Peripheral vision and pattern recognition: a review - strasburger - main

Peripheral_Vision.doc
subject responses in > 20 young subjects. The model starts with a trade-off function between
character size and log recognition contrast threshold, approximated by a hyperbola (first row in
Figure 17). Its asymptotes, log C off and S off , are both shifted with increasing eccentricity, each
by a linear function (left graph). The equations can be solved for character size S as a function
of eccentricity (second row in the figure), where log C appears as a parameter in the
denominator. For high contrast C, the denominator in that equation becomes mostly constant
except for high eccentricity (because log1 = 0), and is thus reduced to conventional M scaling
(black straight line at 100%). At lower contrast, the graphs are bent upwards and at low contrast
quickly approach infinity (colored lines). That equation therefore represents a generalization of
M scaling. Finally, correct performance can be predicted by the psychometric function (Figure
17, third row), which shows the percentage of correct answers P c as a function of log
normalized contrast (c/C). Threshold contrast C (from the trade-off function) acts as position
parameter and shifts the psychometric function horizontally. The slope has been shown to be
largely independent of stimulus size and position (Strasburger, 2001b). The lower asymptote is
given by the rate of guessing γ, i.e., by the inverse of the number of alternatives. The equations
predict contrast and size thresholds for recognition, and the proportion of correct recognition, for
singly presented characters of arbitrary contrast, size, and position in the visual field (the
anisotropy is not incorporated since it is comparably small).
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