Papers available on cognitive development, knowledge and learning, cognitive consistency

[Tom Shultz]

The following four papers are available at the WWW site for LNSC
(Laboratory for Natural and Simulated Cognition at McGill University).

http://www.psych.mcgill.ca/labs/lnsc/html/Lab-Home.html

Alternatively, ftp addresses are given for each paper.

Shultz, T. R., Schmidt, W. C., Buckingham, D., & Mareschal, D. (1995).
Modeling cognitive development with a generative connectionist algorithm
(pp. 205-261). In T. J. Simon & G. S. Halford (Eds.), Developing cognitive
competence: New approaches to process modeling. Hillsdale, NJ: Erlbaum.

One of the key unsolved problems in cognitive development is the precise
specification of developmental transition mechanisms. In this chapter, we
focus on the applicability of a specific generative connectionist
algorithm, cascade-correlation (Fahlman & Lebiere, 1990), as a process
model of transition mechanisms. Generative connectionist algorithms build
their own network topologies as they learn, allowing them to simulate both
qualitative and quantitative developmental changes. We compare and contrast
cascade-correlation, Piaget's notions of assimilation and accommodation,
Papert's little known but historically relevant genetron model,
conventional back-propagation networks, and rule-based models.

Specific cascade-correlation models of a wide range of developmental
phenomena are presented. These include the balance scale task; concepts of
potency and resistance in causal reasoning; seriation; integration of the
concepts of distance, time, and velocity; and personal pronouns.
Descriptions of these simulations stress the degree to which the models
capture the essential known psychological phenomena, generate new testable
predictions, and provide explanatory insights. In several cases, the
simulation results underscore clear advantages of connectionist modeling
techniques. Abstraction across the various models yields a set of
domain-general constraints for cognitive development. Particular
domain-specific constraints are identified. Finally, the models demonstrate
that connectionist approaches can be successful even on relatively
high-level cognitive tasks.

ftp: ego.psych.mcgill.ca/pub/shultz/cog-comp.ps.gz
--------------------------------------------------------------------------------

Shultz, T. R., & Lepper, M. R. (1995). Cognitive dissonance reduction as
constraint satisfaction. Technical Report No. 2195, McGill Papers in
Cognitive Science, McGill University, Montréal.

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.

ftp: ego.psych.mcgill.ca/pub/shultz/cog-diss.ps.gz
--------------------------------------------------------------------------------

Shultz, T. R., Oshima-Takane, Y., & Takane, Y. (1995). Analysis of
unstandardized contributions in cross connected networks. In D. Touretzky,
G. Tesauro, & T. K. Leen, (Eds). Advances in Neural Information Processing
Systems 7 (pp. 601-608). Cambridge, MA: MIT Press.

Understanding knowledge representations in neural nets has been a difficult
problem. Principal components analysis (PCA) of contributions (products of
sending activations and connection weights) has yielded valuable insights
into knowledge representations, but much of this work has focused on the
correlation matrix of contributions. The present work shows that analyzing
the variance-covariance matrix of contributions yields more valid  insights
by taking account of weights.

ftp: ego.psych.mcgill.ca/pub/shultz/contcros.ps.gz
--------------------------------------------------------------------------------

Tetewsky, S. J., Shultz, T. R., & Takane, Y. (1995). Training regimens and
function compatibility: Implications for understanding the effects of
knowledge on concept learning. Proceedings of the Seventeenth Annual
Conference of the Cognitive Science Society (pp. 304-309). Hillsdale, NJ:
Erlbaum.

Previous research has indicated that breaking a task into subtasks can both
facilitate and interfere with learning in neural networks.  Although these
results appear to be contradictory, they actually reflect some underlying
principles governing learning in neural networks.  Using the
cascade-correlation learning algorithm, we devised a concept learning task
that would let us specify the conditions under which subtasking would
facilitate or interfere with learning. The results indicated that
subtasking facilitated learning when the initial subtask involved learning
a function compatible with that characterizing the rest of the task, and
inhibited learning when the initial subtask involved a function
incompatible with the rest of the task.  These results were then discussed
with regard to their implications for understanding the effect of knowledge
on concept learning.

ftp: ego.psych.mcgill.ca/pub/shultz/regimens.ps.gz
--------------------------------------------------------------------------------

A variety of other papers in the areas of cognitive development, knowledge
and learning, analyzing knowledge representations in neural networks, and
cognitive consistency can be found at the LNSC site.

Tom


------------------------------------------------------------------------
Thomas R. Shultz, Professor, Department of Psychology, McGill University
1205 Penfield Ave., Montreal, Quebec, Canada H3A 1B1
Phone: 514-398-6139   Fax: 514-398-4896   Email: shultz at psych.mcgill.ca
WWW: http://www.psych.mcgill.ca/labs/lnsc/html/Lab-Home.html
------------------------------------------------------------------------