12 found
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  1.  19
    Autonomous Processing in Parallel Distributed Processing Networks.Michael R. W. Dawson & Don P. Schopflocher - 1992 - Philosophical Psychology 5 (2):199-219.
    This paper critically examines the claim that parallel distributed processing (PDP) networks are autonomous learning systems. A PDP model of a simple distributed associative memory is considered. It is shown that the 'generic' PDP architecture cannot implement the computations required by this memory system without the aid of external control. In other words, the model is not autonomous. Two specific problems are highlighted: (i) simultaneous learning and recall are not permitted to occur as would be required of an autonomous system; (...)
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  2. On the Subsymbolic Nature of a PDP Architecture That Uses a Nonmonotonic Activation Function.Michael R. W. Dawson & C. Darren Piercey - 2001 - Minds and Machines 11 (2):197-218.
    PDP networks that use nonmonotonic activation functions often produce hidden unit regularities that permit the internal structure of these networks to be interpreted (Berkeley et al., 1995; McCaughan, 1997; Dawson, 1998). In particular, when the responses of hidden units to a set of patterns are graphed using jittered density plots, these plots organize themselves into a set of discrete stripes or bands. In some cases, each band is associated with a local interpretation. On the basis of these observations, Berkeley (2000) (...)
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  3. Interpreting the Internal Structure of a Connectionist Model of the Balance Scale Task.Michael R. W. Dawson & Corinne Zimmerman - 2003 - Brain and Mind 4 (2):129-149.
    One new tradition that has emerged from early research on autonomous robots is embodied cognitive science. This paper describes the relationship between embodied cognitive science and a related tradition, synthetic psychology. It is argued that while both are synthetic, embodied cognitive science is antirepresentational while synthetic psychology still appeals to representations. It is further argued that modern connectionism offers a medium for conducting synthetic psychology, provided that researchers analyze the internal representations that their networks develop. The paper then provides a (...)
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  4.  35
    PDP Networks Can Provide Models That Are Not Mere Implementations of Classical Theories.Michael R. W. Dawson, David A. Medler & Istvan S. N. Berkeley - 1997 - Philosophical Psychology 10 (1):25-40.
    There is widespread belief that connectionist networks are dramatically different from classical or symbolic models. However, connectionists rarely test this belief by interpreting the internal structure of their nets. A new approach to interpreting networks was recently introduced by Berkeley et al. (1995). The current paper examines two implications of applying this method: (1) that the internal structure of a connectionist network can have a very classical appearance, and (2) that this interpretation can provide a cognitive theory that cannot be (...)
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  5.  19
    Making a Middling Mousetrap.Michael R. W. Dawson & Istvan Berkeley - 1993 - Behavioral and Brain Sciences 16 (3):454-455.
  6.  27
    To What Extent Do Beliefs Affect Apparent Motion?Richard D. Wright & Michael R. W. Dawson - 1994 - Philosophical Psychology 7 (4):471-491.
    A number of studies in the apparent motion literature were examined using the cognitive penetrability criterion to determine the extent to which beliefs affect the perception of apparent motion. It was found that the interaction between the perceptual processes mediating apparent motion and higher order processes appears to be limited. In addition, perceptual and inferential beliefs appear to have different effects on perceived motion optimality and direction. Our findings suggest that the system underlying apparent motion perception has more than one (...)
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  7.  1
    Interpreting the Internal Structure of a Connectionist Model of the Balance Scale Task.Michael R. W. Dawson & Corinne Zimmerman - 2003 - Brain and Mind 4 (2):129-149.
    One new tradition that has emerged from early research on autonomous robots is embodied cognitive science. This paper describes the relationship between embodied cognitive science and a related tradition, synthetic psychology. It is argued that while both are synthetic, embodied cognitive science is antirepresentational while synthetic psychology still appeals to representations. It is further argued that modern connectionism offers a medium for conducting synthetic psychology, provided that researchers analyze the internal representations that their networks develop. The paper then provides a (...)
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  8.  16
    Constraining Tag-Assignment From Above and Below.Michael R. W. Dawson - 1989 - Behavioral and Brain Sciences 12 (3):400-402.
  9.  45
    Better Theories Are Needed to Distinguish Perception From Cognition.Michael R. W. Dawson & C. Darren Piercey - 1999 - Behavioral and Brain Sciences 22 (3):374-375.
    Pylyshyn argues that many of the methods used to study perception are too coarse to detect the distinction between perceptual and cognitive processing. We suggest that the reason for this is that the theories used to guide research in perception are at fault. More powerful theories – for instance, computer simulations – will be required to identify where perception ends and where cognition begins.
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  10.  21
    Feature Development, Object Concepts, and the Scope Slip.Michael R. W. Dawson - 2001 - Behavioral and Brain Sciences 24 (6):1146-1147.
    Schyns et al.'s (1998) target article raises a conflict between the need for a fixed functional architecture in an explanatory cognitive science and the need for a system to learn to detect new features. This conflict can be resolved by avoiding the scope slip in which properties of objects are erroneously viewed as being properties of their representations.
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  11. FINSTs, Tag-Assignment and the Parietal Gazetteer.Michael R. W. Dawson - 1992 - Behavioral and Brain Sciences 15 (4):730-731.
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  12.  25
    Using Perceptrons to Explore the Reorientation Task.Michael R. W. Dawson, Debbie M. Kelly, Marcia L. Spetch & Brian Dupuis - 2010 - Cognition 114 (2):207-226.
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