From Egocentric to Allocentric Spatial Behavior: A Computational Model of Spatial Development

Abstract

Psychological experiments on children's development of spatial knowledge suggest that experience at self-locomotion and visual tracking are important factors. Yet, the mechanism underlying development is unknown. We propose a robot that learns to track a target object mentally (i.e., maintaining a representation of an object's position when outside the field of view) as a model for spatial development. Mental tracking is considered as prediction of an object's position, given the previous environmental state and motor commands and the current environment state resulting from movement. Following Jordan and Rumelhart's (1992) forward modeling architecture, the system consists of two components: an inverse model of sensory input to desired motor commands and a forward model of motor commands to desired sensory input (goals). The robot was tested on the "three cups"paradigm (in which children are required, under various movement conditions, to select the cup containing the hidden object). Consistent with child development, in the absence of the capacity for self-locomotion, the robot makes errors that are self-center-based. When given the ability for self-locomotion, the robot responds allocentrically.

The role of locomotion in the acquisition and transfer of spatial knowledge in children

“…Hiraki, Sashima and Phillips (1998) proposed a computational model of spatial development. They proposed a robot that learned to track a target mentally, and demonstrated that, in the absence of the capacity for self‐locomotion, the robot made errors that were self‐centered.…”

“…Therefore, the effect of locomotion on spatial performance also depends on the spatial test applied. If children have to remember a spatial routine that can be solved by egocentric, self‐centered strategies, they might depend less on active locomotion during exploration than when solving an orientation test demanding the use of allocentric orientation strategies, as is suggested by the findings of Hiraki et al (1998).…”

Finding Your Way from the Bed to the Kitchen: Reenacting and Recombining Sensorimotor Episodes Learned from Human Demonstration

“…Two early implementations of simulation-like mechanisms of this kind were the "Connectionist Navigational Map (CNM)" of Chrisley (1990) and the simulated robot (arm) "Murphy" by Mel (1991). The theoretical motivation behind the connectionist navigational map was the transition from non-conceptual to conceptual knowledge [see, e.g., Barsalou (1999)], and later models have focused on, for example, allocentric spatial knowledge (Hiraki et al, 1998), learning the spatial layout of a maze-like environment [e.g., Jirenhed et al (2001) and Hoffmann and Möller (2004)], obstacle avoidance (Gross et al, 1999), and robot dreams .…”

Simulations

“…Barsalou, 1999). Later models have investigated the following types of behaviors: a) allocentric spatial knowledge (Hiraki, et al, 1998) b) learning the topological structure of a maze like environment, identified as successful navigation, either physically or mentally (Hoffmann & Möller, 2004;Jirenhed et al 2001;Jirenhed, 2001;Nolfi & Tani, 1999;Tani & Nolfi, 1998;Svensson, Morse & Ziemke, 2009[IV]; Ziemke et al, 2005), c) goal-directed navigation: the robots are given a goal-configuration and should find the goal by using the cognitive map as internal/planning resource (Baldassarre, 2002;Hoffmann & Möller, 2004;Tani, 1996), d) obstacle avoidance (Gross et al, 1999), and e) problem solving (van Dartel, 2005) This chapter discusses some of the issues addressed by these previous computational models and also describes an approach to model the development of simulation in robots inspired by the so-called inception of simulation (InSim) hypothesis (Svensson, Thill, and Ziemke, 2013[VI]; Thill and Svensson, 2011[V]). For a more complete review of previous computational models of simulation, see (Svensson, 2007).…”