For more than a century the paradigm inspiringcognitive neuroscience has been modular and localist.Contemporary research in functional brain imaginggenerally relies on methods favorable to localizingparticular functions in one or more specific brainregions. Meanwhile, connectionist cognitive scientistshave celebrated the computational powers ofdistributed processing, and pioneered methods forinterpreting distributed representations. This papertakes a connectionist approach to functionalneuroimaging. A tabulation of 35 PET (positronemission tomography) experiments strongly indicatesdistributed function for at least the ''medium sized''anatomical units, the cortical Brodmann areas. Moreimportant, when these PET (...) experiments were interpretedas distributed representations, multidimensionalscaling revealed a ''brain activation space'' with asalient structure organized primarily by the sensorymodality of the stimulus, and secondarily by the typeof motor response. These results suggest that currentanalytical techniques in functional neuroimagingshould be augmented by distributed processinganalyses, and that these analyses may lead to manydiscoveries about the structure of ''inner space.''. (shrink)

Time underlies many interesting human behaviors. Thus, the question of how to represent time in connectionist models is very important. One approach is to represent time implicitly by its effects on processing rather than explicitly (as in a spatial representation). The current report develops a proposal along these lines first described by Jordan (1986) which involves the use of recurrent links in order to provide networks with a dynamic memory. In this approach, hidden unit patterns are fed back to themselves: (...) the internal representations which develop thus reflect task demands in the context of prior internal states. A set of simulations is reported which range from relatively simple problems (temporal version of XOR) to discovering syntactic/semantic features for words. The networks are able to learn interesting internal representations which incorporate task demands with memory demands: indeed, in this approach the notion of memory is inextricably bound up with task processing. These representations reveal a rich structure, which allows them to be highly context‐dependent, while also expressing generalizations across classes of items. These representations suggest a method for representing lexical categories and the type/token distinction. (shrink)