Re-thinking stages of cognitive development: An appraisal of connectionist models of the balance scale task. Quinlan, P. T, van der Maas, H. L J, Jansen, B. R J, Booij, O., & Rendell, M. Cognition, 103(3):413-59, 2007.
doi  abstract   bibtex   
The present paper re-appraises connectionist attempts to explain how human cognitive development appears to progress through a series of sequential stages. Models of performance on the Piagetian balance scale task are the focus of attention. Limitations of these models are discussed and replications and extensions to the work are provided via the Cascade-Correlation algorithm. An application of multi-group latent class analysis for examining performance of the networks is described and these results reveal fundamental functional characteristics of the networks. Evidence is provided that strongly suggests that the networks are unable to acquire a mastery of torque and, although they do recover certain rules of operation that humans do, they also show a propensity to acquire rules never previously seen.
@Article{Quinlan2007,
  author   = {Philip T Quinlan and Han L J van der Maas and Brenda R J Jansen and Olaf Booij and Mark Rendell},
  journal  = {Cognition},
  title    = {Re-thinking stages of cognitive development: {A}n appraisal of connectionist models of the balance scale task.},
  year     = {2007},
  number   = {3},
  pages    = {413-59},
  volume   = {103},
  abstract = {The present paper re-appraises connectionist attempts to explain how
	human cognitive development appears to progress through a series
	of sequential stages. Models of performance on the Piagetian balance
	scale task are the focus of attention. Limitations of these models
	are discussed and replications and extensions to the work are provided
	via the Cascade-Correlation algorithm. An application of multi-group
	latent class analysis for examining performance of the networks is
	described and these results reveal fundamental functional characteristics
	of the networks. Evidence is provided that strongly suggests that
	the networks are unable to acquire a mastery of torque and, although
	they do recover certain rules of operation that humans do, they also
	show a propensity to acquire rules never previously seen.},
  doi      = {10.1016/j.cognition.2006.02.004},
  keywords = {16574091},
}

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