Reliability-dependent contributions of visual orientation cues in parietal cortex

Abstract

Creating accurate 3D representations of the world from 2D retinal images is a fundamental task for the visual system. However, the reliability of different 3D visual signals depends inherently on viewing geometry, such as how much an object is slanted in depth. Human perceptual studies have correspondingly shown that texture and binocular disparity cues for object orientation are combined according to their slant-dependent reliabilities. Where and how this cue combination occurs in the brain is currently unknown. Here, we search for neural correlates of this property in the macaque caudal intraparietal area (CIP) by measuring slant tuning curves using mixed-cue (texture + disparity) and cue-isolated (texture or disparity) planar stimuli. We find that texture cues contribute more to the mixed-cue responses of CIP neurons that prefer larger slants, consistent with theoretical and psychophysical results showing that the reliability of texture relative to disparity cues increases with slant angle. By analyzing responses to binocularly viewed texture stimuli with conflicting texture and disparity information, some cells that are sensitive to both cues when presented in isolation are found to disregard one of the cues during cue conflict. Additionally, the similarity between texture and mixed-cue responses is found to be greater when this cue conflict is eliminated by presenting the texture stimuli monocularly. The present findings demonstrate reliability-dependent contributions of visual orientation cues at the level of the CIP, thus revealing a neural correlate of this property of human visual perception.

Keywords: 3D orientation; cue combination; perspective; reliability; vision.

Fig. 1.

Perspective geometry constrains the reliability of texture cues. (A) Brick wall viewed at four slants: 0°, 20°, 45°, and 65°. Rotation by a fixed amount (e.g., ∆s = 20°) results in greater texture changes at larger slants (Bottom) compared to smaller slants (Top). The colored lines illustrate that the convergence of parallel lines in a 2D image due to perspective accelerates with slant, making texture cues more reliable at larger slants. This property of perspective geometry is highlighted in the rightmost parts of the diagrams, where the lines are reproduced on top of each other. Note the greater difference in slopes in the Bottom vs. Top diagrams. (B) Summary of human perceptual studies showing how the reliability and weighting of texture and disparity cues for 3D surface orientation depend on slant. Data with regression lines are plotted for five subjects from studies by Knill and Saunders (5) and Hillis et al. (6). (Left and Middle) Texture and disparity cue reliabilities computed from measured discrimination thresholds as a function of slant. Whereas texture reliability consistently increases with slant, disparity reliability is comparatively flat. (Right) The weight with which texture cues contribute to the perceived slant of a mixed-cue stimulus increases (hence, the disparity weight decreases) with slant, as predicted if texture and disparity cues are combined according to their reliabilities.

Fig. 2.

Visual stimuli and slant–tilt tuning. (A) Three sets of stimuli defining planar surfaces. Column 1 illustrates mixed-cue CKB stimuli. Column 2 illustrates texture stimuli with the same pattern as the CKB stimuli but zero disparity (TXT). The TXT stimuli could be presented monocularly (mTXT) or binocularly (bTXT). Column 3 illustrates RDS stimuli for assessing sensitivity to disparity cues. The yellow dot at the center of each stimulus is the fixation point (directly in front of the monkey). (B) Slant–tilt tuning curve of a CIP neuron measured with the CKB stimuli. Slant is the radial variable, and tilt is the angular variable. The firing rate is color coded, with red hues indicating a higher firing rate. The peak of the tuning curve lies in the upper left quadrant, indicating the cell preferred a planar surface with the upper left side closest to the monkey. The black line marks the slant tuning curve passing through the cell’s preferred surface orientation. Each tested tilt axis (e.g., the one specified by the black dot and line) is labeled and marked with a colored dot. (Inset) The preferred planar surface. (C) Slant tuning curve measured at each tilt axis is plotted in the colors indicated in B. Fits are von Mises functions. Mean responses are baseline-subtracted, and error bars are SEM.

Fig. 3.

Slant tuning curves. (A–D) Slant tuning curves of four cells illustrating the range of observed responses. Tuning curves were measured using the following stimuli: (i) CKB (black), (ii) RDS (blue), (iii) bTXT (green), and (iv) mTXT (magenta) for some cells. Fitted curves are von Mises functions (drawn for significantly tuned responses only; ANOVA, P < 0.05). Mean responses are baseline-subtracted, and error bars are SEM. (A) Cell that was tuned for the RDS stimuli but not the bTXT stimuli. (B) Cell that was tuned for the RDS stimuli but not the bTXT or mTXT stimuli. (C) Cell that was tuned for both the RDS and bTXT stimuli. (D) Cell that was tuned for both the RDS and mTXT stimuli but not the bTXT stimuli. Texture stimuli were not presented monocularly to the cells in A and C. Note that all of the bTXT responses in A, B, and D were similar in amplitude to the RDS and CKB responses to a frontoparallel plane (s = 0°).

Fig. 4.

Comparison of slant preferences measured with mixed-cue and cue-isolated stimuli. The histogram on the diagonal shows the difference in preferred slants, with crosses marking population averages. Values in the upper left portion of the histogram indicate larger cue-isolated than mixed-cue (CKB) slant preferences. Binocular disparity (RDS, n = 57), monocularly viewed texture (mTXT, n = 14), and binocularly viewed texture (bTXT, n = 28) slant preferences are shown. The unity line is black.

Fig. 5.

Contributions of texture and disparity cues depend on cue reliability. (A) Z-scored partial correlations between mixed-cue (CKB) and cue-isolated (RDS and TXT) slant tuning curves were used to classify responses as dominated by disparity or texture cues (n = 71). ●, cell was significantly tuned for both cues; ○, cell was significantly tuned for one cue. (B) The difference in Z-scored partial correlations is plotted as a function of the preferred CKB slant (mTXT, n = 15; bTXT, n = 32). Type II regression lines (minimizing the perpendicular distance between the data points and the regression line; SI Methods) are plotted for both the mTXT and bTXT data. (C) Accounted variance (r²) between the CKB and RDS/TXT tuning curves as a function of the preferred CKB slant. There was no relationship for the RDS (n = 58) tuning curves, and there were significant positive relationships for both the mTXT (n = 15) and bTXT (n = 32) tuning curves. Type II regression lines are shown in the same colors. For the mTXT data in B and C, if responses were measured for both eyes, each data point is plotted (connected by a thin black line) but the average was used in the regression. (D) Accounted variance between CKB and texture tuning curves measured binocularly (bTXT) vs. monocularly (mTXT) (n = 22). Eliminating the cue conflict that exists when texture-only stimuli are viewed binocularly by presenting them monocularly increased the r² between the CKB and TXT responses. This result is also reflected in the vertical offset between the bTXT and mTXT regression lines in C.