scholarly journals Skin Response Time Point Reference

2020 ◽  
Author(s):  
Keyword(s):  
Response Time ◽  
Skin Response ◽  
Time Point

scholarly journals Multimodal-Multisensory Experiments

2020 ◽  
Author(s):  
Moein Razavi ◽  
Takashi Yamauchi ◽  
Vahid Janfaza ◽  
Anton Leontyev ◽  
Shanle Longmire-Monford ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Response Time ◽  
Galvanic Skin Response ◽  
Low Cost ◽  
Body Movement ◽  
Human Mind ◽  
Body Motion ◽  
Mouse Cursor ◽  
Skin Response ◽  
Electroencephalogram Eeg

The human mind is multimodal. Yet most behavioral studies rely on century-old measures of behavior—task accuracy and latency (response time). Multimodal and multisensory analysis of human behavior creates a better understanding of how the mind works. The problem is that designing and implementing these experiments is technically complex and costly. This paper introduces versatile and economical means of developing multimodal-multisensory human experiments. We provide an experimental design framework that automatically integrates and synchronizes measures including electroencephalogram (EEG), galvanic skin response (GSR), eye-tracking, virtual reality (VR), body movement, mouse/cursor motion and response time. Unlike proprietary systems (e.g., iMotions), our system is free and open-source; it integrates PsychoPy, Unity and Lab Streaming Layer (LSL). The system embeds LSL inside PsychoPy/Unity for the synchronization of multiple sensory signals—gaze motion, electroencephalogram (EEG), galvanic skin response (GSR), mouse/cursor movement, and body motion—with low-cost consumer-grade devices in a simple behavioral task designed by PsychoPy and a virtual reality environment designed by Unity. This tutorial shows a step-by-step process by which a complex multimodal-multisensory experiment can be designed and implemented in a few hours. When conducting the experiment, all of the data synchronization and recoding of the data to disk will be done automatically.

scholarly journals Investigating the role of attention in the identification of associativity shortcuts using a microgenetic measure of implicit shortcut use

2020 ◽  
Vol 73 (7) ◽  
pp. 1017-1035
Author(s):  
Joanne Eaves ◽  
Camilla Gilmore ◽  
Nina Attridge
Keyword(s):  
Visual Attention ◽  
Response Time ◽  
Response Time Data ◽  
Time Data ◽  
Trial Number ◽  
Mathematics Problems ◽  
Strategy Identification ◽  
Novel Method ◽  
Time Point

Many mathematics problems can be solved in different ways or by using different strategies. Good knowledge of arithmetic principles is important for identifying and using strategies that are more sophisticated. For example, the problem “6 + 38 − 35” can be solved through a shortcut strategy where the subtraction “38 − 35 = 3” is performed before the addition “3  + 6  = 9,” a strategy that is derived from the arithmetic principle of associativity. However, both children and adults make infrequent use of this shortcut and the reasons for this are currently unknown. To uncover these reasons, new sensitive measures of strategy identification and use must first be developed, which was one goal of our research. We built a novel method to detect the time-point when individuals first identify an arithmetic strategy, based on trial-by-trial response time data. Our second goal was to use this measure to investigate the contribution of one particular factor, attention, in the identification of the associativity shortcut. In two studies, we found that manipulating visual attention made no difference to the number of people who identified the shortcut, the trial number on which they first identified it, or their accuracy and response time for solving shortcut problems. We discuss the theoretical and methodological contribution of our findings and argue that the origin of people’s difficulty with associativity shortcuts may lie beyond attention.

scholarly journals A Sensitive and Rapid Method for Detecting Formaldehyde in Brain Tissues

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Xiangpei Yue ◽  
Yaoyue Zhang ◽  
Wen Xing ◽  
Yutong Chen ◽  
Chenyang Mu ◽  
...  
Keyword(s):  
Response Time ◽  
Rapid Method ◽  
Spectrophotometric Method ◽  
Room Temperature ◽  
Limit Of Detection ◽  
Highly Sensitive ◽  
High Level ◽  
Optimal Ph ◽  
Yellow Compound ◽  
Time Point

The existing methods for detecting formaldehyde (FA) in brain samples are expensive and require sophisticated experimental procedures. Here, we established a highly sensitive and selective spectrophotometric method, which is based on a reaction in which FA reacts with colorless reagent 4-amino-3-penten-2-one (Fluoral-P) to produce a yellow compound, 3,5-diacetyl-1,4-dihydrolutidine (DDL), which can be detected by a spectrophotometer at 420 nm at room temperature. The sensitive response time point was found to be at the first hour, and the optimal pH of derivative reaction was pH 6.0. The limit of detection (LOD) and the limits of quantization (LOQ) for detecting FA were 0.5 μM and 2.5 μM, respectively. Using this method, an abnormally high level of FA was detected in both the brains of FA-injected mice and autopsy hippocampus tissues from patients with Alzheimer’s disease. This finding suggests that the modified Fluoral-P method is effective for measuring levels of FA in the brains.

scholarly journals Multimodal-Multisensory Experiments: Design and Implementation

2020 ◽  
Author(s):  
Moein Razavi ◽  
Takashi Yamauchi ◽  
Vahid Janfaza ◽  
Anton Leontyev ◽  
Shanle Longmire-Monford ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Response Time ◽  
Galvanic Skin Response ◽  
Low Cost ◽  
Body Movement ◽  
Human Mind ◽  
Body Motion ◽  
Mouse Cursor ◽  
Skin Response ◽  
Electroencephalogram Eeg

AbstractThe human mind is multimodal. Yet most behavioral studies rely on century-old measures of behavior - task accuracy and latency (response time). Multimodal and multisensory analysis of human behavior creates a better understanding of how the mind works. The problem is that designing and implementing these experiments is technically complex and costly. This paper introduces versatile and economical means of developing multimodal-multisensory human experiments. We provide an experimental design framework that automatically integrates and synchronizes measures including electroencephalogram (EEG), galvanic skin response (GSR), eye-tracking, virtual reality (VR), body movement, mouse/cursor motion and response time. Unlike proprietary systems (e.g., iMotions), our system is free and open-source; it integrates PsychoPy, Unity and Lab Streaming Layer (LSL). The system embeds LSL inside PsychoPy/Unity for the synchronization of multiple sensory signals - gaze motion, electroencephalogram (EEG), galvanic skin response (GSR), mouse/cursor movement, and body motion - with low-cost consumer-grade devices in a simple behavioral task designed by PsychoPy and a virtual reality environment designed by Unity. This tutorial shows a step-by-step process by which a complex multimodal-multisensory experiment can be designed and implemented in a few hours. When conducting the experiment, all of the data synchronization and recoding of the data to disk will be done automatically.

Temporal Prediction Errors Affect Short-Term Memory Scanning Response Time

2016 ◽  
Vol 63 (6) ◽  
pp. 333-342
Author(s):  
Roberto Limongi ◽  
Angélica M. Silva
Keyword(s):  
Response Time ◽  
Short Term Memory ◽  
Estimation Error ◽  
Predictive Coding ◽  
Time Estimation ◽  
Prediction Errors ◽  
Memory Scanning ◽  
Short Term ◽  
Term Memory ◽  
Temporal Prediction

Abstract. The Sternberg short-term memory scanning task has been used to unveil cognitive operations involved in time perception. Participants produce time intervals during the task, and the researcher explores how task performance affects interval production – where time estimation error is the dependent variable of interest. The perspective of predictive behavior regards time estimation error as a temporal prediction error (PE), an independent variable that controls cognition, behavior, and learning. Based on this perspective, we investigated whether temporal PEs affect short-term memory scanning. Participants performed temporal predictions while they maintained information in memory. Model inference revealed that PEs affected memory scanning response time independently of the memory-set size effect. We discuss the results within the context of formal and mechanistic models of short-term memory scanning and predictive coding, a Bayes-based theory of brain function. We state the hypothesis that our finding could be associated with weak frontostriatal connections and weak striatal activity.

A Response-Time Investigation of Vehicle Stereotypes

2000 ◽  
Author(s):  
Michael Anthony ◽  
Robert W. Fuhrman
Keyword(s):  
Response Time
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