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Cleeremans: The search for the computational correlates of consciousness

Major biases and stereotypes in group judgments are reviewed and modeled from a recurrent connectionist perspective. These biases are in the areas of group impression formation (illusory correlation), group differentiation (accentuation), stereotype change (dispersed vs. concentrated distribution of inconsistent information), and group homogeneity. All these phenomena are illustrated with well-known experiments, and simulated with an autoassociative network architecture with linear activation update and delta learning algorithm for adjusting the connection weights. All the biases were successfully reproduced in the simulations. The discussion centers on how the particular simulation specifications compare with other models of group biases and how they may be used to develop novel hypotheses for testing the connectionist modeling approach and, more generally, for improving theorizing in the field of social biases and stereotype change. Petite, attractive, intelligent, WSF, 30, fond of music, theatre, books, travel, seeks warm, affectionate, fun-loving man to share life’s pleasures with view to lasting relationship. Send photograph. Please no

A central theme in recent research on attitudes is the distinction between deliberate, “explicit” attitudes and automatic, “implicit” attitudes. The present article provides an integrative review of the available evidence on implicit and explicit attitude change that is guided by a distinction between associative and propositional processes. Whereas associative processes are characterized by mere activation independent of subjective truth or falsity, propositional reasoning is concerned with the validation of evaluations and beliefs. The proposed Associative-Propositional Evaluation Model (APE Model) makes specific assumptions about the mutual interplay of the two processes, implying several mechanisms that lead to symmetric or asymmetric changes in implicit and explicit attitudes. The model integrates a broad range of empirical evidence and implies several new predictions for implicit and explicit attitude change.

Through the use of double task conditions, the sequence learning (SL) paradigm offers unique opportunities to study the relationships between learning and attention. In their original study, Nissen & Bullemer (1987) argued that a secondary tone-counting task prevents SL because it exhausts participants’ attentional resources. Other authors have instead suggested that the detrimental effects of tone-counting are due to scheduling conflicts between performing the main and secondary tasks rather than to attentional load. Frensch & Miner (1994), for instance, suggested that the secondary task impairs sequence learning because it lengthens the response-to-stimulus interval (RSI) and hence makes it less likely for relevant contingencies to be represented together in short-term memory, — a condition for learning. Stadler (1995), on the other hand, argued that the secondary task introduces variability in the RSI and disrupts the organization of the sequence into chunks. Further, according to Willingham, Greenberg & Cannon Thomas (1997) manipulation of the RSI influences performance but not sequence learning per se. The goal of this paper is to further explore and clarify the role of the RSI in the SL paradigm. To do so, we systematically manipulated the RSI, and assessed performance through different objective and subjective measures. In contrast to previous results, we found that increasing the RSI improves explicit SL. We further show how a neural network model based on the Simple Recurrent Network can account for our data, even though the model neither uses decay nor develops chunked, declarative representations of the sequence. These findings suggest that RSI effects in SL are rooted in the temporal dynamics of learning.

Using a simple change detection task involving vertical and horizontal stimuli, I. M. Thornton and D. Fernandez-Duque (2000) showed that the implicit detection of a change in the orientation of an item influences performance in a subsequent orientation judgment task. However, S. R. Mitroff, D. J. Simons, and S. L. Franconeri (2002) were not able to replicate this finding after correcting for confounds and thus attributed Thornton and Fernandez-Duque’s results to methodological artifacts. Because Mitroff et al.’s failure to replicate might in turn have stemmed from several methodological differences between their study and those of Thornton and Fernandez-Duque (2000) and Fernandez-Duque and Thornton (2003), the current authors set out to conduct a further replication in which they corrected all known methodological biases identified so far. The results suggest that implicit change detection indeed occurs: People’s conscious decisions about the orientation of an item appear to be influenced by previous undetected changes in the orientation of other items in the display. Implications of this finding in light of current theories of visual awareness are discussed.

Attractor networks, which map an input space to a discrete output space, are useful for pattern completion---cleaning up noisy or missing input features. However, designing a net to have a given set of attractors is notoriously tricky; training procedures are CPU intensive and often produce spurious attractors and ill-conditioned attractor basins. These difficulties occur because each connection in the network participates in the encoding of multiple attractors. We describe an alternative formulation of attractor networks in which the encoding of knowledge is local, not distributed. Although localist attractor networks have similar dynamics to their distributed counterparts, they are much easier to work with and interpret. We propose a statistical formulation of localist attractor net dynamics, which yields a convergence proof and a mathematical interpretation of model parameters. We present simulation experiments that explore the behavior of localist attractor networks, show...

The radical plasticity thesis — 2 In this chapter, I sketch a conceptual framework which takes it as a starting point that conscious and unconscious cognition are rooted in the same set of interacting learning mechanisms and representational systems. On this view, the extent to which a representation is conscious depends in a graded manner on properties such as its stability in time or its strength. Crucially, these properties are accrued as a result of learning, which is in turn viewed as a mandatory process that always accompanies information processing. From this perspective, consciousness is best characterized as involving (1) a graded continuum defined over “quality of representation”, such that availability to consciousness and to cognitive control correlates with quality, and (2) the implication of systems of metarepresentations. A first implication of these ideas is that the main function of consciousness is to make flexible, adaptive control over behavior possible. A second, much more speculative implication, is that we learn to be conscious. This I call the “radical

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Noun class information is a crucial component of the interface between the lexicon and the grammar. In order to explain linguistic productivity it is necessary to assume that linguistic rules are defined not over specific words, but classes of word. This is not only true given the classical distinction between lexicon and grammar, but also in 'emergentist ' views which see no clear separation between these two systems (Ellis, 1998; Tomasello, 2000). Even though the latter stress the lexical-specificity of many 'grammatical rules', it is still recognised that adult productivity can only be explained if words are grouped into classes, even if those classes do not map neatly onto traditional linguistic categories. The way in which words are grouped into grammatical classes is therefore an important issue in understanding language development, particularly in explaining the leap from lexical learning to grammar learning. Noun classes, such as grammatical gender, are fundamentally abstract, grammatical, notions (Corbett, 1991). However, attempts have been made to uncover subtle phonological and semantic cues that can be used to predict a word's gender (Kelly, 1992). For example, masculine nouns in German are more likely to be monosyllabic, and monosyllabic words that are masculine contain more consonants than those of other classes. In French, feminine nouns tend to end in closed stressed syllables (e.g. personne, tomate, viande), and masculine nouns tend to end in open stressed syllables (e.g. avion, bruit, chapeau, bain). There are also a number of characteristic derivational morphemes