Christian Lebiere

An integrated theory of the mind.

Adaptive control of thought-rational (ACT-R; J. R. Anderson & C. Lebiere, 1998) has evolved into a theory that consists of multiple modules but also explains how these modules are integrated to produce coherent cognition. The perceptual-motor modules, the goal module, and the declarative memory module are presented as examples of specialized systems in ACT-R. These modules are associated with distinct cortical regions. These modules place chunks in buffers where they can be detected by a production system that responds to patterns of information in the buffers. At any point in time, a single production rule is selected to respond to the current pattern. Subsymbolic processes serve to guide the selection of rules to fire as well as the internal operations of some modules. Much of learning involves tuning of these subsymbolic processes. A number of simple and complex empirical examples are described to illustrate how these modules function singly and in concert.

ACT-R: a theory of higher level cognition and its relation to visual attention

The ACT-R system is a general system for modeling a wide range of higher level cognitive processes. Recently, it has been embellished with a theory of how its higher level processes interact with a visual interface. This includes a theory of how visual attention can move across the screen, encoding information into a form that can be processed by ACT-R. This system is applied to modeling several classic phenomena in the literature that depend on the speed and selectivity with which visual attention can move across a visual display. ACT-R is capable of interacting with the same computer screens that subjects do and, as such, is well suited to provide a model for tasks involving human-computer interaction. In this article, we discuss a demonstration of ACT-Rs application to menu selection and show that the ACT-R theory makes unique predictions, without estimating any parameters, about the time to search a menu. These predictions are confirmed.

Conditional routing of information to the cortex: a model of the basal ganglia's role in cognitive coordination.

The basal ganglia play a central role in cognition and are involved in such general functions as action selection and reinforcement learning. Here, we present a model exploring the hypothesis that the basal ganglia implement a conditional information-routing system. The system directs the transmission of cortical signals between pairs of regions by manipulating separately the selection of sources and destinations of information transfers. We suggest that such a mechanism provides an account for several cognitive functions of the basal ganglia. The model also incorporates a possible mechanism by which subsequent transfers of information control the release of dopamine. This signal is used to produce novel stimulus-response associations by internalizing transferred cortical representations in the striatum. We discuss how the model is related to production systems and cognitive architectures. A series of simulations is presented to illustrate how the model can perform simple stimulus-response tasks, develop automatic behaviors, and provide an account of impairments in Parkinsons and Huntingtons diseases.

The Newell Test for a theory of cognition.

Newell (1980; 1990) proposed that cognitive theories be developed in an effort to satisfy multiple criteria and to avoid theoretical myopia. He provided two overlapping lists of 13 criteria that the human cognitive architecture would have to satisfy in order to be functional. We have distilled these into 12 criteria: flexible behavior, real-time performance, adaptive behavior, vast knowledge base, dynamic behavior, knowledge integration, natural language, learning, development, evolution, and brain realization. There would be greater theoretical progress if we evaluated theories by a broad set of criteria such as these and attended to the weaknesses such evaluations revealed. To illustrate how theories can be evaluated we apply these criteria to both classical connectionism (McClelland & Rumelhart 1986; Rumelhart & McClelland 1986b) and the ACT-R theory (Anderson & Lebiere 1998). The strengths of classical connectionism on this test derive from its intense effort in addressing empirical phenomena in such domains as language and cognitive development. Its weaknesses derive from its failure to acknowledge a symbolic level to thought. In contrast, ACT-R includes both symbolic and sub-symbolic components. The strengths of the ACT-R theory derive from its tight integration of the symbolic component with the sub-symbolic component. Its weaknesses largely derive from its failure, as yet, to adequately engage in intensive analyses of issues related to certain criteria on Newells list.

SAL: an explicitly pluralistic cognitive architecture

The SAL cognitive architecture is a synthesis of two well-established constituents: ACT-R, a hybrid symbolic-subsymbolic cognitive architecture, and Leabra, a neural architecture. These component architectures have vastly different origins yet suggest a surprisingly convergent view of the brain, the mind and behaviour. Furthermore, both of these architectures are internally pluralistic, recognising that models at a single level of abstraction cannot capture the required richness of behaviour. In this article, we offer a brief principled defence of epistemological pluralism in cognitive science and artificial intelligence, and elaborate on the SAL architecture as an example of how pluralism can be highly effective as an approach to research in cognitive science.

The Dynamics of Cognition: An ACT-R Model of Cognitive Arithmetic

ZusammenfassungForschungsarbeiten zur Kognitiven A-rithmetik untersuchen die mentale Repräsentation von Zah-len und arithmetischen Fakten sowie die kognitiven Prozesse die diese generieren, abrufen und manipulieren. Das Span-nungsfeld zwischen der scheinbar einfachen formalen Struk-tur dieses Aufgabenbereichs und den Schwierigkeiten, die Kinder bei seiner Bewaltigung haben, stellt einen einzigarti-gen Zugang zum Studium kognitiver Prozesse dar. Der vor-liegende Beitrag präsentiert einen Erklärungsansatz der zen-tralen Befunde des Forschungsgebietes auf der Grundlage ei-nes ACT-R Modells zur Lebenszeit-Simulation des Erwerbs arithmetischen Wissens. Die Anwendung der Bayesischen Lernmechanismen der ACT-R Architektur zeigen auf, wie sich diese Befunde auf die statistische Struktur des Aufga-bengebiets zurückführen lassen. Aus den präzisen Vorher-sagen der Simulation werden sowohl Hinweise zur Vermitt-lung arithmetischen Wissens abgeleitet als auch Erkenntnisse über die Architektur ACT-R selbst gewonnen. Im Rahmen einer formalen Analyse wird gezeigt, daß sich die vorge-stellte Simulation als dynamisches System betrachten läßt, dessen Lernergebnis unmittelbar von Parametern der Archi-tektur abhangt. Eine Untersuchung der Sensitivität der Parameter der Simulation belegt, daß die Werte, die zur besten Anpassung an die empirischen Daten fiihren, auch eine in einer optimalen Performanz resultieren. Die Implikationen dieses Ergebnis für die grundlegende Adaptivität menschli-cher Kognition werden diskutiert.AbstractCognitive arithmetic studies the mental representation of numbers and arithmetic facts and the processes that create, access, and manipulate them. The contradiction between the apparent straightforwardness of its exact formal structure and the difficulties that every child faces in mastering it provides an important window into human cognition. An ACT-R model is proposed which accounts for the central results of the field through a single simulation of a lifetime of arithmetic learning. The use of the architecture’s Bayesian learning mechanisms explains how these effects arise from the statistics of the task. Because of the precise predictions of the simulation, a number of lessons are derived concerning the teaching of arithmetic and the ACT-R architecture itself. A formal analysis establishes that the simulation can be viewed as a dynamical system whose ultimate learning outcome is fundamentally dependent upon some architectural parameters. Finally, an empirical study of the sensitivity of the simulation to its parameters determines that the values that yield the best fit to the data also provide optimal performance. The implications of these findings for the fundamental adaptivity of human cognition are discussed.

Simple games as dynamic, coupled systems: randomness and other emergent properties

From a game theory perspective the ability to generate random behaviors is critical. However, psychological studies have consistently found that individuals are poor at behaving randomly. In this paper we investigated the possibility that the randomness mechanism lies not within the individual players but in the interaction between the players. Provided that players are influenced by their opponents past behavior, their relationship may constitute a state of reciprocal causation [Cognitive Science 21 (1998) 461], in which each player simultaneously affects and is affected by the other player. The result of this would be a dynamic, coupled system. Using neural networks to represent the individual players in a game of paper, rock, and scissors, a model of this process was developed and shown to be capable of generating chaos-like behaviors as an emergent property. In addition, it was found that by manipulating the control parameters of the model, corresponding to the amount of working memory and the perceived values of different outcomes, that the game could be biased in favor of one player over the other, an outcome not predicted by game theory. Human data was collected and the results show that the model accurately describes human behavior. The results and the model are discussed in light of recent theoretical advances in dynamic systems theory and cognition.

Sequence Learning in the ACT-R Cognitive Architecture: Empirical Analysis of a Hybrid Model

This chapter presents a model of sequence learning based on the ACT-R cognitive architecture. This model acquires sequences by gradually learning small pieces of the sequence in symbolic structures called chunks. The availability of those chunks is a function of their numerical parameters, which are estimated by the architectures Bayesian learning mechanisms to reflect the structure of the environment. Therefore, the sequence is represented both explicitly in the symbolic chunks as well as implicitly in their real-valued parameters. This chapter presented a detailed analysis of the models sensitivity to its parameters and experimental conditions. The main conclusion is that two central characteristics of cognitive models, stochasticity and forgetting, are in fact essential in optimizing the performance of the model. We also studied alternative knowledge representations and found them to have advantages but also serious shortcomings relative to the standard model. This model is related to a number of other ACT-R models applied to a wide range of domains, and it is hoped that both the techniques used here and the conclusions drawn are applicable to a wide range of cognitive domains.

A functional model of sensemaking in a neurocognitive architecture

Sensemaking is the active process of constructing a meaningful representation (i.e., making sense) of some complex aspect of the world. In relation to intelligence analysis, sensemaking is the act of finding and interpreting relevant facts amongst the sea of incoming reports, images, and intelligence. We present a cognitive model of core information-foraging and hypothesis-updating sensemaking processes applied to complex spatial probability estimation and decision-making tasks. While the model was developed in a hybrid symbolic-statistical cognitive architecture, its correspondence to neural frameworks in terms of both structure and mechanisms provided a direct bridge between rational and neural levels of description. Compared against data from two participant groups, the model correctly predicted both the presence and degree of four biases: confirmation, anchoring and adjustment, representativeness, and probability matching. It also favorably predicted human performance in generating probability distributions across categories, assigning resources based on these distributions, and selecting relevant features given a prior probability distribution. This model provides a constrained theoretical framework describing cognitive biases as arising from three interacting factors: the structure of the task environment, the mechanisms and limitations of the cognitive architecture, and the use of strategies to adapt to the dual constraints of cognition and the environment.

Cognitive Agents Interacting in Real and Virtual Worlds

INTRODUCTION This chapter describes agents, based on the ACT-R cognitive architecture, which operate in real robotic and virtual synthetic domains. The virtual and robotic task domains discussed here share nearly identical challenges from the agent modeling perspective. Most importantly, these domains involve agents that interact with humans and each other in real-time in a three-dimensional space. This chapter describes a unified approach to developing ACT-R agents for these environments that takes advantage of the synergies presented by these environments. In both domains, agents must be able to perceive the space they move through (i.e., architecture, terrain, obstacles, objects, vehicles, etc.). In some cases the information available fromperception is raw sensor data, whereas in other cases it is at a much higher level of abstraction. Similarly, in both domains actions can be specified and implemented at a very low level (e.g., through the movement of individual actuators or simulated limbs) or at a much higher level of abstraction (e.g., moving to a particular location, which depends on other low-level actions). Controlling programs for both robots and synthetic agents must operate on some representation of the external environment that is created through the processing of sensory input. Thus, the internal robotic representation of the external world is in effect a simulated virtual environment. Many of the problems in robotics then hinge on being able to create a sufficiently rich and abstract internal representation of the world from sensor data that captures the essential nuances necessary to perceive properly (e.g., perceiving a rock rather than a thousand individual pixels from a camera sensor bitmap) and a sufficiently abstract representation of actions to allow it to act properly.