insights. SocialGNN reproduces human social interaction judgements in both controlled and natural videos using only visual information, but requires relational, graph structure and processing to do so. Together, this work suggests that social interaction recognition is a core human ability that relies on specialized, structured visual representations.
Organizer: Hector Penagos Organizer Email: cbmm-contact@mit.edu
