Abstract not found

This article proposes an adaptive connectionist model that implements an attributional account of cognitive dissonance. The model represents an attitude as the connection between the attitude object and behavioral-affective outcomes. Dissonance arises when circumstantial constraints induce a mismatch between the model's (mental) prediction and discrepant behavior or affect. Reduction of dissonance by attitude change is accomplished through long-lasting changes in the connection weights using the error-correcting delta learning algorithm. The model can explain both the typical effects predicted by dissonance theory as well as some atypical effects (i.e., reinforcement effect), using this principle of weight changes and by giving a prominent role to affective experiences. The model was implemented in a standard feedforward connectionist network. Computer simulations showed an adequate fit with several classical dissonance paradigms (inhibition, initiation, forced compliance, free choice & misattribution), as well as novel studies that underscore the role of affect. A comparison with an earlier constraint satisfaction approach (Shultz & Lepper, 1996) indicates that the feedforward implementation provides a similar fit with these human data, while avoiding a number of shortcomings of this previous model.

The dual-network memory model is designed to be a neurobiologically plausible manner of avoiding catastrophic interference. We discuss a number of advantages of this model and potential clues that the model has provided in the areas of memory consolidation, category-specific deficits, anterograde and retrograde amnesia. We discuss a surprising result about how this class of models handles episodic ("snap-shot") memory --- namely, that they seem to be able to handle both episodic and abstract memory --- and discuss two other promising areas of research involving these models. 1.

Everyone agrees that real cognition requires much more than static pattern recognition. In particular, it requires the ability to learn sequences of patterns (or actions) But learning sequences really means being able to learn multiple sequences, one after the other, wi thout the most recently learned ones erasing the previously learned ones. But if catastrophic interference is a problem for the sequential learning of individual patterns, the problem is amplified many times over when multiple sequences of patterns have to be learned consecutively, because each new sequence consists of many linked patterns. In this paper we will present a connectionist architecture that would seem to solve the problem of multiple sequence learning using pseudopatterns.

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.

Most artificial neural networks suffer from the problem of catastrophic for-getting, where previously learnt information is suddenly and completely lost when new information is learnt. Memory in real neural systems does not appear to suffer from this unusual behaviour. In this thesis we discuss the problem of catastrophic forgetting in Hopfield networks, and investi-gate various potential solutions. We extend the pseudorehearsal solution of Robins (1995) enabling it to work in this attractor network, and compare the results with the unlearning procedure proposed by Crick and Mitchison (1983). We then explore a familiarity measure based on the energy profile of the learnt patterns. By using the ratio of high energy to low energy parts of the network we can robustly distinguish the learnt patterns from the large number of spurious “fantasy ” patterns that are common in these networks. This energy ratio measure is then used to improve the pseudorehearsal solu-tion so that it can store 0.3N patterns in the Hopfield network, significantly

Abstract. Consolidation has been implemented in two ways: as straight rehearsal of patterns or as pseudorehearsal, in which pseudoitems are created by sampling attractors or input–output combinations from the network. Although both implementations have been investigated by several authors, few have explored how it is decided which pattern or pseudoitem is consolidated. Controlling consolidation is not trivial, as it is susceptible to a corruption. In runaway consolidation, one or two patterns monopolize all consolidation resources and come to dominate the entire network. Runaway consolidation is analysed, and three solutions are explored. Suppressing transmission in the connections in which consolidation takes place is shown to work best. Placing bounds on connections or unlearning attractors also alleviates runaway consolidation, though less effectively so.

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.

Abstract — Handling catastrophic forgetting is an interesting and challenging topic in modeling the memory mechanisms of the human brain using machine learning models. From a more general point of view, catastrophic forgetting reflects the stability-plasticity dilemma, which is one of the several dilemmastobeaddressedinlearningsystems:toretainthe stored memory while learning new information. Different to the existing approaches, we introduce a Pareto-optimality based multi-objective learning framework for alleviating catastrophic learning. Compared to the single-objective learning methods, multi-objective evolutionary learning with the help of pseudorehearsal is shown to be more promising in dealing with the stability-plasticity dilemma. I.

Building on the human memory model that consider LTM to be similar to a distributed network (McClelland, McNaughton & O'Reilly, 1995), and informed by the recent solutions to catastrophic forgetting that suppose memories are dynamically maintained in a dual architecture through a memory self-refreshing mechanism (Ans & Rousset, 1997, 2000; Ans et al., 2002, 2004; French, 1997), we checked whether false memories of never seen (target) items can be created in humans by exposure to "pseudo-patterns " generated from random input in an artificial neural network (previously trained on the target items). In a behavioral experiment using an opposition method it is shown that the answer is yes: Though the pseudo-patterns presented to the participants were selected so as to resemble (both at the exemplar and the prototype level) more the control items than the target items, the participants exhibited more familiarity for the target items previously learned by the artificial neural network. This behavioral result analogous to the one found in simulations indicates that humans, like distributed neural networks, are able to make use of the information the memory self-refreshing mechanism is based upon. The implications of these findings are discussed in the framework of memory consolidation.