Implicit Learning Deficits in Autism: A Neurocomputational Account

Implicit Learning Deficits in Autism: A Neurocomputational Account Trent Kriete & David C. Noelle (tkriete,dnoelle@ucmerced.edu) Program in Cognitive Science, University of California, Merced, 5200 N. Lake Road Merced, CA 95343 USA Abstract in PFC/DA interactions can account for patterns of execu- tive dysfunction in autism (Kriete & Noelle, 2005, 2006) and how the same neural deficits can explain stimulus overselec- tivity in people with autism along with the associated diffi- culties in generalizing learned skills to novel contexts (Kri- ete & Noelle, 2008). In this paper, we present a neurocom- putational model of implicit learning performance in people with autism, demonstrating that abnormal PFC/DA interac- tions can explain the observations in this domain, as well. Implicit learning is learning that occurs without any aware- ness of the specific knowledge acquired during the process. Researchers have suggested that people with autism have a core deficit in their ability to implicitly learn about the in- herent relationships that exist between objects and situations in the world (Mostofsky, Goldberg, & Landa, 2000; Klinger, Klinger, & Pohlig, 2006). Klinger et al. argue that impaired implicit learning results in difficulties in recognizing the rela- tionships that exist across experiences, likely leading to prob- lems forming general knowledge about categories of items and types of situations. Difficulties in generalizing learned knowledge to new situations are commonly observed in peo- ple with autism, and these difficulties frequently act as a cen- tral obstacle to the development of behaviors needed for au- tonomy and independent living. Thus, a precise characteriza- tion of the mechanisms responsible for these generalization deficits would be very valuable to any effort to design ways to mitigate these serious issues in people with autism. In this paper, we first describe our general computational account of PFC/DA interactions, and we review our previous work, showing that abnormalities in these neural systems can account for the patterns of executive dysfunction and stimulus overselectivity observed in autism. We then review a common paradigm for assessing implicit learning, the Serial Response Time Task (SRTT), and we revisit experimental results that make use of this task to demonstrate impairments in implicit learning in people with autism. A neurocomputational model of healthy performance on the SRTT is then described, and the results of introducing a PFC/DA deficit into this model are reported. The model results are fit to both healthy and autistic SRTT performance data appearing in the literature, demonstrating the ability of a PFC/DA interaction abnormal- ity to account for the lack of implicit learning in autism. We close with a general discussion. Experiments using the Serial Response Time Task (SRTT) have suggested that implicit learning is impaired in people with autism. Implicit learning is learning that occurs without ex- plicit awareness of the knowledge being acquired. Researchers have suggested that poor implicit learning could be a major factor in other profound learning difficulties exhibited by peo- ple with autism. In this report, we use a neurocomputational model of the SRTT to show that disturbed interactions between the mesolimbic dopamine (DA) system and the prefrontal cor- tex (PFC) may underlie the implicit learning problems ob- served in autism. This model is shown to fit reaction time data from the literature for both individuals with autism and nor- mally developing controls. This work expands on a previous body of research showing that abnormal DA/PFC interactions can account for a disparate collection of autistic behavioral patterns, suggesting that a common neurological mechanism might produce executive dysfunction, stimulus overselectivity, and impaired implicit learning in this population. Keywords: Autism; Implicit Learning; Serial Response Time Task (SRTT); Prefrontal Cortex (PFC); Dopamine (DA) Introduction Autism is a complex developmental disorder characterized by deficits across physical, social, and cognitive domains. Cognitive difficulties are found in tasks assessing executive function, “mind reading” abilities, integration of informa- tion, attention, and generalizing learned abilities to novel contexts (Hughes, Russell, & Robbins, 1994; Baron-Cohen, Leslie, & Frith, 1985; Frith, 1989). In addition, physi- cal motor abnormalities, an increased prevalence of seizure disorders, motor stereotypies, and repetitive behaviors of- ten accompany the diagnosis (Rinehart, Bradshaw, Brere- ton, & Tonge, 2001; Tuchman & Rapin, 2002). Com- plicating matters further, people with autism often possess “islets” of spared cognitive functioning in some areas, in- cluding visiospatial tasks such as the embedded figures test and Weschler’s block design task (Shah & Frith, 1983). In- deed, the diversity and variance of the traits of autism has led to a recent proposal to abandon any attempt to find a sin- gle “monolithic” cause underlying all aspects of the disor- der (Happe, Ronald, & Plomin, 2006). While we are sympathetic to the notion that all behavioral aspects of autism are unlikely to have a single common neu- rological cause, our previous explorations of computational models of the prefrontal cortex (PFC), and its interactions with the mesolimbic dopamine (DA) system, have suggested that a disparate collection of behavioral patterns observed in autism might stem from a common deficit in these neural sys- tems. We propose that abnormal PFC/DA interactions may underlie multiple aspects of this disorder, providing a neu- roscientific account of how these aspects are related. Our previous computational work has demonstrated how deficits Previous Work Our proposal is that deficits in the mesolimbic dopamine (DA) system and/or abnormalities in how dopamine modu- lates the prefrontal cortex (PFC) can account for the behav- ioral profiles of people with autism across a diverse range of