reviews: 
Comments: This work examines how neural networks make predictions on action-recognition video frames and provides a comparative analysis with human behavioral performance. The authors experimentally manipulate stimuli to isolate bodies from backgrounds, demonstrating that both neural networks and humans achieve high accuracy rates across full stimuli, isolated bodies, and isolated backgrounds. The performance gap between human body-only and background-only conditions is notable. A newly proposed network architecture, inspired by modular principles from neuroscience, demonstrates improved performance and stronger alignment with human behavior patterns. However, the critical limitation undermining this contribution is the reliance on poorly controlled and inadequate datasets, which produces findings that are difficult to interpret and potentially artifacts of data quality issues. The core experimental results lack statistical rigor—Fig. 1 (bottom left) purportedly shows comparable performance across conditions, but visual inspection suggests a clear hierarchy (ORIG >> BG >> Body) without proper error bars or statistical testing. The observation that humans achieve >0.7 accuracy using only background information indicates three serious problems: the dataset lacks sufficient difficulty, background information acts as a confounding variable, and temporal information appears unnecessary for this task. Moreover, the proposed architecture exhibits a confounding training paradigm—it is trained exclusively on body-only stimuli yet shows improvement only on body-only test conditions, while showing no gains on background-only conditions, with training differences potentially explaining performance improvements rather than architectural innovations. Critical methodological information is absent, such as quantitative validation of the YOLO v8 body-background separation quality. The manuscript presents only a single primary figure with insufficient supporting material, falling well below disciplinary standards. Essential elements are completely lacking: no error bars, no multi-model comparisons, no cross-dataset validation, no ablation studies, and no systematic investigation of critical variables such as stimulus size effects.
Strengths: Creating networks for action recognition holds significant value for real-world applications. Additionally, evaluating network performance relative to human performance represents an important research direction for bridging the gap between machine and human visual perception.
Weaknesses: The fundamental issue is that poorly controlled and substandard datasets produce results that lack reliability and interpretability. In Fig. 1 (bottom left), the claim that baseline models achieve equivalent performance across ORIG, body, and background conditions is unsupported by the actual data presented. The results lack error bars and statistical validation. From visual inspection alone, the pattern appears to be ORIG >> BG >> Body, contradicting the stated claim. The finding that humans exceed 0.7 accuracy using background-only frames raises three critical concerns: (1) the task is insufficiently challenging, (2) background information functions as a major confound, and (3) temporal dynamics are superfluous for this overly simple task. The proposed architecture suffers from a critical confound: it undergoes different training procedures than baseline models. When trained on body-only data, it improves on body-only evaluation; when trained on background-only data, no improvement is observed (again without error bars or statistical analysis). These improvements may simply reflect training differences rather than architectural superiority. Missing from the work is any empirical demonstration of YOLO v8's effectiveness at separating bodies from backgrounds. The manuscript contains only one primary figure and minimal additional content, failing to meet basic field standards. Critical elements are absent: no error bars, no multi-model evaluations, no cross-dataset assessments, no ablation studies, and no systematic examination of key variables such as stimulus size.
Recommendation: 1: strong reject
Reviewer confidence: 5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.
Soundness/correctness: 1 poor
Clarity: 1 poor