Labs affiliated with Rutgers Center for Cognitive Science (RuCCS) are the:
- [Dr. Feldman, Jacob] Visual Cognition Laboratory
- [Dr. Gallistel, Randy] Behavioral Genetics of Memory Lab
- [Dr. Gelman, Rochel] Laboratory for Cognitive Development and Learning
- [Dr. Hanson, Steve] Rutgers University Brain Imaging Center (RUBIC)
- [Dr. Leslie, Alan] Cognitive Development Lab
- [Dr. Musolino, Julien] Psycholinguistics Lab
- [Dr. Papathomas, Thomas] Lab of Vision Research
- [Dr. Pylyshyn, Zenon] Visual Attention Laboratory
- [Dr. Rigdon, Mary] Decision and Economic Sciences Lab
- [Dr. Singh, Manish] Perceptual Science Lab
- [Dr. Stich, Stephen] Research Group on Evolution and Higher Cognition
- [Dr. Stone, Matthew] The VILLAGE (Vision, Interaction, Language, Logic and Graphics Environment Lab)
- [Dr. Stromswold, Karin] Language Acquisition and Neurolinguistics Lab
- [Dr. Syrett, Kristen] Developmental Language Studies
At the present time the principal contributing disciplines are psychology, computer science, linguistics and philosophy, although at Rutgers there are individuals with strong interests in cognitive science located in a variety of departments -- perhaps a greater range of academic departments than is typical of the field at large. For example, the department of biomedical engineering and the CAIP Center (Computer Aids for Industrial Productivity) have special strength in the area of computational and biological perception and speech recognition. Several groups in the mathematics and electrical engineering departments have strong interests in aspects of computational architecture and neural networks, as do, of course, the neuroscience researchers in the center for behavioral and neural sciences. Click on any discipline below to visit its site.
The study of learning and conceptual change at Rutgers ranges across disciplines—computer science, lingusitics, psychology and philosophy—and levels of analysis—the molecular, cellular, behavioral, and computational. Work in the different disciplines and at the different levels of analysis is integrated by a shared concern for the questions of domain specificity and initial data representations. The first question concerns the interplay between learning mechanisms tailored to particular learning problems (for example, learning language, or learning spatial layouts) and learning mechanisms that operate without regard to the structure of the material to be learned (for example, associative learning mechanisms). The second question focuses on the problem of choosing an initial representation of experience that facilitates the development of a more economical and effective representation as more experience is gained. The problem of the learner's initial representation is central to language learning, to the success or failure of many machine learning algorithms, and to the ability of many animals to extract from their experience a representation of the temporal and spatial structure of their environment.
Several labs at Rutgers pursue research on learning at the neurobiological level: Randy Gallistel (Psychology), Louis Matzel (Psychology), Tim Otto (Psychology), Tracey Shors (Psychology), Gleb Shumyatsky (Molecular Genetics), Mark West (Psychology). These labs combine the behavioral level of analysis with electrophysiological, anatomical, and molecular levels of analysis.
Issues in the learning of language promote interaction between another large group of cognitive scientists at Rutgers : Mark Baker, Jerry Fodor, Lila Gleitman, Rochel Gelman, Alvin Goldman, Jane Grimshaw, Ernie Lepore, Alan Prince, Steve Stich, Karin Stromswold, Krysten Syrett and Bruce Tesar.
Concept learning is studied both experimentally (Jacob Feldman's, Alan Leslie, Rochel Gelman) and from a formal and philosophical perspective (Jerry Fodor, Ernie Lepore, Alvin Goldman). Within this group, there are cross-laboratory focii on the learning of numerical concepts, the distinction between the living and non-living (animacy), and the emergence of a theory of mind (intentionality).
RuCCS is well-known for its commitment to developmental cognitive science. We believe that development is not itself a distinct topic area, but rather that developmental questions are central to every topic area in cognitive science. This is reflected in the interests of the core RuCCS faculty listed below.
We have two core labs that are devoted to the study of cognitive development, Rochel Gelman's Cognitive Development and Learning Lab and Alan Leslie's Cognitive Development Lab. Both labs are equipped for work with young children and infants. The development of vision is also studied in the Kowler lab. Both the Musolino and Stromswold labs focus on the acquisition of language. All of these labs are in the same building as RuCCS.
The acquisition of concepts is a long-standing and active interest of philosopher, Jerry Fodor. Lila Gleitman studies the acquisition of syntax and is a visiting professor at RuCCS each fall term. We have a number of linguists with strong interests in universal grammar and learnability, including Alan Prince, Jane Grimshaw, Mark Baker, and Bruce Tesar.
Core faculty with developmental interests:
- Jerry Fodor (Philosophy and RuCCS)
- Rochel Gelman (Psychology and RuCCS)
- Lila Gleitman (U. Penn, Psychology and RuCCS)
- Eileen Kowler (Psychology and RuCCS)
- Alan Leslie (Psychology and RuCCS)
- Julien Musolino (Psychology and RuCCS)
- Karin Stromswold (Psychology and RuCCS)
- Kristen Syrett (Linguistics and RuCCS)
A number of RuCCS affiliates also have labs devoted to the study of development (see List of Affiliates).
Foundations and Cognitive Architecture
Foundations & Cognitive Architecture
A third cluster, which cuts across the first two areas and also encompasses other theoretical fields in which RuCCS has particular strength, concerns the question of the architecture of the cognitive system. This includes the nature of the underlying (computational) mechanisms of cognition, as well as the way in which these mechanisms encode information and the way that both the information and the mechanisms may change over time through learning or development. These are among basic theoretical and foundational questions on which RuCCS members have written extensively. For example, Jerry Fodor, Zenon Pylyshyn, Brian McLaughlin and Robert Matthews have written on constraints on Cognitive Architecture, including its modularity and the requirements placed on it by the empirical facts of systematicity and productivity. These writers have argued, among other things, that the representational systems provided by the architecture must meet certain general but powerful constraints which exclude, for example, "prototypes" or other noncompositional representational system, from being by itself adequate to the task. Similarly, Pylyshyn has written extensively on the inadequacy of such special forms of representations as those that we are intuitively drawn to in explaining the nature of mental imagery, known as the "picture theory" of representations underlying mental imagery. Areas in which RuCCS faculty have been leaders involve an analysis of the nature of concepts and their mental representation, the nature of mental representations, and the fundamental problems faced by Connectionist and other nonsymbolic models in addressing the phenomena of human cognition (on which Fodor, Pylyshyn, McLaughlin and Alan Prince have written extensively).
Some RuCCS members or associates are concerned with the problem of representing knowledge in general (Thorne McCarty, Hyam Hirsh, Alex Borgida) or in particular domains, such as language or vision or planning and problem-solving (see the description of relevant research clusters). Some are concerned with understanding how representations change in response to relevant information -- i.e., learning in persons and machines (Hirsh and Schmidt in the case of general knowledge; Fodor, Jane Grimshaw, Matthews, Prince, Karin Stromswold in the case of language; Leslie and Kovacs in the case of the development of the cognitive and visual system in infancy). Others are concerned to show how certain mechanisms are innate and/or universal or how they develop ontogenetically. For example, Alan Leslie has shown that sophisticated cognitive mechanisms are present at very early stages of infancy (e.g. perception of causation and objecthood), whereas other mechanisms take a number of years to develop (e.g. ascribing mental states, such as beliefs and desires, to others). Rochel Gellman’s work on the acquisition of numerical competence falls in this category since it seeks universal cognitive mechanisms that can form the basis of this competence. Other RuCCS members (Ilona Kovacs, Randy Gallistel) have a strong connection with the neuroscience community and are concerned with the question of how behavioral and neural methods and findings can inform one another in the pursuit of more adequate theories.
The study of universals of language and perception also sheds light on the nature of the cognitive architecture. In this vein Grimshaw and Prince have argued that both phonological and syntactic differences among languages can be accounted for by the ordering which they place on a finite set of universal but violable constraints. Stromswold has analyzed empirical evidence showing that certain structures are acquired despite impoverished evidence that lacks negative feedback. Mark Baker has extensively documented the enormous commonality that lies below the surface of the world’s languages. Michael Leyton, Jacob Feldman have shown that certain primitive form properties may be the (possibly innate and universal) basis for the encoding of shape in general.
The development of computational models of cognition raises both philosophical and methodological questions that many RuCCS members have addressed (e.g. Steven Stich, Fodor, Pylyshyn). From the particular perspective of RuCCS researchers, the attempt to build specific computational models in domains such as language comprehension and visual perception (especially in the work of Michael Leyton, Feldman and Manish Singh) goes hand in hand with designing an underlying architecture that satisfy strong computational, linguistic, geometrical and psychological constraints. When this is done then the model's match to the observed human behavior is seen to be an inescapable consequence of that architecture, rather than based on mere mimicry. For example, Grimshaw and Prince have investigated the architectural requirements of a model of human parsing based on principles developed from Prince et al's Optimality Theory.