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“Cognitive Development”
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Glasersfeld E. von (1974) Jean Piaget and the radical constructivist epistemology
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Cohen L. B., Chaput H. H. & Cashon C. H. (2002) A constructivist model of infant cognition. Cognitive Development 17: 1323–1343.
Cohen L. B.
,
Chaput H. H.
&
Cashon C. H.
(
2002
)
A constructivist model of infant cognition
.
Cognitive Development
17: 1323–1343.
Copy Citation
We propose six Information-Processing Principles (IPPs) that together describe a constructive, hierarchical system by which infants come to understand objects and events in the world around them. We then demonstrate the applicability of these principles to four specific domains of infant perception and/or cognition, (i.e., form perception, object unity, complex pattern perception, and understanding of causal events). In each case empirical developmental changes appear to be consistent with the IPPs. We then present the Constructivist Learning Architecture, a computational model of infant
cognitive development
. This model is based on the IPPs, and uses self-organizing, neurally based techniques from Kohonen (1997) and Hebb (1949). We then apply the model to the complex domain of infant understanding of causal events, and replicate many of the developmental changes found empirically. Finally, we discuss the applicability of this constructivist approach to infant
cognitive development
in general.
Key words:
information-processing principles
,
constructivist learning architecture
,
infant cognition.
Glasersfeld E. von (1981) The conception and perception of number. In: Wagner S. & Geeslin W. E. (eds.) Modeling mathematical cognitive development. Clearinghouse for Science, Mathematics and Environmental Education, Columbus OH: 15–46. https://cepa.info/1353
Glasersfeld E. von
(
1981
)
The conception and perception of number
.
In: Wagner S. & Geeslin W. E. (eds.)
Modeling mathematical
cognitive development
. Clearinghouse for Science, Mathematics and Environmental Education, Columbus OH: 15–46.
Fulltext at https://cepa.info/1353
Copy Citation
Key words:
cognition
,
mathematics
Glasersfeld E. von (1993) Das Radikale in Jean Piagets Konstruktivismus [The radical in Jean Piaget’s constructivism]. In: Duit R. & Gräber W. (eds.) Kognitive Entwicklung und Lernen der Naturwissenschaften [Cognitive development and learning in the natural science]. IPN, Kiel, Germany: 46–62. https://cepa.info/1443
Glasersfeld E. von
(
1993
)
Das Radikale in Jean Piagets Konstruktivismus [The radical in Jean Piaget’s constructivism]
.
In: Duit R. & Gräber W. (eds.)
Kognitive Entwicklung und Lernen der Naturwissenschaften [
Cognitive development
and learning in the natural science]
. IPN, Kiel, Germany: 46–62.
Fulltext at https://cepa.info/1443
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Key words:
radical constructivism
,
Jean Piaget
Haith M. M. (2013) Emergent constructivism has its place – Among other possibilities. Cognitive Development 2(28): 144–147.
Haith M. M.
(
2013
)
Emergent constructivism has its place – Among other possibilities
.
Cognitive Development
2(28): 144–147.
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Commentary on “Stepping off the pendulum: Why only an action-based approach can transcend the nativist–empiricist debate” by J. Allen and M. Bickhard.
Haith M. M. (2013) Emergent constructivism has its place – Among other possibilities. Commentary on “Stepping off the pendulum: Why only an action-based approach can transcend the nativist–empiricist debate” by J. Allen and M. Bickhard. Cognitive Development 28: 144–147. https://cepa.info/4505
Haith M. M.
(
2013
)
Emergent constructivism has its place – Among other possibilities. Commentary on “Stepping off the pendulum: Why only an action-based approach can transcend the nativist–empiricist debate” by J. Allen and M. Bickhard
.
Cognitive Development
28: 144–147.
Fulltext at https://cepa.info/4505
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Mareschal D. & Shultz T. R. (1996) Generative connectionist networks and constructivist cognitive development. Cognitive Development 11(4): 571–603.
Mareschal D.
&
Shultz T. R.
(
1996
)
Generative connectionist networks and constructivist
cognitive development
.
Cognitive Development
11(4): 571–603.
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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 children’s 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 Fodor’s (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.
Parisi D. & Schlesinger M. (2002) Artificial life and Piaget. Cognitive Development 17: 1301–1321. https://cepa.info/2783
Parisi D.
&
Schlesinger M.
(
2002
)
Artificial life and Piaget
.
Cognitive Development
17: 1301–1321.
Fulltext at https://cepa.info/2783
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Artificial Life is the study of all phenomena of the living world through their repro¬duction in artificial systems. We argue that Artificial Life models of evolution and devel¬opment offer a new set of theoretical and methodological tools for investigating Piaget’s ideas. The concept of an Artificial Life Neural Network (ALNN) is first introduced, and contrasted with the study of other recent approaches to modeling development. We then illustrate how several key elements of Piaget’s theory of
cognitive development
(e.g., sensorimotor schemata, perception-action integration) can be investigated within the Ar¬tificial Life framework. We conclude by discussing possible new directions of Artificial Life research that will help to elaborate and extend Piaget’s developmental framework.
Key words:
Artificial life
,
Piaget
,
evolution
Richards J., Steffe L. P. & Glasersfeld E. von (1981) Reflections on interdisciplinary research teams. In: Wagner S. & Geeslin W. E. (eds.) Modeling mathematical cognitive development. Clearinghouse for Science, Mathematics and Environmental Education, Columbus OH: 135–143. https://cepa.info/1354
Richards J.
,
Steffe L. P.
&
Glasersfeld E. von
(
1981
)
Reflections on interdisciplinary research teams
.
In: Wagner S. & Geeslin W. E. (eds.)
Modeling mathematical
cognitive development
. Clearinghouse for Science, Mathematics and Environmental Education, Columbus OH: 135–143.
Fulltext at https://cepa.info/1354
Copy Citation
Key words:
science
Spencer J. P., Austin A. & Schutte A. R. (2012) Contributions of dynamic systems theory to cognitive development. Cognitive Development 27(4): 401–418. https://cepa.info/5883
Spencer J. P.
,
Austin A.
&
Schutte A. R.
(
2012
)
Contributions of dynamic systems theory to
cognitive development
.
Cognitive Development
27(4): 401–418.
Fulltext at https://cepa.info/5883
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We examine the contributions of dynamic systems theory to the field of
cognitive development
, focusing on modeling using dynamic neural fields. After introducing central concepts of dynamic field theory (DFT), we probe empirical predictions and findings around two examples – the DFT of infant perseverative reaching that explains Piaget’s A-not-B error and the DFT of spatial memory that explain changes in spatial cognition in early development. Review of the literature around these examples reveals that computational modeling is having an impact on empirical research in
cognitive development
; however, this impact does not extend to neural and clinical research. Moreover, there is a tendency for researchers to interpret models narrowly, anchoring them to specific tasks. We conclude on an optimistic note, encouraging both theoreticians and experimentalists to work toward a more theory-driven future.
Key words:
dynamic systems theory
,
spatial memory
,
perseveration
,
neural networks
,
object concept.
Ziemke T. (2001) Are robots embodied? In: Balkenius C., Zlatev J., Kozima H., Dautenhahn K. & Breazeal C. (eds.) Proceedings of the First International Workshop on Epigenetic Robotics: Modeling Cognitive Development in Robotic Systems. LUCS 85. Lund University, Lund: 75–93. https://cepa.info/7226
Ziemke T.
(
2001
)
Are robots embodied?
.
In: Balkenius C., Zlatev J., Kozima H., Dautenhahn K. & Breazeal C. (eds.)
Proceedings of the First International Workshop on Epigenetic Robotics: Modeling
Cognitive Development
in Robotic Systems. LUCS 85
. Lund University, Lund: 75–93.
Fulltext at https://cepa.info/7226
Copy Citation
Embodiment has become an important concept in many areas of cognitive science. There are, however, very different notions of exactly what embodiment is and what kind of body is required for what kind of embodied cognition. Hence, while many would agree that humans are embodied cognizers, there is much less agreement on what kind of artefact could be considered as embodied. This paper identifies and contrasts five different notions of embodiment which can roughly be characterized as (1) structural coupling between agent and environment, (2) historical embodiment as the result of a history of structural coupling, (3) physical embodiment, (4) “organismoid’ embodiment, i.e. organism-like bodily form (e.g., humanoid robots), and (5) organismic embodiment of autopoietic, living systems.
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