Ron Sun

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Ron Sun is a cognitive scientist who made significant contributions to computational psychology and other areas of cognitive science and artificial intelligence. He is currently professor of cognitive sciences at Rensselaer Polytechnic Institute, and formerly the James C. Dowell Professor of Engineering and Professor of Computer Science at University of Missouri. He received his Ph.D. in 1992 from Brandeis University.

Contents

Overview

His many research interests center around the study of human cognition and psychology, especially in the areas of cognitive architectures, human reasoning and learning, cognitive social simulation, and hybrid connectionist-symbolic models. Over the years, his work has been wide-ranging, and spans cognitive science, psychology, philosophy, computer science, artificial intelligence, and social sciences.

He has been known for his work in cognitive modeling (computational psychology). For his paper on integrating rule-based and connectionist models for accounting for human everyday reasoning, he received the 1991 David Marr Award from Cognitive Science Society. For his work on human skill learning, he received the 2008 Hebb Award from the International Neural Network Society. In 2013, he received a Leadership and Vision award from the president of INNS. He is an IEEE Fellow and a fellow of Association for Psychological Science.

He was the founding co-editor-in-chief of the journal Cognitive Systems Research , and serves on the editorial boards of many other journals. He was the general chair and the program chair of CogSci 2006, and the program chair of IJCNN 2007. He was a member of the governing boards of Cognitive Science Society and of International Neural Networks Society. He served as the president of INNS for two years from January 2011 to December 2012.

Research

Throughout the past two decades, he has been conducting research in the fields of computational psychology and hybrid connectionist neural network (i.e., neural symbolic models). In particular, he applied these models to research on human skill acquisition. Specifically, he has worked on the integrated effect of "top-down" and "bottom-up" learning in human skill acquisition, [1] [2] in a variety of task domains, for example, navigation tasks, [3] reasoning tasks, and implicit learning tasks. [4] This inclusion of bottom-up learning processes has been revolutionary in cognitive science, because most previous models of learning had focused exclusively on top-down learning (whereas human learning clearly happens in both directions). This research has culminated with the development of an integrated cognitive architecture that can be used to provide a qualitative and quantitative explanation of empirical psychological learning data. The model, CLARION, is a hybrid neural network that can be used to simulate problem solving and social interactions as well. More importantly, CLARION was the first psychological model that proposed an explanation for the "bottom-up learning" mechanisms present in human skill acquisition: His numerous papers on the subject have brought attention to this neglected area in cognitive science.

Relatedly, he has done pioneering work on dual process theory. Also known as two-system or two-level theories, his dual-process theories posit the co-existence of and the interaction between implicit and explicit processes. [5] [6]

Another strand of his work is a theoretical model of creative problem solving. [7] In this work (with S. Helie), he proposed an integrative theory that is much broader in explanatory scope and used it to account for a range of empirical phenomena.

Yet another strand is what he called cognitive social sciences --- the re-unification of the cognitive and social sciences through grounding the social sciences in the cognitive sciences. [8] [9]

He also attempted the difficult task of laying the theoretical and meta-theoretical foundation for computational cognitive modeling (or computational psychology).

Books

Related Research Articles

<span class="mw-page-title-main">Cognitive science</span> Interdisciplinary scientific study of cognitive processes

Cognitive science is the interdisciplinary, scientific study of the mind and its processes. It examines the nature, the tasks, and the functions of cognition. Mental faculties of concern to cognitive scientists include language, perception, memory, attention, reasoning, and emotion; to understand these faculties, cognitive scientists borrow from fields such as linguistics, psychology, artificial intelligence, philosophy, neuroscience, and anthropology. The typical analysis of cognitive science spans many levels of organization, from learning and decision to logic and planning; from neural circuitry to modular brain organization. One of the fundamental concepts of cognitive science is that "thinking can best be understood in terms of representational structures in the mind and computational procedures that operate on those structures."

Artificial consciousness (AC), also known as machine consciousness (MC), synthetic consciousness or digital consciousness, is the consciousness hypothesized to be possible in artificial intelligence. It is also the corresponding field of study, which draws insights from philosophy of mind, philosophy of artificial intelligence, cognitive science and neuroscience. The same terminology can be used with the term "sentience" instead of "consciousness" when specifically designating phenomenal consciousness.

<span class="mw-page-title-main">Connectionism</span> Cognitive science approach

Connectionism is the name of an approach to the study of human mental processes and cognition that utilizes mathematical models known as connectionist networks or artificial neural networks. Connectionism has had many 'waves' since its beginnings.

A cognitive model is an approximation of one or more cognitive processes in humans or other animals for the purposes of comprehension and prediction. There are many types of cognitive models, and they can range from box-and-arrow diagrams to a set of equations to software programs that interact with the same tools that humans use to complete tasks. In terms of information processing, cognitive modeling is modeling of human perception, reasoning, memory and action.

<span class="mw-page-title-main">ACT-R</span>

ACT-R is a cognitive architecture mainly developed by John Robert Anderson and Christian Lebiere at Carnegie Mellon University. Like any cognitive architecture, ACT-R aims to define the basic and irreducible cognitive and perceptual operations that enable the human mind. In theory, each task that humans can perform should consist of a series of these discrete operations.

A cognitive architecture refers to both a theory about the structure of the human mind and to a computational instantiation of such a theory used in the fields of artificial intelligence (AI) and computational cognitive science. The formalized models can be used to further refine a comprehensive theory of cognition and as a useful artificial intelligence program. Successful cognitive architectures include ACT-R and SOAR. The research on cognitive architectures as software instantiation of cognitive theories was initiated by Allen Newell in 1990.

Computational cognition is the study of the computational basis of learning and inference by mathematical modeling, computer simulation, and behavioral experiments. In psychology, it is an approach which develops computational models based on experimental results. It seeks to understand the basis behind the human method of processing of information. Early on computational cognitive scientists sought to bring back and create a scientific form of Brentano's psychology.

Mathematical psychology is an approach to psychological research that is based on mathematical modeling of perceptual, thought, cognitive and motor processes, and on the establishment of law-like rules that relate quantifiable stimulus characteristics with quantifiable behavior. The mathematical approach is used with the goal of deriving hypotheses that are more exact and thus yield stricter empirical validations. There are five major research areas in mathematical psychology: learning and memory, perception and psychophysics, choice and decision-making, language and thinking, and measurement and scaling.

Hybrid intelligent system denotes a software system which employs, in parallel, a combination of methods and techniques from artificial intelligence subfields, such as:

John Robert Anderson is a Canadian-born American psychologist. He is currently professor of Psychology and Computer Science at Carnegie Mellon University.

<span class="mw-page-title-main">Allan R. Wagner</span>

Allan R. Wagner was an American experimental psychologist and learning theorist, whose work focused upon the basic determinants of associative learning and habituation. He co-authored the influential Rescorla–Wagner model of Pavlovian conditioning (1972) as well as the Standard Operating Procedures or "Sometimes Opponent Process" (SOP) theory of associative learning (1981), the Affective Extension of SOP and the Replaced Elements Model (REM) of configural representation. His research involved extensive study of the conditioned eyeblink response of the rabbit, of which he was one of the initial investigators (1964).

In psychology, a dual process theory provides an account of how thought can arise in two different ways, or as a result of two different processes. Often, the two processes consist of an implicit (automatic), unconscious process and an explicit (controlled), conscious process. Verbalized explicit processes or attitudes and actions may change with persuasion or education; though implicit process or attitudes usually take a long amount of time to change with the forming of new habits. Dual process theories can be found in social, personality, cognitive, and clinical psychology. It has also been linked with economics via prospect theory and behavioral economics, and increasingly in sociology through cultural analysis.

Axel Cleeremans is a Research Director with the National Fund for Scientific Research (Belgium) and a professor of cognitive science with the Department of Psychology of the Université Libre de Bruxelles, Brussels.

<span class="mw-page-title-main">CLARION (cognitive architecture)</span>

Connectionist Learning with Adaptive Rule Induction On-line (CLARION) is a computational cognitive architecture that has been used to simulate many domains and tasks in cognitive psychology and social psychology, as well as implementing intelligent systems in artificial intelligence applications. An important feature of CLARION is the distinction between implicit and explicit processes and focusing on capturing the interaction between these two types of processes. The system was created by the research group led by Ron Sun.

Some of the research that is conducted in the field of psychology is more "fundamental" than the research conducted in the applied psychological disciplines, and does not necessarily have a direct application. The subdisciplines within psychology that can be thought to reflect a basic-science orientation include biological psychology, cognitive psychology, neuropsychology, and so on. Research in these subdisciplines is characterized by methodological rigor. The concern of psychology as a basic science is in understanding the laws and processes that underlie behavior, cognition, and emotion. Psychology as a basic science provides a foundation for applied psychology. Applied psychology, by contrast, involves the application of psychological principles and theories yielded up by the basic psychological sciences; these applications are aimed at overcoming problems or promoting well-being in areas such as mental and physical health and education.

In behavioral psychology, parallel constraint satisfaction processes (PCSP) is a model of human behavior that integrates connectionism, neural networks, and parallel distributed processing models.

The LIDA cognitive architecture is an integrated artificial cognitive system that attempts to model a broad spectrum of cognition in biological systems, from low-level perception/action to high-level reasoning. Developed primarily by Stan Franklin and colleagues at the University of Memphis, the LIDA architecture is empirically grounded in cognitive science and cognitive neuroscience. In addition to providing hypotheses to guide further research, the architecture can support control structures for software agents and robots. Providing plausible explanations for many cognitive processes, the LIDA conceptual model is also intended as a tool with which to think about how minds work.

The Troland Research Awards are an annual prize given by the United States National Academy of Sciences to two researchers in recognition of psychological research on the relationship between consciousness and the physical world. The areas where these award funds are to be spent include but are not limited to areas of experimental psychology, the topics of sensation, perception, motivation, emotion, learning, memory, cognition, language, and action. The award preference is given to experimental work with a quantitative approach or experimental research seeking physiological explanations.

<span class="mw-page-title-main">Embodied cognition</span> Interdisciplinary theory

Embodied cognition is the concept suggesting that many features of cognition are shaped by the state and capacities of the organism. The cognitive features include a wide spectrum of cognitive functions, such as perception biases, memory recall, comprehension and high-level mental constructs and performance on various cognitive tasks. The bodily aspects involve the motor system, the perceptual system, the bodily interactions with the environment (situatedness), and the assumptions about the world built the functional structure of organism's brain and body.

Neuro-symbolic AI is a type of artificial intelligence that integrates neural and symbolic AI architectures to address the weaknesses of each, providing a robust AI capable of reasoning, learning, and cognitive modeling. As argued by Leslie Valiant and others, the effective construction of rich computational cognitive models demands the combination of symbolic reasoning and efficient machine learning. Gary Marcus, argued, "We cannot construct rich cognitive models in an adequate, automated way without the triumvirate of hybrid architecture, rich prior knowledge, and sophisticated techniques for reasoning." Further, "To build a robust, knowledge-driven approach to AI we must have the machinery of symbol manipulation in our toolkit. Too much useful knowledge is abstract to proceed without tools that represent and manipulate abstraction, and to date, the only known machinery that can manipulate such abstract knowledge reliably is the apparatus of symbol manipulation."

References

  1. R. Sun, Duality of the Mind. Lawrence Erlbaum Associates, Mahwah, NJ. 2002.
  2. R. Sun, Anatomy of the Mind: Exploring Psychological Mechanisms and Processes with the Clarion Cognitive Architecture. Oxford University Press, New York. 2016.
  3. R. Sun, E. Merrill, and T. Peterson, From implicit skills to explicit knowledge: A bottom-up model of skill learning. Cognitive Science, Vol.25, No.2, pp.203-244. 2001.
  4. R. Sun, P. Slusarz, and C. Terry, The interaction of the explicit and the implicit in skill learning: A dual-process approach. Psychological Review, Vol.112, No.1, pp.159-192. 2005.
  5. R. Sun, Integrating Rules and Connectionism for Robust Commonsense Reasoning. John Wiley and Sons, New York, NY. 1994.
  6. R. Sun, Duality of the Mind: A Bottom-up Approach towards Cognition. Taylor and Francis. 2002.
  7. S. Helie and R. Sun, Incubation, insight, and creative problem solving: A unified theory and a connectionist model. Psychological Review, Vol.117, No.3, pp.994-1024. 2010.
  8. R. Sun, (ed.) Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation. Cambridge University Press, New York. 2006.
  9. R. Sun, Grounding Social Sciences in Cognitive Sciences. MIT Press, Cambridge MA. 2012.