Neural representations of haptic object size in the human brain revealed by multivoxel fMRI patterns

Representational Similarity Analysis for Tracking Neural Correlates of Haptic Learning on a Multimodal Device

A goal of wearable haptic devices has been to enable haptic communication, where individuals learn to map information typically processed visually or aurally to haptic cues via a process of cross-modal associative learning. Neural correlates have been used to evaluate haptic perception and may provide a more objective approach to assess association performance than more commonly used behavioral measures of performance. In this article, we examine Representational Similarity Analysis (RSA) of electroencephalography (EEG) as a framework to evaluate how the neural representation of multifeatured haptic cues changes with association training. We focus on the first phase of cross-modal associative learning, perception of multimodal cues. A participant learned to map phonemes to multimodal haptic cues, and EEG data were acquired before and after training to create neural representational spaces that were compared to theoretical models. Our perceptual model showed better correlations to the neural representational space before training, while the feature-based model showed better correlations with the post-training data. These results suggest that training may lead to a sharpening of the sensory response to haptic cues. Our results show promise that an EEG-RSA approach can capture a shift in the representational space of cues, as a means to track haptic learning.

Aging and the visual perception of object size

An experiment evaluated the ability of 30 younger and older adults to visually judge object size under three conditions: (1) full cue, (2) in the dark, with linear perspective, and (3) in complete darkness. Each observer made repeated judgments for the same square stimuli (the task was to adjust a separation until it matched the perceived size of the squares), enabling an evaluation of precision as well as accuracy. The judgments were just as accurate in the dark with linear perspective condition as in the full cue condition, indicating that linear perspective serves as an important source of optical information to support the perception of object size). In contrast, in complete darkness (where linear perspective information was unavailable), the accuracy of the observers' judgments was poor. Finally, there was no difference in either the accuracy or the precision of the observers' judgments between the two age groups, despite the fact that the older adults were more than 50 years older than the younger adults (mean age of the younger and older adults was 22.3 and 74.1 years, respectively). The ability to visually perceive object size is well maintained with increasing age, unlike a number of other important visual abilities.

Decoding grasp and speech signals from the cortical grasp circuit in a tetraplegic human

The cortical grasp network encodes planning and execution of grasps and processes spoken and written aspects of language. High-level cortical areas within this network are attractive implant sites for brain-machine interfaces (BMIs). While a tetraplegic patient performed grasp motor imagery and vocalized speech, neural activity was recorded from the supramarginal gyrus (SMG), ventral premotor cortex (PMv), and somatosensory cortex (S1). In SMG and PMv, five imagined grasps were well represented by firing rates of neuronal populations during visual cue presentation. During motor imagery, these grasps were significantly decodable from all brain areas. During speech production, SMG encoded both spoken grasp types and the names of five colors. Whereas PMv neurons significantly modulated their activity during grasping, SMG's neural population broadly encoded features of both motor imagery and speech. Together, these results indicate that brain signals from high-level areas of the human cortex could be used for grasping and speech BMI applications.