of the Nonconscious Acquisition A Symbolic Model

This article presents counter evidence against Smolensky’s theory thot 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 wellestablished, perceived correlation between “conscious” and “symbolic” on the one hand, and between “nonconscious” and “subsymbolic” on the other, does not exist.

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

[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]  Paul E. Utgoff,et al.  Incremental Induction of Decision Trees , 1989, Machine Learning.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[23]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

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

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

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

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