Advanced theory of mind in children with mild intellectual disability and deaf or hard of hearing children: A two-year longitudinal study in middle childhood

The present study investigates the development of advanced theory of mind (AToM) among typically developing (TD) children, children with mild intellectual disability (MID), and deaf or hard of hearing (DHH) children. The 2-year longitudinal study comprised three waves and included a large sample of children from Poland in middle childhood aged around 7.5–9.5 years (N = 779; M = 7.7, SD = 0.92 at wave 1). The analysis of children’s understanding of second-order false belief and the Faux-Pas Recognition Test showed that TD children outperformed children with MID and DHH children on both measures. At 7.5 years, almost 60% of the TD children correctly solved the second-order false belief task; correct performance at 7.5 years in children with MID and DHH children was 27 and 38% respectively. Two years later, correct performance rose to 80% (TD children), 45% (children with MID), and 63% (DHH children). Despite these differences, the speed of AToM development did not differ across the groups. The development of faux-pas recognition followed a non-linear pattern, with TD children showing no further significant development after mid-elementary school. Our findings show differences in AToM development between TD children, children with MID, and DHH children, and they suggest that children’s development of AToM may follow different developmental pathways, depending on the aspect of AToM under study.     

Neuropsychological assessment could distinguish among different clinical phenotypes of progressive supranuclear palsy: A Machine Learning approach

Progressive supranuclear palsy (PSP) is a rare, rapidly progressive neurodegenerative disease. Richardson’s syndrome (PSP-RS) and predominant parkinsonism (PSP-P) are characterized by wide range of cognitive and behavioural disturbances, but these variants show similar cognitive pattern of alterations, leading difficult differential diagnosis. For this reason, we explored with an Artificial Intelligence approach, whether cognitive impairment could differentiate the phenotypes. Forty Parkinson's disease (PD) patients, 25 PSP-P, 40 PSP-RS, and 34 controls were enrolled following the consensus criteria diagnosis. Participants were evaluated with neuropsychological battery for cognitive domains. Random Forest models were used for exploring the discriminant power of the cognitive tests in distinguishing among the four groups. The classifiers for distinguishing diseases from controls reached high accuracies (86% for PD, 95% for PSP-P, 99% for PSP-RS). Regarding the differential diagnosis, PD was discriminated from PSP-P with 91% (important variables: HAMA, MMSE, JLO, RAVLT_I, BDI-II) and from PSP-RS with 92% (important variables: COWAT, JLO, FAB). PSP-P was distinguished from PSP-RS with 84% (important variables: JLO, WCFST, RAVLT_I, Digit span_F). This study revealed that PSP-P, PSP-RS and PD had peculiar cognitive deficits compared with healthy subjects, from which they were discriminated with optimal accuracies. Moreover, high accuracies were reached also in differential diagnosis. Most importantly, Machine Learning resulted to be useful to the clinical neuropsychologist in choosing the most appropriate neuropsychological tests for the cognitive evaluation of PSP patients.