Human participants and recurrent (“connectionist”) neural networks were both trained on a categorization system abstractly similar to natural language systems involving irregular (“strong”) classes and a default class. Both the humans and the networks exhibited staged learning and a generalization pattern reminiscent of the Elsewhere Condition (Kiparsky, 1973). Previous connectionist accounts of related phenomena have often been vague about the nature of the networks’ encoding systems. We analyzed our network using dynamical systems theory, revealing topological and geometric properties that can (...) be directly compared with the mechanisms of non-connectionist, rule-based accounts. The results reveal that the networks “contain” structures related to mechanisms posited by rule-based models, partly vindicating the insights of these models. On the other hand, they support the one mechanism (OM), as opposed to the more than one mechanism (MOM), view of symbolic abstraction by showing how the appearance of MOM behavior can arise emergently from one underlying set of principles. The key new contribution of this study is to show that dynamical systems theory can allow us to explicitly characterize the relationship between the two perspectives in implemented models. (shrink)

Regularly inflected forms often behave differently in language production than irregular forms. These differences are often used to argue that irregular forms are listed in the lexicon but regular forms are produced by rule. Using an experimental speech production task with adults, it is shown that overtensing errors, where a tensed verb is used in place of an infinitive, predominantly involve irregular forms, but that the differences may be due to phonological confounds, not to regularity per se. Errors involve vowel‐changing (...) irregular forms more than suffixing inflected forms, with at best a small difference between regular ‐ed and irregular ‐en. Frequency effects on overtensing errors require a model in which the past‐tense and base forms of the verb are in competition and in which activation functions are nonlinear, and rule out models with specialized subnetworks for past‐tense forms. Implications for theories of language production are discussed. (shrink)

Although debates continue, studies of cognition in infancy suggest that knowledge begins to emerge early in life and constitutes part of humans' innate endowment. Early-developing knowledge appears to be both domain-specific and task-specific, it appears to capture fundamental constraints on ecologically important classes of entities in the child's environment, and it appears to remain central to the commonsense knowledge systems of adults.

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. (shrink)

Mental representations have continuous as well as discrete, combinatorial properties. For example, while predominantly discrete, phonological representations also vary continuously; this is reflected by gradient effects in instrumental studies of speech production. Can an integrated theoretical framework address both aspects of structure? The framework we introduce here, Gradient Symbol Processing, characterizes the emergence of grammatical macrostructure from the Parallel Distributed Processing microstructure (McClelland, Rumelhart, & The PDP Research Group, 1986) of language processing. The mental representations that emerge, Distributed Symbol Systems, (...) have both combinatorial and gradient structure. They are processed through Subsymbolic Optimization–Quantization, in which an optimization process favoring representations that satisfy well-formedness constraints operates in parallel with a distributed quantization process favoring discrete symbolic structures. We apply a particular instantiation of this framework, λ-Diffusion Theory, to phonological production. Simulations of the resulting model suggest that Gradient Symbol Processing offers a way to unify accounts of grammatical competence with both discrete and continuous patterns in language performance. (shrink)

In this article, I survey the integrated connectionist/symbolic (ICS) cognitive architecture in which higher cognition must be formally characterized on two levels of description. At the microlevel, parallel distributed processing (PDP) characterizes mental processing; this PDP system has special organization in virtue of which it can be characterized at the macrolevel as a kind of symbolic computational system. The symbolic system inherits certain properties from its PDP substrate; the symbolic functions computed constitute optimization of a well-formedness measure called Harmony. The (...) most important outgrowth of the ICS research program is optimality theory (Prince & Smolensky, 1993/2004), an optimization based grammatical theory that provides a formal theory of cross-linguistic typology. Linguistically, Harmony maximization corresponds to minimization of markedness or structural ill-formedness. Cognitive explanation in ICS requires the collaboration of symbolic and connectionist principles. ICS is developed in detail in Smolensky and Legendre (2006a); this article is a précis of and guide to those volumes. (shrink)

A number of theoretical positions in psychology—including variants of case‐based reasoning, instance‐based analogy, and connectionist models—maintain that abstract rules are not involved in human reasoning, or at best play a minor role. Other views hold that the use of abstract rules is a core aspect of human reasoning. We propose eight criteria for determining whether or not people use abstract rules in reasoning, and examine evidence relevant to each criterion for several rule systems. We argue that there is substantial evidence (...) that several different inferential rules, including modus ponens, contractual rules, causal rules, and the law of large numbers, are used in solving everyday problems. We discuss the implications for various theoretical positions and consider hybrid mechanisms that combine aspects of instance and rule models. (shrink)

Evidence is presented from a polyglot savant to suggest that double dissociations between linguistic and nonverbal abilities are more important than Müller's target article implies. It is also argued that the special nature of syntax makes its assimilation to other aspects of language or to nonhuman communication systems radically implausible.

Multiple explanatory frameworks may be required to provide an adequate account of human cognition. This paper embeds the classical account within a neural network framework, exploring the encoding of syntacticallystructured objects over the synchronicdiachronic characteristics of networks. Synchronic structure is defined in terms of temporal binding and the superposition of states. To accommodate asymmetric relations, synchronic structure is subject to the type uniqueness constraint. The nature of synchronic structure is shown to underlie Xbar theory that characterizes the phrasal structure of (...) human languages. The derivation differentiates core Xbar properties from languagespecific properties. (shrink)

Müller's review of the neuroscientific evidence undermines nativist claims for autonomous syntax and the argument from the poverty of the stimulus. Generativists will appeal to data from language acquisition, but here too there is growing evidence against the nativist position. Epigenetic naturalism, the developmental alternative to nativism, can be extended to epigenetic socionaturalism, acknowledging the importance of sociocultural processes in language and cognitive development.

Abstract A survey of different senses of the term ?representation? is presented. The presentation is guided by the appraisal that this key term is employed in the cognitive literature in different senses and that the distinction between these is not always explicitly stated or appreciated. Furthermore, the different senses seem to be associated with different rationales for the postulation of representation. Given that there may be a lack of convergence between the various senses of the construct in question and the (...) rationales for its postulation, a clear differentiation of these may be crucial for any evaluation of the role of representations in cognition and in cognitive theory. (shrink)

Quite unexpectedly, cognitive psychologists find their field intimately connected to a whole new intellectual landscape that had previously seemed remote, unfamiliar, and all but irrelevant. Yet the proliferating connections tying together the cognitive and evolutionary communities promise to transform both fields, with each supplying necessary principles, methods, and a species of rigor that the other lacks. (Cosmides and Tooby, 1994, p. 85).

In their critique of connectionist models Fodor and Pylyshyn (1988) dismiss such models as not being cognitive or psychological. Evaluating Fodor and Pylyshyn's critique requires examining what is required in characterizating models as 'cognitive'. The present discussion examines the various senses of this term. It argues the answer to the title question seems to vary with these different senses. Indeed, by one sense of the term, neither representa-tionalism nor connectionism is cognitive. General ramifications of such an appraisal are discussed and (...) alternative avenues for cognitive research are suggested. (shrink)

Rumelhart and McClelland's chapter about learning the past tense created a degree of controversy extraordinary even in the adversarial culture of modern science. It also stimulated a vast amount of research that advanced the understanding of the past tense, inflectional morphology in English and other languages, the nature of linguistic representations, relations between language and other phenomena such as reading and object recognition, the properties of artificial neural networks, and other topics. We examine the impact of the Rumelhart and McClelland (...) model with the benefit of 25 years of hindsight. It is not clear who “won” the debate. It is clear, however, that the core ideas that the model instantiated have been assimilated into many areas in the study of language, changing the focus of research from abstract characterizations of linguistic competence to an emphasis on the role of the statistical structure of language in acquisition and processing. (shrink)

According to the language of thought (LOT) approach and the related computational theory of mind (CTM), thinking is the processing of symbols in an inner mental language that is distinct from any public language. Herein, I explore a deep problem at the heart of the LOT/CTM program—it has yet to provide a plausible conception of a mental symbol.

Plant and animal species are information-processing entities of such complexity, integration, and adaptive competence that it may be scientifically fruitful to consider them intelligent. The possibility arises from the analogy between learning and evolution, and from recent developments in evolutionary science, psychology and cognitive science. Species are now described as spatiotemporally localized individuals in an expanded hierarchy of biological entities. Intentional and cognitive abilities are now ascribed to animal, human, and artificial intelligence systems that process information adaptively, and that manifest (...) problem-solving abilities. The structural and functional similarities between such systems and species are extensive, although they “are usually obscured by population-genetic metaphors that have nonetheless contributed much to our understanding of evolution.In this target article, I use Sewall Wright's notion of the “adaptive landscape” to compare the performances of evolving species to those of intelligent organisms. With regard to their adaptive achievements and the kinds of processes by which they are achieved, biological species compare very favorably to intelligent animals by virtue of interactions between populations and their environments, between ontogeny and phylogeny, and between natural, interdemic, organic, and species selection. Addressing the question of whether species are intelligent could help to refine our ideas about species, evolution, and intelligence, and could open new lines of empirical and theoretical inquiry in many disciplines. (shrink)

Plant and animal species are information-processing entities of such complexity, integration, and adaptive competence that it may be scientifically fruitful to consider them intelligent. The possibility arises from the analogy between learning and evolution, and from recent developments in evolutionary science, psychology and cognitive science. Species are now described as spatiotemporally localized individuals in an expanded hierarchy of biological entities. Intentional and cognitive abilities are now ascribed to animal, human, and artificial intelligence systems that process information adaptively, and that manifest (...) problem-solving abilities. The structural and functional similarities between such systems and species are extensive, although they “are usually obscured by population-genetic metaphors that have nonetheless contributed much to our understanding of evolution.In this target article, I use Sewall Wright's notion of the “adaptive landscape” to compare the performances of evolving species to those of intelligent organisms. With regard to their adaptive achievements and the kinds of processes by which they are achieved, biological species compare very favorably to intelligent animals by virtue of interactions between populations and their environments, between ontogeny and phylogeny, and between natural, interdemic, organic, and species selection. Addressing the question of whether species are intelligent could help to refine our ideas about species, evolution, and intelligence, and could open new lines of empirical and theoretical inquiry in many disciplines. (shrink)

In this prcis we focus on phenomena central to the reaction against similarity-based theories that arose in the 1980s and that subsequently motivated the approach to semantic knowledge. Specifically, we consider (1) how concepts differentiate in early development, (2) why some groupings of items seem to form or coherent categories while others do not, (3) why different properties seem central or important to different concepts, (4) why children and adults sometimes attest to beliefs that seem to contradict their direct experience, (...) (5) how concepts reorganize between the ages of 4 and 10, and (6) the relationship between causal knowledge and semantic knowledge. The explanations our theory offers for these phenomena are illustrated with reference to a simple feed-forward connectionist model. The relationships between this simple model, the broader theory, and more general issues in cognitive science are discussed. (shrink)

This paper introduces a special issue of Cognitive Science initiated on the 25th anniversary of the publication of Parallel Distributed Processing (PDP), a two-volume work that introduced the use of neural network models as vehicles for understanding cognition. The collection surveys the core commitments of the PDP framework, the key issues the framework has addressed, and the debates the framework has spawned, and presents viewpoints on the current status of these issues. The articles focus on both historical roots and contemporary (...) developments in learning, optimality theory, perception, memory, language, conceptual knowledge, cognitive control, and consciousness. Here we consider the approach more generally, reviewing the original motivations, the resulting framework, and the central tenets of the underlying theory. We then evaluate the impact of PDP both on the field at large and within specific subdomains of cognitive science and consider the current role of PDP models within the broader landscape of contemporary theoretical frameworks in cognitive science. Looking to the future, we consider the implications for cognitive science of the recent success of machine learning systems called “deep networks”—systems that build on key ideas presented in the PDP volumes. (shrink)

We agree with Müller's epigenetic view of evolution and ontogeny and applaud his multilevel perspective. With him, we stress the importance in ontogeny of progressive specialisation rather than prewired structures. However, we argue that he slips from “speech” to “language” and that, in seeking homologies, these two levels need to be kept separate in the analysis of evolution and ontogeny.