Ka Chun Ken Wu

This study investigated the impact of Chinese dyslexia subtypes on English literacy skills (i.e., reading fluency and dictation) in Hong Kong children. Eighty-four Cantonese-speaking children officially diagnosed with dyslexia (M_age = 103 months) and 48 age-matched typically developing (TD) children were tested. Cluster analysis with performances on Chinese syllable awareness (CSA), Chinese phonemic awareness (CPA), Chinese phonological memory (CPM), Chinese orthographic awareness (COA), and matrix reasoning (MR) yielded three cognitive subtypes: the phonological deficit (PD) subtype, the orthographic deficit (OD) subtype, and the global deficit (GD) subtype. After controlling for English language experience, age, and gender, all three dyslexia subtype groups performed significantly worse in English word reading fluency and dictation than the TD children. In addition, the PD group performed worse in English PA; the OD group performed worse in English OA; and the GD group performed worse in all English skills except English PM. We compared the level of impairment in literacy between languages and dyslexia subtypes. In word reading fluency, all subtype groups experienced less impairment in English than Chinese, while the OD group showed the largest English advantage. In dictation, only the OD group showed a significant language effect favoring English. The findings suggest that different subtypes of Chinese dyslexia bear different risks for difficulties in English literacy.

Reward positivity (RewP), an event-related potential observed 250–300 ms after feedback, is hypothesized to reflect the dopaminergic response to the reward prediction error (RPE) during the reward processing. However, the traditional grand averaging approach of ERP analyses cannot answer whether RewP is merely a response to RPE valence in a categorical way (i.e., better-than-expected or worse-than-expected) or reflects the computation of RPE in a parametric way. In this study, we take a model-based approach to explore the effect of RPE on RewP. Specifically, we use the hierarchical Bayesian modelling to estimate individual parameter under the reinforcement learning model and extract the trial-by-trial RPE as the regressor for model-based EEG analysis.

Thirty-seven healthy adults (19 male, 18 female, Mean age = 26.97) performed four blocks of probabilistic reversal learning task while we acquire their EEG response using a 128-channel system. The preprocessed data were segmented into 1000 ms epoch from -200 ms before to 800 ms after the feedback slides. The reinforcement learning model based on Rescorla Wagner model with separate learning rates for positive and negative feedbacks was fitted with the choice data of the subjects to estimate the hyper-parameter and individual parameters using the hBayesDM package. Using the fitted parameters, the trial-by-trial RPE is generated and input as the trial-by-trial regressors for the model-based EEG analysis using the LIMO-EEG plugin of EEGLAB.

Traditional ERP analysis found a P200, FRN, and P300 effect of feedback valence at FCz (Fig. 1A). For the model-based analysis, one-sample t-test is applied to condition contrast (reward vs. non-reward) and RPE contrast (positive RPE vs. negative RPE). While no significant cluster is found for condition effect after correction for multiple comparisons using spatiotemporal clustering, the results reveal a significant modulation of ERP for positive RPE than for negative RPE (Fig. 1B – cluster started at 212 ms and ends at 268 ms encompassing frontocentral electrodes, mean beta value = 0.68, 95% CI [-0.07 1.42], maximum t-value = 5.20 at 232 ms channel F6, corrected p-value = 0.003). The results support that a more positive RPE predicts a more positive EEG response at frontocentral region, though the corresponding time is earlier than the typical RewP time window. Model-based analysis provides an alternative angle to the average-based RewP and shed a new light on the temporal dynamic of reward computation. It provides direct evidence that the “early” RewP encode positive RPE in a parametric way.