Inflectional morphology has been taken as a paradigmatic example of rule-governed grammatical knowledge (Pinker, 1999). The plausibility of this claim may be related to the fact that it is mainly based on studies of English, which has a very simple inflectional system. We examined the representation of inflectional morphology in Serbian, which encodes number, gender, and case for nouns. Linguists standardly characterize this system as a complex set of rules, with disagreements about their exact form. We present analyses of a (...) large corpus of nouns which showed that, as in English, Serbian inflectional morphology is quasiregular: It exhibits numerous partial regularities creating neighborhoods that vary in size and consistency. We then asked whether a simple connectionist network could encode this statistical information in a manner that also supported generalization. A network trained on 3,244 Serbian nouns learned to produce correctly inflected phonological forms from a specification of a word's lemma, gender, number, and case, and generalized to untrained cases. The model's performance was sensitive to variables that also influence human performance, including surface and lemma frequency. It was also influenced by inflectional neighborhood size, a novel measure of the consistency of meaning to form mapping. A word-naming experiment with native Serbian speakers showed that this measure also affects human performance. The results suggest that, as in English, generating correctly inflected forms involves satisfying a small number of simultaneous probabilistic constraints relating form and meaning. Thus, common computational mechanisms may govern the representation and use of inflectional information across typologically diverse languages. (shrink)

We distinguish between literal and metaphorical applications of Bayesian models. When intended literally, an isomorphism exists between the elements of representation assumed by the rational analysis and the mechanism that implements the computation. Thus, observation of the implementation can externally validate assumptions underlying the rational analysis. In other applications, no such isomorphism exists, so it is not clear how the assumptions that allow a Bayesian model to fit data can be independently validated.

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)

Kabbalah and Ecology is a groundbreaking book that resets the conversation about ecology and the Abrahamic traditions. David Mevorach Seidenberg challenges the anthropocentric reading of the Torah, showing that a radically different orientation to the more-than-human world of nature is not only possible, but that such an orientation also leads to a more accurate interpretation of scripture, rabbinic texts, Maimonides and Kabbalah. Deeply grounded in traditional texts and fluent with the physical sciences, this book proposes not only a new (...) understanding of God's image but also a new direction for restoring religion to its senses and to a more alive relationship with the more-than-human, both with nature and with divinity. (shrink)

The productive use of English past tense morphology in school-aged children (N= 74; 3 years, 8 months to 13 years, 5 months) is explored using on elicited production task. Errors represented 20% of the responses overall. Virtually all of the children demonstrated productivity with regular (e.g., good) and irregular patterns (zero-marking, e.g., sit + sit; vowel-change, e.g., ride -+ rid). Overall frequency of errors decreased with age, yet the tendency for certain types of irregularizations increased in the older groups. Analyses (...) across items indicated that all error types were predicted by combinations of item frequency, phonological chamcteristics of stems and past tense forms, and aspects of phonological past tense “neighborhoods.” Contrary to “hybrid” or dual-mechanism models incorporating a phonologically-insensitive default mechanism (e.g., Prasada & Pinker, 1993), these results suggest that children’s productivity with regular and irregular patterns is consistent with a phonologically-based constraint satisfaction system similar to that implemented in connectionist models (Daugherty & Seidenberg, 1992; Plunkett 81 Marchman, 1991,1993). (shrink)

Roderick Seidenberg, a friend of Lewis Mumford, was an architect with a sociological perspective on technology. He understood that technology had become a system and that it needed to be situated within a historical context to understand its impact on humans. In this article, his work is compared to that of Jacques Ellul.

Languages and writing systems result from satisfying multiple constraints related to learning, comprehension, production, and their biological bases. Orthographies are not optimal because these constraints often conflict, with further deviations due to accidents of history and geography. Things tend to even out because writing systems and the languages they represent exhibit systematic trade-offs between orthographic depth and morphological complexity.