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  • 1
    Electronic Resource
    Electronic Resource
    A Decision-Tree Model of Balance Scale Development (1994)
    Springer
    Machine learning 24 (1994), S. 203-230 
    Details
    ISSN: 0885-6125
    Keywords: Cognitive development ; balance scale ; C4.5 ; symbolic learning algorithms.
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We present an alternative model of human cognitive development on the balance scale task. Study of this task has inspired a wide range of human and computational work. The task requires that children predict the outcome of placing a discrete number of weights at various distances on either side of a fulcrum. Our model, which features the symbolic learning algorithm C4.5 as a transition mechanism, exhibits regularities found in the human data including orderly stage progression, U-shaped development, and the torque difference effect. Unlike previous successful models of the task, the current model uses a single free parameter, is not restricted in the size of the balance scale that it can accommodate, and does not require the assumption of a highly structured output representation or a training environment biased towards weight or distance information. The model makes a number of predictions differing from those of previous computational efforts.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1018062826803
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    Paper (German National Licenses)
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  • 2
    Electronic Resource
    Electronic Resource
    A decision-tree model of balance scale development (1996)
    Springer
    Machine learning 24 (1996), S. 203-230 
    Details
    ISSN: 0885-6125
    Keywords: Cognitive development ; balance scale ; C4.5 ; symbolic learning algorithms
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We present an alternative model of human cognitive development on the balance scale task. Study of this task has inspired a wide range of human and computational work. The task requires that children predict the outcome of placing a discrete number of weights at various distances on either side of a fulcrum. Our model, which features the symbolic learning algorithm C4.5 as a transition mechanism, exhibits regularities found in the human data including orderly stage progression. U-shaped development, and the torque difference effect. Unlike previous successful models of the task, the current model uses a single free parameter, is not restricted in the size of the balance scale that it can accommodate, and does not require the assumption of a highly structured output representation or a training environment biased towards weight or distance information. The model makes a number of predictions differing from those of previous computational efforts.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00058612
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    Paper (German National Licenses)
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  • 3
    Electronic Resource
    Electronic Resource
    Modeling Cognitive Development on Balance Scale Phenomena (1994)
    Springer
    Machine learning 16 (1994), S. 57-86 
    Details
    ISSN: 0885-6125
    Keywords: cognitive development ; balance scale ; connectionist learning ; cascade-correlation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract 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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1022630902151
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    Paper (German National Licenses)
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  • 4
    Electronic Resource
    Electronic Resource
    Modeling cognitive development on balance scale phenomena (1994)
    Springer
    Machine learning 16 (1994), S. 57-86 
    Details
    ISSN: 0885-6125
    Keywords: cognitive development ; balance scale ; connectionist learning ; cascade-correlation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract 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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00993174
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    Paper (German National Licenses)
    Fulltext
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