New trends in cognitive science : Integrative approaches to learning and development

A new trend in Cognitive Science is the use of artificial agents and systems to investigate learning and development of complex organisms in natural environments. This work, in contrast with traditional AI work, takes into account principles of neural development, problems of embodiment, and complexities of the environment. Current and future promises and challenges for this approach are defined and outlined. 1. Historical changes in Modeling: Towards Adaptive, Interactive Intelligence A widely circulated story about early Cognitive Science efforts has it that Marvin Minsky assigned an undergraduate student computer vision as a summer project. The anecdote builds irony with historical hindsight, as successive theoretical trends in the behavioral and computational sciences each have run into the barrier of Real Complexity: the monumentally interactive intricacies of organism-environment dynamics that give rise to human thought and action. Cognitive Scientists have repeatedly discovered that such prosaic skills as producing phonemes or tracking objects are quite challenging to model on anything approaching the scale of an actual organism. A growing appreciation for neural and cognitive complexity, anatomical structure and function of real organisms’ brains and bodies, and rich ecological structures have mandated a reconceptualization of models of intelligent behavior. The mandate is to push beyond the symbolic models of human information processing of the 1980’s and 1990’s, and to meaningfully elaborate on early work on neural networks by incorporating relevant information about neuroscience (e.g., chemistry, physiology, anatomy), concerns about embodiment (e.g., perception-action systems, biomechanics, motor control), and sensitivity to cognitive ecology (e.g., information such as sociograms and ethnographic data at different levels of detail). Of course, such models must also accurately model high-quality behavioral data from the organism of interest, be they rat or human, infant or adult. In recent years a community of researchers has made strides in these theoretical and empirical directions. Their work is bringing new questions and problems to the foreground, and demonstrating innovative empirical approaches, as exemplified by contributions to this issue. Although the methods and questions are quite varied, a “family resemblance” of recurring concerns or positions can be discerned (though perhaps not all the contributors would agree with all these points): • Cognition occurs in the context of complex structures, both physical and social, in the environment. In many ways this structure alters, and even sometimes simplifies, the computational and behavioral tasks faced Ac ce pte d m an us cri pt by active agents in those environments. • Problems of embodiment are substantial and important [17]. We cannot fully understand intelligent behavior without understanding how the information-processing system is embedded in its “platform” for acting and perceiving. • Cognition is sub-symbolic and distributed. However, it is not all of one simple type (e.g., unsupervised Hebbian learning). In discerning the types of learning and cognition, we may take inspiration from neuroscience evidence. • Nativist accounts that attribute high-order cognition to experience-independent products of the genome are not consistent with developmental neuroscience, embryology, or genetics, much less general principles of epigenesis. Such accounts, while sometimes pragmatic in initial models of cognition, are suboptimal and should be avoided or explicitly marked as simplifying assumptions. More generally, many proponents of these positions and others have questioned or even rejected the traditional theoretical framework of cognitive psychology and AI. As summarized by Christiansen and Hooker [16], most theories in cognitive science implicitly assume general centralized control models. Such models place a disembodied, Cartesian mind at the center of a Ptolemean cognitive universe, wherein the environment (including the body) is separate and subordinate [22][56]. This standard model ignores issues of embodiment and the environment, is theoretically problematic [16], and runs contrary to findings from many disciplines. For example, Pentland [55] has shown that a great deal of people’s impending behavior can be predicted by where they are and who they are with. Note that not all the social sciences have historically subscribed to this centralized control model: for example, the opposite problem can be seen in pure ethnographic approaches that emphasize relativism, where the environment is given full causal power without considering common neural and perceptual-motor attributes of individuals within and between cultural groups. The alternative is to reject “either/or” models of both extremes. Instead, we assume that adequate models of cognitive functions require an accurate account of the tendencies and variability of real behavior, a detailed model of the body that provides for and executes the brain’s computation, a detailed model of the functions and processes of the neural systems, and detailed models and descriptions of patterns of information on various spatial, temporal, and cultural scales, within the environment. Put otherwise, we assume the major challenge for Cognitive Science is systems modeling. Systems Modeling: Testing and falsifying formal theories about specific cognitive functions of organisms with vastly complex nervous system and vastly complex perceptual and motor capacities, interacting in real time and space with highly diverse and changeable environments. Some proponents go further in breaking from traditional AI, cognitive psychology, linguistics, and anthropology/sociology. Historically, these fields have mostly ignored developmental/epigenetic concerns. Now, however, we know enough about brain development, and about socio-cultural effects on infants’/children’s thinking, to infer that a developmental history must be part of any account of mature cognitive functioning. A description of the mature cognitive “profile,” while necessary, cannot yield a full explanatory account. Worse, a myopic consideration of adult cognition leads to systematic misconceptions about the nature of adult capacities and development [46][69]. Mature functioning is a product of protracted learning and development in constant interaction with genetically mediated, heterochronous processes of neural change [28][42][58]. Thus, although most papers in this volume do not explicitly consider children of a particular age, or seek to model precise developmental changes, many deal with cognitive processes that are centrally relevant to infants and children, and are controversial: for example, face processing [Bartlett], inductive inference [Nelson & Cottrell], and syntax acquisition [Desai]. Such difficult natural learning problems can be re-cast problems in developmental terms, which we shall call Developmental Systems Modeling: Developmental Systems Modeling: Testing and falsifying formal theories about specific cognitive functions of developing organisms with emerging, vastly complex nervous system and emerging vastly complex afferent and efferent potentials, through a history of interaction in real time and space Ac ce pte d m an us cri pt within highly diverse environments that change on multiple time scales, ranging from moment-bymoment changes to long-term changes over the organism’s lifespan. Many proponents of modern approaches to developmental systems modeling realize the limits imposed by disciplinary boundaries, and seek inspiration, and expertise, from multiple disciplines. For example, computational models often can be improved by careful attention to what is neurally plausible, and what are the relevant details of human behavior and cognition. Psychology research benefits from richer grounding in the neural underpinnings of thought and behavior, and from attempts to generate rigorous, well-specified models of cognition. Cognitive neuroscience work benefits from a deeper grasp of the importance of organisms perceiving and acting on a real world in real time, and from a greater appreciation of how human cognition if affected by the odd environment of an imaging magnet. All of these disciplines can benefit from greater knowledge of biophysics, embryology, ethnography, genetics, linguistics, physical anthropology, and animal behavior. What empirical problems are of interest to modern proponents of developmental systems modeling? The wide-ranging list is challenging, controversial, and substantive. It includes such problems as face processing, scene processing, attention, word learning, imitation, shared attention, working memory, navigation, articulation, multimodal perception, fluid motor control, self-awareness, object recognition, and others. The papers in this issue exemplify only a few possible interdisciplinary approaches to a few trenchant problems in cognitive science. They were borne of an effort to gain insights about autonomous learning and development by creating a forum for researchers in machine learning, robotics, neuroscience, and developmental psychology. The International Conferences on Development and Learning have a brief but energetic history. They began as a Workshop on Development and Learning funded by NSF and DARPA in 2000. The next meeting was hosted in 2002 by MIT; the third by UC-San Diego and the Salk Institute in 2004; the fourth by Osaka University in 2005, and the fifth by Indiana University at Bloomington in 2006. The next ICDL meeting will be held in London, UK, in 2007. The papers in this issue were submitted for peer review as expanded versions of presentations from the 2004 meeting. We now consider how the contributions to this volume exemplify and advance interdisciplinary approaches to modeling developing complex organisms and agents. We organize the discussion around the theoretical challenges addressed