Improving Acceptance to Sensory Substitution: A Study on the V2A-SS Learning Model Based on Information Processing...

Area of Science:

• Neuroscience
• Biomedical Engineering
• Human-Computer Interaction

Background:

• The visual sensory organ (VSO) is crucial for daily function; damage severely impacts activities.
• Visual-to-Auditory Sensory Substitution (V2A-SS) uses neuroplasticity to transfer visual data to auditory channels, offering a solution for vision impairment.
• Technological advancements in AI and IT address V2A-SS limitations like resolution and bandwidth, increasing its potential applicability.

Purpose of the Study:

• To investigate effective learning strategies for accelerating the acceptance and integration of V2A-SS technology.
• To introduce and evaluate a novel V2A-SS learning model based on Information Processing Learning Theory (IPLT).

Main Methods:

• Developed a V2A-SS learning model incorporating "concept acquisition, rehearsal, assessment" stages.
• Compared the proposed IPLT-based model against simple random repetition learning and an optimized V2A-SS algorithm (Mel-Scaled Frequency Mapping).

Main Results:

• The proposed IPLT-based learning model demonstrated an 11% increase in recognition rates.
• This improvement significantly surpassed the 2.72% gain from optimizing the V2A-SS algorithm alone.
• The model effectively reduced the learning curve and enhanced user adaptation to V2A-SS.

Conclusions:

• A structured learning model is essential for bridging the gap between V2A-SS technical feasibility and practical application.
• Developing effective learning strategies alongside technological advancements is crucial for accelerating the adoption of V2A-SS and harnessing neuroplasticity.