Driving factors of auditory category learning success
Our brain learns to update its mental model of the environment by abstracting sensory experiences for adaptation and survival. Learning to categorize sounds is one essential abstracting process for high-level human cognition, such as speech perception, but it is also challenging due to the variable nature of auditory signals and their dynamic contexts. To overcome these learning challenges and enhance learner performance, it is essential to identify the impact of learning-related factors in developing better training protocols. Here, we conducted an extensive meta-analysis of auditory category learning studies, including a total of 111 experiments and 4,521 participants, and examined to what extent three hidden factors (i.e., variability, intensity, and engagement) derived from 12 experimental variables contributed to learning success (i.e., effect sizes). Variables related to intensity and training variability outweigh others in predicting learning effect size. Activation likelihood estimation (ALE) meta-analysis of the neuroimaging studies revealed training-induced systematic changes in the frontotemporal-parietal networks. Increased brain activities in speech and motor-related auditory-frontotemporal regions and decreased activities in cuneus and precuneus areas are associated with increased learning effect sizes. These findings not only enhance our understanding of the driving forces behind speech and auditory category learning success, along with its neural changes, but also guide researchers and practitioners in designing more effective training protocols that consider the three key aspects of learning to facilitate learner success.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Single-word Auditory Attention Decoding Using Deep Learning Model
Identifying auditory attention by comparing auditory stimuli and corresponding brain responses, is known as auditory attention decoding (AAD). The majority of AAD algorithms utilize the so-called envelope entrainment mec…
Deep LearningEEGTowards a Human-Centred Cognitive Model of Visuospatial Complexity in Everyday Driving
We develop a human-centred, cognitive model of visuospatial complexity in everyday, naturalistic driving conditions. With a focus on visual perception, the model incorporates quantitative, structural, and dynamic attribu…
BenchmarkingA Single Model Explains both Visual and Auditory Precortical Coding
Precortical neural systems encode information collected by the senses, but the driving principles of the encoding used have remained a subject of debate. We present a model of retinal coding that is based on three constr…
From sound to meaning in the auditory cortex: A neuronal representation and classification analysis
The neural mechanisms underlying the comprehension of meaningful sounds are yet to be fully understood. While previous research has shown that the auditory cortex can classify auditory stimuli into distinct semantic cate…
Cognitive factor forming an individual constituent in a driver model inferred from multiplicatory relationships between cognitive sub-factors
In order to reproduce human behaviour in dynamic traffic situations, a computational representation of the requisite mental processes used to carry out the complex driving tasks is required. A single cognitive factor has…