A Symbolic Model of the Nonconscious Acquisition of Information

This article presents counter evidence against Smolensky's theory that human intuitive/nonconscious congnitive processes can only be accurately explained in terms of subsymbolic computations carried out in artificial neural networks. We present symbolic learning models of two well-studied, complicated cognitive tasks involving nonconscious acquisition of information: learning production rules and artificial finite state grammars. Our results demonstrate that intuitive learning does not imply subsymbolic computation, and that the already well-established, perceived correlation between “conscious” and “symbolic” on the one hand, and between “nonconscious” and “subsymbolic” on the other, does not exist.

[1]  Stuart K. Card,et al.  Evaluation of mouse, rate-controlled isometric joystick, step keys, and text keys, for text selection on a CRT , 1987 .

[2]  M. A. Stadler,et al.  On learning complex procedural knowledge. , 1989, Journal of experimental psychology. Learning, memory, and cognition.

[3]  Paul A. Kolers,et al.  Memorial Consequences of Automatized Encoding. , 1975 .

[4]  Douglas H. Fisher,et al.  A Case Study of Incremental Concept Induction , 1986, AAAI.

[5]  Philip J. Stone,et al.  Experiments in induction , 1966 .

[6]  James L. McClelland,et al.  Learning the structure of event sequences. , 1991, Journal of experimental psychology. General.

[7]  Paul Smolensky,et al.  Tensor Product Variable Binding and the Representation of Symbolic Structures in Connectionist Systems , 1990, Artif. Intell..

[8]  J. Kihlstrom.  The cognitive unconscious. , 1987, Science.

[9]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[10]  John R. Anderson,et al.  Knowledge Compilation: Mechanisms for the Automatization of Cognitive Skills. , 1980 .

[11]  G. S. Snoddy.  Learning and stability: a psychophysiological analysis of a case of motor learning with clinical applications. , 1926 .

[12]  A. Reber.  Implicit learning and tacit knowledge , 1993 .

[13]  Pawel Lewicki,et al.  Acquisition of procedural knowledge about a pattern of stimuli that cannot be articulated , 1988, Cognitive Psychology.

[14]  P. Smolensky.  On the proper treatment of connectionism , 1988, Behavioral and Brain Sciences.

[15]  Carla H. Lagorio,et al.  Psychology , 1929, Nature.

[16]  C. Ling,et al.  Answering the connectionist challenge: a symbolic model of learning the past tenses of English verbs , 1993, Cognition.

[17]  P. Smolensky.  THE CONSTITUENT STRUCTURE OF CONNECTIONIST MENTAL STATES: A REPLY TO FODOR AND PYLYSHYN , 2010 .

[18]  E. R. Crossman.  A THEORY OF THE ACQUISITION OF SPEED-SKILL∗ , 1959 .

[19]  Allen and Rosenbloom Paul S. Newell,et al.  Mechanisms of Skill Acquisition and the Law of Practice , 1993 .

[20]  H. Hoffman,et al.  Unconscious acquisition of complex procedural knowledge. , 1987 .

[21]  J. Fodor,et al.  Connectionism and cognitive architecture: A critical analysis , 1988, Cognition.

[22]  R. Seibel.  DISCRIMINATION REACTION TIME FOR 1,023-ALTERNATIVE TASK. , 1963, Journal of experimental psychology.

[23]  Geoffrey E. Hinton.  Tensor Product Variable Binding and the Representation of Symbolic Structures in Connectionist Systems , 1991 .

[24]  Peter Friedland.  Acquisition of Procedural Knowledge from Domain Experts , 1981, IJCAI.

[25]  W. Freeman.  Second Commentary: On the proper treatment of connectionism by Paul Smolensky (1988) - Neuromachismo Rekindled , 1989 .

[26]  Pawel Lewicki,et al.  Nonconscious Social Information Processing , 1986 .

[27]  P. Lewicki,et al.  Nonconscious acquisition of information. , 1992, The American psychologist.

[28]  Ryszard S. Michalski,et al.  The AQ15 Inductive Learning System: An Overview and Experiments , 1986 .

[29]  E. Hartley,et al.  Religious Affiliation and Open-Mindedness in Binocular Resolution , 1963, Perceptual and motor skills.

[30]  U NEISSER,et al.  Searching for Ten Targets Simultaneously , 1963, Perceptual and motor skills.