(language), as investigated in con-nectionist networks. The networks are trained to process complex sentences involving relative clauses, number agreement, and several types of verb argument structure. Training fails in the case of networks which are fully formed and ‘adultlike ’ in their capacity. Training

cognitive) theory of mind. And sure enough, though behaviorism all but disappeared, associationism has un-dergone a striking revival that started in the mid-1980s with “con-nectionist ” models of associative learning in massively parallel net-works. (A connectionist network models the activity

to the seemingly unitary mental faculty of attention remains unclear. We describe a con-nectionist model of human attentional networks to explore the possible interplays among the networks from a computational perspective. This model is developed in the framework of leabra (local, error-driven, and associative

Recurrent neural networks (RNNs) are con-nectionist models of sequential data that are naturally applicable to the analysis of natural language. Recently, “depth in space ” — as an orthogonal notion to “depth in time ” — in RNNs has been investigated by stacking mul-tiple layers of RNNs and shown

We present the "Multi-State Time Delay Neural Network " (MS-TDNN) as an extension of the TDNN to robust word recognition. Unlike most other hybrid methods. the MS-TDNN embeds an alignment search procedure into the con-nectionist architecture. and allows for word level supervision

The development of reading skill and bases of developmental dyslexia were explored using con-nectionist models. Four issues were examined: the acquisition of phonological knowledge prior to reading, how this knowledge facilitates learning to read, phonological and non phonological bases of dyslexia