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Growing Minds, Integrating Senses: Neural and Computational Insights into Age-related Changes in Audio-Visual and Tactile-Visual Learning in Children

Nina Raduner, Carmen Providoli, Sarah Di Pietro, Maya Schneebeli, Iliana I. Karipidis, Ella Casimiro, Saurabh Bedi, Michael Von Rhein, Nora Maria Raschle, Christian C. Ruff, Silvia Brem

Posted on: 27 June 2025

Preprint posted on 23 June 2025

Learning isn’t one-size-fits-all, especially when your brain’s still under construction. This study reveals how kids fine-tune their senses and use feedback to power up thinking.

Selected by Clara Vo-Dignard, Sarra Chebaane, Farhan Jahan, uMontreal Neuro preLighters

Categories: neuroscience

Background

Multisensory learning (MSL) is a fundamental process in child development, enabling information integration across different sensory modalities, such as auditory, tactile, and visual input. It supports essential skills like language acquisition, social interaction, and environmental navigation.

However, how the ability to learn from multisensory inputs evolves during childhood, how different sensory modalities influence this learning process, and how neural networks support this remains underexplored. This preprint investigates age-related changes in audio-visual (AV) and tactile-visual (TV) associative learning by examining how children between the ages of 5 and 13 form cross-sensory associations. The study offers insights into the underlying neural mechanisms and developmental trajectories that support multisensory integration and learning.

Study design

The authors employed an integrative and comprehensive methodological approach to explore how multisensory learning develops in childhood. First, 67 typically developing children participated in a carefully designed multisensory learning task. During this task, the children learned associations between visual symbols and either auditory sounds or tactile vibrations, receiving feedback after each choice. The authors used computational modelling to characterize the cognitive processes involved, specifically combining reinforcement learning and drift diffusion models. These models allowed them to capture how children updated their expectations based on reward prediction errors and how efficiently they made decisions.

Meanwhile, functional magnetic resonance imaging (fMRI) was conducted to identify the neural correlates underlying these learning processes. Brain activity was examined during both stimulus presentation and feedback processing, and it was analyzed in relation to the computational parameters like values, learning rates, and reward prediction errors. Ultimately, statistical analyses were conducted, employing linear mixed models for behavioural and computational data, alongside whole-brain and targeted region-of-interest analyses to explore how multisensory integration and learning differed across sensory modalities and changed with development.

By combining behavioural tasks, computational modelling, and neuroimaging, the approach of the preprint authors provides a rich, multi-layered understanding of how multisensory learning unfolds and changes throughout childhood.

Key findings

  • Tactile-visual learning is more challenging for children, regardless of age.

The study found that tactile-visual tasks led to lower accuracy and slower reaction times than the audio-visual tasks task across all ages. This suggests that tactile stimuli demand more focused attention and detailed encoding, making them inherently more challenging than auditory stimuli. Computational modelling further revealed longer non-decision times, wider decision boundaries and lower drift rates for TV. The results are consistent with behavioural data, indicating that children require more information accumulation and engage in slower decision-making processes during TV tasks.

  • Older children exhibit faster processing and decision-making.

The researchers combined behavioural data with drift diffusion modelling to examine reaction times and drift rates, revealing that older children demonstrated improved efficiency in both association learning tasks. This was evidenced by faster reaction times and higher drift rates, reflecting enhanced speed and accumulation of sensory information during decision-making processes. These findings support the notion that older children increasingly engage high-order cortical regions, which are associated with more efficient multisensory processing and the effective integration of information across sensory modalities.

  • Older children are more sensitive to negative feedback.

Using fMRI and computational modelling to analyze brain activity, the researchers found that older children exhibited a greater sensitivity to negative feedback, leading to more effective learning adjustments. This was linked to heightened activation in the anterior insula, which integrates emotional and cognitive information. The increased response to negative prediction errors suggests that older children use emotional cues to guide decision-making. In contrast, younger children rely more on basic learning mechanisms. These findings indicate a developmental shift toward more flexible and emotionally informed learning strategies.

Why we chose to highlight this preprint

This study brings forward novel and meaningful findings for understanding how multisensory processing and learning, key drivers of cognitive growth, evolve through typical childhood development. By taking into account a variety of learning parameters, the authors manage to encompass the full complexity of the phenomenon. They offer insight into the cognitive strategies children use to produce behavioural responses and how feedback is integrated during learning, across both audio-visual and tactile-visual modalities.

One of the study’s strengths lies in its multidisciplinary and innovative approach. By combining functional neuroimaging, behavioural data, and computational modelling, the authors provide a more complete picture of the neural mechanisms involved in different aspects of multisensory learning. Their findings build on previous hypotheses in the field and highlight a developmental shift: with age and brain maturation, children increasingly rely on higher-order cortical regions and broader neural networks. The study clearly addresses its initial research questions, bridges previous gaps in the literature and makes way for more comprehensive studies of learning as a whole. These insights hold promise for informing strategies to better support learning in children.

Questions for the authors

  • Given that the task used is a multisensory associative forced choice learning task that might differ from a regular context, can the results and learning parameters obtained be expected to generalize to real-world learning? Would having more tasks have enhanced that generalizability?
  • In the same prospect, is the learning seen through the unique task session sufficient to reflect the gradual learning process in children?
  • Do you think motivation could have significantly affected the performances seen and potentially the results, especially in younger children who might be less feedback-drive?
  • Would you expect similar patterns to emerge in non-visual modalities, and would studying them be interesting when considering multisensory learning?
  • Would you be able to discuss how those findings could be applied in future research to children with different contexts, for example, the presence of deafness in the child or parents, or the study of learning disabilities?

 

 

 

 

 

 

 

Tags: brain, learning, neurodevelopment

doi: https://doi.org/10.1242/prelights.40907

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