A fundamental question in visual perception is to characterize how information from sensory input is integrated with prior probabilities. The role of prior probabilities is controversial for elementary visual processes, which are often believed to be immune from higher-level influences. In this paper, we demonstrate such influences. We tested human observers' abilities to discriminate stereoscopic depth defined by points embedded in a biological pattern—a human figure. Our results indicate that the internal representation of a walking human figure imposed constraints on depth discrimination of a static stimulus. This constraint was manifested when the stimulus was recognized as a human figure. When the expected human figure configuration (forearms having equal length) was inconsistent with the sensory input information, discrimination of forearm lengths was impaired. In contrast, when there was no inconsistency (the hand–hip distance on the left was not expected to be equal to that on the right), discrimination between the two distances was improved, presumably because the human figure configuration provided a more accurate frame of reference for stereoscopic depth. Both the impairment and improvement were due to changes of discrimination sensitivity rather than decision bias. Our findings support the view that visual perception is an inference process constrained by Bayesian prior probabilities.

^{2}) on a computer display (Silicon Graphics, Inc.) that was equipped with stereo shutter goggles (60 Hz). The viewing distance was 57 cm. The height and width of the stimulus were 13 and 6 deg in visual angle, respectively. The luminance of the red, green, and blue points was 19.9 cd/m

^{2}. Each point subtended 0.03 deg in visual angle. Each stimulus was shown for 4 s. Participants discriminated between the red and green 3D distances by pressing one of two response keys. No feedback was provided. There was a 1-s pause between a response and the next trial. The simulated 3D length difference between the forearms at the given viewing distance was selected from one of the following six levels: 0.34, 0.67, 1.33, 3.33, 5.34, and 7.34 mm. The color and length assignments to the forearms were randomized. The mean of the two lengths was held constant at 16 mm.

*d*′. We also analyzed the data by computing discrimination sensitivity

*d*′ and bias

*β*at each of the six length differences. A standard ANOVA yielded results completely consistent with the cumulative Gaussian analysis. This was true for Experiments 2 and 3 also.

*SD*) decreased after recognition (from an

*SD*of 3.36 to 4.28 mm,

*t*(16) = 2.23,

*p*= .041, all

*t*tests reported in this paper were two tailed). The change in response bias was nonsignificant,

*t*(16) = 0.79,

*p*= .44; mean bias = −0.29 mm. The nonsignificant negative value of the mean bias indicates that participants slightly preferred judging the distance between the red dots as longer. This trend might have resulted from the fact that “red” corresponded to the right response button in the experiment, and the majority of the participants (14/17) were right-handed.

*SD,*1/sensitivity; changed from 3.92 to 3.86 mm,

*t*(16) = 0.13,

*p*= .90). The change of bias was also nonsignificant (bias changed from −0.26 to −0.32,

*t*(16) = 0.29,

*p*= .78; see Figure 3).

*t*(16) = 2.67,

*p*= .02; whereas the bias did not change,

*t*(16) = 0.06,

*p*= .95; mean bias = −0.01 mm (see Figure 4). We further verified the hypothesis that the standard deviations (1/sensitivity) from Session 1 of all the three experiments were approximately equal,

*F*(2,48) = 0.94 < 1. Similarly, we found that the biases in Session 1 of these experiments were approximately equal (mean = −0.22 mm),

*F*(2,48) = 0.04 < 1. No reliable difference in bias was found in Session 2 of these experiments either (

*F*< 1). Figure 5 shows individual participant's sensitivity and bias in Experiments 1 and 3.

*a priori*expected location of a point and its sensory information, the peaks of the prior and the likelihood coincided. Therefore, the posterior estimation of each point's location was more accurate, after recognition—the variance of the distribution becomes smaller after recognition. Because distance estimation between two points has a probability distribution that is the convolution of the distributions of the two points' positions, distance estimation was therefore more accurate after recognition. According to signal detection theory, when the interval between two Gaussians (here representing the two distance estimations to be discriminated) remains constant while their variance is reduced, discrimination sensitivity

*d*′ increases.

*δ*function). In this situation, each point's posterior positional estimation is completely determined by the prior while the likelihood function is irrelevant. Because the prior indicates that the forearms are equal in length, discrimination sensitivity would be zero.

_{0}is the expected depth of the point for a natural posture of a human body. At the same time, the likelihood function, that is, the binocular disparity information δ conditioned on the point's z-coordinate, can be written as

_{0}and its estimate from binocular disparities δ, z

_{0}= δ. Hence, the posterior probability distribution of this point's z-position is

^{2}+ 1/σ

_{p}

^{2})

^{−1}(recall that in a convolution, variances add up). Let us denote Δz as the difference between the left hand–hip z-distance and the right hand–hip z-distance. Then, discrimination sensitivity after recognition, according to standard signal detection theory in a 2AFC discrimination, is

_{0}is now the peak of the prior after recognition (i.e., the expected position of a hand in a normal body posture).

^{2}z

_{0}+ σ

_{p}

^{2}δ)/(σ

^{2}+ σ

_{p}

^{2}). Therefore, because one forearm in the stimulus is shorter than expected and the other forearm longer by the same amount, the absolute difference between the lengths of the two forearms is

_{0}− δ| before recognition. The discrimination sensitivity d′ after recognition is

_{p}

^{2})/(σ

^{2}+ σ

_{p}

^{2}) ≤ 1 from before recognition, whereas the standard deviation (the denominator of the d′ equation) is the square root of the same factor. Thus, the signal is reduced more than the uncertainty is. The second intuition is that when the prior is completely uncertain, σ

_{p}→∞, d′ is unchanged as compared to before recognition. When the prior is completely certain, σ

_{p}→0, it overrules the likelihood function, hence the posterior is completely determined by the prior. Because the prior says that the forearms should be equally long, discrimination is at chance, d′ = 0. When we consider σ

_{p}as decreasing from infinity down to zero, there is no reason for the resultant d′ to be nonmonotonic. Hence, when the prior indicates that the difference should be smaller, discrimination sensitivity will decrease no matter how uncertain the prior is.

*Journal of Vision*,, 5(1), 58–70., http://journalofvision.org/5/1/6/, doi:10.1167/5.1.6. [PubMed] [Article] [CrossRef] [PubMed]