Human studies have shown that gaze shifts are mostly driven by the task. One explanation is that fixations gather information about task relevant properties, where task relevance is signalled by reward. This thesis pursues primarily an engineering science goal to determine what mechanisms a rational decision maker could employ to select a gaze location optimally, or near optimally, given limited information and limited computation time. To do so we formulate and characterise three computational models of gaze shifting (implemented on a simulated humanoid robot), which use lookahead to imagine the informational effects of possible gaze fixations. Our first model selects the gaze that most reduces uncertainty in the scene (Unc), the second maximises expected rewards by reducing uncertainty (Rew+Unc), and the third maximises the expected gain in cumulative reward by reducing uncertainty (Rew+Unc+Gain). We also present an integrated account of a visual search process into the Rew+Unc+Gain gaze scheme. Our secondary goal is concerned with the way in which humans might select the next gaze location. We compare the hand-eye coordination timings of our models to previously published human data, and we provide evidence that only the models that incorporate both uncertainty and reward (Rew+Unc and Rew+Unc+Gain) match human data.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:577853 |
| Date | January 2013 |
| Creators | Nunez-Varela, Jose Ignacio |
| Publisher | University of Birmingham |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://etheses.bham.ac.uk//id/eprint/4444/ |
Page generated in 0.002 seconds