The Effects of Exposure to Red and Blue Light on Physiological Indices and Time Estimation

Perception ◽  
1985 ◽  
Vol 14 (1) ◽  
pp. 19-29 ◽  
Author(s):  
John A Caldwell ◽  
Gary E Jones
Keyword(s):  
Repeated Measures ◽  
Time Estimation ◽  
Peak Frequency ◽  
Dependent Measure ◽  
Physiological Indices ◽  
Power Spectral ◽  
Eeg Data ◽  
Verbal Estimation ◽  
Discriminant Analyses ◽  
Two Phases

The effects of exposure to red, white, and blue lights on time estimation and physiological indices were examined. Sixty subjects were exposed to a total of four presentation series of red, white, and blue lights. There were two phases of the experiment: a verbal estimation phase in which subjects were required to count out loud the length of each color while measures of eyeblinks, skin conductance, pulse volume, heart rate, and EEG activity were obtained; and a production phase in which subjects were required to produce several intervals while measures of EEG were obtained. The data on each dependent measure were subjected to three-way repeated-measures ANOVAS. EEG data were digitized and analyzed with power spectral, peak frequency, percentage of alpha activity, and discriminant analyses. Results indicate that color did not exert consistent significant effects on any of the dependent measures and raise serious questions about the assumption that ‘warm’ colors are more arousing than ‘cool’ colors.

scholarly journals Sensorimotor Synchronization with Higher Metrical Levels in Music Shortens Perceived Time

2020 ◽  
Vol 37 (4) ◽  
pp. 263-277 ◽  
Author(s):  
David Hammerschmidt ◽  
Clemens Wöllner
Keyword(s):  
Repeated Measures ◽  
Time Estimation ◽  
Finger Tapping ◽  
Circular Statistics ◽  
Repeated Measures Design ◽  
Trial Stimulus ◽  
Verbal Estimation ◽  
Perception Of Time ◽  
Directing Attention ◽  
Metrical Level

The aim of the present study was to investigate if the perception of time is affected by actively attending to different metrical levels in musical rhythmic patterns. In an experiment with a repeated-measures design, musicians and nonmusicians were presented with musical rhythmic patterns played at three different tempi. They synchronized with multiple metrical levels (half notes, quarter notes, eighth notes) of these patterns using a finger-tapping paradigm and listened without tapping. After each trial, stimulus duration was judged using a verbal estimation paradigm. Results show that the metrical level participants synchronized with influenced perceived time: actively attending to a higher metrical level (half notes, longer intertap intervals) led to the shortest time estimations, hence time was experienced as passing more quickly. Listening without tapping led to the longest time estimations. The faster the tempo of the patterns, the longer the time estimation. While there were no differences between musicians and nonmusicians, those participants who tapped more consistently and accurately (as analyzed by circular statistics) estimated durations to be shorter. Thus, attending to different metrical levels in music, by deliberately directing attention and motor activity, affects time perception.

scholarly journals A Three-Phase Fundamental Diagram from Three-Dimensional Traffic Data

Axioms ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 17
Author(s):  
Maria Laura Delle Delle Monache ◽  
Karen Chi ◽  
Yong Chen ◽  
Paola Goatin ◽  
Ke Han ◽  
...  
Keyword(s):  
Dimensional Space ◽  
Three Dimensional ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Traffic Data ◽  
Fundamental Diagram ◽  
Model Based Clustering ◽  
Three Phase ◽  
Characterization Of Solutions ◽  
Two Phases

This paper uses empirical traffic data collected from three locations in Europe and the US to reveal a three-phase fundamental diagram with two phases located in the uncongested regime. Model-based clustering, hypothesis testing and regression analyses are applied to the speed–flow–occupancy relationship represented in the three-dimensional space to rigorously validate the three phases and identify their gaps. The finding is consistent across the aforementioned different geographical locations. Accordingly, we propose a three-phase macroscopic traffic flow model and a characterization of solutions to the Riemann problems. This work identifies critical structures in the fundamental diagram that are typically ignored in first- and higher-order models and could significantly impact travel time estimation on highways.

scholarly journals Categorisation of EEG suppression using enhanced feature extraction for SUDEP risk assessment

2020 ◽  
Vol 20 (S12) ◽  
Author(s):  
Juan C. Mier ◽  
Yejin Kim ◽  
Xiaoqian Jiang ◽  
Guo-Qiang Zhang ◽  
Samden Lhatoo
Keyword(s):  
Sensitivity Analysis ◽  
Feature Extraction ◽  
Random Forest ◽  
Window Size ◽  
Extraction Process ◽  
Unexpected Death ◽  
Power Spectral ◽  
Eeg Data ◽  
Boosted Decision Trees ◽  
Future Work

Abstract Background Sudden Unexpected Death in Epilepsy (SUDEP) has increased in awareness considerably over the last two decades and is acknowledged as a serious problem in epilepsy. However, the scientific community remains unclear on the reason or possible bio markers that can discern potentially fatal seizures from other non-fatal seizures. The duration of postictal generalized EEG suppression (PGES) is a promising candidate to aid in identifying SUDEP risk. The length of time a patient experiences PGES after a seizure may be used to infer the risk a patient may have of SUDEP later in life. However, the problem becomes identifying the duration, or marking the end, of PGES (Tomson et al. in Lancet Neurol 7(11):1021–1031, 2008; Nashef in Epilepsia 38:6–8, 1997). Methods This work addresses the problem of marking the end to PGES in EEG data, extracted from patients during a clinically supervised seizure. This work proposes a sensitivity analysis on EEG window size/delay, feature extraction and classifiers along with associated hyperparameters. The resulting sensitivity analysis includes the Gradient Boosted Decision Trees and Random Forest classifiers trained on 10 extracted features rooted in fundamental EEG behavior using an EEG specific feature extraction process (pyEEG) and 5 different window sizes or delays (Bao et al. in Comput Intell Neurosci 2011:1687–5265, 2011). Results The machine learning architecture described above scored a maximum AUC score of 76.02% with the Random Forest classifier trained on all extracted features. The highest performing features included SVD Entropy, Petrosan Fractal Dimension and Power Spectral Intensity. Conclusion The methods described are effective in automatically marking the end to PGES. Future work should include integration of these methods into the clinical setting and using the results to be able to predict a patient’s SUDEP risk.

Prefrontal Cortex Acetylcholine Release, EEG Slow Waves, and Spindles Are Modulated by M2 Autoreceptors in C57BL/6J Mouse

2002 ◽  
Vol 87 (6) ◽  
pp. 2817-2822 ◽  
Author(s):  
Christopher L. Douglas ◽  
Helen A. Baghdoyan ◽  
Ralph Lydic
Keyword(s):  
Prefrontal Cortex ◽  
Slow Wave ◽  
Cortical Activation ◽  
Mouse Strains ◽  
Potential Contribution ◽  
Ach Release ◽  
Power Spectral ◽  
Eeg Data ◽  
Muscarinic Cholinergic ◽  
Eeg Power

Recent evidence suggests that muscarinic cholinergic receptors of the M2 subtype serve as autoreceptors modulating acetylcholine (ACh) release in prefrontal cortex. The potential contribution of M2 autoreceptors to excitability control of prefrontal cortex has not been investigated. The present study tested the hypothesis that M2 autoreceptors contribute to activation of the cortical electroencephalogram (EEG) in C57BL/6J (B6) mouse. This hypothesis was evaluated using microdialysis delivery of the muscarinic antagonist AF-DX116 (3 nM) while simultaneously quantifying ACh release in prefrontal cortex, number of 7- to 14-Hz EEG spindles, and EEG power spectral density. Mean ACh release in prefrontal cortex was significantly increased ( P < 0.0002) by AF-DX116. The number of 7- to 14-Hz EEG spindles caused by halothane anesthesia was significantly decreased ( P < 0.0001) by dialysis delivery of AF-DX116 to prefrontal cortex. The cholinergically induced cortical activation was characterized by a significant ( P < 0.05) decrease in slow-wave EEG power. Together, these neurochemical and EEG data support the conclusion that M2 autoreceptor enhancement of ACh release in prefrontal cortex activates EEG in contralateral prefrontal cortex of B6 mouse. EEG slow-wave activity varies across mouse strains, and the results encourage comparative phenotyping of cortical ACh release and EEG in additional mouse models.

scholarly journals CNN-based Framework using Spatial Dropping for Enhanced Interpretation of Neural Activity in Motor Imagery Classification

2020 ◽  
Author(s):  
Diego Fabian Collazos Huertas ◽  
Andres Marino Alvarez Meza ◽  
German Castellanos Dominguez
Keyword(s):  
Wavelet Transform ◽  
Motor Imagery ◽  
Continuous Wavelet Transform ◽  
Brain Activity ◽  
Machine Learning Techniques ◽  
Continuous Wavelet ◽  
Brain Response ◽  
Power Spectral ◽  
Learning Techniques ◽  
Eeg Data

Abstract Interpretation of brain activity responses using Motor Imagery (MI) paradigms is vital for medical diagnosis and monitoring. Assessed by machine learning techniques, identification of imagined actions is hindered by substantial intra and inter subject variability. Here, we develop an architecture of Convolutional Neural Networks (CNN) with enhanced interpretation of the spatial brain neural patterns that mainly contribute to the classification of MI tasks. Two methods of 2D-feature extraction from EEG data are contrasted: Power Spectral Density and Continuous Wavelet Transform. For preserving the spatial interpretation of extracting EEG patterns, we project the multi-channel data using a topographic interpolation. Besides, we include a spatial dropping algorithm to remove the learned weights that reflect the localities not engaged with the elicited brain response. Obtained results in a bi-task MI database show that the thresholding strategy in combination with Continuous Wavelet Transform improves the accuracy and enhances the interpretability of CNN architecture, showing that the highest contribution clusters over the sensorimotor cortex with differentiated behavior between μ and β rhythms.

scholarly journals Decreased P2 Waveform Reflects Impaired Brain Executive Function Induced by 12 h of Low Homeostatic Sleep Pressure: Evidence From an Event-Related Potential Study

2021 ◽  
Vol 15 ◽  
Author(s):  
Lingjing Zeng ◽  
Haijing Wu ◽  
Jialu Li ◽  
Haiteng Wang ◽  
Songyue Xie ◽  
...  
Keyword(s):  
Executive Function ◽  
Response Time ◽  
Visual Processing ◽  
Repeated Measures ◽  
Young Male ◽  
Event Related Potential ◽  
Peak Latency ◽  
Peak Amplitude ◽  
Eeg Data ◽  
The Impact

Homeostatic sleep pressure can cause cognitive impairment, in which executive function is the most affected. Previous studies have mainly focused on high homeostatic sleep pressure (long-term sleep deprivation); thus, there is still little related neuro-psycho-physiological evidence based on low homeostatic sleep pressure (12 h of continuous wakefulness) that affects executive function. This study aimed to investigate the impact of lower homeostatic sleep pressure on executive function. Our study included 14 healthy young male participants tested using the Go/NoGo task in normal resting wakefulness (10:00 am) and after low homeostatic sleep pressure (10:00 pm). Behavioral data (response time and accuracy) were collected, and electroencephalogram (EEG) data were recorded simultaneously, using repeated measures analysis of variance for data analysis. Compared with resting wakefulness, the participants’ response time to the Go stimulus was shortened after low homeostatic sleep pressure, and the correct response rate was reduced. Furthermore, the peak amplitude of Go–P2 decreased significantly, and the peak latency did not change significantly. For NoGo stimulation, the peak amplitude of NoGo–P2 decreased significantly (p &lt; 0.05), and the peak latency was significantly extended (p &lt; 0.05). Thus, the P2 wave is likely related to the attention and visual processing and reflects the early judgment of the perceptual process. Therefore, the peak amplitude of Go–P2 and NoGo–P2 decreased, whereas the peak latency of NoGo–P2 increased, indicating that executive function is impaired after low homeostatic sleep pressure. This study has shown that the P2 wave is a sensitive indicator that reflects the effects of low homeostatic sleep pressure on executive function, and that it is also an important window to observe the effect of homeostatic sleep pressure and circadian rhythm on cognitive function.

Anticipated Reward and Time Estimation in Young Navajo Children

1978 ◽  
Vol 47 (3) ◽  
pp. 1011-1014 ◽  
Author(s):  
Norman G. Dinges ◽  
Gwen Tollefson ◽  
George A. Parks ◽  
Albert R. Hollenbeck
Keyword(s):  
Repeated Measures ◽  
Time Estimation ◽  
Motivating Factors ◽  
Repeated Measures Design

Time estimations of 1-min. intervals using the method of reproduction by 112 Navajo children were measured under neutral and reward conditions in a repeated-measures design. Anticipated reward (a piece of candy) significantly reduced the accuracy of time estimations. In contrast to previous research the girls were more accurate than the boys; however, the effect of reward was more pronounced for girls than for boys. These data suggest that motivating factors play an important role in the reproduction of time by Navajo children.

Effect of Septal Perforations on Measures of Nasal Resistance

1997 ◽  
Vol 34 (2) ◽  
pp. 129-134 ◽  
Author(s):  
Thomas W. Guyette ◽  
Bonnie E. Smith
Keyword(s):  
Outcome Measures ◽  
Repeated Measures ◽  
Dependent Measure ◽  
Nasal Resistance ◽  
Septal Perforation ◽  
Analog Model ◽  
Repeated Measures Anova ◽  
Nominal Scale ◽  
Independent Variables ◽  
Septal Perforations

Objective The purpose of this investigation was to determine the effect of septal perforations on posterior and anterior rhinomanometric measures of nasal resistance In an analog model. Design The data were analyzed using a repeated-measures ANOVA. Nasal resistance was the dependent variable, while type of rhinomanometry, septal perforation size, and position of resistance (proximal vs. distal) were nominal scale independent variables. Participants The analog model used in this study was similar to that described by Warren and Devereux (1966), except that the nasal cavities of the model were modified to create septal perforations. Outcome Measures The main dependent measure was nasal resistance. Results An important finding of this investigation was that septal perforations resulted in large differences (> 3 cm H2O/L/sec) between posterior and anterior nasal resistance values in the bilateral proximal resistor condition. Conclusions Anterior rhinomanometry may underestimate true nasal resistance when a septal perforation Is present, because the septal perforation prevents accurate measurement of nasopharyngeal pressure. Posterior rhinomanometric measures should accurately reflect nasal resistance despite septal perforations, because the perforation does not invalidate the estimate of nasopharyngeal pressure.

Time Estimation: Effects of Cognitive Task, Presentation Rate, and Delay

1996 ◽  
Vol 83 (3_suppl) ◽  
pp. 1387-1394 ◽  
Author(s):  
William F. Vitulli ◽  
Heather A. Shepard
Keyword(s):  
Repeated Measures ◽  
Cognitive Task ◽  
Digit Span ◽  
Presentation Rate ◽  
Time Estimation ◽  
Stimulus Presentation ◽  
Subjective Time ◽  
Allocation Model ◽  
Mixed Analysis ◽  
Within Subjects

The variables of which subjective time is a function extend throughout a myriad of “people, places, and things.” This study measured subjective estimations of time as a function of complexity of cognitive task including arithmetic and recall (within subjects), the rate of stimulus (digit span) information (between subjects), and the delay between stimulus presentation and estimations of time (between subjects). A mixed analysis of variance 2 × 2 × 3 (repeated-measures) factorial design showed that retrospective time estimations were significantly different as a function of the main effects of rate of digit presentation and delay. Men and women showed no differences with no significant interactions so their data were pooled. Quotient values for ratios of delayed versus immediate estimates and slow versus fast rates showed overestimates of “real time.” Explanations based on the “storage-size model,” the “attention-allocation” model, and comparisons with Pedri and Hesketh's 1993 data are discussed.

Sign in / Sign up

Export Citation Format

Share Document