Experimental Brain Research

, Volume 183, Issue 1, pp 61–74 | Cite as

Rapid recalibration based on optic flow in visually guided action

  • Brett R. FajenEmail author
Research Article


Action capabilities are always subject to limits. Whether on foot or in a vehicle, people can only move so fast, slow down so quickly, and turn so sharply. The successful performance of almost any perceptual-motor task requires actors to learn and continually relearn their ever-changing action capabilities. Such learning can be considered an example of perceptual-motor calibration. The present study includes two experiments designed to address basic questions about the nature of this calibration process. Subjects performed a simulated braking task, using a foot pedal to slow down to a stop in front of an obstacle in the path of motion. At one point in the experiment, the strength of the brake was increased or decreased unbeknownst to subjects, and behavior before and after the change in brake strength was analyzed for evidence of recalibration. Experiment 1 showed that actors rapidly recalibrate following a change in brake dynamics, even when they are unaware of the change. In Experiment 2, the scene turned black one second after braking was initiated. Subjects still recalibrated following the change in brake strength, suggesting that information in the sensory consequences of the initial brake adjustment is sufficient for recalibration, even in the absence of feedback about the outcome (i.e., in terms of final position error) of the task. Discussion focuses on the critical but often overlooked role of calibration in continuously controlled visually guided action, and the nature of the information used for recalibration.


Optic Flow Grip Force Stop Sign Action Capability Brake Pedal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research was supported by a grant from the National Science Foundation (BCS 0236734). I thank Mark Stenpeck and Brian Richmond for programming the simulation and Sarah Bowie for collecting the data.


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Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  1. 1.Department of Cognitive ScienceRensselaer Polytechnic InstituteTroyUSA

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