Simulating individual differences in language ability and genetic differences in FOXP2 using a neural network model of the SRT task

Simulating individual differences in language ability and genetic differences in FOXP2 using a neural network model of the SRT task Joseph C. Toscano (joseph-toscano@uiowa.edu) Dept. of Psychology, University of Iowa, Iowa City, IA 52242 USA Kathryn L. Mueller (kathryn-mueller@uiowa.edu) Dept. of Communication Sciences and Disorders, University of Iowa, Iowa City, IA 52242 USA Bob McMurray (bob-mcmurray@uiowa.edu) Dept. of Psychology, University of Iowa, Iowa City, IA 52242 USA J. Bruce Tomblin (j-tomblin@uiowa.edu) Dept. of Communication Sciences and Disorders, University of Iowa, Iowa City, IA 52242 USA Abstract The aim of the current paper is to investigate the relation- ship between individual differences (both differences in lan- guage ability and genetic differences) and functional proper- ties of language processing using a computational model of the serial reaction time (SRT) task. The SRT task measures participants’ ability to learn pattern sequences. Variation in performance on the SRT task has been associated with both language ability (Tomblin, Mainela-Arnold, & Zhang, 2007b) and genetic differences (Tomblin, Christiansen, Bjork, Iyen- gar, & Murray, 2007a). Given this, and the fact that sequence processing is a critical component of language use, this task provides a useful paradigm for studying these relationships. Recent work has shown that individual differences in language development are related to differences in procedural learning, as measured by the serial reaction time (SRT) task. Perfor- mance on this task has also been shown to be associated with common genetic variants in FOXP2. To investigate what these differences can tell us about the functional properties of lan- guage processing, we present a computational model of the SRT task. We varied parameters in the model to observe their effects on performance in the task. We found that the com- bined effect of several model parameters produced changes in the learning trajectory that were similar to those observed be- haviorally. Keywords: language processing; specific language impair- ment; FOXP2; procedural learning; serial reaction time task; computational modeling; simple recurrent networks Individual differences in language abilities Introduction The mechanisms that underlie language use emerge over the course of development through the integration of multiple biological and environmental factors (Elman et al., 1997). Much previous research has focused on whether these mecha- nisms are language-specific or domain-general (Christiansen & Chater, 2008). Regardless of which is the case, we must specify how different factors interact to give rise to language. One way to study the mechanisms involved in language is to look at individual differences in language ability. Recently, the use of molecular genetics has emerged as a tool for inves- tigating these differences. However, the use of genetics to study complex cognitive processes, like language, presents a challenge: how do we address questions regarding the role of genes when they are so far removed from language process- ing? Similarly, how do we assess the role of individual genes when it is unlikely that there is a one-to-one correspondence between genes and specific characteristics of language? As a first step, we need a way to observe the effects of func- tional properties of language processing on behavior. Com- putational models offer a tool for doing this. The units in a neural network model, for instance, correspond to functional (rather than structural) units in the system. Thus, computa- tional models may be useful for examining how genetic fac- tors relate to the functional organization of cognitive systems. One area in which differences in language ability have been extensively studied is specific language impairment (SLI). SLI is a relatively common developmental disorder character- ized by difficulty acquiring language in the absence of gross cognitive or sensory impairments, and despite adequate ex- perience and educational opportunities (Tomblin, Records, & Zhang, 1996). Typically, research criteria for SLI classi- fication require that the individual falls 1.15 SD below the mean on a range of standardized assessments of language while falling in the normal range for non-verbal intelligence (Tomblin et al., 1996). Children with SLI have deficits in various language abil- ities, such as mopho-syntactic processing, phonological processing, word learning, and spoken word recognition (Leonard, 1998; McGregor, Newman, Reilly, & Capone, 2002; McMurray, Samelson, Lee, & Tomblin, 2010). In many ways, these children demonstrate language abilities associ- ated with typically developing younger peers. They have smaller vocabularies, use shorter, simpler syntactical con- structions, and make more morphological errors than would be expected for children their age (McGregor, Friedman, Reilly, & Newman, 2002). A range of possible hypotheses for SLI have been pro- posed, and include deficits in temporal-perceptual processing, generalized slowing, problems with phonological processing, and deficits in working memory (Bishop, North, & Donlan,