Unified Theories Of Cognition Research Articles

How many heuristics are enough? Testing a parsimonious theory of possibility generation in problem-solving

Despite widespread recognition of the need for general-purpose mechanisms in unified theories of cognition, accounts of human thinking often propose that performance is best explained by task- or problem-specific knowledge and heuristics, elements found even in theories proposing general-purpose mechanisms. Here, we argue that many of the effects explained by task- or problem-specific knowledge or heuristics can be better explained by two general mechanisms: maximisation of progress and learning from experience. In Experiment 1, performance on the Taxicab problem, variously described as requiring or not requiring insight, was unaffected by removal of content presumed to trigger a problem-specific heuristic. In Experiment 2, the order of move selection in the Hobbits and Orcs problem, usually described as a transformation problem that does not require insight to solve, was successfully modelled by a maximisation heuristic with conceptual encoding of intermediate states, irrespective of problem context. In Experiment 3, solution rates to the eight-coin puzzle, a problem generally accepted as requiring insight to solve, were increased by cueing moves. A parsimonious theory, based on progress maximisation plus conceptual learning during solution, appears sufficient to model performance across a wide range of problems.

Explanations in cognitive science: unification versus pluralism

The debate between the defenders of explanatory unification and explanatory pluralism has been ongoing from the beginning of cognitive science and is one of the central themes of its philosophy. Does cognitive science need a grand unifying theory? Should explanatory pluralism be embraced instead? Or maybe local integrative efforts are needed? What are the advantages of explanatory unification as compared to the benefits of explanatory pluralism? These questions, among others, are addressed in this Synthese’s special issue. In the introductory paper, we discuss the background of the questions, distinguishing integrative theorizing from building unified theories. On the one hand, integrative efforts involve collaboration between various disciplines, fields, approaches, or theories. These efforts could even be quite temporary, without establishing any long-term institutionalized fields or disciplines, but could also contribute to developing new interfield theories. On the other hand, unification can rely on developing complete theories of mechanisms and representations underlying all cognition, as Newell’s “unified theories of cognition”, or may appeal to grand principles, as predictive coding. Here, we also show that unification in contemporary cognitive science goes beyond reductive unity, and may involve various forms of joint efforts and division of explanatory labor. This conclusion is one of the themes present in the content of contributions constituting the special issue.

AHA: A general cognitive architecture for Darwinian agents

Abstract Unified theories of cognition have traditionally played a vital role in understanding the human mind. In the animal behavior field, however, acceptance of holistic views on the behavioral phenotype that includes diverse cognitive and behavioral traits is rather slow. Studying adaptation and evolution of behavior, especially complex cognition and decision making, requires integrative models applicable to a range of species. We describe a general cognitive architecture and a modeling framework for studying evolution and adaptation of behavior and cognition that we call Adapted Heuristics and Architecture (AHA). AHA is non-symbolic, rule-based and grounded in general neurobiological mechanisms. It integrates the whole organism with its genome, physiology, hormones, perception, emotion, motivation and cognition in an agent based model environment. The method lets us investigate various scenarios for the evolution of cognition, decision making and emergence of subjective phenomena. We illustrate the potential feasibility of the framework with a model of simple forms of self-awareness.

Could a Unified Theory of Cognition and Emotion Further the Transdiagnostic Perspective? A Critical Analysis Using Interacting Cognitive Subsystems as a Case Example

There is evidence that common processes underlie psychological disorders transdiagnostically. A challenge for the transdiagnostic movement is accounting for such processes theoretically. Theories of psychological disorders are traditionally restricted in scope, often explaining specific aspects of a disorder. The alternative to such ‘micro-theories’ is developing frameworks which explain general human cognition, so called ‘macro-theories’, and applying these systematically to clinical phenomena. Interacting Cognitive Subsystems (ICS) [Teasdale, J.D., & Barnard, P.J. (1993). Affect, cognition and change: Re-modelling depressive thought, Lawrence Erlbaum Associates, Hove] is a macro-theory which aims to explain aspects of information processing. The aim of this review is to examine whether ICS provides a useful platform for understanding common processes which maintain psychological disorders. The core principles of ICS are explained and theoretical papers adopting ICS to explain a particular psychological disorder or symptom are considered. Dysfunctional schematic mental models, reciprocal interactions between emotional and intellectual beliefs, as well as attention and memory processes, are identified as being important to the maintenance of psychological disorders. Concrete examples of how such variables can be translated into novel therapeutic strategies are given. The review concludes that unified theories of cognition and emotion have the potential to drive forward developments in transdiagnostic thinking, research and treatment.

The SP Theory of Intelligence: Distinctive Features and Advantages

This paper aims to highlight distinctive features of the SP theory of intelligence, realized in the SP computer model, and its apparent advantages compared with some AI-related alternatives. Perhaps most importantly, the theory simplifies and integrates observations and concepts in AI-related areas, and has potential to simplify and integrate of structures and processes in computing systems. Unlike most other AI-related theories, the SP theory is itself a theory of computing, which can be the basis for new architectures for computers. Fundamental in the theory is information compression via the matching and unification of patterns and, more specifically, via a concept of multiple alignment. The theory promotes transparency in the representation and processing of knowledge, and unsupervised learning of natural structures via information compression. It provides an interpretation of aspects of mathematics and an interpretation of phenomena in human perception and cognition. concepts in the theory may be realized in terms of neurons and their inter-connections (SP-neural). These features and advantages of the SP system are discussed in relation to AI-related alternatives: the concept of minimum length encoding and related concepts, how computational and energy efficiency in computing may be achieved, deep learning in neural networks, unified theories of cognition and related research, universal search, Bayesian networks and some other models for AI, IBM's Watson, solving problems associated with big data and in the development of intelligence in autonomous robots, pattern recognition and vision, the learning and processing of natural language, exact and inexact forms of reasoning, representation and processing of diverse forms of knowledge, and software engineering. In conclusion, the SP system can provide a firm foundation for the long-term development of AI and related areas, and at the same time, it may deliver useful results on relatively short timescales.

Between architecture and model: Strategies for cognitive control

One major limitation of current cognitive architectures is that models are typically constructed in an “empty” architecture, and that the knowledge specifications (typically production rules) are specific to the particular task. This means that general cognitive control strategies have to be implemented for each specific model, which means a lack of consistency and constraint. Alternatively, cognitive control can be implemented as a part of the architecture itself, which is often implausible, because many strategies are learned and differ among individuals. A third solution is to assume cognitive control consists of learned strategies that can be used for many different tasks. The PRIMs theory (Taatgen, 2013) provides a modeling framework for this type of reuse. The approach is discussed using the example of working memory control in which I show that three different working memory tasks share the same strategic components to store and recall items. The broader impact of the work is that it shows that general cognitive skills may play a much more important role in understanding many aspects of cognition that are traditionally considered to be part of either the cognitive architecture or specific task knowledge, and therefore provides an important stepping stone towards the larger goal of unified theories of cognition.

Unified theories of cognition.

Unified theories of cognition (UTCs) offer an alternative to the modal 'divide and conquer' methodology within cognitive science and attempt to address the full range of cognitive activity within a single theoretical framework. These theories, also termed 'cognitive architectures' are generally computational in nature and are intended to model, at some degree of fidelity, human cognition in a broad range of tasks. This style of research has numerous advantages, not the least of which being that the actual human cognitive system is itself an integrated system and many important tasks require bringing integrated capabilities to bear. There are also drawbacks, particularly dealing with the incompleteness of the knowledge base in cognitive science and the difficulty of evaluating such theories. Three architectures are profiled, each one representing a different 'home' discipline: from AI, Soar; from cognitive psychology, Adaptive Control of Thought-Rational; and from neuroscience, Leabra. Future directions for UTCs include expansion into branches of cognition not already well represented, such as spatial cognition, and increasing attention to cognitive moderators such as emotion and fatigue. Overall, this is a powerful research strategy that is likely to remain an important part of cognitive science for the foreseeable future. WIREs Cogn Sci 2012, 3:431-438. doi: 10.1002/wcs.1180 For further resources related to this article, please visit the WIREs website.

Criteria for the Design and Evaluation of Cognitive Architectures

Cognitive architectures are unified theories of cognition that take the form of computational formalisms. They support computational models that collectively account for large numbers of empirical regularities using small numbers of computational mechanisms. Empirical coverage and parsimony are the most prominent criteria by which architectures are designed and evaluated, but they are not the only ones. This paper considers three additional criteria that have been comparatively undertheorized. (a) Successful architectures possess subjective and intersubjective meaning, making cognition comprehensible to individual cognitive scientists and organizing groups of like-minded cognitive scientists into genuine communities. (b) Successful architectures provide idioms that structure the design and interpretation of computational models. (c) Successful architectures are strange: They make provocative, often disturbing, and ultimately compelling claims about human information processing that demand evaluation.

Friends and Foes of Theory Construction in Psychological Science

Newell (1973) criticized the use of vague theoretical dichotomies to account for narrowly defined empirical phenomena. Many of the problems raised by Newell persist today. We argue that these problems derive not from any peculiarity of psychological science but from the hindrances inherent to empirical theory testing. To show the contemporary relevance of these problems, we present two modern illustrations of the encumbrances faced by dichotomy-based research, we review some attempts to rely on nonempirical criteria to overcome the empirical impediments in theory testing, and we bring the question of theoretical mimicry to bear on these problems. Next, we discuss an alternative to theoretical dichotomies: the Unified Theories of Cognition (Newell, 1990). Finally, we introduce the “new experimentalism” approach in philosophy of science (Mayo, 1996), which provides a new perspective on theory construction in psychological science. We conclude with suggestions on how this new perspective can be implemented.

Cognitive Constraints on Decision Making under Uncertainty

Models of decision making under uncertainty should be grounded in general cognitive processes reflecting pervasive constraints from the nature of our environment. Developing integrated models applicable across different tasks provides converging constraints that increase the predictiveness of models to new situations. Decision making is such a rich discipline that it is often considered in relative isolation, leading to entire fields devoted to specialized aspects and domains. Decision making under uncertainty can be better understood through the prism of general theories of cognition, constrained by representations and mechanisms developed to account for the much broader range of human activities (Anderson and Lebiere, 2003). This argument is an elaboration of Simon's bounded rationality (e.g., Simon, 1957) approach to constraining the rationality of optimal decision making by the cognitive limitations of the decision maker. Further, those cognitive limitations, and more generally the entire decision making process, should be modeled in a computational framework that captures in detail not only the cognitive mechanisms and representations involved (Newell, 1990) but also motivational processes (Kruglanski et al., 2007; Kruglanski and Gigerenzer, 2011) as well as perceptual (especially attentional) and motor processes (Card et al., 1983) to reflect the constraints of the task environment. Finally, decision making involves not simply raw cognitive processes but also knowledge and strategies on how to approach the problem (Gigerenzer et al., 1999). Fortunately, unified theories of cognition enable the representation of declarative and procedural knowledge constructs in a way that interacts with the constraints of the cognitive and perceptual processes to provide a rich account of performance in the task. Providing a detailed but unified computational account of those factors and their interaction across a wide range of tasks is essential for a deeper understanding of human decision making under uncertainty, as it involves general cognitive processes that are not limited to specific paradigms but take place across all human activities. We illustrate those points by briefly describing a number of instances of our recent line of research in models of decision making. In particular, we want to highlight the importance of applying the same modeling approach to widely different paradigms of decision making (including domains that are not usually considered part of decision making) in order to bring the maximum force of converging constraints onto the problem. Indeed, the main issue with many decision making tasks is not that they are too difficult to model, but instead that too many distinct models provide roughly equivalent accounts of the data, making it hard to determine which provide a fundamental understanding of human decision making processes and which are merely well-fitted parametric descriptions of human performance.

Implementation and evaluation of a hypercube-based method for spatiotemporal exploration and analysis

This paper presents the results obtained with a new type of spatiotemporal topological dimension implemented within a hypercube, i.e., within a multidimensional database (MDDB) structure formed by the conjunction of several thematic, spatial and temporal dimensions. Our goal is to support efficient SpatioTemporal Exploration and Analysis (STEA) in the context of Automatic Position Reporting System (APRS), the worldwide amateur radio system for position report transmission. Mobile APRS stations are equipped with GPS navigation systems to provide real-time positioning reports. Previous research about the multidimensional approach has proved good potential for spatiotemporal exploration and analysis despite a lack of explicit topological operators (spatial, temporal and spatiotemporal). Our project implemented such operators through a hierarchy of operators that are applied to pairs of instances of objects. At the top of the hierarchy, users can use simple operators such as “same place”, “same time” or “same time, same place”. As they drill down into the hierarchy, more detailed topological operators are made available such as “adjacent immediately after”, “touch during” or more detailed operators. This hierarchy is structured according to four levels of granularity based on cognitive models, generalized relationships and formal models of topological relationships. In this paper, we also describe the generic approach which allows efficient STEA within the multidimensional approach. Finally, we demonstrate that such an implementation offers query run times which permit to maintain a “train-of-thought” during exploration and analysis operations as they are compatible with Newell's cognitive band (query runtime<10 s) (Newell, A., 1990. Unified theories of cognition. Harvard University Press, Cambridge MA, 549 p.).

A New Temporary Maintenance System, Working Cognition: A Construct to Incorporate Working Memory in Unified Theories of Cognition

A New Temporary Maintenance System, Working Cognition: A Construct to Incorporate Working Memory in Unified Theories of Cognition

Utility of Cognitive Models

In ergonomics the approach of cognitive modeling is crucial for the design of human-machine systems. With regard to the user interface design of future manufacturing systems eleven requirements for cognitive models are introduced. These requirements are used to evaluate a total of five cognitive models with regard to three ergonomic design paradigms: (1) the design of decision support systems, (2) the design for minimized stress and strain, and (3) the design for wholistic tasks and personal development. The evaluated cognitive models include Caccibue's Cognitive Simulation Model, Card's et al. Model Human Information Processor, and Rasmussen's Model of Human Performance. In addition, two explicatory models are evaluated: Newell's Unified Theories of Cognition and Anderson's ACT-R model. The results demonstrate a superiority of the Rasmussen approach concerning the first and third design paradigm. Concerning the second design paradigm Newell's Unified Theories of Cognition have the best evaluation result.

Book review: Artificial Intelligence in Perspective by Daniel G. Bobrow (Ed.) (The MIT Press 1993)

The past and present of Artificial Intelligence have been nicely presented in the book, "Artificial Intelligence in Perspective". This book is dedicated to Allen Newell, one of the founders of artificial intelligence (AI). The book looks at the achievements of AI between 1971 and 1986 and examines their present relevance. The editor had invited about thirty authors whose papers had been heavily cited to write commentary on the impact and relevance of their original work from a contemporary perspective. The second part of the book consists of several reviews of Newell's book, "Unified Theories of Cognition." Two of his research collaborators responded to the issues raised by the reviewers. The book ends with an interview with Newell. The book covers a wide range of issues (making it a good reference), points to some unsolved problems in AI, and raises some new issues. It is relevant for researchers in AI, postgraduate students and AI practitioners.

Soar and the case for unified theories of cognition

Despite the potential importance to cognitive psychology of unified theories no attempt has been made to assess concretely the methodological problems that such theorising produces. This paper addresses this issue of unified theorising, and in particular the arguments for unified theories put forward by Newell (1990). Close examination of these arguments reveals that Newell's approach does not adequately counter the difficulties which beset the grand theories of the 1930s, nor the problems of irrelevant specification which arise in modern computational psychological work. These difficulties do not prevent the development of unified theories, but they do pose serious problems, problems which it is argued can only be met by rigorous empirical testing together with extreme methodological sensitivity. The methodological concerns lead us to examine Soar, perhaps the most well-developed unified theory, from methodological, computational, and empirical perspectives. Our conclusions are that, whilst Soar represents an impressive body of research, its methodological foundations are insecure, it is ill specified as a computational/psychological theory, and under empirical testing it does not stand up to close scrutiny as a unified theory. The Soar research programme as it currently stands thus fails to meet the necessary methodological demands imposed by unified theorising.

Unified Theories of Cognition

Unified Theories of Cognition

On Unified Theories of Cognition: a response to the reviews

This is a tough one. Clearly, neither of us is Allen Newell, and there is no way that we can do the job he would have liked to have done in responding to these thought-provoking reviews of Unified Theories of Cognition (henceforth, UTC). However, it was very important to Allen-and is very important to us--that the work on Soar and unified theories of cognition carry on. So, here we will do what we can based on our combined understanding of Soar, cognitive science, Allen, and how he would have likely responded to the issues raised by these reviews. In this latter, we have been greatly aided by a prepublication print of [ 12] that was graciously provided to us by the editors of Behavioral and Brain Sciences, and by Newell's follow-on paper to UTC [13]. In general, we will try to be clear about when we are conveying how we think Allen would have responded, and when we are just giving our own views.

In pursuit of mind… the research of Allen Newell

Allen Newell was one of the founders and truly great scientists of AI. His contributions included foundational concepts and ground-breaking systems. His career was defined by the pursuit of a single, fundamental issue: the nature of the human mind. This article traces his pursuit from his early work on search and list processing in systems such as the LOGIC THEORIST and the GENERAL PROBLEM SOLVER; through his work on problem spaces, human problem solving, and production systems; through his final work on unified theories of cognition and SOAR.

Précis of Unified theories of cognition.

The book presents the case that cognitive science should turn its attention to developing theories of human cognition that cover the full range of human perceptual, cognitive, and action phenomena. Cognitive science has now produced a massive number of high-quality regularities with many microtheories that reveal important mechanisms. The need for integration is pressing and will continue to increase. Equally important, cognitive science now has the theoretical concepts and tools to support serious attempts at unified theories. The argument is made entirely by presenting an exemplar unified theory of cognition both to show what a real unified theory would be like and to provide convincing evidence that such theories are feasible. The exemplar is SOAR, a cognitive architecture, which is realized as a software system. After a detailed discussion of the architecture and its properties, with its relation to the constraints on cognition in the real world and to existing ideas in cognitive science, SOAR is used as theory for a wide range of cognitive phenomena: immediate responses (stimulus-response compatibility and the Sternberg phenomena); discrete motor skills (transcription typing); memory and learning (episodic memory and the acquisition of skill through practice); problem solving (cryptarithmetic puzzles and syllogistic reasoning); language (sentence verification and taking instructions); and development (transitions in the balance beam task). The treatments vary in depth and adequacy, but they clearly reveal a single, highly specific, operational theory that works over the entire range of human cognition, SOAR is presented as an exemplar unified theory, not as the sole candidate. Cognitive science is not ready yet for a single theory - there must be multiple attempts. But cognitive science must begin to work toward such unified theories.

On putting the cart before the horse: Taking perception seriously in unified theories of cognition.

On putting the cart before the horse: Taking perception seriously in unified theories of cognition.