Frank H. Guenther

Generalization in motor control is the extent to which motor learning affects movements in situations different than those in which it originally occurred. Recent data on orofacial speech movements indicates that motor sequence learning generalizes to novel syllable sequences containing phonotactically illegal, but previously practiced, consonant clusters. Practicing an entire syllable, however, results in even larger performance gains compared to practicing just its clusters. These patterns of generalization could reflect language-general changes in phonological memory storage and/or inter-articulator coordination during motor sequence learning. To disentangle these factors, we conducted two experiments in which talkers intensively practiced producing novel syllables containing illegal onset and coda clusters over two consecutive days. During the practice phases of both experiments, we observed that, through repetition, talkers gradually produced the syllables with fewer errors, indicative of learning. After learning, talkers were tested for generalization to single syllables (Experiment 1) or syllable pairs (Experiment 2) that overlapped to varying degrees with the practiced syllables. Across both experiments, we found that performance improvements from practicing syllables with illegal clusters partially generalized to novel syllables that contained those clusters, but performance was more error prone if the clusters occurred in a different syllable position (onset versus coda) as in practice, demonstrating that inter-articulator coordination is contextually sensitive. Furthermore, changing the position of a cluster was found to be more deleterious to motor performance during the production of the second syllables in syllable pairs, which required talkers to store more phonological material in memory prior to articulation, compared to single syllables. This interaction effect reveals a complex interplay between memory storage and inter-articulator coordination on generalization in speech motor sequence learning.

In this cross-sectional study, we examined the relationship between cortical thickness and performance on several verbal repetition tasks in a cohort of patients with primary progressive aphasia in order to test predictions generated by theoretical accounts of phonological working memory that predict phonological content buffers in left posterior inferior frontal sulcus and supramarginal gyrus. Cortical surfaces were reconstructed from magnetic resonance imaging scans from 42 participants diagnosed with primary progressive aphasia. Cortical thickness was measured in a set of anatomical regions spanning the entire cerebral cortex. Correlation analyses were performed between cortical thickness and average score across three phonological working memory-related tasks: the Repetition sub-test from the Western Aphasia Battery, a forward digit span task, and a backward digit span task. Significant correlations were found between average working memory score across tasks and cortical thickness in left supramarginal gyrus and left posterior inferior frontal sulcus, in support of prior theoretical accounts of phonological working memory. Exploratory whole-brain correlation analyses performed for each of the three behavioural tasks individually revealed a distinct set of positively correlated regions for each task. Comparison of cortical thickness measures from different primary progressive aphasia sub-types to cortical thickness in age-matched controls further revealed unique patterns of atrophy in the different subtypes.

In this cross-sectional study, we examined the relationship between cortical thickness and performance on several verbal repetition tasks in a cohort of patients with primary progressive aphasia (PPA) in order to test predictions generated by theoretical accounts of phonological working memory (PWM) that predict phonological content buffers in left posterior inferior frontal sulcus (pIFS) and supramarginal gyrus (SMG). Cortical surfaces were reconstructed from magnetic resonance imaging scans from 42 participants diagnosed with PPA. Cortical thickness was measured in a set of anatomical regions spanning the entire cerebral cortex. Correlation analyses were performed between cortical thickness and average score across three PWM related tasks: the Repetition subtest from the Western Aphasia Battery, a forward digit span task, and a backward digit span task. Significant correlations were found between average PWM score across tasks and cortical thickness in left SMG and left pIFS, in support of prior theoretical accounts of PWM. Exploratory whole-brain correlation analyses performed for each of the three behavioral tasks individually revealed a distinct set of positively-correlated regions for each task. Comparison of cortical thickness measures from different PPA subtypes to cortical thickness in age-matched controls further revealed unique patterns of atrophy in the different PPA subtypes.

Effective human supervision of robots can be key for ensuring correct robot operation in a variety of potentially safety-critical scenarios. This paper takes a step towards fast and reliable human intervention in supervisory control tasks by combining two streams of human biosignals: muscle and brain activity acquired via EMG and EEG, respectively. It presents continuous classification of left and right hand-gestures using muscle signals, time-locked classification of error-related potentials using brain signals (unconsciously produced when observing an error), and a framework that combines these pipelines to detect and correct robot mistakes during multiple-choice tasks. The resulting hybrid system is evaluated in a “plug-and-play” fashion with 7 untrained subjects supervising an autonomous robot performing a target selection task. Offline analysis further explores the EMG classification performance, and investigates methods to select subsets of training data that may facilitate generalizable plug-and-play classifiers.