Neurophysiological Correlates of Visual Statistical Learning in Adults and Children

Neurophysiological Correlates of Visual Statistical Learning in Adults and Children Ethan Jost (ejost@slu.edu), Christopher M. Conway (cconway6@slu.edu), J.D. Purdy (jdpurdy@slu.edu), Michelle A. Hendricks (mpatte20@slu.edu) Department of Psychology, Saint Louis University, 3511 Laclede Avenue St. Louis, MO 63103 USA Abstract Wippich, 2003), and age-related implicit learning differences have been found between seven-year-old children and adults (Barry, 2007). In most cases, where developmental differences in implicit learning is found, adults out-perform children. It perhaps is not surprising to see a developmental progression in statistical learning, with more basic abilities acquired first, followed by more involved processing components (Saffran, 2003). On the other hand, an alternative is that young children might actually possess better or more efficient learning mechanisms than adults. Some proposals take the somewhat paradoxical stance that cognitive limitations and/or reduced input may confer a computational advantage for learning (Conway, Ellefson, & Christiansen, 2003; Elman, 1993; Newport, 1990), which may explain the presence of sensitive periods in language development. To explore these issues, the current study aims to investigate the neural mechanisms of visual statistical learning in both adults and children using the event related potential (ERP) technique. Some previous studies have used ERPs to investigate implicit learning in adults, with particular components associated with the acquisition of rule-based or statistical sequential patterns. One type of response, the N2, is observed as a negative- going deflection in voltage potential, appearing approximately 200 ms after the presentation of a stimulus. Another, the P300, is a positive deflection that appears about 300 ms after stimulus onset (Eimer, Goschke, Schlaghecken, & Stuermer, 1996; Ferdinand, Mecklinger, & Kray, 2008). These ERP components often appear together in learning studies (Schlaghecken, Stuermer, & Eimer, 2000), and it has been suggested that both components reflect stimulus-evaluation processes (Russeler & Roesler, 2000). Ferdinand et al. (2008) argue that the N2 indexes the detection of deviant stimuli, while the P300 reflects the evaluation of incoming information and the updating of contextual representations. It may also be possible to interpret these two components in terms of the degree to which automatic vs. controlled processing are involved, with the earlier N2 reflecting more automatic or implicit learning mechanisms. For the present study, we developed a novel learning paradigm – based on the classic oddball task – that is conducive to the measurement of ERPs in both adults and children. The task involved viewing a stream of visual stimuli that contained covert statistical patterns governing the probability of a target stimulus occurring. ERPs were Implicit statistical learning refers to the acquisition of statistical patterns occurring under incidental learning conditions. Although statistical learning is central to the development of many cognitive domains, such as language, its developmental trajectory is largely unspecified. Furthermore, few studies have attempted to examine the underlying neural mechanisms supporting statistical learning, in adults or in children. In this study, we used a novel visual statistical learning paradigm that allowed us to investigate the neurophysiological correlates of learning in adults, older children (aged 9-12), and younger children (aged 6-9). The results depict a nuanced picture of the development of statistical learning, involving the use of two distinct neurocognitive processing mechanisms associated with the N2 and P300 components. In addition, the results suggest that the children’s brains acquired the statistical structure quicker than the adults, with learning- related ERP components emerging after comparably less exposure to the patterns. Keywords: statistical learning, event-related potentials (ERP), cognitive development Introduction Implicit learning refers to the automatic, unconscious, and effortless acquisition of information that generally results in knowledge that is difficult to express verbally (Cleeremans, Destrebecqz, & Boyer, 1998; Eimer, Goschke, Schlaghecken, & Sturmer, 1996). One type of implicit learning is statistical learning, which refers to the acquisition of statistical patterns in the environment (Perruchet & Pacton, 2006). Implicit and statistical learning are believed to be important contributors to aspects of cognitive development, such as language acquisition (Conway, Bauernschmidt, Huang, & Pisoni, 2009). However, few still have probed the neural mechanisms mediating statistical learning in adults let alone children, making it difficult to specify the neurocognitive development of these learning processes. Although Reber (1993) suggested that implicit learning is developmentally invariant, other researchers have provided evidence of developmental differences in implicit learning. In an fMRI study, Thomas, Hunt Vizueta, Sommer, Durston, Yang, and Worden (2004) found behavioral and functional neuroimaging differences in implicit learning between children 7 to 11 years of age and adults (also, see McNealy, Mazziota, & Dapretto, 2010). Furthermore, a pattern of development in implicit learning abilities has been detected between six-year-old and ten-year old children (Mecklenbrauker, Hupbach, &