Dissociated Neural Representations of Content and Ordinal Structure in Auditory Sequence Memory

2020 ◽  
Author(s):  
Ying Fan ◽  
Qiming Han ◽  
Simeng Guo ◽  
Huan Luo
2008 ◽  
Vol 3 (4) ◽  
pp. 178-186 ◽  
Author(s):  
Adam T. Tierney ◽  
Tonya R. Bergeson ◽  
David B. Pisoni

2020 ◽  
Author(s):  
Ying Fan ◽  
Qiming Han ◽  
Simeng Guo ◽  
Huan Luo

AbstractWhen retaining a sequence of auditory tones in working memory (WM), two forms of information – frequency (content) and ordinal position (structure) – have to be maintained in the brain. Here, we employed a time-resolved multivariate decoding analysis on content and structure information separately to examine their neural representations in human auditory WM. We demonstrate that content and structure are stored in a dissociated manner and show distinct characteristics. First, each tone is associated with two separate codes in parallel, characterizing its frequency and ordinal position, respectively. Second, during retention, a structural retrocue reactivates structure but not content, whereas a following white noise triggers content but not structure. Third, structure representation remains unchanged whereas content undergoes a transformation throughout memory progress. Finally, content reactivations during retention correlate with WM behavior. Overall, our results support a factorized content-structure representation in auditory WM, which might help efficient memory formation and storage by generalizing stable structure to new auditory inputs.


2020 ◽  
Author(s):  
Miriam E. Weaverdyck ◽  
Mark Allen Thornton ◽  
Diana Tamir

Each individual experiences mental states in their own idiosyncratic way, yet perceivers are able to accurately understand a huge variety of states across unique individuals. How do they accomplish this feat? Do people think about their own anger in the same ways as another person’s? Is reading about someone’s anxiety the same as seeing it? Here, we test the hypothesis that a common conceptual core unites mental state representations across contexts. Across three studies, participants judged the mental states of multiple targets, including a generic other, the self, a socially close other, and a socially distant other. Participants viewed mental state stimuli in multiple modalities, including written scenarios and images. Using representational similarity analysis, we found that brain regions associated with social cognition expressed stable neural representations of mental states across both targets and modalities. This suggests that people use stable models of mental states across different people and contexts.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Meir Meshulam ◽  
Liat Hasenfratz ◽  
Hanna Hillman ◽  
Yun-Fei Liu ◽  
Mai Nguyen ◽  
...  

AbstractDespite major advances in measuring human brain activity during and after educational experiences, it is unclear how learners internalize new content, especially in real-life and online settings. In this work, we introduce a neural approach to predicting and assessing learning outcomes in a real-life setting. Our approach hinges on the idea that successful learning involves forming the right set of neural representations, which are captured in canonical activity patterns shared across individuals. Specifically, we hypothesized that learning is mirrored in neural alignment: the degree to which an individual learner’s neural representations match those of experts, as well as those of other learners. We tested this hypothesis in a longitudinal functional MRI study that regularly scanned college students enrolled in an introduction to computer science course. We additionally scanned graduate student experts in computer science. We show that alignment among students successfully predicts overall performance in a final exam. Furthermore, within individual students, we find better learning outcomes for concepts that evoke better alignment with experts and with other students, revealing neural patterns associated with specific learned concepts in individuals.


Author(s):  
Suchitra Krishnamachari ◽  
Manoj Kumar ◽  
So Hyun Kim ◽  
Catherine Lord ◽  
Shrikanth Narayanan

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