Models of allocentric coding for reaching in naturalistic visual scenes

To reach to objects, humans rely on relative positions of target objects to surrounding objects (allocentric) as well as to their own bodies (egocentric). Previous studies demonstrated that scene configuration and object relevancy to the task modulates the combination weights of allocentric and egocentric information. Egocentric coding for reaching is studied extensively; however, how allocentric information is coupled and used in reaching is unknown. Using a computational approach, we show that clustering mechanisms for allocentric coding combined with causal Bayesian integration of allocentric and egocentric information can account for the observed reaching behavior. To further understand allocentric coding, we propose two strategies, global vs. distributed landmark clustering (GLC vs. DLC). Both models can replicate the current data but each has distinct implications. GLC efficiently encodes the scene relative to a single virtual reference but loses all the local structure information. In contrary, DLC stores more redundant inter-object relationship information. Consequently, DLC is more sensitive to the changes of the scene. Further experiments must differentiate between the two proposed strategies.