Bridging levels of analysis: Comment on McClelland et al. and Griffiths et al.

Trends in Cognitive Sciences

Volumn 14, Issue 8, 2010, Pages 344-345

  Kruschke, John K ^a  

^a INDIANA UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BEHAVIOR; COGNITION; HUMAN; LETTER; PROBABILITY; STATISTICAL MODEL; THEORY;

COGNITION; HUMANS; MODELS, PSYCHOLOGICAL; MODELS, STATISTICAL; NONLINEAR DYNAMICS;

EID: 77955277121     PISSN: 13646613     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.tics.2010.05.007     Document Type: Letter

References (8)

1. McClelland J.L., et al. Letting structure emerge: connectionist and dynamical systems approaches to cognition. Trends Cogn. Sci. 2010, 14:348-356.
2. Griffiths T.L., et al. Probabilistic models of cognition: exploring representations and inductive biases. Trends Cogn. Sci. 2010, 14:357-364.
3. Xu F., Tenenbaum J.B. Word learning as Bayesian inference. Psychol. Rev. 2007, 114:245-272.
4. Sanborn, A.N. et al. Rational approximations to rational models: alternative algorithms for category learning. Psychol. Rev. (in press).
5. Macdonald C., Macdonald G. Connectionism: Debates on Psychological Explanation 1995, Wiley-Blackwell.
6. McCauley R.N. Levels of explanation and cognitive architectures. Blackwell Companion to Cognitive Science 1998, 611-624. Blackwell. W. Bechtel, G. Graham (Eds.).
7. Kruschke J.K. ALCOVE: an exemplar-based connectionist model of category learning. Psychol. Rev. 1992, 99:22-44.
8. Kruschke J.K. Locally Bayesian learning with applications to retrospective revaluation and highlighting. Psychol. Rev. 2006, 113:677-699.