Affiliations: [a] Department of Psychology, The George Washington University, Washington, DC, USA | [b] Department of Mechanical and Aerospace Engineering, The George Washington University, Washington, DC, USA
Correspondence:
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Corresponding author: John W. Philbeck, Department of Psychology, 2125 G. Street, NW, The George Washington University, Washington, DC 20052, USA. Tel.: +1 202 994 6313; Fax: +1 202 994 1602; E-mail: [email protected]
Abstract: Non-sensory (cognitive) inputs can play a powerful role in monitoring one's self-motion. Previously, we showed that access to spatial memory dramatically increases response precision in an angular self-motion updating task [1]. Here, we examined whether spatial memory also enhances a particular type of self-motion updating – angular path integration. "Angular path integration" refers to the ability to maintain an estimate of self-location after a rotational displacement by integrating internally-generated (idiothetic) self-motion signals over time. It was hypothesized that remembered spatial frameworks derived from vision and spatial language should facilitate angular path integration by decreasing the uncertainty of self-location estimates. To test this we implemented a whole-body rotation paradigm with passive, non-visual body rotations (ranging 40°–140°) administered about the yaw axis. Prior to the rotations, visual previews (Experiment 1) and verbal descriptions (Experiment 2) of the surrounding environment were given to participants. Perceived angular displacement was assessed by open-loop pointing to the origin (0°). We found that within-subject response precision significantly increased when participants were provided a spatial context prior to whole-body rotations. The present study goes beyond our previous findings by first establishing that memory of the environment enhances the processing of idiothetic self-motion signals. Moreover, we show that knowledge of one's immediate environment, whether gained from direct visual perception or from indirect experience (i.e., spatial language), facilitates the integration of incoming self-motion signals.
