Explicitness With Psychological Ground

Behavioral and Brain Sciences, 1988

A set of hypotheses is formulated for a connectionist approach to cognitive modeling. These hypotheses are shown to be incompatible with the hypotheses underlying traditional cognitive models. The connectionist models considered are massively parallel numerical computational systems that are a kind of continuous dynamical system. The numerical variables in the system correspond semantically to fine-grained features below the level of the concepts consciously used to describe the task domain. The level of analysis is intermediate between those of symbolic cognitive models and neural models. The explanations of behavior provided are like those traditional in the physical sciences, unlike the explanations provided by symbolic models.Higher-level analyses of these connectionist models reveal subtle relations to symbolic models. Parallel connectionist memory and linguistic processes are hypothesized to give rise to processes that are describable at a higher level as sequential rule appli...

Arxiv preprint quant-ph/0404068, 2004

In this article 1 review the connectionist framework for modeling psychological processes, and I examine the role of connectionist models in empirical psychology. I illustrate how modeling can reveal the empirical implications of general principles, and I point out that the connectionist framework is particularly apt for formalizing certain proposed processing principles. The framework has led to the discovery of new classes of explanations for basic findings; it has led to unified accounts of disparate or contradictory phenomena; and it has shed light on the relevance of certain types of evidence for basic questions about the nature of the processing system. 10 1988 Academic POW. IIK

1990

The character of computational modelling of cognition depends on an underlying theory of representation. Classical cognitive science has exploited the syntax/semantics theory of representation that derives from logic. But this has had the consequence that the kind of psychological explanation supported by classical cognitive science is conceptualist: psychological phenomena are modelled in terms of relations that hold between concepts, and between the sensors/effectors and concepts. This kind of explanation is inappropriate for the Proper Treatment of Connectionism . Is there an alternative theory of representation that retains the advantages of classical theory, but which does not force psychological explanation into the conceptualist mould? I outline such an alternative by introducing an experience-based notion of nonconceptual content and by showing how a complex construction out of nonconceptual content can satisfy classical constraints on cognition. The psychologically fundamental structure of cognition is not the structure that holds between concepts, but, rather, the structure within concepts. The theory of the representational structure within concepts allows psychological phenomena to be explained as the progressive emergence of objectivity. This can be modelled computationally by means of the computational processes of a perspective-dependence-reducing transformer. This device may be thought of as a generalisation of a cognitive map, which includes the processes of map-formation and map use. It forms computational structures which take nonconceptual contents as inputs and yield nonconceptual contents as outputs, but do so in a way which makes the resulting capacity of the system less and less dependent on any particular perspectives, yielding satisfactory performance from any point of view.

Synthese, 2001

ABSTRACT. In “Representations without Rules, Connectionism and the Syntactic Argument”, Kenneth Aizawa argues against the view that connectionist nets can be un-derstood as processing representations without the use of representation-level rules, and he ...

Synthese, 1993

Cognitive Science: A Multidisciplinary Journal, 2008

The remarkable successes of the physical sciences have been built on highly general quantitative laws, which serve as the basis for understanding an enormous variety of specific physical systems. How far is it possible to construct universal principles in the cognitive sciences, in terms of which specific aspects of perception, memory, or decision making might be modelled? Following Shepard (e.g., 1987), it is argued that some universal principles may be attainable in cognitive science. Here we propose two examples: The simplicity principle (which states that the cognitive system prefers patterns that provide simpler explanations of available data); and the scale-invariance principle, which states that many cognitive phenomena are independent of the scale of relevant underlying physical variables, such as time, space, luminance, or sound pressure.

Stability of activation, while it may be necessary for information to become available to consciousness, is not sufficient to produce phenomenal experience. We suggest that consciousness involves access to information and that access makes information symbolic. From this perspective, implicit representations exist, and are best thought of as sub-symbolic. Crucially, such representations can be causally efficacious in the absence of consciousness.

I discuss a connectionist model, based on Elman's (1990, 1991) Simple Recurrent Network, of the acquisition of complex syntactic structure. While not intended as a detailed model of the process children go through in acquiring natural languages, the model helps clarify concepts that may be useful for understanding the development of complex abilities. It provides evidence that connectionist learning can produce stage-wise development emergently. It is consistent with prior work on connectionist models emphasizing their capability of computing in ways that are not possible within the symbolic paradigm (Siegelmann, 1999). On the other hand, it suggests that one mechanism of the symbolic paradigm (a pushdown automaton) may be identified with an attractor of the learning process. Thus, the model provides a concrete example of what might be called "emergence of new conceptual structure during development" and suggests that we need to use both dynamical systems theory and symbolic computation theory to make sense of it.

2019

Connectionism is an approach to neural-networks-based cognitive modeling that encompasses the recent deep learning movement in artificial intelligence. Connectionist models center on statistical inference within neural networks with empirically learnable parameters, which can be represented as graphical models. More recent approaches focus on learning and inference within hierarchical generative models. Contra influential and ongoing critiques, I argue in this dissertation that the connectionist approach to cognitive science possesses in principle (and, as is becoming increasingly clear, in practice) the resources to model even the most rich and distinctly human cognitive capacities, such as abstract, conceptual thought and natural language comprehension and production. Consonant with much previous philosophical work on connectionism, I argue that a core principle—that proximal representations in a vector space have similar semantic values—is the key to a successful connectionist account of the systematicity and productivity of thought, language, and other core cognitive phenomena.