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Human observers can perceive their direction of heading with a precision of about a degree. Several computational models of the processes underpinning the perception of heading have been proposed. In the present study we set out to assess which of four candidate models best captured human performance; the four models we selected reflected key differences in terms of approach and methods to modelling optic flow processing to recover movement parameters. We first generated a performance profile for human observers by measuring how performance changed as we systematically manipulated both the quantity (number of dots in the stimulus per frame) and quality (amount of 2D directional noise) of the flow field information. We then generated comparable performance profiles for the four candidate models. Models varied markedly in terms of both their performance and similarity to human data. To formally assess the match between the models and human performance we regressed the output of each of the four models against human performance data. We were able to rule out two models that produced very different performance profiles to human observers. The remaining two shared some similarities with human performance profiles in terms of the magnitude and pattern of thresholds. However none of the models tested could capture all aspect of the human data.

Heading recovery from optic flow: comparing performance of humans and computational models