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Missing Pieces: How Humans and GPT-4.1 Detect Absence and Predict the Unseen

Year: 2025

Type: conference-paper

Abstract: We investigate the semantic reasoning capabilities of GPT-4.1 through a comparative study with human participants across three visual domains: object absence recognition, social inference, and temporal prediction. Our evaluation introduces a benchmark designed to assess the detection of absences and the prediction of unseen events. GPT-4.1 exceeds human performance in social context cases, where structured patterns support inference. In object-based cases, model performance is more variable, with failures occurring when crucial visual cues are subtle or embedded in dense scenes. Temporal reasoning is most challenging for both humans and GPT-4.1, though the model struggles when inference depends on narratively significant but less-prominent physical details.

DOI: 10.1145/3746276.3760466

Authors: Petar Mladenov, Jasmin Heierli, Alexandre de Spindler, Elena Gavagnin

Institutions: Lucerne University of Applied Sciences and Arts, ZHAW Zurich University of Applied Sciences

Language: English

Topic: Multimodal Machine Learning Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

OpenAlex ID: W4415519228

FWCI:

Cites: 5

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