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)

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)

It has been well documented how language-specific cues may be used for word segmentation. Here, we investigate what role a language-independent phonological universal, the sonority sequencing principle (SSP), may also play. Participants were presented with an unsegmented speech stream with non-English word onsets that juxtaposed adherence to the SSP with transitional probabilities. Participants favored using the SSP in assessing word-hood, suggesting that the SSP represents a potentially powerful cue for word segmentation. To ensure the SSP influenced the segmentation process (i.e., (...) during learning), we presented two additional groups of participants with either (a) no exposure to the stimuli prior to testing or (b) the same stimuli with pauses marking word breaks. The SSP did not influence test performance in either case, suggesting that the SSP is important for word segmentation during the learning process itself. Moreover, the fact that SSP-independent segmentation of the stimulus occurred (in the latter control condition) suggests that universals are best understood as biases rather than immutable constraints on learning. (shrink)

In our opinion, the discontinuity between extant human and nonhuman minds is much broader and deeper than most researchers admit. We are happy to report that Evans & Levinson's (E&L's) target article strongly corroborates our unpopular hypothesis, and that the comparative evidence, in turn, bolsters E&L's provocative argument. Both a Universal Grammar and the “mental continuity” between human and nonhuman minds turn out to be modern myths.

Our response takes advantage of the wide-ranging commentary to clarify some aspects of our original proposal and augment others. We argue against the generative critics of our coevolutionary program for the language sciences, defend the use of close-to-surface models as minimizing cross-linguistic data distortion, and stress the growing role of stochastic simulations in making generalized historical accounts testable. These methods lead the search for general principles away from idealized representations and towards selective processes. Putting cultural evolution central in understanding language (...) diversity makes learning fundamental in the cognition of language: increasingly powerful models of general learning, paired with channelled caregiver input, seem set to manage language acquisition without recourse to any innate “universal grammar.” Understanding why human language has no clear parallels in the animal world requires a cross-species perspective: crucial ingredients are vocal learning (for which there are clear non-primate parallels) and an intention-attributing cognitive infrastructure that provides a universal base for language evolution. We conclude by situating linguistic diversity within a broader trend towards understanding human cognition through the study of variation in, for example, human genetics, neurocognition, and psycholinguistic processing. (shrink)

It has been well documented how language‐specific cues may be used for word segmentation. Here, we investigate what role a language‐independent phonological universal, the sonority sequencing principle (SSP), may also play. Participants were presented with an unsegmented speech stream with non‐English word onsets that juxtaposed adherence to the SSP with transitional probabilities. Participants favored using the SSP in assessing word‐hood, suggesting that the SSP represents a potentially powerful cue for word segmentation. To ensure the SSP influenced the segmentation process (i.e., (...) during learning), we presented two additional groups of participants with either (a) no exposure to the stimuli prior to testing or (b) the same stimuli with pauses marking word breaks. The SSP did not influence test performance in either case, suggesting that the SSP is important for word segmentation during the learning process itself. Moreover, the fact that SSP‐independent segmentation of the stimulus occurred (in the latter control condition) suggests that universals are best understood as biases rather than immutable constraints on learning. (shrink)