scholarly journals Response-dependent effects on near-threshold detection performance: Saccades versus manual responses

1984 ◽  
Vol 35 (6) ◽  
pp. 543-546 ◽  
Author(s):  
Howard C. Hughes ◽  
James V. Kelsey
Keyword(s):  
Detection Performance ◽  
Threshold Detection ◽  
Manual Responses ◽  
Near Threshold

scholarly journals Neurocognitive dynamics of near-threshold voice signal detection and affective voice evaluation

2020 ◽  
Vol 6 (50) ◽  
pp. eabb3884
Author(s):  
Huw Swanborough ◽  
Matthias Staib ◽  
Sascha Frühholz
Keyword(s):  
Signal Detection ◽  
Fundamental Problem ◽  
Dynamic Properties ◽  
Signal To Noise Ratio ◽  
Affective Meaning ◽  
Communication Signals ◽  
Threshold Detection ◽  
Voice Signal ◽  
Detection Point ◽  
Near Threshold

Communication and voice signal detection in noisy environments are universal tasks for many species. The fundamental problem of detecting voice signals in noise (VIN) is underinvestigated especially in its temporal dynamic properties. We investigated VIN as a dynamic signal-to-noise ratio (SNR) problem to determine the neurocognitive dynamics of subthreshold evidence accrual and near-threshold voice signal detection. Experiment 1 showed that dynamic VIN, including a varying SNR and subthreshold sensory evidence accrual, is superior to similar conditions with nondynamic SNRs or with acoustically matched sounds. Furthermore, voice signals with affective meaning have a detection advantage during VIN. Experiment 2 demonstrated that VIN is driven by an effective neural integration in an auditory cortical-limbic network at and beyond the near-threshold detection point, which is preceded by activity in subcortical auditory nuclei. This demonstrates the superior recognition advantage of communication signals in dynamic noise contexts, especially when carrying socio-affective meaning.

Writing in Aphasia Rehabilitation: Cursive vs Manuscript

1976 ◽  
Vol 41 (4) ◽  
pp. 523-529 ◽  
Author(s):  
Daniel R. Boone ◽  
Harold M. Friedman
Keyword(s):  
Correct Response ◽  
The Other ◽  
Read Aloud ◽  
Writing Style ◽  
Reading And Writing ◽  
Manual Responses ◽  
Significant Difference ◽  
Written Form ◽  
The Individual ◽  
Read Number

Reading and writing performance was observed in 30 adult aphasic patients to determine whether there was a significant difference when stimuli and manual responses were varied in the written form: cursive versus manuscript. Patients were asked to read aloud 10 words written cursively and 10 words written in manuscript form. They were then asked to write on dictation 10 word responses using cursive writing and 10 words using manuscript writing. Number of words correctly read, number of words correctly written, and number of letters correctly written in the proper sequence were tallied for both cursive and manuscript writing tasks for each patient. Results indicated no significant difference in correct response between cursive and manuscript writing style for these aphasic patients as a group; however, it was noted that individual patients varied widely in their success using one writing form over the other. It appeared that since neither writing form showed better facilitation of performance, the writing style used should be determined according to the individual patient’s own preference and best performance.

Hearing in Mice via GSR Audiometry

1963 ◽  
Vol 6 (4) ◽  
pp. 359-368 ◽  
Author(s):  
Charles I. Berlin
Keyword(s):  
Inverse Relationship ◽  
Sound Pressure ◽  
Single Units ◽  
Sensitivity Curve ◽  
Basic Tool ◽  
Eighth Nerve ◽  
Pure Tones ◽  
Conditioned Responses ◽  
Frequency Intensity ◽  
Near Threshold

Hearing in mice has been difficult to measure behaviorally. With GSR as the basic tool, the sensitivity curve to pure tones in mice has been successfully outlined. The most sensitive frequency-intensity combination was 15 000 cps at 0-5 dB re: 0.0002 dyne/cm 2 , with responses noted from 1 000 to beyond 70 000 cps. Some problems of reliability of conditioning were encountered, as well as findings concerning the inverse relationship between the size of GSR to unattenuated tones and the sound pressure necessary to elicit conditioned responses at or near threshold. These data agree well with the sensitivity of single units of the eighth nerve of the mouse.

Reliability of odor measurements near threshold.

1988 ◽  
Author(s):  
Birgitta Berglund ◽  
Lennart Hoegman ◽  
Ingegerd Johansson
Keyword(s):  
Near Threshold

NEAR THRESHOLD MEASUREMENTS OF THE K SHELL SATELLITES OF CO AND N2

1987 ◽  
Vol 48 (C9) ◽  
pp. C9-773-C9-776 ◽  
Author(s):  
J. FELDHAUS ◽  
A. REIMER ◽  
J. SCHIRMER ◽  
A. M. BRADSHAW ◽  
U. BECKER ◽  
...  
Keyword(s):  
Near Threshold

CNN performance dependence on linear image processing

2020 ◽  
Vol 2020 (10) ◽  
pp. 310-1-310-7
Author(s):  
Khalid Omer ◽  
Luca Caucci ◽  
Meredith Kupinski
Keyword(s):  
Image Processing ◽  
Texture Classification ◽  
Full Rank ◽  
Detection Performance ◽  
Ideal Observer ◽  
Training Data ◽  
Image Texture ◽  
Training Images ◽  
Analytic Expressions ◽  
Linear Compression

This work reports on convolutional neural network (CNN) performance on an image texture classification task as a function of linear image processing and number of training images. Detection performance of single and multi-layer CNNs (sCNN/mCNN) are compared to optimal observers. Performance is quantified by the area under the receiver operating characteristic (ROC) curve, also known as the AUC. For perfect detection AUC = 1.0 and AUC = 0.5 for guessing. The Ideal Observer (IO) maximizes AUC but is prohibitive in practice because it depends on high-dimensional image likelihoods. The IO performance is invariant to any fullrank, invertible linear image processing. This work demonstrates the existence of full-rank, invertible linear transforms that can degrade both sCNN and mCNN even in the limit of large quantities of training data. A subsequent invertible linear transform changes the images’ correlation structure again and can improve this AUC. Stationary textures sampled from zero mean and unequal covariance Gaussian distributions allow closed-form analytic expressions for the IO and optimal linear compression. Linear compression is a mitigation technique for high-dimension low sample size (HDLSS) applications. By definition, compression strictly decreases or maintains IO detection performance. For small quantities of training data, linear image compression prior to the sCNN architecture can increase AUC from 0.56 to 0.93. Results indicate an optimal compression ratio for CNN based on task difficulty, compression method, and number of training images.

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