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.

[1]  James L. McClelland.  Parallel Distributed Processing: Implications for Cognition and Development , 1988 .

[2]  Pat Langley,et al.  A general theory of discrimination learning , 1987 .

[3]  Peter N. Chletsos.  Cognitive Assessment of Proportional Reasoning Strategies. , 1989 .

[4]  Thomas R. Shultz,et al.  Simulating Stages of Human Cognitive Development With Connectionist Models , 1991, ML.

[5]  H.L.J. van der Maas,et al.  Stagewise cognitive development: an application of catastrophe theory. , 1992, Psychological review.

[6]  R. Siegler.  Three aspects of cognitive development , 1976, Cognitive Psychology.

[7]  Annette Karmiloff-Smith,et al.  Nature, Nurture and PDP: Preposterous Developmental Postulates? , 1992 .

[8]  L. Squire.  Memory and Brain , 1987 .

[9]  Richard K. Belew,et al.  Interposing an Ontogenetic Model Between Genetic Algorithms and Neural Networks , 1992, NIPS.

[10]  R. Stavy,et al.  U-shaped behavioral growth , 1982 .

[11]  Annette Karmiloff-Smith,et al.  Children's problem solving , 1984 .

[12]  D. Klahr,et al.  The representation of children's knowledge. , 1978, Advances in child development and behavior.

[13]  J. Wilder.  The Origins of Intelligence in Children , 1954 .

[14]  Christian Lebiere,et al.  The Cascade-Correlation Learning Architecture , 1989, NIPS.

[15]  Philippe G. Schyns.  A Modular Neural Network Model of Concept Acquisition , 1991 .

[16]  Thomas R. Shultz,et al.  Modeling cognitive development with a generative connectionist algorithm , 1995 .

[17]  I. Ahmed,et al.  The Neurobiology of Memory: Concepts, Findings, Trends , 1990 .

[18]  E. Butterfield,et al.  Are children's rule-assessment classifications invariant across instances of problem types? , 1986, Child development.

[19]  A. Newell.  Unified Theories of Cognition , 1990 .

[20]  Robert S. Siegler,et al.  Children's thinking, 2nd ed. , 1991 .

[21]  William C. Schmidt,et al.  A Cascade-Correlation Model of Balance Scale Phenomena , 2000 .

[22]  V. Marchman,et al.  U-shaped learning and frequency effects in a multi-layered perception: Implications for child language acquisition , 1991, Cognition.

[23]  Stevan Harnad,et al.  Categorical Perception and the Evolution of Supervised Learning in Neural Nets , 1991 .

[24]  G. Oden.  Concept, Knowledge, and Thought , 1987 .

[25]  Timothy D. Wilson,et al.  Telling more than we can know: Verbal reports on mental processes. , 1977 .

[26]  Jeffrey L. Elman,et al.  Learning and Evolution in Neural Networks , 1994, Adapt. Behav..

[27]  Eric A. Jenkins,et al.  How Children Discover New Strategies , 1989 .

[28]  K. Plunkett,et al.  Connectionism and developmental theory , 1992 .

[29]  C. Brainerd.  Judgments and explanations as criteria for the presence of cognitive structures. , 1973 .

[30]  K. A. Ericsson,et al.  Verbal reports as data. , 1980 .

[31]  Robert S. Siegler.  When do children learn? The relationship between existing knowledge and learning , 1980 .

[32]  R. Taraban,et al.  Language learning: Cues or rules? , 1989 .

[33]  R. Siegler.  Developmental Sequences within and between Concepts. , 1981 .