Understanding normal and impaired word reading: Computational principles in quasi-regular domains.

Abstract

We develop a connectionist approach to processing in quasi-regular domains, as exemplified by English word reading. A consideration of the shortcomings of a previous implementation (Seidenberg & McClelland, 1989, Psych. Rev.) in reading nonwords leads to the development of orthographic and phonological representations that capture better the relevant structure among the written and spoken forms of words. In a number of simulation experiments, networks using the new representations learn to read both regular and exception words, including low-frequency exception words, and yet are still able to read pronounceable nonwords as well as skilled readers. A mathematical analysis of the effects of word frequency and spelling-sound consistency in a related but simpler system serves to clarify the close relationship of these factors in influencing naming latencies. These insights are verified in subsequent simulations, including an attractor network that reproduces the naming latency data directly in its time to settle on a response. Further analyses of the network's ability to reproduce data on impaired reading in surface dyslexia support a view of the reading system that incorporates a graded division-of-labor between semantic and phonological processes. Such a view is consistent with the more general Seidenberg and McClelland framework and has some similarities with-but also important differences from-the standard dual-route account.Many aspects of language can be characterized as quasiregular-the relationship between inputs and outputs is systematic but admits many exceptions. One such task is the mapping between the written and spoken forms of English words. Most words are regular (e.g., GAVE, MINT) in that their pronunciations adhere to standard spelling-sound correspondences. There are, however, many irregular or exception words (e.g., HAVE, PINT) whose pronunciations violate the standard correspondences. To make matters worse, some spelling patterns have a range of pronunciations with none clearly predominating (e.g., OWN in DOWN, TOWN, BROWN, This research was supported financially by the National Institute of Mental Health (Grants MH47566 and MH00385), the National Institute on Aging (Grant Ag10109), the National Science Foundation (Grant ASC-9109215), and the McDonnell-Pew Program in Cognitive Neuroscience (Grant T89-01245-016).We thank Marlene Behrmann, Derek Besner, Max Coltheart, Joe Devlin, Geoff Hinton, and Eamon Strain for helpful discussions and comments. We also acknowledge Derek Besner, Max Coltheart, and Michael McCloskey for directing attention to many of the issues addressed in this paper.Correspondence concerning this paper should be sent to Dr. David C. Plaut, Department of Psychology, Carnegie Mellon University, Pittsburgh, PA 15213-3890, plaut@cmu.edu. CROWN vs. KNOWN, SHOWN, GROWN, THROWN, or OUGH in COUGH, ROUGH, BOUGH, THOUGH, THROUGH). Nonetheless, in the face of this complexity, skilled readers pronounce written words quickly and accurately, and can also use their knowledge...

Rethinking Eliminative Connectionism

“…For example, Plaut, McClelland, Seidenberg, and Patterson (1996) wrote that A rule-based approach has considerable intuitive appeal [but. .…”

Generalization from newly learned words reveals structural properties of the human reading system.

“…Taken together, the standard Plaut et al (1996) model simulated the behavioral results relatively well and captured all of the qualitative patterns in the behavioral data. Importantly, the model simulated the key finding that exception learning impedes regularization of probes as a function of anchor regularity using the stimuli of the behavioural experiment.…”

“…For the exception probes, the crucial drop in regularization that was seen with participants was also produced by the model, albeit visibly smaller. For ambiguous probes, regularization rates dropped in the simulation and behavioral data, and by a similar amount in both.Taken together, the standard Plaut et al (1996) model simulated the behavioral results relatively well and captured all of the qualitative patterns in the behavioral data. Importantly, the model simulated the key finding that exception learning impedes regularization of probes as a function of anchor regularity using the stimuli of the behavioural experiment.…”

“…Warping 5 To better understand how internal representations in the Plaut et al (1996) model made it both generalize appropriately and learn exceptions, Kim, Pitt, and Myung (2013) performed a series of simulations to flesh out how representations of regularities and exceptions were organized in that model. Intuitively, the characteristics of the representations of regular and exception pronunciations would seem to be at odds: learning an exception like pint should hinder the model's ability to generalize its knowledge of how int should be pronounced when encountering new words (e.g., kint, bint, gint).…”

“…To do either in a meaningful way requires also modeling participant variability, which has yet to be introduced into the current model. In addition, in model evaluation, it is most important to first establish a good qualitative fit.The Plaut et al (1996) model is a three-layer, feed forward neural network that contains 105 grapheme input units, 100 hidden units, and 61 phoneme output units. The hidden units make it possible for the model to learn complex mappings between input and output that are necessary in a deep orthography like English, and warping occurs in the connections between layers.…”