Vision Transformers must resolve figure-ground organization by choosing between completions driven by local geometric evidence and those favored by global organizational priors, giving rise to a characteristic perceptual ambiguity. We aim to locate where the canonical Gestalt prior convexity is realized within the internal components of BEiT. Using a controlled perceptual conflict based on synthetic shapes of darts, we systematically mask regions that equally admit either a concave completion or a convex completion. We show that BEiT reliably favors convex completion under this competition. Projecting internal activations into the model's discrete visual codebook space via logit attribution reveals that this preference is governed by identifiable functional units within transformer substructures. Specifically, we find that figure-ground organization is ambiguous through early and intermediate layers and resolves abruptly in later layers. By decomposing the direct effect of attention heads, we identify head L0H9 acting as an early seed, introducing a weak bias toward convexity. Downscaling this single attention head shifts the distributional mass of the perceptual conflict across a continuous decision boundary, allowing concave evidence to guide completion.
Figures & tables
Figure 1: A visual stimulus of perceptual conflict. We define the conflict region (gray overlay) of interest by masking the patch -wise set difference between the concave shape (dark region) and its convex hull (red outline), inducing competition between local evidence and global priors.
Figure 2: Figure–ground preference across layers and heads. (a) Layer-wise logit attribution of the masked patch representation, showing signed residual-stream contributions favoring convex (positive) or concave (negative) completion across depth; points show medians with variability. (b) Head-wise logit attribution for attention, encoding the direction and magnitude of each head’s contribution, with marginal summaries indicating aggregate effects.