List of Current Projects

Agent-Based Models of Social Intelligence Based on Behavioral Heuristics Informed by Cognitive Neurosciences

Our broader objective is to develop a new perspective  or modeling economic interactions that goes beyond the standard paradigm of Homo economicus, in which the recent financial crisis has demonstrated important efficiencies.

Application of Bayesian truth serum to forecasting, and collaborative discussion

As human knowledge grows, the problem of combining multiple opinions to find truth is becoming both more important and more difficult to solve (Tetlock, 2006).

Biological Foundations of Language

The origin of human language has sometimes been called possibly “the hardest problem of science.” (Christiansen and Kirby, 2003).

Combining human and machine predictions using "boosting" algorithms

This project will focus on new approaches for combining human and machine predictions in order to produce a more accurate combined predictor. To do this, we will use algorithms developed for machine learning (specifically, Freund and Schapire’s AdaBoost algorithm), and explore ways that human agent predictions can be combined into stronger predictors.

Computing causes, counterfactuals, and responsibility: theoretical analyses, probabilistic models and psychophysical studies

No abstract is currently available for this project

Health Intelligence: Individualized care by detecting subpopulations from patient data

Imagine a health system that uses data that has already been collected to rank your treatment options by how they have benefitted a population similar to you, and that continues to learn from the collective experience of the real-world patients and doctors to simplify the approval of new drugs and improve our understanding of human disease.

This research project is funded in part by Center for Biomedical Innovation (CBI)

High-Throughput In Vivo Causal Assessment of Neural Circuits of Intelligence

What are the neural circuits, and neural activity patterns, within the brain that causally implement the neural computations that we commonly associate with the terms “cognition” and “intelligence”? We here propose to perform a causal high-throughput screen of neural circuit targets to reveal how a cognitive behavior is implemented by the brain.

Language research: Connecting Computer Science, Psychology and Linguistics

The proposed project seeks to target key open questions in language structure and use by bringing together researchers in three areas of language research, in all of which MIT is already a world leader: statistical natural language processing, formal linguistics, and psycholinguistics.

Learning Causal Action Selection via Motor Babble and Observation

How can robotic systems learn to move their own bodies in purposeful ways, to gain facility in basic tasks such as moving through the environment and manipulating objects?  We are investigating representations and methods with which an agent can initiate motor actions while observing the consequences of these actions in its sensory inputs, in effect learning how a given motor action changes the relationship between the agent's body and the external environment.

Life-long Mapping

While much recent work in mobile robotics focuses on developing algorithms for mapping and localization, the neuroscience community is trying to understand how animals and humans map their environment and localize themselves. These mostly independent efforts are highly complementary and combining them should be beneficial for both fields. We propose to explore the connections between state of the art algorithms in this area in robotics and in neuroscience.

Phonotactic Universals and Automatic Speech Recognition

No abstract is currently available for this project

Symbolic Ideation in Motor Planning

A key question in the study of both biological and artificial intelligence is the extent to which symbolic processing is required for planning and execution of motor activities such as mobility and manipulation, and the extent to which environmental aspects or objects give rise to observable brain activity. We call this process “symbolic ideation.”

Understanding food affordances

We want to build a robot that can help a disabled user eat an ordinary meal at an ordinary dining table. The user tells the robot “get me a grape” or “get me some hummus,” and the robot uses its fingers or a utensil to acquire the food and convey it to the user’s mouth. This simple task offers a microcosm of problems in AI, perception, and robotics.

Vision for the Birds

We will perform an initial technical demonstration of vision-based control of aggressive UAV maneuvers.

When Are Groups More Intelligent Than Individuals?

The goal of our proposed work is to provide a systematic framework for understanding the impact of group interactions and decisions on the emergent group intelligence. Our focus will be on investigating the effects of individual strategic behavior and objectives, and the network structure on the resulting outcomes.