Source-linked AI summary
A Human-Factors Guided Cognitive Model of Visuospatial Complexity in Embodied Active Vision
Vasiliki Kondyli, Jakob Suchan, Mehul Bhatt
TL;DR
Static, isolated complexity measures do not adequately represent perception in dynamic, multimodal environments. The paper proposes an embodied active-vision model organized across multiple attribute categories and demonstrates that driving-scene complexity emerges from their interaction, supporting human-centered dataset and system evaluation.
Problem
Existing complexity models often focus on isolated visual properties or static 2D scenes, overlooking the multimodal and embodied nature of real-world perception.
Method
The paper develops a cognitive model integrating quantitative, structural, dynamic, auditory, and interactional attributes to analyze driving scenes and guide benchmark and VR applications.
Results
The driving-scene analysis shows that visuospatial complexity is emergent and multimodal, with temporal and interactional richness contributing beyond visual density alone.
Takeaways & Limitations
The framework supports human-centered benchmark design, VR studies of active vision, and explainable computational analysis of dynamic 3D environments.
Abstract
from arXiv · showhide
We propose a novel framework for the analysis of multimodal data -- encompassing visual, auditory, and spatial stimuli -- foregrounding the role of complexity in embodied perception and interaction in dynamic, naturalistic settings. Grounded in theories of embodied cognition and active vision, we argue that embodied perceptual complexity emerges from an agent's dynamic engagement with the environment and must be analyzed holistically, as a combination of qualitative and quantitative attributes pertaining to, for instance, visuospatial and auditory features. Building on previous work on visual complexity, we expand this into a categorization of diverse complexity attributes -- quantitative, structural, dynamic, auditory, and interactional -- that together characterize multimodal complexity. We demonstrate how this model provides a theoretical framework for characterizing aspects of visuospatial complexity and their interactions, specifically in the context of everyday driving. We also discuss practical applications of the proposed model for creating and evaluating benchmark datasets (e.g., in driving) that centralize cognitive human factors, as well as applications aimed at systematically investigating the effect of visuospatial complexity on human active vision from the viewpoint of visual perception research. The proposed framework lays the foundation for automated methods that interpret complexity in 3D dynamic environments from a human-centered perspective, serving as a semantic template for explainable computational analysis of visuospatial complexity with a categorical focus on cognitive human factors.
1. Introduction
The paper proposes a human-factors framework for analyzing multimodal complexity as embodied agents perceive and interact with dynamic environments. It extends visual-complexity analysis toward driving applications and explainable computational methods.
- Traditional visual-complexity models based on static, unimodal data are insufficient for dynamic environments containing visual, auditory, and spatial inputs.
- The framework treats visuospatial complexity as emerging from active engagement with the environment and combines qualitative and quantitative attributes.
- The framework provides a semantic foundation for explainable computational analysis of complexity in dynamic 3D environments.
- Embodied cognition and active vision motivate analyzing perception through continuous interaction between action and multimodal sensory feedback.
- The model integrates low-level visual attributes with higher-level semantic annotations to characterize naturalistic driving scenes.
- Applications include human-factors-oriented driving benchmark datasets and interactive virtual-reality experiments studying active vision.
2. Modelling Visual Complexity
Existing visual-complexity research provides useful computational and behavioral measures, but it often isolates scene properties and remains centered on static 2D imagery. The paper argues for a broader multimodal, embodied account.
- Visual complexity is commonly defined through scene detail, variability, or intricacy and measured using clutter, object density, and scene entropy.
- Low-Level Scene Features: Low-level metrics such as feature congestion, edge density, and texture entropy quantify visual irregularity and predict clutter or search difficulty.
- Structural Organization: Structural attributes capture spatial organization, with regularity, symmetry, and orderly layouts associated with lower perceived complexity and faster interpretation.
- Scene Dynamics and Semantics: Dynamic analysis requires temporal measures such as Temporal Information and optical flow, while auditory cues also contribute to perceived complexity.
- Existing models often treat visual clutter, structure, motion, audio, or social context separately and overlook multimodal embodied perception in real-world interaction.
3. Human-Centred Complexity Model for Active Vision
The proposed model views complexity as dynamic, egocentric, and goal-driven rather than as a fixed environmental property. It integrates spatial, temporal, and multimodal cues with the observer’s capabilities and context.
- The framework defines complexity as emerging from continuous interaction between an observer and the surrounding context.
- It addresses prior emphasis on static features by integrating spatial, temporal, auditory, and social dimensions as complexity unfolds over time.
- A visually simple single frame can become cognitively demanding when experienced as a dynamic sequence involving pedestrians, cyclists, head movements, and sounds.
- Quantitative attributes include physical-space dimensions and functional clutter that are immediately accessible to human observers.
VISUOSPATIAL COMPLEXITY Description
The model profiles visuospatial complexity through quantitative, structural, dynamic, auditory, and interactional attributes, combining computational measures with semantic annotations. Driving-scene comparisons show that complexity depends on the interplay of visual, temporal, auditory, and social factors.
- Quantitative Attributes: Quantitative attributes include object quantity, color and shape variety, object and edge density, luminance, saliency, and target-background similarity.
- Structural Attributes: Structural attributes describe repetition, symmetry, order, and homogeneity or heterogeneity in the arrangement of scene elements.
- Structural Attributes: High regularity, repetition, and symmetry are associated with lower visuospatial complexity, whereas heterogeneous organization increases it.
- Dynamic Attributes: Dynamic attributes track temporal changes in scene quantities and moving objects, which shape visual attention and detection during cognitive tasks.
- Auditory Attributes: Auditory attributes include semantically relevant sounds that affect perception, attention, cognitive load, reaction time, and situational awareness.
- Multimodal Interaction Attributes: Multimodal interaction attributes cover verbal and non-verbal signals whose meanings vary with task, environment, social roles, and dynamics.
- The analysis combines image-based metrics with bounding-box annotations and qualitative annotations of motion, audio, and interaction types.
- Comparative Analysis: Tokyo has higher clutter and lower SSIM, whereas Paris combines lower clutter with unpredictable pedestrian behavior, rapid motion changes, and task-related audio.
4. Application the Complexity Model: Dataset and System Evaluation
The model supports two applications: human-factors benchmarking of driving scenes and controlled VR experiments that vary visuospatial complexity. Together, these applications represent complexity through combined visual, semantic, interactive, auditory, and temporal attributes.
- Dataset and system evaluation: The framework guides benchmark-dataset creation and evaluation for cognitive and perceptual technologies in dynamic, naturalistic environments.It is intended to represent perceptual and cognitive demands in human-centered autonomous driving.
- Benchmark dataset creation and complexity-based scaling: 27 real-world driving scenes are placed on a low-to-high visuospatial complexity scale using composite low-level and high-level attributes.The composite score incorporates visual clutter alongside semantics, interactivity, and multimodal cues.
- Benchmark dataset creation and complexity-based scaling: Comparable overall complexity can arise from different attribute combinations, such as higher clutter in Hong Kong versus more motion, interaction, and audio cues in Bombay.This motivates a multidimensional rather than single-attribute characterization of scene complexity.
- The effect of visuospatial complexity in active vision: The VR driving study combines immersive embodied experience with precise control over visuospatial complexity attributes.Participants navigated a virtual city while detecting behavior or object changes, including driving-relevant hazards.
- The effect of visuospatial complexity in active vision: 24.8% detection at a 0-second event interval, with 1.633-second average reaction time, showed impaired performance when two events occurred simultaneously.Detection improved for 1–5-second intervals and then plateaued, indicating a non-linear temporal sensitivity threshold.
- The effect of visuospatial complexity in active vision: The model provides a foundation for designing behavioral experiments and future adaptive driver-assistance and human-centered AI systems.Its structured manipulation of environmental variables supports analysis in dynamic, embodied contexts.
5. Outlook
The outlook extends the framework toward human-evaluated complexity measures, dataset benchmarking, and computational systems for explainable visual sensemaking. These directions aim to align complexity analysis with subjective perception and human-centered autonomous-system evaluation.
- Qualitative human evaluation: Future human-subject studies will collect quantitative ratings and qualitative distinctions of systematically parameterized visuospatial-complexity stimuli.These judgments are intended to align computational complexity measures with human perceptual experience.
- Benchmarking for human-factor assistive technologies: The framework can identify underrepresented conditions and combinations of scene attributes across machine-learning and cognitive-vision datasets.This supports evaluation of perceptual and interaction demands in embodied contexts.
- Computational cognitive vision: Embedding human-centered perceptual complexity into dataset construction and evaluation metrics supports explainable and cognitively grounded autonomous systems.The framework is presented as a scalable basis for design, evaluation, and benchmarking.
- Computational cognitive vision: The framework is being connected with integrated vision-and-semantics research for active, explainable visual sensemaking in autonomous vehicles.This connection supports a declarative computational framework combining knowledge representation with visual processing.