Natural languages contain many layers of sequential structure, from the distribution of phonemes within words to the distribution of phrases within utterances. However, most research modeling language acquisition using artificial languages has focused on only one type of distributional structure at a time. In two experiments, we investigated adult learning of an artificial language that contains dependencies between both adjacent and non-adjacent words. We found that learners rapidly acquired both types of regularities and that the strength of the adjacent statistics (...) influenced learning of both adjacent and non-adjacent dependencies. Additionally, though accuracy was similar for both types of structure, participants’ knowledge of the deterministic non-adjacent dependencies was more explicit than their knowledge of the probabilistic adjacent dependencies. The results are discussed in the context of current theories of statistical learning and language acquisition. (shrink)

People tend to perceive ongoing continuous activity as series of discrete events. This partitioning of continuous activity may occur, in part, because events correspond to dynamic patterns that have recurred across different contexts. Recurring patterns may lead to reliable sequential dependencies in observers' experiences, which then can be used to guide perception. The current set of simulations investigated whether this statistical structure within events can be used 1) to develop stable internal representations that facilitate perception and 2) to learn when (...) to update such representations in a self-organizing manner. These simulations demonstrate that experience with recurring patterns enables a system to accurately predict upcoming stimuli within an event, to identify boundaries between such events based on transient increases in prediction error, and to use such boundaries to improve prediction about subsequent activities. (shrink)

In recent decades it has become increasingly clear that a substantial amount of cognitive work goes on independent of consciousness. The research has been carried out largely under two rubrics, implicit learning and implicit memory. The former has been concerned primarily with the acquisition of knowledge independent of awareness and the latter with the manner in which memories not readily available to conscious recall or recognition play a role in behavior; collectively these operations comprise the essential functions of the cognitive (...) unconscious. This paper reviews the recent history of work on these issues, identifies some of the problems confronting researchers, and introduces a theoretical framework based on principles of evolutionary biology within which to view them. The argument is that the cognitive unconscious, despite its apparent sophistication, is of considerable evolutionary antiquity and antedates conscious cognitive systems by a considerable amount of time. Various entailments of this evolutionary perspective are explored including such issues as phylogeny and ontogeny of implicit processes, the robustness of the implicit functions as displayed by the capacity to resist disruptions from psychological and neurological disorders, the relationship between implicit cognition and intelligence, and individual differences in implicit cognitive abilities. (shrink)

The hallmark of implicit learning is that complex knowledge can be acquired unconsciously. The second order conditionals of Reed and Johnson were developed to be complex, and they are popular materials for implicit learning research. Recently, it was demonstrated that in a sequence made noisy , shared features of the SOCs may be learned explicitly . What are these shared features? We hypothesized that low reversal frequency may play a significant role. We have varied reversal frequency, and discovered that reversal (...) frequency affected response times, inclusion exclusion behavior, and recognition ratings. Not only does it appear to be important to distinguish implicit and explicit knowledge, but also to distinguish what the knowledge is of. (shrink)

Eliminative materialism is a popular view of the mind which holds that propositional attitudes, the typical units of our traditional understanding, are unsupported by modern connectionist psychology and neuroscience, and consequently that propositional attitudes are a poor scientific postulate, and do not exist. Since our traditional folk psychology employs propositional attitudes, the usual argument runs, it too represents a poor theory, and may in the future be replaced by a more successful neurologically grounded theory, resulting in a drastic improvement in (...) our interpersonal relationships. I contend that these eliminativist arguments typically run together two distinct capacities: the folk psychological mechanisms which we use to understand one another, and scientific and philosophical guesses about the structure of those understandings. Both capacities are ontologically committed and therefore empirical. However, the commitments whose prospects look so dismal to the eliminativist, in particular the causal and logical image of propositional attitudes, belong to the guesses, and not necessarily to the underlying mechanisms. It is the commitments of traditional philosophical perspectives about the operation of our folk psychology which are contradicted by␣new evidence and modeling methods in connectionist psychology. Our actual folk psychology was not clearly committed to causal, sentential propositional attitudes, and thus is not directly threatened by connectionist psychology. (shrink)

Consciousness remains a mystery—“a phenomenon that people do not know how to think about—yet” (Dennett, , p. 21). Here, I consider how the connectionist perspective on information processing may help us progress toward the goal of understanding the computational principles through which conscious and unconscious processing differ. I begin by delineating the conceptual challenges associated with classical approaches to cognition insofar as understanding unconscious information processing is concerned, and to highlight several contrasting computational principles that are constitutive of the connectionist (...) approach. This leads me to suggest that conscious and unconscious processing are fundamentally connected, that is, rooted in the very same computational principles. I further develop a perspective according to which the brain continuously and unconsciously learns to redescribe its own activity itself based on constant interaction with itself, with the world, and with other minds. The outcome of such interactions is the emergence of internal models that are metacognitive in nature and that function so as to make it possible for an agent to develop a (limited, implicit, practical) understanding of itself. In this light, plasticity and learning are constitutive of what makes us conscious, for it is in virtue of our own experiences with ourselves and with other people that our mental life acquires its subjective character. The connectionist framework continues to be uniquely positioned in the Cognitive Sciences to address the challenge of identifying what one could call the “computational correlates of consciousness” (Mathis & Mozer, ) because it makes it possible to focus on the mechanisms through which information processing takes place. (shrink)

While the study of implicit learning is nothing new, the field as a whole has come to embody — over the last decade or so — ongoing questioning about three of the most fundamental debates in the cognitive sciences: The nature of consciousness, the nature of mental representation (in particular the difficult issue of abstraction), and the role of experience in shaping the cognitive system. Our main goal in this chapter is to offer a framework that attempts to integrate current (...) thinking about these three issues in a way that specifically links consciousness with adaptation and learning. Our assumptions about this relationship are rooted in further assumptions about the nature of processing and of representation in cognitive systems. When considered together, we believe that these assumptions offer a new perspective on the relationships between conscious and unconscious processing and on the function of consciousness in cognitive systems. (shrink)