Explicit and implicit depth-cue integration: evidence of systematic biases with real objects

Author:

Campagnoli Carlo,Hung Bethany,Domini Fulvio

Abstract

AbstractIn a previous series of experiments using virtual stimuli, we found evidence that 3D shape estimation agrees to a superadditivity rule of depth-cue combination. According to this rule, adding depth cues leads to greater perceived depth magnitudes and, in principle, to depth overestimation. The mechanism underlying the superadditivity effect can be fully accounted for by a normative theory of cue integration, through the adaptation of a model of cue integration termed the Intrinsic Constraint (IC) model. As for its nature, it remains unclear whether superadditivity is a byproduct of the artificial nature of virtual environments, causing explicit reasoning to infiltrate behavior and inflate the depth judgments when a scene is richer in depth cues, or the genuine output of the process of depth-cue integration. In the present study, we addressed this question by testing whether the IC model’s prediction of superadditivity generalizes beyond VR environments to real world situations. We asked participants to judge the perceived 3D shape of cardboard prisms through a matching task. To assay the potential influence of explicit control over those perceptual estimates, we also asked participants to reach and hold the same objects with their fingertips and we analyzed the in-flight grip size during the reaching. Using physical objects ensured that all visual information was fully consistent with the stimuli’s 3D structure without computer-generated artifacts. We designed a novel technique to carefully control binocular and monocular 3D cues independently from one another, allowing to add or remove depth information from the scene seamlessly. Even with real objects, participants exhibited a clear superadditivity effect in both explicit and implicit tasks. Furthermore, the magnitude of this effect was accurately predicted by the IC model. These results confirm that superadditivity is an inherent feature of depth estimation.

Publisher

Cold Spring Harbor Laboratory

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