Center for Cognitive Science
The Puzzle of the Mind
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Center for Cognitive Science The Puzzle of the Mind |
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Last Update: 14 December 2012
Note: |
in
(unless otherwise noted), North Campus,
and are open to the public.
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Background readings
for each lecture are available to UB faculty and students on UB Learns. Once you have logged in
to UB Learns, select "Center for Cognitive Science" → "Course
Documents" →
"Background Readings for Spring 2012". If you are affiliated
with UB and do not have access to our UBLearns website, please contact
Gail Mauner, mauner@buffalo.edu.
25 January 2012
Graduate Student Open House
1 February 2012
Princeton Neuroscience Institute and Department of Psychology, Princeton University
Hierarchical reinforcement learning
ABSTRACT:
Research on human and animal behavior has long emphasized its hierarchical structure, according to which tasks are comprised of subtask sequences, which are themselves built of simple actions. The hierarchical structure of behavior has also been of enduring interest within neuroscience, where it has been widely considered to reflect prefrontal cortical functions. In recent work, we have been reexamining behavioral hierarchy and its neural substrates from the point of view of recent developments in computational reinforcement learning. Specifically, we've been considering at a set of approaches known collectively as hierarchical reinforcement learning, which extend the reinforcement learning paradigm by allowing the learning agent to aggregate actions into reusable subroutines or skills. A close look at the components of hierarchical reinforcement learning suggests how they might map onto neural structures, in particular regions within the dorsolateral and orbital prefrontal cortex. It also suggests specific ways in which hierarchical reinforcement learning might provide a complement to existing psychological models of hierarchically structured behavior. A particularly important question that hierarchical reinforcement learning brings to the fore is that of how learning identifies new action routines that are likely to provide useful building blocks in solving a wide range of future problems. Here and at many other points, hierarchical reinforcement learning offers an appealing framework for investigating the computational and neural underpinnings of hierarchically structured behavior. In addition to introducing the theoretical framework, I'll describe a first set of neuroimaging and behavioral studies, in which we have begun to test specific predictions.
RECOMMENDED READING:
15 February 2012
University of Maryland
Department of Linguistics
Discontinuities in Syntactic Development
ABSTRACT:
When children acquire a given piece of linguistic knowledge, they must also learn to tie this knowledge to deployment systems that enable robust speech production and comprehension in real time. This talk will explore the degree to which these acquisition processes are interleaved and the degree to which development in each system impacts development in the other. Through two case studies, we will examine the ways that an immature deployment system impacts both understanding and learning. This work helps to clarify the independent contributions of statistical-distributional analysis and constraints on structural representations in the acquisition of syntax.
RECOMMENDED READING:
22 Feburary 2012
Business Meeting (PARK 208)
Department of Psychology
Harvard University
UB Center for Cognitive Science Colloquium
Wednesday, 29 February 2012, 2:00 P.M.
280 Park Hall
The Origin of Concepts: A Case Study of Natural Number
ABSTRACT:
A theory of conceptual development must specify the innate representational primitives, must characterize the ways in which the initial state differs from the adult state, and must characterize the processes through which one is transformed into the other. I will defend three claims: 1) With respect to the initial state, the innate stock of primitives is not limited to sensory, perceptual, or sensory-motor representations; rather, there are also innate conceptual representations. 2) With respect to developmental change, conceptual development consists of episodes of qualitative change, resulting in systems of representation that are more powerful than, and sometimes incommensurable with, those from which they are built. 3) With respect to a learning mechanism that achieves conceptual discontinuity, I offer Quinian bootstrapping.
RECOMMENDED READING:
ABSTRACT:
In this talk I present a case study of infants' representations of people, intentional agency, social relations, and morality to illustrate the methods and arguments in favor of claims that human infants are endowed with rich innate representational resources.
7 March 2012
School of Electrical and Computer Engineering
Purdue University
Mediating Cross-Modal Perception, Motor Control, Language, and Reasoning with Common and Deep Semantic Representations
ABSTRACT:
Human intelligence is tightly intertwined. Multiple perceptual modalities,like vision and audition, and multiple motor modalities, like manipulation and locomotion, inform, influence, and mediate each other though multiple thought modalities, like language and reasoning. Yet most computer intelligence research is compartmentalized into disjoint fields like computer vision, robotics, natural language, and AI. Understanding human intelligence and emulating it computationally will require common semantic representations across all these modalities. I will present our concerted effort to develop just that: common representations that allow rich and deep semantic interaction between computer vision, robotics, and natural language. I will present an overview of three projects that exemplify our efforts to do this: sentential description of video, assembly imitation, and learning to play board games.
In the first project, short video clips depicting people performing actions are processed to produce descriptions of the observed actions as common English verbs, descriptions of the participant objects as noun phrases,properties of those objects as adjectival modifiers in those noun phrases, the spatial relations between those participants as prepositional phrases, and characteristics of the action as adverbial modifiers. In the second, one robot builds an assembly out of Lincoln Logs while a second robot observes the assembled structure and describes it in English to a third robot who must replicate that structure from the English description. In the third, two robots play a board game, driven by an English description of the rules, while a third robot observes the play and must infer the rules from visual observation and use the learned rules to drive robotic play.
Joint work with Andrei Barbu, Daniel Barrett, Alexander Bridge, Ryan Buffington, Zachary Burchill, Tommy Chang, Dan Coroian, Sven Dickinson, Sanja Fidler, Seongwoon Ko, Alex Levinshtein, Aaron Michaux, Sam Mussman, Siddharth Narayanaswamy, Dhaval Salvi, Lara Schmidt, Jiangnan Shangguan, Brian Thomas, Jarrell Waggoner, Song Wang, Jinliang Wei, Yifan Yin, Haonan Yu, and Zhiqi Zhang.
RECOMMENDED READING:
14 March 2012
Spring break
21 March 2012
Department of Philosophy, at the University of Pennsylvania.
The Center for Neuroscience and Society, the Center for Cognitive
Neuroscience and the Institute for Research in Cognitive Science.
Concepts: a Pragmatist Theory
In this talk I devise a theory of the nature of concepts that is descended
from the "conceptual atomist" position (associated with Jerry Fodor) but
which adds a much needed psychological dimension to the atomist's view. I
discuss the relation between this account of concepts and positions on
concepts in psychology, such as the prototype theory, and I relate my view
of concepts to work in cognitive science in the connectionist tradition.
RECOMMENDED READING:
28 March 2012
UB Department of Computer Science and Engineering
Affiliated Faculty, Department of Philosophy
Affiliated Faculty, Department of Linguistics
and Center for Cognitive Science
Toward Deep Understanding of Short Intelligence Messages
ABSTRACT:
We have been developing a software system to analyze short English messages presumably written by human informants or human intelligence gatherers, and represent the information in the messages in a graph-based knowledge representation formalism. The graphs from multiple messages will later be combined, and used to provide information to intelligence analysts. Our system uses: GATE, the General Architecture for Text Engineering, for natural language processing tools such as tokenizing, part-of-speech tagging, named-entity recognition, and anaphora resolution; the Stanford Dependency Parser; a graphical editor for human intra-message reference resolution; Cyc to provide additional ontological information; and a syntax-semantics mapper of our design to convert a graph of syntactic information into a propositional graph of semantic/conceptual information based on the FrameNet system of deep lexical semantics and represented using the SNePS knowledge representation and reasoning system. In this technology-oriented talk, I will describe the various parts of this system, and show how an English message gets transformed into a SNePS propositional graph representing the information in the message.
RECOMMENDED READING:
4 April 2012
Department of Communicative Sciences and Disorders
NYU Steinhardt School of Culture, Education, and Human Development
Semantic feature processing in stroke-aphasia: Preliminary findings
ABSTRACT:
There is growing evidence that the cognitive and neuroanatomical representations of object concepts emerge from their underlying semantic feature structure. Much of this evidence has been obtained from work with neuropsychological patient populations, such as those who present with degenerative disorders such as semantic dementia or chronic brain damage due to disease processes such as herpes simplex encephalitis. Less is known about the nature of semantic feature processing, and impairment thereof, in patients with language impairment (i.e., aphasia) consequent to stroke. This is a surprising gap in the literature considering that many of the behavioral treatments for the word retrieval deficits observed in these patients are predicated on semantic feature cueing. In this talk, we will discuss an assessment designed to examine responsiveness to different types of semantic feature cues, and their relationship with different categories of object concepts, in patients with stroke-aphasia. Preliminary findings will also be explored.
RECOMMENDED READING:
11 April 2012
Department of Modern Languages and Cultures
Rochester Institute of Technology
Length-based phrase-ordering in Japanese and its interaction with canonicality
ABSTRACT:
Speakers change the order of words and phrases within a sentence for a variety of reasons. One example is word-order changes based on the length of phrases in a sentence. English speakers tend to exhibit the "Short-before-long" tendency, i.e., they postpone a long phrase until after a short phrase (e.g., Hawkins, 1994; Stallings, MacDonald, and O'Seaghdha, 1998). In contrast, speakers of Japanese and Korean show the "Long-before-short" tendency (e.g., Hawkins, 1994; Yamashita and Chang, 2001; Kondo and Yamashita, 2011). Several proposals have been made to account for both phenomena, yet more data that explore them in depth are necessary. In this talk I will highlight some of the hypotheses of the Long-before-short tendency in Japanese and present my analysis of a corpus of spontaneous speech. The study, which employed the probability of filler occurrence as a measure of production load, suggests that the Long-before-short tendency in Japanese may not be accounted for by the efficiency of the production load, and furthermore the tendency interacts with the degree of canonicality of each sentence structure.
RECOMMENDED READING:
18 April 2012
Department of Psychology
Vanderbilt University
Predicting the neural and behavioral dynamics of perceptual decisions
ABSTRACT:
How do humans and non-human primates make perceptual decision about an object's category, identity, or importance? In our work, we formally contrast competing hypotheses about perceptual decision making mechanisms using computational models that are tested on how well or how poorly they predict behavioral and neural dynamics. Our starting point is a well known class of models that assume that perceptual decisions are made by a noisy accumulation of perceptual evidence to a response boundary. Our efforts have focused on developing models of the perceptual evidence that drives this accumulation process and testing whether and how these mechanisms are instantiated in the brain. After introducing the general framework and briefly reviewing past work, I will focus on recent projects that associate perceptual evidence with one class of neurons and the accumulation process with another class of neurons recorded from monkeys trained to make perceptual decisions by a saccadic eye movement. I will highlight novel approaches we have taken to relate cognitive-level explanations and neural-level explanations, using neural data to constrain cognitive theories and using cognitive theories to explain neural dynamics.
RECOMMENDED READING:
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