Boicho Kokinov

Dynamics and Automaticity of Context: A Cognitive Modeling Approach

AI and psychological approaches to context are contrasted and the dynamic and automatic nature of the continuous context change in human cognition is emphasized. A dynamic theory of context is presented which defines context as the dynamic state of human mind. It describes the interaction between memory, perception, and reasoning in forming context as well as how they are influenced by context. A general cognitive architecture, DUAL, is presented that implements the mechanisms of context formation and accounts for the context-sensitivity of human cognition. A model of human problem solving, AMBR, has been built upon the DUAL architecture and the simulation experiments performed with it produce data that are coherent with experimental data on human problem solving.

Simulating Context Effects in Problem Solving with AMBR

This paper presents a computer simulation of context effects on problem solving with AMBR -- a model of human analogy-making. It demonstrates how perceiving some incidental objects from the environment may change the way the problem is being solved. It also shows that the timing of this perception is important: while the context element may have crucial influence during the initial stages of problem solving it has virtually no effect during the later stages. The simulation also explores the difference between an explicit hint condition where the focus of attention is drawn towards a context situation which is analogous to the target problem and an implicit context condition where an arbitrary object from the environment makes us remind an old episode.

Analogy in decision-making, social interaction, and emergent rationality

Colmans reformulation of rational theory is challenged in two ways. Analogy-making is suggested as a possible candidate for an underlying and unifying cognitive mechanism of decision-making, one which can explain some of the paradoxes of rationality. A broader framework is proposed in which rationality is considered as an emerging property of analogy-based behavior.

The Interplay of Analogy-Making with Active Vision and Motor Control in Anticipatory Robots

This chapter outlines an approach to building robots with anticipatory behavior based on analogies with past episodes. Anticipatory mechanisms are used to make predictions about the environment and to control selective attention and top-down perception. An integrated architecture is presented that perceives the environment, reasons about it, makes predictions and acts physically in this environment. The architecture is implemented in an AIBO robot. It successfully finds an object in a house-like environment. The AMBR model of analogy-making is used as a basis, but it is extended with new mechanisms for anticipation related to analogical transfer, for top down perception and selective attention. The bottom up visual processing is performed by the IKAROS system for brain modeling. The chapter describes the first experiments performed with the AIBO robot and demonstrates the usefulness of the analogy-based anticipation approach.

SIMILARITY IN ANALOGICAL REASONING

Abstract A computational model of similarity assessment in the context of analogical reasoning is proposed. Three types of similarity are defined: associative, semantic and structural and their specific role in the process of analogical reasoning is discussed. Mechanisms for similarity computation are proposed on the basis of a hybrid cognitive architecture (DUAL). The interaction between the three types of similarity is discussed. Finally, a number of experimental facts is explained in terms of the model. In particular, the dynamic and context-dependent aspects of similarity as well as why it is not a transitive and symmetric relation are discussed.

Building robots with analogy-based anticipation

A new approach to building robots with anticipatory behavior is presented. This approach is based on analogy with a single episode from the past experience of the robot. The AMBR model of analogy-making is used as a basis, but it is extended with new agent-types and new mechanisms that allow anticipation related to analogical transfer. The role of selective attention on retrieval of memory episodes is tested in a series of simulations and demonstrates the context sensitivity of the AMBR model. The results of the simulations clearly demonstrated that endowing robots with analogy-based anticipatory behavior is promising and deserves further investigation.

The role of the motor system in conceptual processing: effects of object affordances beyond response interference.

Numerous behavioral and neuro-imaging studies have demonstrated that the motor system is activated when people are presented with manipulable objects. However it remains a matter of debate whether these results should be interpreted as evidence that certain conceptual processes employ motor programs. In order to address this issue, we conducted two experiments which required participants to assess the functions of tool-like objects and respond verbally. The results demonstrate that action affordances may constrain performance in tasks which are not based on the stimulus-response compatibility paradigm. We argue that this finding supports the causal role of the motor system in conceptual processing and that it cannot be explained by spreading of activation and response interference.

Context-sensitivity of human memory: episode connectivity and its influence on memory reconstruction

This paper is testing a DUAL-based model of memory. The model assumes decentralized representation of episodes as a coalition of agents and analogical transfer processes as the basis for memory reconstruction of our past. It is a model of active reconstruction thereby allowing memory insertions and blending of episodes. The experiment explores the role of the degree of internal connectivity of the coalition representing the episode on the outcome of the reconstruction process. It demonstrates that the more the links between the elements of the episode are, the higher the number of details we recall, and the lesser the intruded elements and the context influence.

Chapter 4 A Cognitive Approach to Context Effects on Individual Decision Making under Risk

Abstract This chapter compares and contrasts various approaches to understanding human decision making under risk, and is trying to formulate requirements for a cognitive economics theory of risky decision making. Then a first attempt is made to put forward such a theory by proposing a cognitive model JUDGEMAP based on the general cognitive architecture DUAL. This allows the model to be integrated with other cognitive processes such as perception, analogical reasoning, spreading activation memory retrieval, etc. The fact that all processes in DUAL are based on local computations and parallel processing allows for modelling the interplay between various cognitive processes during the decision-making process, in particular the model predicts that the unconscious and automatic process of spreading activation will influence the conscious process of argument building and comparison. This prediction is tested and confirmed by a psychological experiment that demonstrates that seemingly remote and irrelevant aspects of the environment can change the decision we make.

Endowing Artificial Systems with Anticipatory Capabilities: Success Cases

This book has provided various theoretical perspectives on anticipatory processes in natural and artificial cognitive systems. Advantages have been proposed and confirmed in various detailed case studies, which may have given the reader detailed insights into anticipatory processes and their importance in various cognitive systems tasks. To wrap up these advantages and give a concluding overview of various current anticipatory process advantages, this final chapter highlights a concise collection of precise success stories of anticipations in artificial cognitive systems. We survey fourteen case studies, which were developed during the EU project MindRACES. In these studies, simulated or real robots were tested in different environmental tasks, which required advanced sensorimotor and cognitive abilities. These abilities included the initiation and control of goal-directed actions, the orientation of attention, finding and reaching goal locations, and performing mental experiments for action selection. All the studies have shown advantages of anticipatory mechanisms compared to reactive mechanisms in terms of increased robot autonomy and adaptivity. In some cases, anticipations even caused the development of new cognitive abilities, which were simply impossible without anticipatory mechanisms. For each case study, we indicate relevant associated publications, in which the interested reader may find further details on the relevant computational architectures, the involved anticipatory mechanisms, as well as on the analytical and quantitative results. While the book as a whole has laid out the theoretical principles and design methodology for such advancements, this final chapter thus provides various possible starting points for further developments in both the surveyed system architectures and the presented solutions to the cognitive tasks addressed.