In everyday tasks, selecting actions in the proper sequence requires a continuously updated representation of temporal context. Many existing models address this problem by positing a hierarchy of processing units, mirroring the roughly hierarchical structure of naturalistic tasks themselves. Such an approach has led to a number of difficulties, including a reliance on overly rigid sequencing mechanisms, an inability to account for context sensitivity in behavior, and a failure to address learning. We consider here an alternative framework, according to which the representation of temporal context is facilitated by recurrent connections within a network mapping from environmental inputs to actions. Applying this approach to a specific, and in many ways prototypical, everyday task (coffee-making), we examine its ability to account for several central characteristics of normal and impaired human performance. The model we consider learns to deal flexibly with a complex set of sequencing constraints, encoding contextual information at multiple time-scales within a single, distributed internal representation. Mildly degrading this context representation leads

The authors investigated the extent to which touch, vision, and audition mediate the processing of statistical regularities within sequential input. Few researchers have conducted rigorous comparisons across sensory modalities; in particular, the sense of touch has been virtually ignored. The current data reveal not only commonalities but also modality constraints affecting statistical learning across the senses. To be specific, the authors found that the auditory modality displayed a quantitative learning advantage compared with vision and touch. In addition, they discovered qualitative learning biases among the senses: Primarily, audition afforded better learning for the final part of input sequences. These findings are discussed in terms of whether statistical learning is likely to consist of a single, unitary mechanism or multiple, modality-constrained ones. The world is temporally bounded: Events do not occur all at once but rather are distributed in time. Therefore, it is crucial for organisms to be able to encode and represent temporal order information. One potential method for encoding temporal order is to learn the statistical relationships of elements within sequential input. This process appears to be important in a diverse set of learning situations, including speech segmentation (Saffran, Newport, & Aslin, 1996), learning orthographic regularities of written words (Pacton, Perruchet, Fayol, & Cleeremans, 2001), visual processing (Fiser & Aslin, 2002), visuomotor learning (e.g., serial reaction time tasks; Cleeremans, 1993) and nonlinguistic, auditory

This article discusses a recurrent connectionist network, simulating empirical phenomena usually explained by current dual-process approaches of attitudes, thereby focusing on the processing mechanisms that may underlie both central and peripheral routes of persuasion. Major findings in attitude formation and change involving both processing modes are reviewed and modeled from a connectionist perspective. We use an autoassociative network architecture with a linear activation update and the delta learning algorithm for adjusting the connection weights. The network is applied to well-known experiments involving deliberative attitude formation, as well as the use of heuristics of length, consensus, expertise, and mood. All these empirical phenomena are successfully reproduced in the simulations. Moreover, the proposed model is shown to be consistent with algebraic models of attitude formation (Fishbein & Ajzen, 1975). The discussion centers on how the proposed network model may be used to unite and formalize current ideas and hypotheses on the processes underlying attitude acquisition and how it can be deployed to develop novel hypotheses in the attitude domain.

How is information transmitted in a group? A multi-agent connectionist model is proposed that combines features of standard recurrent models to simulate the process of information uptake, integration and memorization within individual agents, with novel aspects that simulate the communication of beliefs and opinions between agents. A crucial aspect in belief updating based on information from other agents is the trust in the information provided, implemented as the consistency with the receiving agents’ existing beliefs. Trust leads to a selective propagation and thus filtering out of less reliable information, and implements Grice’s (1975) maxims of quality and quantity in communication. By studying these communicative aspects within the framework of standard models of information processing, the unique contribution of communicative mechanisms beyond intra-personal factors was explored in simulations of key phenomena involving persuasive communication and polarization, lexical acquisition, spreading of stereotypes and rumors, and a lack of sharing unique information in group decisions.

This research examined choice behaviour and probability judgement in a counterintuitive reasoning problem called the Monty Hall problem (MHP). In Experiments 1 and 2 we examined whether learning from a simulated card game similar to the MHP affected how people solved the MHP. Results indicated that the experience with the card game affected participants ’ choice behaviour, in that participants selected to switch in the MHP. However, it did not affect their understanding of the objective probabilities. This suggests that there is dissociation between implicit knowledge gained from the task and the explicit understanding as to why switching was the best strategy. In Experiment 3, the number of prizes and doors were manipulated to examine how participants construed the problem space of the MHP. Results revealed that participants partition the probability judgement to reflect the number of prizes over the number of unopened doors. Human reasoning does not always adhere to the formal rules of logic (e.g., using expected utility or Bayes theorem). Instead, considerable research has revealed that people often neglect formal rules when deriving solutions to problems (Stanovich, 1999). People often fail to incorporate to base-rate information (e.g., Fischhoff & Bar-Hillel, 1984), fail to adhere to the rules of probability theory (e.g., Hammerton, 1973), and fail to follow rules dictated by expected utility theory (e.g., Lichtenstein & Slovic, 1971). Why do people produce these errors?

While statistical learning (SL) and language acquisition have been perceived as intertwined, such a view must contend with theoretical and empirical challenges. Against the backdrop of criticism leveled at early associationist efforts to account for language, a key concern for current SL approaches is whether it may suffice to enable the detection of long-distance relationships akin to those ubiquitously abounding in natural language. In Experiment 1, we extend results from previous work on the learning of nonadjacent dependencies to the learning of long-distance relations spanning three intervening elements; such learning is shown to obtain under two separate contexts. In Experiment 2, we additionally test the strength of SL and language's proposed relatedness by documenting the nature of correlations in individual differences between the two. Both experiments support the thesis that SL may overlap with mechanisms for language, while raising questions as to the singularity or duality of such underlying mechanism(s).

We investigated the extent to which touch, vision, and audition are similar in the ways they mediate the processing of statistical regularities within sequential input. While previous research has examined statistical/sequential learning in the visual and auditory domains, few researchers have conducted rigorous comparisons across sensory modalities; in particular, the sense of touch has been virtually ignored in such research. Our data reveal commonalities between the ways in which these three modalities afford the learning of sequential information. However, the data also suggest that in terms of sequential learning, audition is superior to the other two senses. We discuss these findings in terms of whether statistical/sequential learning is likely to consist of a single, unitary mechanism or multiple, modality-constrained ones.

Implicit learning – broadly construed as learning without awareness – is a complex, multifaceted phenomenon that defies easy

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