The kinetic depth effect and optic flow--II. First- and second-order motion

We use a difficult shape identification task to analyze how humans extract 3D surface structure from dynamic 2D stimuli--the kinetic depth effect (KDE). Stimuli composed of luminous tokens moving on a less luminous background yield accurate 3D shape identification regardless of the particular token used (either dots, lines, or disks). These displays stimulate both the 1st-order (Fourier-energy) motion detectors and 2nd-order (nonFourier) motion detectors. To determine which system supports KDE, we employ stimulus manipulations that weaken or distort 1st-order motion energy (e.g. frame-to-frame alternation of the contrast polarity of tokens) and manipulations that create microbalanced stimuli which have no useful 1st-order motion energy. All manipulations that impair 1st-order motion energy correspondingly impair 3D shape identification. In certain cases, 2nd-order motion could support limited KDE, but it was not robust and was of low spatial resolution. We conclude that 1st-order motion detectors are the primary input to the kinetic depth system. To determine minimal conditions for KDE, we use a two frame display. Under optimal conditions, KDE supports shape identification performance at 63-94% of full-rotation displays (where baseline is 5%). Increasing the amount of 3D rotation portrayed or introducing a blank inter-stimulus interval impairs performance. Together, our results confirm that the human KDE computation of surface shape uses a global optic flow computed primarily by 1st-order motion detectors with minor 2nd-order inputs. Accurate 3D shape identification requires only two views and therefore does not require knowledge of acceleration.