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The edge orientation entropy of natural scenes is associated with infant visual preferences and adult aesthetic judgements
Statistical regularities of oriented edges in natural scenes, ‘edge co-occurrence statistics’, are associated with adults’ aesthetic responses, with greater preference for some images when the degree of randomness in the orientation of edges (Edge Orientation Entropy, EOE) across an image is relatively high. Here, we investigate whether this spatial image statistic is also associated with infants’ visual preferences. We measure infant looking time for images of building façades previously used to identify the relationship between EOE and adult aesthetic judgements. Twenty-six 4–9-month-old infants and 29 adults looked freely at pairs of the images. Infants and adults both looked longest at images where all edge orientations are about equally likely to occur (high 1st-order EOE), and at images with low correlation of edge orientations across the image (high 2nd-order EOE). Infant looking time and adult pleasantness judgements were also strongly related: infants looked longer at the building façades that adults liked. Our results suggest that even as young as 4-months, infants’ spatial vision is sensitive to edge co-occurrence statistics that are typical of natural scenes and faces, where edges are more evenly distributed across orientations. We discuss the implications for understanding the sensory component of adult aesthetic judgements, as well as the role of natural scene statistics in infant perception.
Adaptive control and state error prediction of flexible manipulators using radial basis function neural network and dynamic surface control method
This paper introduces a novel control strategy for managing the uncertainties in flexible joint manipulators, incorporating a Radial Basis Function Neural Network (RBFNN) with Adaptive Dynamic Surface Control (ADSC). This strategy innovatively utilizes RBFNN to precisely approximate uncertain system dynamics and integrates a nonlinear damping term to effectively counteract external disturbances, enhancing the overall control accuracy. We have also developed an adaptive law that updates neural network weights and system parameters in real-time, ensuring the system’s adaptability to dynamic changes. The application of the Lyapunov method ensures that all signals within the closed-loop system remain semi-globally uniformly bounded, significantly reducing tracking errors. Moreover, we introduce the use of Long Short-Term Memory (LSTM) networks for predictive analysis of state data, which further confirms the robustness and effectiveness of our control method through extensive simulations. The distinctive integration of these technologies and their practical validation through comparative simulations underscore the innovative aspects of our approach in addressing real-world challenges in flexible manipulators.