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)

The acquisition of English noun and verb morphology is modeled using a single-system connectionist network. The network is trained to produce the plurals and past tense forms of a large corpus of monosyllabic English nouns and verbs. The developmental trajectory of network performance is analyzed in detail and is shown to mimic a number of important features of the acquisition of English noun and verb morphology in young children. These include an initial error-free period of performance on both nouns and (...) verbs followed by a period of intermittent over-regularization of irregular nouns and verbs. Errors in the model show evidence of phonological conditioning and frequency effects. Furthermore, the network demonstrates a strong tendency to regularize denominal verbs and deverbal nouns and masters the principles of voicing assimilation. Despite their incorporation into a single-system network, nouns and verbs exhibit some important differences in their profiles of acquisition. Most importantly, noun inflections are acquired earlier than verb inflections. The simulations generate several empirical predictions that can be used to evaluate further the suitability of this type of cognitive architecture in the domain of inflectional morphology. (shrink)

The acquisition of English noun and verb morphology is modeled using a single-system connectionist network. The network is trained to produce the plurals and past tense forms of a large corpus of monosyllabic English nouns and verbs. The developmental trajectory of network performance is analyzed in detail and is shown to mimic a number of important features of the acquisition of English noun and verb morphology in young children. These include an initial error-free period of performance on both nouns and (...) verbs followed by a period of intermittent over-regularization of irregular nouns and verbs. Errors in the model show evidence of phonological conditioning and frequency effects. Furthermore, the network demonstrates a strong tendency to regularize denominal verbs and deverbal nouns and masters the principles of voicing assimilation. Despite their incorporation into a single-system network, nouns and verbs exhibit some important differences in their profiles of acquisition. Most importantly, noun inflections are acquired earlier than verb inflections. The simulations generate several empirical predictions that can be used to evaluate further the suitability of this type of cognitive architecture in the domain of inflectional morphology. (shrink)

Theory-ladenness of perception and cognition is pervasive and variable. Emerging maturationally natural perception and cognition, which are on-line, fast, automatic, unconscious, and, by virtue of their selectivity, theoretical in import, if not in form, define normal development. They contrast with off-line, slow, deliberate, conscious perceptual and cognitive judgments that reflective theories, including scientific ones, inform. Although culture tunes MN systems, their emergence and operation do not rely on culturally distinctive inputs. The sciences advance radically counter-intuitive representations that depart drastically from (...) MN systems’ deliverances. Extensive experience with RCI scientific theories can result in a practiced naturalness with their perceptual and cognitive consequences; nevertheless, automatic MN verdicts persistently intrude. Fodor suggests that the uniformity of the biases MN systems introduce can serve as a theory-neutral means for adjudicating scientific disputes. Findings about vision challenge Fodor’s proposal for circumventing problems that MN theory-ladenness presents. These considerations indicate that RCI scientific ideas are difficult to learn, master, and deploy; consequently, the corrective import of science’s social and institutional arrangements plays a critical role in its epistemic stature. (shrink)

Most words in natural languages are polysemous; that is, they have related but different meanings in different contexts. This one‐to‐many mapping of form to meaning presents a challenge to understanding how word meanings are learned, represented, and processed. Previous work has focused on solutions in which multiple static semantic representations are linked to a single word form, which fails to capture important generalizations about how polysemous words are used; in particular, the graded nature of polysemous senses, and the flexibility and (...) regularity of polysemy use. We provide a novel view of how polysemous words are represented and processed, focusing on how meaning is modulated by context. Our theory is implemented within a recurrent neural network that learns distributional information through exposure to a large and representative corpus of English. Clusters of meaning emerge from how the model processes individual word forms. In keeping with distributional theories of semantics, we suggest word meanings are generalized from contexts of different word tokens, with polysemy emerging as multiple clusters of contextually modulated meanings. We validate our results against a human‐annotated corpus of polysemy focusing on the gradedness, flexibility, and regularity of polysemous sense individuation, as well as behavioral findings of offline sense relatedness ratings and online sentence processing. The results provide novel insights into how polysemy emerges from contextual processing of word meaning from both a theoretical and computational point of view. (shrink)

Some empirical evidence in the artificial language acquisition literature has been taken to suggest that statistical learning mechanisms are insufficient for extracting structural information from an artificial language. According to the more than one mechanism (MOM) hypothesis, at least two mechanisms are required in order to acquire language from speech: (a) a statistical mechanism for speech segmentation; and (b) an additional rule-following mechanism in order to induce grammatical regularities. In this article, we present a set of neural network studies demonstrating (...) that a single statistical mechanism can mimic the apparent discovery of structural regularities, beyond the segmentation of speech. We argue that our results undermine one argument for the MOM hypothesis. (shrink)

Children learn their native language by exposure to their linguistic and communicative environment, but apparently without requiring that their mistakes be corrected. Such learning from “positive evidence” has been viewed as raising “logical” problems for language acquisition. In particular, without correction, how is the child to recover from conjecturing an over-general grammar, which will be consistent with any sentence that the child hears? There have been many proposals concerning how this “logical problem” can be dissolved. In this study, we review (...) recent formal results showing that the learner has sufficient data to learn successfully from positive evidence, if it favors the simplest encoding of the linguistic input. Results include the learnability of linguistic prediction, grammaticality judgments, language production, and form-meaning mappings. The simplicity approach can also be “scaled down” to analyze the learnability of specific linguistic constructions, and it is amenable to empirical testing as a framework for describing human language acquisition. (shrink)

How people learn chunks or associations between adjacent items in sequences was modelled. Two previously successful models of how people learn artificial grammars were contrasted: the CCN, a network version of the competitive chunker of Servan‐Schreiber and Anderson [J. Exp. Psychol.: Learn. Mem. Cogn. 16 (1990) 592], which produces local and compositionally‐structured chunk representations acquired incrementally; and the simple recurrent network (SRN) of Elman [Cogn. Sci. 14 (1990) 179], which acquires distributed representations through error correction. The models' susceptibility to two (...) types of interference was determined: prediction conflicts, in which a given letter can predict two other letters that appear next with an unequal frequency; and retroactive interference, in which the prediction made by a letter changes in the second half of training. The predictions of the models were determined by exploring parameter space and seeing howdensely different regions of the space of possible experimental outcomes were populated by model outcomes. For both types of interference, human data fell squarely in regions characteristic of CCN performance but not characteristic of SRN performance. (shrink)

This paper describes different types of models for the evolution of communication and language. It uses the distinction between signals, symbols, and words for the analysis of evolutionary models of language. In particular, it show how evolutionary computation techniques, such as artificial life, can be used to study the emergence of syntax and symbols from simple communication signals. Initially, a computational model that evolves repertoires of isolated signals is presented. This study has simulated the emergence of signals for naming foods (...) in a population of foragers. This type of model studies communication systems based on simple signal-object associations. Subsequently, models that study the emergence of grounded symbols are discussed in general, including a detailed description of a work on the evolution of simple syntactic rules. This model focuses on the emergence of symbol-symbol relationships in evolved languages. Finally, computational models of syntax acquisition and evolution are discussed. These different types of computational models provide an operational definition of the signal/symbol/word distinction. The simulation and analysis of these types of models will help to understand the role of symbols and symbol acquisition in the origin of language. (shrink)

Scholars studying the origins and evolution of language are also interested in the general issue of the evolution of cognition. Language is not an isolated capability of the individual, but has intrinsic relationships with many other behavioral, cognitive, and social abilities. By understanding the mechanisms underlying the evolution of linguistic abilities, it is possible to understand the evolution of cognitive abilities. Cognitivism, one of the current approaches in psychology and cognitive science, proposes that symbol systems capture mental phenomena, and attributes (...) cognitive validity to them. Therefore, in the same way that language is considered the prototype of cognitive abilities, a symbol system has become the prototype for studying language and cognitive systems. Symbol systems are advantageous as they are easily studied through computer simulation (a computer program is a symbol system itself), and this is why language is often studied using computational models. (shrink)