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Baese-Berk, M.

Publications and source records attributed to Baese-Berk, M..

2 recordsLinked to original sources

Motor automaticity in natural keyboard typing

Certain features of everyday motor skills become automatic while others remain controlled. Here we use a novel keyboard typing task to investigate whether motor automaticity depends on the frequency of naturally learned motor sequences. Participants type five-letter strings that vary in their word and bigram (two-letter sequence) frequency in natural language, allowing us to examine the influence of prior exposure without laboratory training. Novel pseudo word strings are tested as well. We find greater sequence frequency in natural language is associated with faster inter-keypress intervals and lower temporal variability within the sequence. In contrast, latencies to initiate a sequence are slower for novel pseudo-word strings but are otherwise insensitive to natural word frequency. We also find individual differences in inter-keypress speed and variability are robust across frequency levels but are unrelated to conventional measures of typing skill. Our method establishes keyboard typing as a scalable, ethologically valid framework for probing features of a naturally acquired human motor skill. This research will help extend laboratory-based studies of motor sequence learning and sets the stage for future investigations of linguo-motor processes. Moreover, our findings demonstrate which features within naturally acquired motor sequences become automatic and that typing proficiency is not determined solely by automaticity.

neuroscience↗

Selectivity to acoustic features of human speech in the auditory cortex of the mouse

A better understanding of the neural mechanisms of speech processing can have a major impact in the development of strategies for language learning and in addressing disorders that affect speech comprehension. Technical limitations in research with human subjects hinder a comprehensive ex-ploration of these processes, making animal models essential for advancing the characterization of how neural circuits make speech perception possible. Here, we investigated the mouse as a model organism for studying speech processing and explored whether distinct regions of the mouse auditory cortex are sensitive to specific acoustic features of speech. We found that mice can learn to categorize frequency-shifted human speech sounds based on differences in formant transitions (FT) and voice onset time (VOT). Moreover, neurons across various auditory cortical regions were selective to these speech features, with a higher proportion of speech-selective neurons in the dorso-posterior region. Last, many of these neurons displayed mixed-selectivity for both features, an attribute that was most common in dorsal regions of the auditory cortex. Our results demonstrate that the mouse serves as a valuable model for studying the detailed mechanisms of speech feature encoding and neural plasticity during speech-sound learning.

neuroscience↗