Evidence and theory ranging from traditional philosophy to contemporary cognitive research support the hypothesis that consciousness has a two-part structure: a focused region of articulated experience surrounded by a field of relatively unarticulated, vague experience.William James developed an especially useful phenomenological analysis of this "fringe" of consciousness, but its relation to, and potential value for, the study of cognition has not been explored. I propose strengthening James′ work on the fringe with a functional analysis: fringe experiences work to radically condense (...) context information in consciousness; are vague because a more explicit representation of context information would overwhelm consciousness′ limited articulation capacity; help mediate retrieval functions in consciousness; and contain a subset of monitoring and control experiences that cannot be elaborated in focal attention and are "ineffable." In general, the phenomenology of the fringe is a consequence of its cognitive functions, constrained by consciousness′ limited articulation capacity. Crucial to monitoring and control is the feeling of "rightness." Rightness functions as a summary index of cognitive integration, representing, in the fringe, the degree of positive fit between a given conscious content and its parallel, unconsciously encoded context. Rightness appears analogous to the connectionist metric of global network integration, known variously as goodness-of-fit, harmony, or minimum energy. (shrink)

The study of the feeling of knowing may have implications for some of the metatheoretical issues concerning consciousness and control. Assuming a distinction between information-based and experience-based metacognitive judgments, it is argued that the sheer phenomenological experience of knowing (''noetic feeling'') occupies a unique role in mediating between implicit-automatic processes, on the one hand, and explicit-controlled processes, on the other. Rather than reflecting direct access to memory traces, noetic feelings are based on inferential heuristics that operate implicitly and unintentionally. Once (...) such heuristics give rise to a conscious feeling that feeling can then affect controlled action. Examination of the cues that affect noetic feelings suggest that not only do these feelings inform controlled action, but they are also informed by feedback from the outcome of that action. (shrink)

Mangan's concept of fringe consciousness is too heavily based on informal introspection, and too all-embracing to constitute a coherent family. It lacks the tight operationalisation needed to identify individual examples of fringe consciousness, and to test Mangan's theoretical account against detailed findings from empirical research. I propose a more focused two-component operationalisation of the fringe. The first component addresses how we can operationalise the consciousness of the fringe; here I draw lessons from research in implicit cognition, and suggest implications for (...) the wider understanding of consciousness. The second component addresses the informational content of the fringe. (shrink)

Non-sensory experiences represent almost all context information in consciousness. They condition most aspects of conscious cognition including voluntary retrieval, perception, monitoring, problem solving, emotion, evaluation, meaning recognition. Many peculiar aspects of non-sensory qualia (e.g., they resist being 'grasped' by an act of attention) are explained as adaptations shaped by the cognitive functions they serve. The most important nonsensory experience is coherence or "rightness." Rightness represents degrees of context fit among contents in consciousness, and between conscious and non-conscious processes. Rightness (not (...) familiarity) is the feeling-of-knowing in implicit cognition. The experience of rightness suggests that neural mechanisms "compute" signals indicating the global dynamics of network integration. (shrink)

Running head: Implicit sequence learning ABSTRACT Can we learn without awareness? Although this issue has been extensively explored through studies of implicit learning, there is currently no agreement about the extent to which knowledge can be acquired and projected onto performance in an unconscious way. The controversy, like that surrounding implicit memory, seems to be at least in part attributable to unquestioned acceptance of the unrealistic assumption that tasks are process-pure, that is, that a given task exclusively involves either implicit (...) or explicit knowledge. (shrink)

Over the past decade, sequence learning has gradually become a central paradigm through which to study implicit learning. In this chapter, we start by briefly summarizing the results obtained with different variants of the sequence learning paradigm. We distinguish three subparadigms in terms of whether the stimulus material is generated either by following a fixed and repeating sequence (e.g., Nissen & Bullemer, 1987), by relying on a complex set of rules from which one can produce several alternative deterministic sequences (e.g., (...) Lewicki, Hill & Bizot, 1988; Stadler, 1989), or by following the output of a probabilistic set of rules such as instantiated by noisy finite-state grammars (Cleeremans & McClelland, 1991; Jiménez, Mendéz & Cleeremans, 1996). Next, we focus on the processes involved in sequence representation and acquisition. We suggest that the sensitivity to the sequential structure observed in the probabilistic subparadigm can only be a result of the acquisition of a representation of the statistical constraints of the material, and that this sensitivity emerges through the operation of mechanisms that are well instantiated by connectionist models such as the Simple Recurrent Network (Elman, 1990; Cleeremans, 1993b). We present new simulation work meant to explore to what extent the model can also account for specific data obtained in a paradigmatic instance of deterministic, rule-based sequence learning task: Lewicki et al. (1988)'s situation. Finally, we report on the results of an experiment that compares learning on otherwise similar deterministic and probabilistic structures, and we show that learning of both types of structures is equivalent only under conditions that maximally hinder explicit acquisition. Taken together, these simulation and experimental data lend support to the claim that implicit learning in all three sequence learning subparadigms can amount to a form of statistical sequence learning. They also suggest that distinguishing among several theories of sequence representation and acquisition may require us to analize the data in great detail. Hopefully, however, some truth can be found in such details.. (shrink)