Thomas R. Shultz

Cognitive dissonance reduction as constraint satisfaction.

A constraint satisfaction neural network model (the consonance model) simulated data from the two major cognitive dissonance paradigms of insufficient justification and free choice. In several cases, the model fit the human data better than did cognitive dissonance theory. Superior fits were due to the inclusion of constraints that were not part of dissonance theory and to the increased precision inherent to this computational approach. Predictions generated by the model for a free choice between undesirable alternatives were confirmed in a new psychological experiment. The success of the consonance model underscores important, unforeseen similarities between what had been formerly regarded as the rather exotic process of dissonance reduction and a variety of other, more mundane psychological processes. Many of these processes can be understood as the progressive application of constraints supplied by beliefs and attitudes.

The Role of Incongruity and Resolution in Children's Appreciation of Cartoon Humor.

Abstract Two experiments were conducted to test a number of predictions derived from a cognitive theory of humor. The theory specified that incongruity and resolution are structural aspects of the joke which a subject must understand in order to fully appreciate the intended humor. The experiments involved presenting elementary school children with a number of cartoons and obtaining measures of both their appreciation and their comprehension of the cartoons. Original, incongruity-removed, and resolution-removed cartoon forms were used to assess the humor-inducing effects of incongruity and resolution. The results indicated a tendency for the child first to identify an incongruity and then proceed to resolve it for each cartoon that he saw. If he was unable to discover the criterial incongruity (i.e., the one intended by the cartoonist), he typically invented a noncriterial incongruity and tried to resolve that. If he was unable to provide the criterial resolution, he typically employed a noncriterial resolution. Whether criterial or noncriterial, incongruity and resolution were both byportant for humor appreciation. This was demonstrated both by comparisons of the funniness of original and altered cartoon forms and by internal analyses of the relation between the comprehension and appreciation measures. Some types of resolution were found to be more comprehensible than others and this was attributed to the different amount of cognitive work that cach resolution type requires. Developmental trends regarding comprehension of incongruity and resolution were attributed to informational, as opposed to structural, factors. A number of possible explanations for developmental diffcrences in appreciation of the cartoons were briefly discussed.

Modeling Cognitive Development on Balance Scale Phenomena

We used cascade-correlation to model human cognitive development on a well studied psychological task, the balance scale. In balance scale experiments, the child is asked to predict the outcome of placing certain numbers of equal weights at various distances to the left or right of a fulcrum. Both stage progressions and information salience effects have been found with children on this task. Cascade-correlation is a generative connectionist algorithm that constructs its own network topology as it learns. Cascade-correlation networks provided better fits to these human data than did previous models, whether rule-based or connectionist. The network model was used to generate a variety of novel predictions for psychological research.

Modeling developmental cognitive neuroscience

In the past few years connectionist models have greatly contributed to formulating theories of cognitive development. Some of these models follow the approach of developmental cognitive neuroscience in exploring interactions between brain development and cognitive development by integrating structural change into learning. We describe two classes of these models. The first focuses on experience-dependent structural elaboration within a brain region by adding or deleting units and connections during learning. The second models the gradual integration of different brain areas based on combinations of experience-dependent and maturational factors. These models provide new theories of the mechanisms of cognitive change in various domains and they offer an integrated framework to study normal and abnormal development, and normal and impaired adult processing.

Generative connectionist networks and constructivist cognitive development

This article presents a novel computational framework for modeling cognitive development. The new modeling paradigm provides a language with which to compare and contrast radically different facets of childrens knowledge. Concepts from the study of machine learning are used to explore the power of connectionist networks that construct their own architectures during learning. These so-called generative algorithms are shown to escape from Fodors (1980) critique of Constructivist development. We describe one generative connectionist algorithm (cascade-correlation) in detail. We report on the successful use of the algorithm to model cognitive development on balance scale phenomena; seriation; the integration of velocity, time, and distance cues; prediction of effect sizes from magnitudes of causal potencies and effect resistances; and the acquisition of English personal pronouns. The article demonstrates that computer models are invaluable for illuminating otherwise obscure discussions.

Comparing online and lab methods in a problem-solving experiment

Online experiments have recently become very popular, and—in comparison with traditional lab experiments— they may have several advantages, such as reduced demand characteristics, automation, and generalizability of results to wider populations (Birnbaum, 2004; Reips, 2000, 2002a, 2002b). We replicated Dandurand, Bowen, and Shultz’s (2004) lab-based problem-solving experiment as an Internet experiment. Consistent with previous results, we found that participants who watched demonstrations of successful problem-solving sessions or who read instructions outperformed those who were told only that they solved problems correctly or not. Online participants were less accurate than lab participants, but there was no interaction with learning condition. Thus, we conclude that online and Internet results are consistent. Disadvantages included high dropout rate for online participants; however, combining the online experiment with the department subject pool worked well.

Free Choice and Cognitive Dissonance Revisited: Choosing “Lesser Evils” Versus “Greater Goods”

Traditional dissonance theory predicts a spreading apart of chosen and rejected alternatives following a decision. More recent constraint satisfaction models of this classic free-choice paradigm suggest that these effects may vary with the overall attractiveness of the choice options. This prediction was tested with 13-year-olds choosing among posters. As in prior computer simulations, a difficult choice between generally less desirable alternatives produced a large increase in participants’ evaluations of the chosen alternative, whereas a difficult choice between generally more desirable alternatives produced a large decrease in evaluations of the rejected alternative. The results were discussed in terms of the relative amounts of dissonance created in the various conditions. The utility of the consonance constraint satisfaction model that generated these novel predictions was stressed.

Covariation and Temporal Contiguity as Principles of Causal Inference in Young Children.

Abstract Four- to seven-year-old children observed a simple physical effect which could be attributed to either a consistent but noncontiguous covariate or a contiguous but inconsistent covariate. When there was a physical rationale for the temporal delay between covariate and effect, children attributed the effect to the consistent but noncontiguous covariate. In the absence of such a rationale, they attributed the effect to the contiguous but inconsistent covariate. It was concluded that neither consistent covariation nor strict temporal contiguity were essential aspects of causal inference for these children.

Knowledge-based cascade-correlation: Using knowledge to speed learning

Research with neural networks typically ignores the role of knowledge in learning by initializing the network with random connection weights. We examine a new extension of a well-known generative algorithm, cascade-correlation. Ordinary cascade-correlation constructs its own network topology by recruiting new hidden units as needed to reduce network error. The extended algorithm, knowledge-based cascade-correlation (KBCC), recruits previously learned sub-networks as well as single hidden units. This paper describes KBCC and assesses its performance on a series of small, but clear problems involving discrimination between two classes. The target class is distributed as a simple geometric figure. Relevant source knowledge consists ofvarious linear transformations ofthe target distribution. KBCC is observed to find, adapt and use its relevant knowledge to speed learning significantly.

A computational analysis of conservation.

An approach to modeling cognitive development with a generative connectionist algorithm is described and illustrated with a new model of conservation acquisition. Among the conservation phenomena captured with this model are acquisition, the problem size effect, the length bias effect, and the screening effect. The simulations suggest novel explanations for sudden jumps in conservation performance (based on new representations of conservation transformations) and for the problem size effect (based on an analog representation of number). The simulations support the correlation-learning explanation of length bias (that length correlates with number during number altering transformations). Some conservation phenomena that so far elude computational modeling attempts are also discussed along with their prospects for capture. Suggestions are made for theorizing about cognitive development as well as about conservation acquisition. A variety of classic puzzles about cognitive development are addressed in the light of this model and similar models of other aspects of cognitive development.