May 9, 2014 - 4:00 pm Venue:  MIT: McGovern Institute Reading Room, 46-5165 Address:  43 Vassar Street, MIT Bldg 46 , Cambridge, MA 02139 United States Speaker/s:  Ben Deen

Collaborator: Nancy Kanwisher, Rebecca Saxe; CBMM Thrust 4 – Social Intelligence

Progress of CBMM Challenge – Social Intelligence

Abstract:

The central aim of Thrust 4 is to understand nonverbal social perception, or the ability to make high-level social inferences from perceptual information, in order to allow the development of models able to answer CBMM challenge questions about people and their social interactions from images and videos. The study of social perception is in its relative infancy: we have a catalog of intriguing behavioral findings on what sorts of inferences can be made, but don’t have a sense of the full scope of these abilities, nor any understanding of their computational basis or neural implementation.

Here, we propose to investigate the functional organization of the neural machinery for social perception as a first step toward understanding the computational and neural architecture of these abilities.  Specifically, we target the superior temporal sulcus, which has been argued to play a role in a large number of social perceptual and cognitive processes: the perception of faces, biological motion, and voices, as well as the ability to understand language and mental state content.

In experiment 1, we use fMRI to measure STS responses during a number of different social perceptual tasks, and compare responses within individual participants.  We find that the STS has a strong spatial organization, with distinct processes eliciting distinct patterns of activity.  Furthermore, we find both evidence for selective STS subregions, which respond strongly to one task and not others, and multifunctional regions in which responses to multiple tasks overlap.

In experiment 2 we further investigate a particularly strong case of overlap from experiment 1 – overlap between responses to dynamic faces and vocal sounds. […]

Apr 25, 2014 - 4:00 pm Venue:  MIT: McGovern Institute Seminar Room, 46-3189 Address:  43 Vassar Street, MIT Bldg 46, Cambridge, MA 02139 United States Speaker/s:  Robert Desimone; Thrust 2 – Circuits for Intelligence and Thrust 5 – Theories of Intelligence

Progress on the CBMM challenge questions: What/Who is there?

Abstract:

We continue the series of weekly discussions and reports on each CBMM challenge question describing progress and problems of ongoing work at CBMM.

Thrust 2 is focused on the Neural Circuits and on how models for the CBMM challenge should be consistent with the brain neural data. One of the projects in the thrust focuses on the neural correlates of visual attention, which plays a key role in the answer to most of the CBMM open set of questions, probably in the form of task-dependent visual routines. I will describe very recent results about the neural correlates of so-called object attention. The voluntary control of visual attention to behaviorally relevant stimuli is thought to involve “top-down” feedback to visual processing areas. For spatially-directed attention, one key source of top-down attention is the frontal eye fields (FEF). My lab found that feedback to visual cortex from FEF causes enhanced responses to stimuli at attended locations, and leads to synchronized neural activity in the gamma frequency range between FEF and visual processing areas. Recent evidence suggests that the pulvinar may also serve as an important relay of attentional feedback to visual cortex, and it may also serve to desynchronize cortical activity in the alpha frequency range. Many aspects of the neural response changes with attention are explained by top-down inputs to a simple cortical circuit containing excitatory and inhibitory neurons. The neural basis of feature, or object attention has been much more difficult to understand.

One possibility is that attention to objects with particular features causes spatially directed attention to be directed to those objects, utilizing known pathways for spatial attention. Another possibility is that attention to objects or features such as faces, colors, or shapes, depends on feedback to visual cells that are selective for those features, biasing activity in favor of those stimuli. Such a mechanism would be similar to what is thought to mediate visual recall memory. We have recently found evidence for both types of mechanisms in prefrontal cortex of humans and monkeys.

Apr 18, 2014 - 4:00 pm Venue:  Harvard University: Northwest Bldg, Room 243 Address:  52 Oxford Street, Harvard University Northwest Building, Cambridge, 02138 Speaker/s:  Winrich Freiwald and Joel Z. Leibo; CBMM Thrust 5 – Theories for Intelligence

Title: On the neural mechanisms of face recognition: from experiments to theory

Abstract:

Object recognition, the ability to identify an object despite vast changes in appearance due to changes in lighting or orientation, is a major accomplishment of the primate brain, as a result of which we can recognize objects with an ease belying the daunting complexity of the computational challenges involved. Understanding not only the algorithms used to answer the questions What is there? Who is there?, but also the underlying neural mechanisms  is a major problem for both the experimental and the theoretical neurosciences – and one of the main items in the CBMM challenge. To decipher the mechanisms of object recognition, we have taken advantage of a unique model system evolution has provided us with. The temporal lobes of macaque monkeys, like those of humans, contain neural machinery to support face recognition. The machinery consists of a fixed number of discrete patches of face-selective cortex that can be localized with functional magnetic brain resonance imaging. The three main organizing features of this system, concentration of cells encoding the same complex object category into modules, spatial separation of modules with different functional roles for face processing, and integration of modules into an interconnected network, make it possible to break down the process of face recognition into its components. In this talk, we will present the experimental data we have obtained over the last years that characterize the major properties of this system. In particular, we will discuss results showing that a mirror symmetric face representation is computed as an intermediate step between view-tuned and view-tolerant representations. In the second part of the talk we will describe how this finding was influential in the early conception and development of M-theory, currently one of the research directions in the CBMM. We will describe and discuss a computational model of the face-processing system that is derived from M-theory and predicts and explains the core experimental finding.  This interdisciplinary project on the neural mechanisms for the processing of a stimulus set of utmost relevance for social cognition, bridges activities between three Thrusts of the CBMM.

Apr 11, 2014 - 4:00 pm Venue:  MIT: McGovern Institute Reading Room, 46-5165 Address:  43 Vassar Street, MIT Bldg 46 , Cambridge, MA 02139 United States Speaker/s:  Dr. Michael Buice, Allen Institute for Brain Science, CBMM Thrust 2: Circuits for Intelligence and Thrust 5: Theories for Intelligence

Progress on the CBMM challenge questions: What/Who is there?

Abstract:
In the Jeopardy/Watson effort every week on Friday there was an evaluation of performance. We continue the series of weekly discussions and reports on each CBMM challenge question [e.g. What is there? What will happen next? What are they doing? etc.] describing progress and problems of ongoing work at CBMM.

The Allen Institute for Brain Science uses a big science approach to neuroscience, integrating experiment, modeling and theory. One of the Institute’s projects, MindScope, is an exploration of the mouse visual system from a multitude of coordinated approaches. I will give an overview of the MindScope project and the “C3” approach – Components, Computation, and Cognition, and discuss how the goals of MindScope relate to the goals of CBMM. Important goals along our Cognition axis are Object Recognition, Attention, and Decision Making in the mouse cortico-thalamic system. The M-Theory framework provides a feedforward architecture which computes invariant representations of the visual field, facilitating fast object recognition which mimics human performance on recognition tasks within about 100ms. Several results suggest that recognition beyond 100ms or so involves feedback. I will discuss some preliminary ideas on extending the M-Theory framework to include feedback, in particular by incorporating elements of probabilistic inference into the model.