Chinese readers' eye movements were simulated in the context of the E-Z Reader model, which was developed to account for the eye movements of readers of English. Despite obvious differences between English and Chinese, the model did a fairly good job of simulating the eye movements of Chinese readers. The successful simulation suggests that the control of eye movements in reading Chinese is similar to that in an alphabetic language such as English.

There are two accounts of how readers of unspaced writing systems know where to move their eyes: saccades are directed toward default targets ; or saccade lengths are adjusted dynamically, as a function of ongoing parafoveal processing. This article reports an eye-movement experiment supporting the latter hypothesis by demonstrating that the slope of the relationship between the saccade launch site on word N and the subsequent fixation landing site on word N + 1 is > 1, suggesting that saccades are (...) lengthened from launch sites that afford more parafoveal processing. This conclusion is then evaluated and confirmed via simulations using implementations of both hypotheses, with a discussion of these results for our understanding of saccadic targeting during reading and existing models of eye-movement control. (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)