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.
RuCCS participates in a graduate training program in perceptual science supported by an IGERT grant from the National Science Foundation.
Training in perception is an interdisciplinary effort designed to give students a solid background in basic perceptual phenomena and formal models of perception drawn from computer science. Training emphasizes both empirical and theoretical issues, and this is accomplished through jointly-supervised research projects and cross-disciplinary course work. The perception community at Rutgers, drawn from the Departments of Psychology, Biomedical Engineering, Computer Science, Linguistics, as well as RuCCS and the Laboratory of Vision Research, covers a wide variety of topics in both early and high-level perception. Faculty maintain active and visible research programs, with the participation of graduate students and postdoctoral researchers. Faculty in perception work at maintaining close ties across academic disciplines and individual areas of expertise, and actively collaborate in supervision of student research.
Principal topics of collaborative efforts are the perception of motion, texture, color, shape and depth, attention, eye movements, motor control, object recognition and classification, with application to both human and machine perception.
Training centers around the jointly-supervised research and also relevant cross-disciplinary courses. These courses, whose development was supported by the NSF IGERT grant, include two foundational courses in Computer Science (CS 503, Computational Thinking, and CS 504, Computational Modeling) which are structured to give all students – regardless of undergraduate experience in computer science – a thorough grounding in the development of computational and cognitive models and their application to human and computational perception. An interdisciplinary laboratory course (Perceptual Science 521, 522, Integrated Methods in Perceptual Science), led by interdisciplinary faculty, provide groups of students with the opportunity to work in teams on original projects that combine computational and experimental aspects.
General Research Information
Research Directions at RuCCS
There are several distinct foci in the current research directions adopted by participants in the Center for Cognitive Science.
- One focus is on fundamental research concerned with understanding the nature of the cognitive processor -- of the architecture of the mind. This research, which involves both theoretical and empirical studies, is concerned with such issues as the processor's resource limits, its memory structures, the forms of representation(s) it uses, the basic operations it makes available, the discipline of sequential and/or parallel execution it permits, restrictions on interprocess communication, decomposition of the processors into modular components, and so on. These are all questions that concern the architecture of the cognitive processor, including its perceptual, memory, reasoning, and motor control capacities.
- A second and complementary focus involves fundamental research into the knowledge and the strategies that people bring to bear in reasoning and in solving problems using the architectural resources provided. This pursuit raises issues of knowledge-acquisition, learning, and representation, and makes close contact with purely formal and computational studies in computational logic and artificial intelligence.
- A third focus is on relating these research issues to the data of biological science -- particularly to the mechanisms studied in neuroscience, and the more microscopic mechanisms studied in cellular biology, biochemistry and genetics. It also involves relating cognitive theories to the data of clinical neurology.
- A fourth focus, which brings together all three of the above areas, concerns the process of perception, both human and machine. The main focus so far has been on vision, visual attention, and the perception of spatial layout. The Laboratory of Vision Research, under the direction of Bela Julesz, and several departments -- notable psychology and biomedical engineering -- have strong research programs in vision research.
- A fifth focus is concerned with investigations of language competence and language use. Language acquisition, language universals, and linguistic performance provide evidence for inferring the nature of mental processes, as well as for the design of models of linguistic capacities. Currently a theoretical approach, called Optimality Theory (see the Optimality Archive) provides a major focus, applying universal principles of language and a form of constraint satisfaction technique to linguistic research. RuCCS theoretical and computational linguists have been developing approaches to linguistic structure within Optimality Theory, which derives the grammar of particular languages from universal constraints.
- A sixth focus brings the empirical research into contact with traditional questions in philosophy of mind, philosophy of language, philosophy of science, epistemology, and also philosophical logic. Rutgers has a particularly strong philosophy group whose members have been concerned with such fundamental issues in cognitive science as the nature of meaning and the foundations of computational, cognitive and intentional processes.
- Finally, many of the research programs sketched above have practical applications in a number of areas of active investigation at Rutgers. Some of these are:
- Application of studies in human and computer vision and attention to the design of remote operation tools, including teleoperation, telerobotics, telelearning and teleconferencing aids.
- Studies of the process of design, and the development of design-aids in selected domains -- particularly aids to engineering and software design.
- Studies of the human-machine interface and the relationship of this design problem to the study of bottlenecks in human information processing. In general, this pursuit is concerned with providing data to help optimize the distribution of labor between human and machine skills.
- The study of computational aids for training, education, collaborative work, and group communication.