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Contextual feedback in object recognition: A biologically inspired computational model and human behavioral study.

Elahe Soltandoost1, Karim Rajaei2, Reza Ebrahimpour3

  • 1University of Padova, Department of Information Engineering, Via Gradenigo 6/b, Padova, 35131, Veneto, Italy; Shahid Rajaei University, Faculty of Engineering, Lavizan, Shahid Babaei Highway, Tehran, 16788-15811, Iran.

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|August 31, 2025
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Summary
This summary is machine-generated.

A new context-based computational model (CBM) improves object recognition with scene context. While effective, human visual perception relies more on intrinsic object processing, especially with occlusion.

Keywords:
Computational modelingFeedback mechanismsObject recognitionScene contextVisual perception

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Area of Science:

  • Computational neuroscience
  • Computer vision
  • Cognitive psychology

Background:

  • Scene context significantly influences visual perception and object recognition, especially in challenging conditions.
  • Top-down mechanisms involving scene information modulating object-selective brain areas are suggested but not fully understood.

Purpose of the Study:

  • To introduce a biologically inspired context-based computational model (CBM) for object recognition.
  • To investigate the role of explicit feedback mechanisms integrating scene context into object recognition.
  • To compare CBM performance against a standard feedforward model and human perception.

Main Methods:

  • Developed a context-based computational model (CBM) with two pathways: Object_CNN for object features and Place_CNN for scene context.
  • Compared CBM to AlexNet (a standard feedforward model) on object recognition tasks with visual degradation and occlusion.
  • Conducted behavioral experiments to compare human object recognition with CBM and AlexNet performance.

Main Results:

  • CBM significantly outperformed AlexNet in object recognition, demonstrating the benefit of contextual feedback for degraded input.
  • Human participants showed a modest benefit from congruent context, particularly with high occlusion levels.
  • Human recognition remained robust even without context, suggesting dominant roles for global shape processing and local recurrent processes.

Conclusions:

  • Contextual feedback can enhance computational model performance for object recognition.
  • Human visual perception employs distinct strategies, relying heavily on intrinsic object processing and local recurrent mechanisms.
  • Future models should integrate context-sensitive feedback with local recurrent processes to better mimic human visual resilience.