How do individuals with Williams syndrome learn a route in a real-world environment?

Individuals with Williams syndrome (WS) show a specific deficit in visuo-spatial abilities. This finding, however, is mainly based on performance on small-scale laboratory-based tasks. This study investigated large-scale route learning in individuals with WS and two matched control groups (moderate learning difficulty group [MLD], typically developing group [TD]). In a non-labelling and a labelling (verbal information provided along the route) condition, participants were guided along one of two unfamiliar 1 km routes with 20 junctions, and then retraced the route themselves (two trials). The WS participants performed less well than the other groups, but given verbal information and repeated experience they learnt nearly all of the turns along the route. The extent of improvement in route knowledge (correct turns) in WS was comparable to that of the control groups. Relational knowledge (correctly identifying spatial relationships between landmarks), compared to the TD group, remained poor for both the WS and MLD groups. Assessment of the relationship between performance on the large-scale route learning task to that on three small-scale tasks (maze learning, perspective taking, map use) showed no relationship for the TD controls, and only a few non-specific associations in the MLD and WS groups

Latent variables and route choice behavior

In the last decade, a broad array of disciplines has shown a general interest in enhancing discrete choice models by considering the incorporation of psychological factors affecting decision making. This paper provides insight into the comprehension of the determinants of route choice behavior by proposing and estimating a hybrid model that integrates latent variable and route choice models. Data contain information about latent variable indicators and chosen routes of travelers driving regularly from home to work in an urban network. Choice sets include alternative routes generated with a branch and bound algorithm. A hybrid model consists of measurement equations, which relate latent variables to measurement indicators and utilities to choice indicators, and structural equations, which link travelers' observable characteristics to latent variables and explanatory variables to utilities. Estimation results illustrate that considering latent variables (i.e., memory, habit, familiarity, spatial ability, time saving skills) alongside traditional variables (e.g., travel time, distance, congestion level) enriches the comprehension of route choice behavior