Running head : COGNITIVE ROBOTS TO ENHANCE PSYCHOLOGY Computational Modeling / Cognitive Robotics Enhances Functional Modeling / Experimental Psychology

This position paper explores the possible contributions to the science of psychology from insights obtained by building and experimenting with cognitive robots. First, the functional modeling so characteristic of experimental psychology is discussed. Next, the computational modeling required for cognitive robotics is described, and possible experiments with them illustrated. Next we argue that cognitive developmental robots where robots that “live” through a development phase like human children, where they learn about their environments in several different modes can provide additional benefits to science of psychology. Finally, the reciprocal interactions between computational modeling/cognitive robotics and functional modeling/experimental psychology are explored. We conclude that each can contribute significantly to the other. Cognitive Robots to Enhance Psychology 3 Computational Modeling/Cognitive Robotics Enhances Functional Modeling/Experimental Psychology Experimental science progresses via a theorize→predict→experiment →theorize cycle during which a theory is built, predictions made from the theory are tested by experimentation, and the theory is revised in the light of empirical findings, tested again, etc. (Beveridge, 1957, Losee, 1980, Salmon, 1990). All scientific theories are, to some extent, both functional and mechanistic in nature. A functional theory describes what can be expected to occur in a given situation. A mechanistic theory speaks to the how of the occurrence, the mechanism that brings it about. Psychological theories are typically functional in nature (Angell, 1907; Block, 1980; Meissner, 1966). Psychologists build functional models that are intended to both explain psychological processes and predict their functionality, that is, what can be expected to happen under various conditions. Although these functional models are quite useful, even essential, they yield little insight into the mechanisms underlying the cognitive processes. Also, they typically model only small pieces of cognition (e.g., memory and attention, Logan, 2002). In contrast, the control system of any robot, by its very nature, must be fully integrated. That is, it must chose its actions based on incoming exogenous or endogenous stimuli utilizing all needed internal processes. Also, the control system of a robot must act through its underlying mechanisms. Almost by definition, the architecture of a cognitive robot, a robot that employs a cognitive architecture to select its next action, is derived from integrated models of the cognition of humans and/or other animals. Its control system is designed using that integrated cognitive architecture, and is structurally coupled to its underlying mechanisms. The control system of Cognitive Robots to Enhance Psychology 4 such a robot cannot be restricted to small pieces of cognition, but would have to be sufficiently broad so as to encompass basic cognitive processes such as perception, episodic memory, selective attention, action selection, and action execution. Higher level cognitive processes such as deliberation, volition, problem solving, developmental learning, and metacognition may be modeled as well. Additionally, it may also be necessary to establish computational frameworks for feelings and emotions to serve as motivators and learning facilitators. A roboticist may also work through something like a theorize→experiment →theorize cycle. A robot is designed and built. Experimentation shows that it doesn’t perform as desired. Its control system and, possibly, the underlying mechanisms are redesigned and rebuilt. More experimentation takes place resulting in more redesigning, etc. The two theorize→experiment →theorize cycles can be amalgamated by means of a cognitive robot able to participate in or to replicate a psychological experiment. The conceptual architecture of the robot would functionally model the psychological process being experimented with on humans or animals. The computational architecture is essentially the same model only acting through the underlying mechanisms. The computational architecture yields insight into the mechanisms underlying the process. The human or animal experiments, together with the replicated experiment with the cognitive robot, serve to test both the functional model and the computational model. Both the high-level functional model and the underlying computational model can then be brought more in line with the results of these experiments. After alterations to the robot suggested by the new version of the architecture are made, new psychological experiments can be designed and carried out to test the current version. The amalgamated cycle continues. Cognitive Robots to Enhance Psychology 5 The overall goal of this paper will be to explore the possible contributions to the science of psychology from insights obtained by building and experimenting with cognitive robots. We begin by discussing several of the benefits to psychology afforded by computational models of human cognition. We then describe several shortcomings associated with computational modeling that can be alleviated by the use of cognitive robotics. The paper then evaluates possible design paradigms for the control structure of a cognitive robot and argues for basing the control mechanism on computational models that are consistent with known psychological evidence. We then describe a number of “new” AI (artificial intelligence) techniques to simulate several of the cognitive processes required for the development of a cognitive robot that may be used in experiments to advance our understanding of human cognition. The paper then argues that cognitive robots that “live” through a development phase like human children, where they learn about their environments in several different modes can provide additional benefits to science of psychology. Next we briefly discuss the feasibility of cognitive robotics and highlight several robotic simulation environments that may be useful for rapid development and experimentation. Finally, we will explore the reciprocal interactions between computational modeling/cognitive robotics and functional modeling/experimental psychology. We hope to make a significant impact in theoretical psychology, as well as catalogue the potential benefits of inter-disciplinary research between these two fields. Cognitive Robotics as an Extension of Computational Modeling The fundamental goal of cognitive science is to obtain a better understanding of human cognition. Towards this end psychologists and cognitive scientists have proposed theories, isolated hypotheses, formulated these as research questions, have designed and conducted Cognitive Robots to Enhance Psychology 6 controlled experiments to evaluate such hypotheses while systematically addressing confounds and alternatives, and have subsequently accepted, rejected, or revised their theories. Computational models have long been a major, and perhaps indispensable, tool in cognitive science by directly contributing to some of the main goals of the field. Many of these model some psychological theory of a particular aspect of cognition, attempting to account for experimental data. Others aspire to be general computational models of cognition. These are usually designed around some unified theory of cognition (Newell, 1990). They include SOAR (Laird, Newell, & Rosenbloom, 1987), ACT-R (Lebiere & Anderson, 1993), CAPS (Just & Carpenter, 1987), Clarion (Sun, 1997), EPAM (Feigenbaum & Simon, 1984), EPIC (Meyer & Kieras, 1997). These models offer certain tangible benefits to enrich the basic theorize→predict→experiment →theorize cycle described above. In this section we briefly describe some of the advantages of computational modeling along with potential shortcomings. Several of the limitations can be alleviated with a cognitive robotic approach as we will discuss shortly. Benefits of Computational Modeling Computational models of human cognition that replicate a broad spectrum of experimental data and offer provocative testable hypotheses have two clear benefits. First, the process of model building is highly conducive to obtaining a deep understanding of the real world phenomenon under consideration. The model building process involves three phases that compel a researcher to delve deep into the mechanistic core of the phenomenon being modeled. These include deciding on the functional requirements and overall goal of the model, isolating the various individual components or building blocks of the model, and devising schemes to bring Cognitive Robots to Enhance Psychology 7 about the interplay between the aforementioned components in order to obtain the desired behavior. Each of these phases requires deep thought and bold innovative theories that deepen understanding of the phenomenon being modeled. The second major benefit of computational modeling lies within the insights that can be gleaned from basic computational principles that underlie the model. The underlying assumption here is that any design decision made in the process of developing a computational model of some psychological process can be interpreted as a hypothesis that can be tested on the human subjects. In this fashion, a computational model may indirectly make qualitative predictions that can be experimentally verified. As an example consider a researcher who decides to endow a cognitive robot with a long term memory (LTM). The first design decision is to select the appropriate computational system that best captures some or ideally all of documented LTM behavior such as recency effects, rehearsal, interference, etc. One possibility is to design a new computational model for LTM as done in popular cognitive architectures such as ACT-R (Lebiere, C., & Anderson, 1993) and SOAR (Laird, Newell, & Rosenbloom, 1987). As an alternative one may select from one of the several existing associative memory models such as Sparse Distributed Me