Empiricists are from Venus, modelers are from Mars: Reconciling experimental and computational approaches in cognitive neuroscience

被引:6
作者
Cowell, Rosemary A. [1 ]
Bussey, Timothy J. [2 ,3 ,4 ]
Saksida, Lisa M. [2 ,3 ,4 ]
机构
[1] Univ Calif San Diego, Dept Psychol, La Jolla, CA 92093 USA
[2] Univ Cambridge, Dept Expt Psychol, Cambridge CB2 3EB, England
[3] MRC, Cambridge CB2 3EB, England
[4] Wellcome Trust Behav & Clin Neurosci Inst, Cambridge CB2 3EB, England
关键词
Computational model; Connectionism; Behavioral neuroscience; Cognitive neuroscience; Levels of organization; Problem space; Biological plausibility; Parsimony; Model parameters; ORBITAL PREFRONTAL CORTEX; COMPLEMENTARY-LEARNING-SYSTEMS; OBJECT RECOGNITION; PERIRHINAL CORTEX; NEURAL-NETWORK; ORBITOFRONTAL CORTEX; CONNECTIONIST MODEL; FACIAL EXPRESSIONS; CORTICAL-LESIONS; EPISODIC MEMORY;
D O I
10.1016/j.neubiorev.2012.08.008
中图分类号
B84 [心理学]; C [社会科学总论]; Q98 [人类学];
学科分类号
03 ; 0303 ; 030303 ; 04 ; 0402 ;
摘要
We describe how computational models can be useful to cognitive and behavioral neuroscience, and discuss some guidelines for deciding whether a model is useful. We emphasize that because instantiating a cognitive theory as a computational model requires specification of an explicit mechanism for the function in question, it often produces clear and novel behavioral predictions to guide empirical research. However, computational modeling in cognitive and behavioral neuroscience remains somewhat rare, perhaps because of misconceptions concerning the use of computational models (in particular, connectionist models) in these fields. We highlight some common misconceptions, each of which relates to an aspect of computational models: the problem space of the model, the level of biological organization at which the model is formulated, and the importance (or not) of biological plausibility, parsimony, and model parameters. Careful consideration of these aspects of a model by empiricists, along with careful delineation of them by modelers, may facilitate communication between the two disciplines and promote the use of computational models for guiding cognitive and behavioral experiments. (C) 2012 Elsevier Ltd. All rights reserved.
引用
收藏
页码:2371 / 2379
页数:9
相关论文
共 68 条
[1]   Computational Influence of Adult Neurogenesis on Memory Encoding [J].
Aimone, James B. ;
Wiles, Janet ;
Gage, Fred H. .
NEURON, 2009, 61 (02) :187-202
[2]  
Anderson J.R., 2007, Integrated Models of Cognitive Systems, P49
[3]   Using fMRI to Test Models of Complex Cognition [J].
Anderson, John R. ;
Carter, Cameron S. ;
Fincham, Jon M. ;
Qin, Yulin ;
Ravizza, Susan M. ;
Rosenberg-Lee, Miriam .
COGNITIVE SCIENCE, 2008, 32 (08) :1323-1348
[4]  
[Anonymous], 2002, Computational Neuroscience of Vision
[5]  
[Anonymous], 2000, Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain
[6]  
[Anonymous], 1986, Parallel Distributed Processing: Explorations in the Microstructure of Cognition
[7]  
[Anonymous], 1993, Rules of the Mind
[8]  
[Anonymous], 1982, Visual perception
[9]   A neuropsychological theory of multiple systems in category learning [J].
Ashby, FG ;
Alfonso-Reese, LA ;
Turken, AU ;
Waldron, EM .
PSYCHOLOGICAL REVIEW, 1998, 105 (03) :442-481
[10]   Computational modeling and empirical studies of hippocampal neurogenesis-dependent memory: Effects of interference, stress and depression [J].
Becker, Suzanna ;
MacQueen, Glenda ;
Wojtowicz, J. Martin .
BRAIN RESEARCH, 2009, 1299 :45-54