Synthesizing Images on Perceptual Boundaries of ANNs for Uncovering and Manipulating Human Perceptual Variability
Journal:
arXiv
Published Date:
May 6, 2025
Abstract
Human decision-making in cognitive tasks and daily life exhibits considerable
variability, shaped by factors such as task difficulty, individual preferences,
and personal experiences. Understanding this variability across individuals is
essential for uncovering the perceptual and decision-making mechanisms that
humans rely on when faced with uncertainty and ambiguity. We present a
computational framework BAM (Boundary Alignment & Manipulation framework) that
combines perceptual boundary sampling in ANNs and human behavioral experiments
to systematically investigate this phenomenon. Our perceptual boundary sampling
algorithm generates stimuli along ANN decision boundaries that intrinsically
induce significant perceptual variability. The efficacy of these stimuli is
empirically validated through large-scale behavioral experiments involving 246
participants across 116,715 trials, culminating in the variMNIST dataset
containing 19,943 systematically annotated images. Through personalized model
alignment and adversarial generation, we establish a reliable method for
simultaneously predicting and manipulating the divergent perceptual decisions
of pairs of participants. This work bridges the gap between computational
models and human individual difference research, providing new tools for
personalized perception analysis.
