Cerebral representation of sequence patterns across multiple presentation formats

Cortex ◽  
2021 ◽  
Author(s):  
Samuel Planton ◽  
Stanislas Dehaene
Keyword(s):  
Sequence Patterns ◽  
Presentation Formats

Dysarthric Sentence Intelligibility

1998 ◽  
Vol 41 (6) ◽  
pp. 1282-1293 ◽  
Author(s):  
Jane Mertz Garcia ◽  
Paul A. Dagenais
Keyword(s):  
Speech Intelligibility ◽  
Presentation Mode ◽  
Contextual Influences ◽  
Motor Functioning ◽  
Video Presentation ◽  
Independent Information ◽  
Communicative Process ◽  
Presentation Formats ◽  
Sentence Intelligibility ◽  
Audio Video

This study examined changes in the sentence intelligibility scores of speakers with dysarthria in association with different signal-independent factors (contextual influences). This investigation focused on the presence or absence of iconic gestures while speaking sentences with low or high semantic predictiveness. The speakers were 4 individuals with dysarthria, who varied from one another in terms of their level of speech intelligibility impairment, gestural abilities, and overall level of motor functioning. Ninety-six inexperienced listeners (24 assigned to each speaker) orthographically transcribed 16 test sentences presented in an audio + video or audio-only format. The sentences had either low or high semantic predictiveness and were spoken by each speaker with and without the corresponding gestures. The effects of signal-independent factors (presence or absence of iconic gestures, low or high semantic predictiveness, and audio + video or audio-only presentation formats) were analyzed for individual speakers. Not all signal-independent information benefited speakers similarly. Results indicated that use of gestures and high semantic predictiveness improved sentence intelligibility for 2 speakers. The other 2 speakers benefited from high predictive messages. The audio + video presentation mode enhanced listener understanding for all speakers, although there were interactions related to specific speaking situations. Overall, the contributions of relevant signal-independent information were greater for the speakers with more severely impaired intelligibility. The results are discussed in terms of understanding the contribution of signal-independent factors to the communicative process.

Robustness of Judgments and Choices for Different Presentation Formats

1991 ◽  
Author(s):  
Richard D. Johnson ◽  
G. Douglas Olsen ◽  
Jordan J. Louviere
Keyword(s):  
Presentation Formats

Flexible k-mers with variable-length indels for identifying binding sequences of protein dimers

2019 ◽  
Vol 21 (5) ◽  
pp. 1787-1797
Author(s):  
Chenyang Hong ◽  
Kevin Y Yip
Keyword(s):  
Binding Proteins ◽  
Affinity Purification ◽  
Classification Performance ◽  
Variable Regions ◽  
Binding Motifs ◽  
New Class ◽  
Protein Dimers ◽  
Sequence Patterns ◽  
Standard Chip ◽  
Exponential Enrichment

Abstract Many DNA-binding proteins interact with partner proteins. Recently, based on the high-throughput consecutive affinity-purification systematic evolution of ligands by exponential enrichment (CAP-SELEX) method, many such protein pairs have been found to bind DNA with flexible spacing between their individual binding motifs. Most existing motif representations were not designed to capture such flexibly spaced regions. In order to computationally discover more co-binding events without prior knowledge about the identities of the co-binding proteins, a new representation is needed. We propose a new class of sequence patterns that flexibly model such variable regions and corresponding algorithms that identify co-bound sequences using these patterns. Based on both simulated and CAP-SELEX data, features derived from our sequence patterns lead to better classification performance than patterns that do not explicitly model the variable regions. We also show that even for standard ChIP-seq data, this new class of sequence patterns can help discover co-bound events in a subset of sequences in an unsupervised manner. The open-source software is available at https://github.com/kevingroup/glk-SVM.

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