We use empirical methods and formal tools to uncover the mechanisms of human learning and inference. We study the computational basis of human learning and inference. Through a combination of mathematical modeling, computer simulation, and behavioral experiments, we try to uncover the logic behind our everyday inductive leaps: constructing perceptual representations, separating "style" and "content" in perception, learning concepts and words, judging similarity or representativeness, inferring causal connections, noticing coincidences, predicting the future. We approach these topics with a range of empirical methods -- primarily, behavioral testing of adults, children, and machines -- and formal tools -- drawn chiefly from Bayesian statistics and probability theory, but also from geometry, graph theory, and linear algebra. Our work is driven by the complementary goals of trying to achieve a better understanding of human learning in computational terms and trying to build computational systems that come closer to the capacities of human learners.

This site was built up by Stephanie Warrick (formerly at the University College London) and is currently maintained by Uwe R. Zimmer uwe.zimmer@ieee.org . Updates and suggestions for additions are welcome from people engaged in academic or non-commercial scientific research.

Soar is a general cognitive architecture for developing systems that exhibit intelligent behavior. Researchers all over the world, both from the fields of artificial intelligence and cognitive science, are using Soar for a variety of tasks. It has been in use since 1983, evolving through many different versions to where it is now Soar, Version 9. In other words, our intention is for Soar to support all the capabilities required of a general intelligent agent. The ultimate in intelligence would be complete rationality which would imply the ability to use all available knowledge for every task that the system encounters.

Project required for graduate credit. This class is suitable for intermediate to advanced undergraduates or graduate students specializing in cognitive science, artificial intelligence, and related fields.

This course gives a mathematical introduction to neural coding and dynamics. Topics include convolution, correlation, linear systems, game theory, signal detection theory, probability theory, information theory, and reinforcement learning. Applications to neural coding, focusing on the visual system are covered, as well as Hodgkin-Huxley and other related models of neural excitability, stochastic models of ion channels, cable theory, and models of synaptic transmission.

The Department was founded by Hans-Lukas Teuber in 1964 as a Department of Psychology, with the then-radical vision that the study of brain and mind are inseparable. Today, at a time of increasing specialization and fragmentation, our goal remains to understand cognition- its processes, and its mechanisms at the level of molecules, neurons, networks of neurons, and cognitive modules. We are unique among neuroscience and cognitive science departments in our breadth, and in the scope of our ambition. We span a very large range of inquiry into the brain and mind, and our work bridges many different levels of analysis including molecular, cellular, systems, computational and cognitive approaches.

An example of object detection and recognition application. Classifier networks are used to inspect, sort, identify, and discriminate minute details in biological or machine systems that human beings cannot discern. They are used in everything from inspecting spark plugs to face recognition. Classifier networks are becoming the basis of machine vision systems. The students' projects are designed to give them practical experience, and to ground graduate students in the field so that they are able to perform this type of research.

Any text editor can be used to view the .ascii, Any number of development tools can be used to compile and run the .c

Like the Roman god Janus, cognitive computing projects can have two faces, their science face and their engineering face. The science face fleshes out the global workspace theory of consciousness into a full cognitive model of how minds work. The engineering face of cognitive computing explores architectural designs for software information agents and cognitive robots that promise more flexible, more human-like intelligence within their domains. This fleshed out global workspace theory is yielding hopefully testable hypotheses about human cognition. The architectures and mechanisms that underlie intelligence and consciousness in humans can be expected to yield information agents, and cognitive robots that learn continualy, adapt readily to dynamic environments, and behave flexibly and intelligently when faced with novel and unexpected situations.

The amount of time employees spend on collaborative business activities is estimated to have increased by 50 percent over the last two decades. Most of us feel crushed under a barrage of meetings, emails, chat, activity streams and more. Despite the endless barrage of productivity tricks and hacks, it has become nearly impossible to get our actual jobs done. Part of the solution lies in better integration of these channels across the digital workplace -- but that only gets us part way there. Cognitive computing and artificial intelligence will play a bigger part in moving us from our current productivity trap to becoming truly effective.