Language acquisition from sparse input without error feedback

Hadley, Robert F.; Cardei, Vlad C.

doi:10.1016/s0893-6080(98)00139-7

Cited by 15 publications

(10 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As discussed in the Introduction, several earlier proposals for semantically systematic connectionist models (Hadley & Cardei, 1999;Hadley & Hayward, 1997;Miikkulainen, 1996) indeed depended on symbolic representations or mechanisms.…”

Section: Implementation Of a Symbol Systemmentioning

confidence: 94%

“…In spite of the evidence that understanding a sentence involves more than the construction of a propositional form, many computational models (e.g., Budiu & Anderson, 2004;Chang, 2002;Desai, 2007;Dominey, 2005;Hadley & Cardei, 1999;Hadley & Hayward, 1997;Mayberry, Crocker, & Knoeferle, in press;St.John & McClelland, 1990) represent sentence meaning as a structural combination of symbols, corresponding to the proposition expressed by the sentence. Contrary to this, Frank, Koppen, Noordman, and Vonk (2003) developed a non-symbolic representational scheme for the meaning of declarative sentences.…”

Section: Sentence Comprehension and Mental Representationmentioning

confidence: 99%

See 1 more Smart Citation

Connectionist semantic systematicity

2009

View full text Add to dashboard Cite

Fodor and Pylyshyn (1988) argue that connectionist models are not able to display systematicity other than by implementing a classical symbol system. This claim entails that connectionism cannot compete with the classical approach as an alternative architectural framework for human cognition.We present a connectionist model of sentence comprehension that does not implement a symbol system yet behaves systematically. It consists in a recurrent neural network that maps sentences describing situations in a microworld, onto representations of these situations. After being trained on particular sentences-situation pairs, the model can comprehend new sentences, even if these describe new situations. We argue that this systematicity arises robustly and in a psychologically plausible manner because it depends on structure inherent in the world.

show abstract

Section: Implementation Of a Symbol Systemmentioning

confidence: 94%

Section: Sentence Comprehension and Mental Representationmentioning

confidence: 99%

Connectionist semantic systematicity

2009

View full text Add to dashboard Cite

show abstract

“…These algorithms provide biologically plausible accounts of changes in neural connectivity (Ashby, Ennis, & Spiering, 2007;Sullivan & de Sa, 2006). Self-organizing connectionist models have been successful in addressing the emergence of new structure in language acquisition, including aspects of vocabulary (Li, Zhao, & MacWhinney, 2007) and syntax (Hadley & Cardei, 1999). They have also proven useful for modeling the emergence of early concepts, such as physical causality (Cohen, Chaput, & Cashon, 2002;Schyns, 1991).…”

Section: Self-organization Of New Structurementioning

confidence: 99%

The dynamics of insight: Mathematical discovery as a phase transition

et al. 2009

View full text Add to dashboard Cite

In recent work in cognitive science, it has been proposed that cognition is a self-organizing, dynamical system. However, capturing the real-time dynamics of cognition has been a formidable challenge. Furthermore, it has been unclear whether dynamics could effectively address the emergence of abstract concepts (e.g., language, mathematics). Here, we provide evidence that a quintessentially cognitive phenomenon-the spontaneous discovery of a mathematical relation-emerges through self-organization. Participants solved a series of gear-system problems while we tracked their eye movements. They initially solved the problems by manually simulating the forces of the gears but then spontaneously discovered a mathematical solution. We show that the discovery of the mathematical relation was predicted by changes in entropy and changes in power-law behavior, two hallmarks of phase transitions. Thus, the present study demonstrates the emergence of higher order cognitive phenomena through the nonlinear dynamics of self-organization.

show abstract

“…) vdV&dK employ competitive inhibition to ensure that within a matrix, the most active assembly along the vertical dimension will bind with the most active assembly along the horizontal dimension. This strategy is precisely analogous to the manner in which conjunctive nodes create bindings in the connectionist parsing model of Stevenson (1994) and the language acquisition models of Hadley and Hayward (1997) and Hadley and Cardei (1999). All three of these systems are capable of processing deeply embedded sentence structures.…”

Section: Ross W Gaylermentioning

confidence: 86%

Distributed neural blackboards could be more attractive

Grüning¹,

Treves²

2006

Behav Brain Sci

View full text Add to dashboard Cite

Human cognition is unique in the way in which it relies on combinatorial (or compositional) structures. Language provides ample evidence for the existence of combinatorial structures, but they can also be found in visual cognition. To understand the neural basis of human cognition, it is therefore essential to understand how combinatorial structures can be instantiated in neural terms. In his recent book on the foundations of language, Jackendoff described four fundamental problems for a neural instantiation of combinatorial structures: the massiveness of the binding problem, the problem of 2, the problem of variables, and the transformation of combinatorial structures from working memory to long-term memory. This paper aims to show that these problems can be solved by means of neural "blackboard" architectures. For this purpose, a neural blackboard architecture for sentence structure is presented. In this architecture, neural structures that encode for words are temporarily bound in a manner that preserves the structure of the sentence. It is shown that the architecture solves the four problems presented by Jackendoff. The ability of the architecture to instantiate sentence structures is illustrated with examples of sentence complexity observed in human language performance. Similarities exist between the architecture for sentence structure and blackboard architectures for combinatorial structures in visual cognition, derived from the structure of the visual cortex. These architectures are briefly discussed, together with an example of a combinatorial structure in which the blackboard architectures for language and vision are combined. In this way, the architecture for language is grounded in perception. Perspectives and potential developments of the architectures are discussed.

show abstract

Language acquisition from sparse input without error feedback

Cited by 15 publications

References 22 publications

Connectionist semantic systematicity

Connectionist semantic systematicity

The dynamics of insight: Mathematical discovery as a phase transition

Distributed neural blackboards could be more attractive

Contact Info

Product

Resources

About