The evolution of a visual-to-auditory sensory substitution device using interactive genetic algorithms

Augmenting perception: How artificial intelligence transforms sensory substitution

What happens when artificial sensors are coupled with the human senses? Using technology to extend the senses is an old human dream, on which sensory substitution and other augmentation technologies have already delivered. Laser tactile canes, corneal implants and magnetic belts can correct or extend what individuals could otherwise perceive. Here we show why accommodating intelligent sensory augmentation devices not just improves but also changes the way of thinking and classifying former sensory augmentation devices. We review the benefits in terms of signal processing and show why non-linear transformation is more than a mere improvement compared to classical linear transformation.

Cross-Modal Correspondences Enhance Performance on a Colour-to-Sound Sensory Substitution Device

Visual sensory substitution devices (SSDs) can represent visual characteristics through distinct patterns of sound, allowing a visually impaired user access to visual information. Previous SSDs have avoided colour and when they do encode colour, have assigned sounds to colour in a largely unprincipled way. This study introduces a new tablet-based SSD termed the ‘Creole’ (so called because it combines tactile scanning with image sonification) and a new algorithm for converting colour to sound that is based on established cross-modal correspondences (intuitive mappings between different sensory dimensions). To test the utility of correspondences, we examined the colour–sound associative memory and object recognition abilities of sighted users who had their device either coded in line with or opposite to sound–colour correspondences. Improved colour memory and reduced colour-errors were made by users who had the correspondence-based mappings. Interestingly, the colour–sound mappings that provided the highest improvements during the associative memory task also saw the greatest gains for recognising realistic objects that also featured these colours, indicating a transfer of abilities from memory to recognition. These users were also marginally better at matching sounds to images varying in luminance, even though luminance was coded identically across the different versions of the device. These findings are discussed with relevance for both colour and correspondences for sensory substitution use.

Improving training for sensory augmentation using the science of expertise

Sensory substitution and augmentation devices (SSADs) allow users to perceive information about their environment that is usually beyond their sensory capabilities. Despite an extensive history, SSADs are arguably not used to their fullest, both as assistive technology for people with sensory impairment or as research tools in the psychology and neuroscience of sensory perception. Studies of the non-use of other assistive technologies suggest one factor is the balance of benefits gained against the costs incurred. We argue that improving the learning experience would improve this balance, suggest three ways in which it can be improved by leveraging existing cognitive science findings on expertise and skill development, and acknowledge limitations and relevant concerns. We encourage the systematic evaluation of learning programs, and suggest that a more effective learning process for SSADs could reduce the barrier to uptake and allow users to reach higher levels of overall capacity.

Sensory substitution: closing the gap between basic research and widespread practical visual rehabilitation

Sensory substitution devices (SSDs) have come a long way since first developed for visual rehabilitation. They have produced exciting experimental results, and have furthered our understanding of the human brain. Unfortunately, they are still not used for practical visual rehabilitation, and are currently considered as reserved primarily for experiments in controlled settings. Over the past decade, our understanding of the neural mechanisms behind visual restoration has changed as a result of converging evidence, much of which was gathered with SSDs. This evidence suggests that the brain is more than a pure sensory-machine but rather is a highly flexible task-machine, i.e., brain regions can maintain or regain their function in vision even with input from other senses. This complements a recent set of more promising behavioral achievements using SSDs and new promising technologies and tools. All these changes strongly suggest that the time has come to revive the focus on practical visual rehabilitation with SSDs and we chart several key steps in this direction such as training protocols and self-train tools.