Connectionism and cognitive architecture: A critical analysis

Fodor, Jerry A.; Pylyshyn, Zenon W.

doi:10.1016/0010-0277(88)90031-5

Cited by 3,291 publications

(1,790 citation statements)

References 18 publications

Supporting

Mentioning

1,739

Contrasting

Unclassified

Order By: Relevance

“…Kovelman, Baker, & Petitto, 2007;PalomarGarcía et al, 2015). Thus, NPSFs are hypothesized to be implemented at the level of the "language of thought" (Fodor, & Pylyshyn, 1988;Marcus, Marblestone, & Dean, 2014). We used these 42 NPSFs, developed to code the semantic properties of English words, testing their ability to generate accurate predictions concerning the neural representations of words in Portuguese.…”

Section: A Generative Mapping Between Word Concepts and Fmri Activatimentioning

confidence: 99%

Commonality of neural representations of sentences across languages: Predicting brain activation during Portuguese sentence comprehension using an English-based model of brain function

et al. 2017

View full text Add to dashboard Cite

Keywords:Cross-language commonality, sentence representations in bilinguals, Predictive modeling of sentence representations, Meta-language brain locations in sentence processing ABSTRACTThe aim of the study was to test the cross-language generative capability of a model that predicts neural activation patterns evoked by sentence reading, based on a semantic characterization of the sentence. In a previous study on English monolingual speakers (Wang, Cherkassky, & Just, submitted), a computational model performed a mapping from a set of 42 concept-level semantic features (Neurally Plausible Semantic Features, NPSFs) as well as 6 thematic role markers to neural activation patterns (assessed with fMRI), to predict activation levels in a network of brain locations. The model used two types of information gained from the English-based fMRI data to predict the activation for individual sentences in Portuguese. First, it used the mapping weights from NPSFs to voxel activation levels derived from the model for English reading. Second, the brain locations for which the activation levels were predicted were derived from a factor analysis of the brain activation patterns during English reading. These meta-language locations were defined by the clusters of voxels with high loadings on each of the four main dimensions (factors), namely people, places, actions and feelings, underlying the neural representations of the stimulus sentences.This cross-language model succeeded in predicting the brain activation patterns associated with the reading of 60 individual Portuguese sentences that were entirely new to the model, attaining accuracies reliably above chance level. The prediction accuracy was not affected by whether the Portuguese speaker was monolingual or Portuguese-English bilingual. The model's confusion errors indicated an accurate capture of the events or states described in the sentence at a conceptual level. Overall, the cross-language predictive capability of the model demonstrates the neural commonality between speakers of different languages in the representations of everyday events and states, and provides an initial characterization of the common meta-language neural basis.

show abstract

Section: A Generative Mapping Between Word Concepts and Fmri Activatimentioning

confidence: 99%

Commonality of neural representations of sentences across languages: Predicting brain activation during Portuguese sentence comprehension using an English-based model of brain function

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Still, there are at least two important ways in which standard parallel distrib-uted processing models clearly differ from the standard picture of symbolmanipulation. First, these models differ with respect to their treatment of compositionality (a topic that is outside the scope of this paper; for discussion see Fodor & Pylyshyn, 1988;Marcus, 1999). Second, these models differ with respect to their treatment of operations over variables, the subject of the current article.…”

Section: Burden Of Proofmentioning

confidence: 99%

Rethinking Eliminative Connectionism

Marcus

1998

Cognitive Psychology

293

180

View full text Add to dashboard Cite

Humans routinely generalize universal relationships to unfamiliar instances. If we are told ''if glork then frum,'' and ''glork,'' we can infer ''frum''; any name that serves as the subject of a sentence can appear as the object of a sentence. These universals are pervasive in language and reasoning. One account of how they are generalized holds that humans possess mechanisms that manipulate symbols and variables; an alternative account holds that symbol-manipulation can be eliminated from scientific theories in favor of descriptions couched in terms of networks of interconnected nodes. Can these ''eliminative'' connectionist models offer a genuine alternative? This article shows that eliminative connectionist models cannot account for how we extend universals to arbitrary items. The argument runs as follows. First, if these models, as currently conceived, were to extend universals to arbitrary instances, they would have to generalize outside the space of training examples. Next, it is shown that the class of eliminative connectionist models that is currently popular cannot learn to extend universals outside the training space. This limitation might be avoided through the use of an architecture that implements symbol manipulation.

show abstract

“…3CAPS combines symbolic computation (i.e., production rule application) with connectionist properties (i.e., activation propagation). The symbolic component provides the computational power that is necessary to do sentence processing (Fodor & Pylyshyn, 1988) as it permits the composition of higher order representations (e.g., parse trees) out of individual elements (e.g., word class information) as specified in production rules, which enable variable binding. The connectionist component provides a way to capture the graded quality that seems typical of normal and aphasic sentence comprehension, such that activation propagation and storage modulate the efficacy of the symbolic component.…”

Section: The Cognitive Architecture: 3capsmentioning

confidence: 99%