About this Research Topic
Causal self-supervised learning (Causal SSL) in artificial intelligence involves discovering and utilizing causal structures from unlabeled data, enabling models to infer causal relationships, not just correlations. Integrating this approach with sensory perception studies can significantly advance the understanding of how sensory inputs are processed and interpreted by both biological systems and computational models. This interdisciplinary effort can deepen insights into the cognitive neuroscience of perception, specifically how perceptual abilities develop, decline, or differ among individuals without direct supervision.
The research topic welcomes submissions on topics related, but not limited, to:
1. Developmental Trajectories in Perception Learning:
Investigate how Causal SSL can model developmental changes in sensory perception across different life stages. This research could use longitudinal data to discern patterns representing developmental trajectories in humans or animal models.
2. Computational Models for Multisensory Integration:
Design computational frameworks that utilize causal relationships to model how multiple sensory inputs (visual, auditory, tactile) are integrated. The aim would be to improve the accuracy and robustness of these models in predicting sensory outcomes in dynamic environments.
3. Enhancing Perceptual Abilities through Causal Learning:
Explore how causal learning mechanisms can enhance perceptual abilities, particularly in contexts where perceptual deficits are present. This could involve creating training regimes that adjust to individual differences in perception, as noted in sensory rehabilitation.
4. Impact of Individual Differences on Causal Learning Models:
Study how individual differences in perception affect the learning processes of Causal SSL models. This could lead to personalized AI systems that adapt to the unique perceptual patterns of users, enhancing user interaction with technology.
5. Causal SSL for Sensory Cognition and Attention:
Develop models that apply causal inference to understand the mechanisms of sensory cognition and attention. Such research could reveal how attention modulates perception and how these processes can be replicated or enhanced in AI systems.
Each of these topics not only aligns with the core themes of the Perception Science section but also contributes to the basic, clinical, or applied sciences as per the section's guidelines. This approach ensures that the submissions are interdisciplinary, leveraging cutting-edge AI methodologies to address fundamental questions in perception science.
The research topic welcomes submissions on topics related, but not limited, to:
1. Developmental Trajectories in Perception Learning:
Investigate how Causal SSL can model developmental changes in sensory perception across different life stages. This research could use longitudinal data to discern patterns representing developmental trajectories in humans or animal models.
2. Computational Models for Multisensory Integration:
Design computational frameworks that utilize causal relationships to model how multiple sensory inputs (visual, auditory, tactile) are integrated. The aim would be to improve the accuracy and robustness of these models in predicting sensory outcomes in dynamic environments.
3. Enhancing Perceptual Abilities through Causal Learning:
Explore how causal learning mechanisms can enhance perceptual abilities, particularly in contexts where perceptual deficits are present. This could involve creating training regimes that adjust to individual differences in perception, as noted in sensory rehabilitation.
4. Impact of Individual Differences on Causal Learning Models:
Study how individual differences in perception affect the learning processes of Causal SSL models. This could lead to personalized AI systems that adapt to the unique perceptual patterns of users, enhancing user interaction with technology.
5. Causal SSL for Sensory Cognition and Attention:
Develop models that apply causal inference to understand the mechanisms of sensory cognition and attention. Such research could reveal how attention modulates perception and how these processes can be replicated or enhanced in AI systems.
Each of these topics not only aligns with the core themes of the Perception Science section but also contributes to the basic, clinical, or applied sciences as per the section's guidelines. This approach ensures that the submissions are interdisciplinary, leveraging cutting-edge AI methodologies to address fundamental questions in perception science.
Keywords: Deep learning, Causal self-supervised learning, Sensory perception
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
