CLARION (cognitive architecture)

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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.

Contents

Clarion Framework Clarion Cognitive Architecture.jpg
Clarion Framework

Overview

CLARION is an integrative cognitive architecture, it is used to explain and simulate cognitive-psychological phenomena, which could potentially lead to an unified explanation of psychological phenomena. There are three layers to the CLARION theory, the first layer is the core theory of mind. The main theories consists of a number of distinct subsystems, which are the essential structures of CLARION, with a dual representational structure in each subsystem (implicit versus explicit representations; Sun et al., 2005). Its subsystems include the action-centered subsystem, the non-action-centered subsystem, the motivational subsystem, and the meta-cognitive subsystem. The second layer consists of the computational models that implements the basic theory, it is more detailed than the first level theory but is still general. The third layer consists of the specific implemented models and simulations of the psychological processes or phenomena. The models of this layer arise from the basic theory and the general computational models.

Dual Representational Structure

The distinction between implicit and explicit processes is fundamental to the Clarion cognitive architecture. [1] This distinction is primarily motivated by evidence supporting implicit memory and implicit learning. Clarion captures the implicit-explicit distinction independently from the distinction between procedural memory and declarative memory. To capture the implicit-explicit distinction, Clarion postulates two parallel and interacting representational systems capturing implicit an explicit knowledge respectively. Explicit knowledge is associated with localist representation and implicit knowledge with distributed representation.

Explicit knowledge resides in the top level of the architecture, whereas implicit knowledge resides in the bottom level. [1] [2] In both levels, the basic representational units are connectionist nodes, and the two levels differ with respect to the type of encoding. In the top level, knowledge is encoded using localist chunk nodes whereas, in the bottom level, knowledge is encoded in a distributed manner through collections of (micro)feature nodes. Knowledge may be encoded redundantly between the two levels and may be processed in parallel within the two levels. In the top level, information processing involves passing activations among chunk nodes by means of rules and, in the bottom level, information processing involves propagating (micro)feature activations through artificial neural networks. Top-down and bottom-up information flows are enabled by links between the two levels. Such links are established by Clarion chunks, each of which consists of a single chunk node, a collection of (micro)feature nodes, and links between the chunk node and the (micro)feature nodes. In this way a single chunk of knowledge may be expressed in both explicit (i.e., localist) and implicit (i.e., distributed) form, though such dual expression is not always required.

The dual representational structure allows implicit and explicit processes to communicate and, potentially, to encode content redundantly. As a result, Clarion theory can account for various phenomena, such as speed-up effects in learning, verbalization-related performance gains, performance gains in transfer tasks, and the ability to perform similarity-based reasoning, in terms of synergistic interaction between implicit and explicit processes. [1] [3] [4] [5] These interactions involve both the flow of activations within the architecture (e.g., similarity-based reasoning is supported by spreading activation among chunks through shared (micro)features) as well as bottom-up, top-down and parallel learning processes. In bottom-up learning, associations among (micro)features in the bottom level are extracted and encoded as explicit rules. In top-down learning, rules in the top level guide the development of implicit associations in the bottom level. Additionally, learning may be carried out in parallel, touching both implicit and explicit processes simultaneously. Through these learning processes knowledge may be encoded redundantly or in complementary fashion, as dictated by agent history. Synergy effects arise, in part, from the interaction of these learning processes. Another important mechanism for explaining synergy effects is the combination and relative balance of signals from different levels of the architecture. For instance, in one Clarion-based modeling study, it has been proposed that an anxiety-driven imbalance in the relative contributions of implicit versus explicit processes may be the mechanism responsible for performance degradation under pressure. [6]

Subsystems

The Clarion cognitive architecture consists of four subsystems.

Action-centered subsystem

The role of the action-centered subsystem is to control both external and internal actions. The implicit layer is made of neural networks called Action Neural Networks, while the explicit layer is made up of action rules. There can be synergy between the two layers, for example learning a skill can be expedited when the agent has to make explicit rules for the procedure at hand. It has been argued that implicit knowledge alone cannot optimize as well as the combination of both explicit and implicit.

Non-action-centered subsystem

The role of the non-action-centered subsystem is to maintain general knowledge. The implicit layer is made of Associative Neural Networks, while the bottom layer is associative rules. Knowledge is further divided into semantic and episodic, where semantic is generalized knowledge, and episodic is knowledge applicable to more specific situations. It is also important to note since there is an implicit layer, that not all declarative knowledge has to be explicit.

Motivational subsystem

The role of the motivational subsystem is to provide underlying motivations for perception, action, and cognition. The motivational system in CLARION is made up of drives on the bottom level, and each drive can have varying strengths. There are low level drives, and also high level drives aimed at keeping an agent sustained, purposeful, focused, and adaptive. The explicit layer of the motivational system is composed of goals. explicit goals are used because they are more stable than implicit motivational states. the CLARION framework views that human motivational processes are highly complex and can't be represented through just explicit representation.

Examples of some low level drives include:

  • food
  • water
  • reproduction
  • avoiding unpleasant stimuli (not mutually exclusive of other low level drives, but separate for the possibility of more specific stimuli)

Examples of some high level drives include:

  • Affiliation and belongingness
  • Recognition and achievement
  • Dominance and power
  • Fairness

There is also a possibility for derived drives (usually from trying to satisfy primary drives) that can be created by either conditioning, or through external instructions. each drive needed will have a proportional strength, opportunity will also be taken into account

Meta-cognitive subsystem

The role of the meta-cognitive subsystem is to monitor, direct, and modify the operations of all the other subsystems. Actions in the meta-cognitive subsystem include: setting goals for the action-centred subsystem, setting parameters for the action and non-action subsystems, and changing an ongoing process in both the action and non-action subsystems.

Learning

Learning can be represented with both explicit and implicit knowledge individually while also representing bottom-up and top-down learning. Learning with implicit knowledge is represented through Q-learning, while learning with just explicit knowledge is represented with one-shot learning such as hypothesis testing. Bottom-up learning (Sun et al., 2001) is represented through a neural network propagating up to the explicit layer through the Rule-Extraction-Refinement algorithm (RER), while top-down learning can be represented through a variety of ways.

Comparison with other cognitive architectures

To compare with a few other cognitive architectures (Sun, 2016):

Theoretical applications

CLARION has been used to account for a variety of psychological data (Sun, 2002, 2016), such as the serial reaction time task, the artificial grammar learning task, the process control task, a categorical inference task, an alphabetical arithmetic task, and the Tower of Hanoi task. The serial reaction time and process control tasks are typical implicit learning tasks (mainly involving implicit reactive routines), while the Tower of Hanoi and alphabetic arithmetic are high-level cognitive skill acquisition tasks (with a significant presence of explicit processes). In addition, extensive work has been done on a complex minefield navigation task, which involves complex sequential decision-making. Work on organizational decision tasks and other social simulation tasks (e.g., Naveh and Sun, 2006), as well as meta-cognitive tasks, has also been initiated.

Other applications of the cognitive architecture include simulation of creativity (Helie and Sun, 2010) and addressing the computational basis of consciousness (or artificial consciousness) (Coward and Sun, 2004).

Related Research Articles

Artificial consciousness (AC), also known as machine consciousness (MC) or synthetic consciousness, is a field related to artificial intelligence and cognitive robotics. The aim of the theory of artificial consciousness is to "Define that which would have to be synthesized were consciousness to be found in an engineered artifact".

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

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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.

Semantic memory refers to general world knowledge that humans have accumulated throughout their lives. This general knowledge is intertwined in experience and dependent on culture. We can learn about new concepts by applying our knowledge learned from things in the past.

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.

In cognitive psychology, chunking is a process by which individual pieces of an information set are bound together into a meaningful whole. The chunks, by which the information is grouped, are meant to improve short-term retention of the material, thus bypassing the limited capacity of working memory and allowing the working memory to be more efficient. A chunk is a collection of basic units that have been grouped together and stored in a person's memory. These chunks can be retrieved easily due to their coherent grouping. It is believed that individuals create higher-order cognitive representations of the items within the chunk. The items are more easily remembered as a group than as the individual items themselves. These chunks can be highly subjective because they rely on an individual's perceptions and past experiences, which are linked to the information set. The size of the chunks generally ranges from two to six items but often differs based on language and culture.

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.

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Psi-theory, developed by Dietrich Dörner at the University of Bamberg, is a systemic psychological theory covering human action regulation, intention selection and emotion. It models the human mind as an information processing agent, controlled by a set of basic physiological, social and cognitive drives. Perceptual and cognitive processing are directed and modulated by these drives, which allow the autonomous establishment and pursuit of goals in an open environment.

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.

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In psychology, implicit memory is one of the two main types of long-term human memory. It is acquired and used unconsciously, and can affect thoughts and behaviours. One of its most common forms is procedural memory, which allows people to perform certain tasks without conscious awareness of these previous experiences; for example, remembering how to tie one's shoes or ride a bicycle without consciously thinking about those activities.

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References

  1. 1 2 3 Sun, Ron (2016-03-01). Anatomy of the Mind: Exploring Psychological Mechanisms and Processes with the Clarion Cognitive Architecture. Oxford University Press. doi:10.1093/acprof:oso/9780199794553.001.0001. ISBN   978-0-19-979455-3.
  2. ↑ Sun, Ron (2001-09-01). Duality of the Mind : A Bottom-up Approach Toward Cognition. Psychology Press. doi:10.4324/9781410604378. ISBN   978-1-4106-0437-8.
  3. ↑ Sun, Ron; Merrill, Edward; Peterson, Todd (2001). "From implicit skills to explicit knowledge: a bottom-up model of skill learning". Cognitive Science. 25 (2): 203–244. doi: 10.1207/s15516709cog2502_2 . ISSN   1551-6709.
  4. ↑ Sun, Ron; Zhang, Xi (2006-06-01). "Accounting for a variety of reasoning data within a cognitive architecture". Journal of Experimental & Theoretical Artificial Intelligence. 18 (2): 169–191. doi:10.1080/09528130600557713. ISSN   0952-813X. S2CID   29729283.
  5. ↑ Domangue, Thomas J.; Mathews, Robert C.; Sun, Ron; Roussel, Lewis G.; Guidry, Claire E. (2004). "Effects of Model-Based and Memory-Based Processing on Speed and Accuracy of Grammar String Generation". Journal of Experimental Psychology: Learning, Memory, and Cognition. 30 (5): 1002–1011. doi:10.1037/0278-7393.30.5.1002. ISSN   1939-1285. PMID   15355132.
  6. ↑ Wilson, Nicholas R.; Sun, Ron; Mathews, Robert C. (2009-07-01). "A motivationally-based simulation of performance degradation under pressure". Neural Networks. 22 (5–6): 502–508. doi:10.1016/j.neunet.2009.06.022. ISSN   0893-6080. PMID   19608380.

Coward, L.A. & Sun, R. (2004). Criteria for an effective theory of consciousness and some preliminary attempts. Consciousness and Cognition, 13, 268-301.

Helie, H. and Sun, R. (2010). Incubation, insight, and creative problem solving: A unified theory and a connectionist model. Psychological Review, 117, 994-1024.

Naveh, I. & Sun, R. (2006). A cognitively based simulation of academic science. Computational and Mathematical Organization Theory, 12, 313-337.

Sun, R. (2002). Duality of the Mind: A Bottom-up Approach Toward Cognition. Mahwah, NJ: Lawrence Erlbaum Associates.

Sun, R. (2016). Anatomy of the Mind: Exploring Psychological Mechanisms and Processes with the Clarion Cognitive Architecture. Oxford University Press, New York.

Sun, R. (2003). A Tutorial on CLARION 5.0. Technical Report, Cognitive Science Department, Rensselaer Polytechnic Institute.

Sun, R., Merrill, E., & Peterson, T. (2001). From implicit skills to explicit knowledge: A bottom-up model of skill learning. Cognitive Science, 25, 203-244. https://web.archive.org/web/20191218033659/http://www.cogsci.rpi.edu/~rsun/

Sun, R., Slusarz, P., & Terry, C. (2005). The interaction of the explicit and the implicit in skill learning: A dual-process approach. Psychological Review, 112, 159-192. https://web.archive.org/web/20191218033659/http://www.cogsci.rpi.edu/~rsun/

Sun, R. & Zhang, X. (2006). Accounting for a variety of reasoning data within a cognitive architecture. Journal of Experimental and Theoretical Artificial Intelligence , 18, 169-191.

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