Effects of hearing loss and spectral shaping on identification and neural response patterns of stop-consonant stimuli

2006 ◽  
Vol 120 (2) ◽  
pp. 915-925 ◽  
Author(s):  
Ashley W. Harkrider ◽  
Patrick N. Plyler ◽  
Mark S. Hedrick
Keyword(s):  
Hearing Loss ◽  
Stop Consonant ◽  
Neural Response ◽  
Response Patterns ◽  
Spectral Shaping

Temporal Encoding of the Voice Onset Time Phonetic Parameter by Field Potentials Recorded Directly From Human Auditory Cortex

1999 ◽  
Vol 82 (5) ◽  
pp. 2346-2357 ◽  
Author(s):  
Mitchell Steinschneider ◽  
Igor O. Volkov ◽  
M. Daniel Noh ◽  
P. Charles Garell ◽  
Matthew A. Howard
Keyword(s):  
Auditory Cortex ◽  
Voice Onset Time ◽  
Onset Time ◽  
Stop Consonant ◽  
Superior Temporal Gyrus ◽  
Response Patterns ◽  
Stop Consonants ◽  
Field Potentials ◽  
Heschl’S Gyrus ◽  
Heschl's Gyrus

Voice onset time (VOT) is an important parameter of speech that denotes the time interval between consonant onset and the onset of low-frequency periodicity generated by rhythmic vocal cord vibration. Voiced stop consonants (/b/, /g/, and /d/) in syllable initial position are characterized by short VOTs, whereas unvoiced stop consonants (/p/, /k/, and t/) contain prolonged VOTs. As the VOT is increased in incremental steps, perception rapidly changes from a voiced stop consonant to an unvoiced consonant at an interval of 20–40 ms. This abrupt change in consonant identification is an example of categorical speech perception and is a central feature of phonetic discrimination. This study tested the hypothesis that VOT is represented within auditory cortex by transient responses time-locked to consonant and voicing onset. Auditory evoked potentials (AEPs) elicited by stop consonant-vowel (CV) syllables were recorded directly from Heschl's gyrus, the planum temporale, and the superior temporal gyrus in three patients undergoing evaluation for surgical remediation of medically intractable epilepsy. Voiced CV syllables elicited a triphasic sequence of field potentials within Heschl's gyrus. AEPs evoked by unvoiced CV syllables contained additional response components time-locked to voicing onset. Syllables with a VOT of 40, 60, or 80 ms evoked components time-locked to consonant release and voicing onset. In contrast, the syllable with a VOT of 20 ms evoked a markedly diminished response to voicing onset and elicited an AEP very similar in morphology to that evoked by the syllable with a 0-ms VOT. Similar response features were observed in the AEPs evoked by click trains. In this case, there was a marked decrease in amplitude of the transient response to the second click in trains with interpulse intervals of 20–25 ms. Speech-evoked AEPs recorded from the posterior superior temporal gyrus lateral to Heschl's gyrus displayed comparable response features, whereas field potentials recorded from three locations in the planum temporale did not contain components time-locked to voicing onset. This study demonstrates that VOT at least partially is represented in primary and specific secondary auditory cortical fields by synchronized activity time-locked to consonant release and voicing onset. Furthermore, AEPs exhibit features that may facilitate categorical perception of stop consonants, and these response patterns appear to be based on temporal processing limitations within auditory cortex. Demonstrations of similar speech-evoked response patterns in animals support a role for these experimental models in clarifying selected features of speech encoding.

Neural response patterns to speech sounds—A model

1983 ◽  
Vol 74 (S1) ◽  
pp. S68-S68
Author(s):  
Pierre L. Divenyi ◽  
Robert V. Shannon ◽  
Stephen R. Saunders
Keyword(s):  
Neural Response ◽  
Response Patterns ◽  
Speech Sounds

How specific is specific phobia? Different neural response patterns in two subtypes of specific phobia

NeuroImage ◽  
2011 ◽  
Vol 56 (1) ◽  
pp. 363-372 ◽  
Author(s):  
Ulrike Lueken ◽  
Johann Daniel Kruschwitz ◽  
Markus Muehlhan ◽  
Jens Siegert ◽  
Jürgen Hoyer ◽  
...  
Keyword(s):  
Specific Phobia ◽  
Neural Response ◽  
Response Patterns

scholarly journals Computational Methods in Social Neuroscience: Recent Advances, New Tools, and Future Directions

2021 ◽  
Author(s):  
Carolyn Parkinson
Keyword(s):  
Computational Methods ◽  
Spatial Scales ◽  
Brain Regions ◽  
Neural Response ◽  
Response Patterns ◽  
Social Environments ◽  
Social Worlds ◽  
Recent Advances ◽  
Social Decisions ◽  
And Behavior

Abstract Recent years have seen a surge of exciting developments in the computational tools available to social neuroscientists. This paper highlights and synthesizes recent advances that have been enabled by the application of such tools, as well as methodological innovations likely to be of interest and utility to social neuroscientists, but that have been concentrated in other sub-fields. Papers in this special issue are emphasized, many of which contain instructive materials (e.g., tutorials, code) for researchers new to the highlighted methods. These include approaches for modeling social decisions, characterizing multivariate neural response patterns at varying spatial scales, using decoded neurofeedback to draw causal links between specific neural response patterns and psychological and behavioral phenomena, examining time-varying patterns of connectivity between brain regions, and characterizing the social networks in which social thought and behavior unfold in everyday life. By combining computational methods for characterizing participants’ rich social environments – at the levels of stimuli, paradigms, and the webs of social relationships that surround people – with those for capturing the psychological processes that undergird social behavior and the wealth of information contained in neuroimaging datasets, social neuroscientists can gain new insights into how people create, understand, and navigate their complex social worlds.

scholarly journals Using the Force: STEM Knowledge and Experience Construct Shared Neural Representations of Engineering Concepts

2017 ◽  
Author(s):  
Joshua S. Cetron ◽  
Andrew C. Connolly ◽  
Solomon G. Diamond ◽  
Vicki V. May ◽  
James V. Haxby ◽  
...  
Keyword(s):  
School Change ◽  
Engineering Students ◽  
Brain Activity ◽  
Neural Response ◽  
Data Driven ◽  
Response Patterns ◽  
Neural Representations ◽  
Hands On ◽  
Machine Learning Approach ◽  
Physical Forces

How does STEM knowledge learned in school change students’ brains? Using fMRI, we presented photographs of real-world structures to engineering students with classroom-based knowledge and hands-on lab experience, examining how their brain activity differentiated them from their “novice” peers not pursuing engineering degrees. A data-driven MVPA and machine- learning approach revealed that neural response patterns of engineering students were convergent with each other and distinct from novices when considering physical forces acting on the structures. Furthermore, informational network analysis demonstrated that the distinct neural response patterns of engineering students reflected relevant concept knowledge: learned categories of mechanical structures. Information about mechanical categories was predominantly represented in bilateral anterior ventral occipitotemporal regions. Importantly, mechanical categories were not explicitly referenced in the experiment, nor does visual similarity between stimuli account for mechanical category distinctions. The results demonstrate how learning abstract STEM concepts in the classroom influences neural representations of objects in the world.

Dissociable Neural Patterns of Facial Identity across Changes in Viewpoint

2010 ◽  
Vol 22 (7) ◽  
pp. 1570-1582 ◽  
Author(s):  
Vaidehi S. Natu ◽  
Fang Jiang ◽  
Abhijit Narvekar ◽  
Shaiyan Keshvari ◽  
Volker Blanz ◽  
...  
Keyword(s):  
Activity Patterns ◽  
Human Perception ◽  
Neural Response ◽  
Behavioral Experiment ◽  
Neural Activation ◽  
Response Patterns ◽  
Activation Patterns ◽  
Facial Identity ◽  
The Face ◽  
High Level

We examined the neural response patterns for facial identity independent of viewpoint and for viewpoint independent of identity. Neural activation patterns for identity and viewpoint were collected in an fMRI experiment. Faces appeared in identity-constant blocks, with variable viewpoint, and in viewpoint-constant blocks, with variable identity. Pattern-based classifiers were used to discriminate neural response patterns for all possible pairs of identities and viewpoints. To increase the likelihood of detecting distinct neural activation patterns for identity, we tested maximally dissimilar “face”–“antiface” pairs and normal face pairs. Neural response patterns for four of six identity pairs, including the “face”–“antiface” pairs, were discriminated at levels above chance. A behavioral experiment showed accord between perceptual and neural discrimination, indicating that the classifier tapped a high-level visual identity code. Neural activity patterns across a broad span of ventral temporal (VT) cortex, including fusiform gyrus and lateral occipital areas (LOC), were required for identity discrimination. For viewpoint, five of six viewpoint pairs were discriminated neurally. Viewpoint discrimination was most accurate with a broad span of VT cortex, but the neural and perceptual discrimination patterns differed. Less accurate discrimination of viewpoint, more consistent with human perception, was found in right posterior superior temporal sulcus, suggesting redundant viewpoint codes optimized for different functions. This study provides the first evidence that it is possible to dissociate neural activation patterns for identity and viewpoint independently.

Sign in / Sign up

Export Citation Format

Share Document