Luca Serrière
Research Subjects
Research Interests
Luca Serrière has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Filipp Schmidt
Justus Liebig University Giessen, Department of Psychology, Giessen, Germany
1 shared publication - Jonathan Adams1 shared publication
Maria Barbara Smorczewska
1 shared publicationMaximilian Jonathan Kothen
1 shared publication- Yaniv Morgenstern
Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam
1 shared publication
Latest Publications
- Open Access
Human and generative AI integrate visual cues differently in a shape completion task
In amodal completion observers perceive complete objects despite partial occlusion. When two object parts are divided by an occluder, completion can result in perceiving one or two objects. This phenomenon involves both lower-level cues (e.g., symmetry, contour continuity) and higher-level cues (e.g., prior knowledge). Experiment 1 investigates how occluder size, familiarity, and symmetry affect human completions using a drawing task. Narrow occluders and asymmetry promote single-shape completions, while familiarity and (global) symmetry promote two-shape interpretations. Good continuation emerges as the strongest cue, with symmetry and familiarity playing increasingly important roles as occluder width increases. Experiment 2 compares human performance with three state-of-the-art generative AI models. Models often generated creative but non-compliant outputs, altering even unoccluded regions. We restricted analysis to instruction-following generations, identified through ratings by naive observers. Among compliant outputs, models showed some human-like biases (e.g., more two-shape completions for wide occluders), but failed with higher-level cues. They did not use symmetry to guide completions and showed reversed familiarity effects. Our findings highlight differences between human and AI completions. Humans integrate low- and high-level cues, whereas compliant outputs from the AI models rely primarily on low-level pattern continuation. Current AI models lack the flexible integration of multiple representational levels that characterize human perception. This work establishes an analytical framework for evaluating whether next-generation models achieve more human-like visual reasoning. Highlights: Humans see one or two objects behind occluders using geometric and semantic cues. Human drawings and generative AI completions show how different cues modulate perception Good continuation dominates human and AI shape completions Symmetry and familiarity guide humans, but not instruction-following AI outputs Humans integrate multiple levels; instruction-following AI engages lower-level-processing.
Open AccessHuman and generative AI integrate visual cues differently in a shape completion task
In amodal completion observers perceive complete objects despite partial occlusion. When two object parts are divided by an occluder, completion can result in perceiving one or two objects. This phenomenon involves both lower-level cues (e.g., symmetry, contour continuity) and higher-level cues (e.g., prior knowledge). Experiment 1 investigates how occluder size, familiarity, and symmetry affect human completions using a drawing task. Narrow occluders and asymmetry promote single-shape completions, while familiarity and (global) symmetry promote two-shape interpretations. Good continuation emerges as the strongest cue, with symmetry and familiarity playing increasingly important roles as occluder width increases. Experiment 2 compares human performance with three state-of-the-art generative AI models. Models often generated creative but non-compliant outputs, altering even unoccluded regions. We restricted analysis to instruction-following generations, identified through ratings by naive observers. Among compliant outputs, models showed some human-like biases (e.g., more two-shape completions for wide occluders), but failed with higher-level cues. They did not use symmetry to guide completions and showed reversed familiarity effects. Our findings highlight differences between human and AI completions. Humans integrate low- and high-level cues, whereas compliant outputs from the AI models rely primarily on low-level pattern continuation. Current AI models lack the flexible integration of multiple representational levels that characterize human perception. This work establishes an analytical framework for evaluating whether next-generation models achieve more human-like visual reasoning. Highlights: Humans see one or two objects behind occluders using geometric and semantic cues. Human drawings and generative AI completions show how different cues modulate perception Good continuation dominates human and AI shape completions Symmetry and familiarity guide humans, but not instruction-following AI outputs Humans integrate multiple levels; instruction-following AI engages lower-level-processing.