Gauss Horns Drivers 2080
Inverse sequential simulation: A new approach for the characterization of hydraulic conductivities demonstrated on a non‐Gaussian field. Requires high‐permeability pathways; Production strategy affects the likelihood and magnitude of gas release; Gas release is likely short‐term, without additional driving forces. GAUSS 2081 HORN DRIVER DOWNLOAD - Postage cost can't be calculated. Just to make everyone ill, here is a pair of Gauss HF in good shape that went for almost free. By hobie1dog, December. And comparation measures in post #57 and amazing FR published by 18 sound is iteresting also )). Horn is the same (?) 2064. (afaik 18s made. As title says: has anyone experience with this 2' driver which seems not commonly available (18sound website says: available on special request).
. and Artem Lenskiy.
Korea University of Technology and Education, Cheonan, South Korea Cognitive performance is defined as the ability to utilize knowledge, attention, memory, and working memory. In this study, we briefly discuss various markers that have been proposed to predict cognitive performance. Next, we develop a novel approach to characterize cognitive performance by analyzing eye-blink rate variability dynamics. Our findings are based on a sample of 24 subjects. The subjects were given a 5-min resting period prior to a 10-min IQ test. During both stages, eye blinks were recorded from Fp1 and Fp2 electrodes. We found that scale exponents estimated for blink rate variability during rest were correlated with subjects' performance on the subsequent IQ test.
This surprising phenomenon could be explained by the person to person variation in concentrations of dopamine in PFC and accumulation of GABA in the visual cortex, as both neurotransmitters play a key role in cognitive processes and affect blinking. This study demonstrates the possibility that blink rate variability dynamics at rest carry information about cognitive performance and can be employed in the assessment of cognitive abilities without taking a test. Introduction A search request for the keyword “IQ” at currently returns 19,599 results, with the number of articles growing every year. Obviously, the study of intelligence has been and continues to be a hot topic of research. Since Alfred Binet developed the first practical Intelligence Quotient (IQ) test , many forms of intelligence have been distinguished. Defined intelligence as the “ability to understand complex ideas, to adapt effectively to the environment, to learn from experience, to engage in various forms of reasoning, and to overcome obstacles by taking thought.” Later, gave a broad definition of intelligence, as being a “biopsychological potential to process information that can be activated in a cultural setting to solve problems or create products that are of value in a culture.” Clearly, this definition encapsulates various ways of defining intelligence. A different approach to defining intelligence is based on the concept of multiple intelligences (MI).
According to MI, there are eight types of intelligence: linguistic, logical-mathematical, visual-spatial, bodily-kinesthetic, musical, interpersonal, intrapersonal, and naturalistic. However, there is an ongoing debate whether the concept of MI has adequate experimental support and a neurophysiological foundation. Yet, another way of looking at intelligence is to consider it as either a single general concept or two distinctive concepts, called fluid and crystallized intelligence. The problem of performance evaluation emerges regardless of the definition of intelligence. An IQ score is thought to be a measure of a person's performance and can be interpreted differently, depending on one's perspective. Some IQ tests only contain problems to assess fluid intelligence; such problems include mathematical analogies and logical and spatial problems.
However, other IQ tests contain problems that require crystallized intelligence, i.e., problems that require prior knowledge or verbal problems. In our experiment, we selected only visual, spatial, and logical problems and removed all verbal problems from the IQ test we used, to focus solely on fluid intelligence or, in terms of the concept of MI, on visual-spatial and logical-mathematical intelligence. The use of an IQ test allowed us to assess the subjects' cognitive performance by activating logical reasoning, visual imagination, and pattern-recognition skills.
The work the subject performed to solve the test included the individual's perception of the amount and difficulty of the task, known as mental workload. The research literature on physiological markers of mental workload includes an extensive body of research to characterize the type and intensity of cognitive processes.
The processes related to mental workload are, for example, attention , perception, memory, learning , language, and higher reasoning. It has been found that mental workload can be assessed by measuring heart and respiration rates, blood pressure, the skin potential response, blink rate and dilation of the pupils. Specifically, this research has documented that the pupils dilate momentarily under a mental load ; in particular, during memory tasks. Windows 7 extreme edition r1 32 bit download. It has been shown that pupil size correlates with intelligence while, on the other hand, a connection between pupil size and dopamine has been demonstrated , indicating a connection between intelligence and dopamine level (; ). It also has been demonstrated that blink patterns are related to certain types of mental workload. This research dates back to the work of, who noticed that the rate of blinking is closely related to “mental tension.” Eye-blink activity has been studied as an index of creativity, in relation to dopamine , and emotional changes. It is known that attentional control, which is a process ensuring that one's actions correspond with one's goals, is related to the magnitude of eye blinks.
Blink rate (BR: the number of blinks per minute) during choice-response tasks can provide a reliable measure of cognitive processing (e.g., in the central nervous system ). In particular, it has been shown that the endogenous eye blink is a response controlled by the cortex.
Its characteristics, like rate and temporal distribution, allow it to be distinguished from voluntary or reflexive eye-lid movements, and it seems to reflect cognitive states. Eye blinks indicate the reallocation of mental resources (e.g., while driving ), cognitive states (e.g., relaxed or engaged in problem solving, or transition points in the processing of information ). Although the rate of spontaneous (i.e., endogenous) eye blinks has been repeatedly found to be related to cognitive processes, it has been recently reported to be modified by level of attention while watching a television screen. There is ongoing research that relates eye-blink rate variability (BRV) dynamics to different types of cognitive processes (, unpublished; ). BRV is a series constructed from stacked-up intervals between eye blinks, which is comparable to the well-established measure of Heart Rate Variability. However, to the best of our knowledge, the question of whether one can predict intelligence from BRV has not been extensively investigated.
In this study, we extracted eye blinks from frontal electrodes of the electroencephalograph (EEG) of subjects while resting and while taking an IQ test. The intervals between peaks of consecutive blinks were stacked up into a series, referred as BRV. We employed the multifractal detrended fluctuation analysis (MFDFA) of each of the BRV series to measure the average rate of change in the variance of inter-blink intervals. We refer to this measure as the α scale exponent. We tested two hypotheses in this study. First, we tested the null hypothesis that BRV while at rest was equal in its α exponents to BRV while solving IQ-test problems. We reasoned that if the hypothesis was rejected, we could conclude that the dynamics of the inter-blink interval were influenced by the mental workload associated with solving IQ problems.
It has been shown that BRV dynamics change under mental workload and the rate of change depends on the task, as different types of tasks engage different cognitive processes. For example, BRV dynamics while reading text are smaller compared to resting, whereas they are larger during memory tests (Lenskiy and Paprocki, unpublished). Ethics Statement The procedure was explained to the subjects before the experiment, but the main purpose of the study was revealed only after the experiment, so that the subjects performed without knowing the purpose, which could subconsciously influence their eye blinks. The experiment caused no harm to, nor had other negative consequences on the subjects. All the subjects provided written consent to participate in the experiment. The study was approved with decision number 17101204 by Institutional Review Board affiliated with the Korea University of Technology and Education.
Experimental Procedure The experiment was conducted in a room that eliminated unexpected changes of light, noise, and temperature. We took into account the readiness of the subjects by ensuring they were capable of performing the task (they were rested, not hungry or stressed, and were fully aware of the procedure) and motivated (however, no incentives were offered). Subjects were asked to inform the investigator about their stress levels and fatigue.
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If necessary, the experiment was postponed. We avoided collecting data during exam periods. The subjects were not under the influence of caffeine or nicotine. The subjects were tested individually using a computer.
Gauss Horns Drivers 2080 1
The experiment setup is shown in Figures,. The experiment consisted of 5 parts: (A) a 5-min resting session, (B) a 10-min IQ-test session, (C) another 5-min resting session, (D) reading a passage of text, and (E) a memory test about the text that was read. In the current work, we focused only on sessions (A) the rest period, and (B) the IQ-test.
Electroencephalography (EEG) was used to record the electrical activity of the participants' brains. Next, the eye blinks were extracted from the recorded EEG signals and stacked up into BRV series. In our work, we incorporated a single exponent α( q = 0) and refer to it as α, which describes the peak of MFS.
Statistical Analysis The normality of BRV was verified using the Shapiro-Wilk test ( p = 0.05), and one-way ANOVA was used to test the hypotheses. Results The estimated average and standard deviation of the blink rate (BR) was 18.27 ± 10.44 and 19.14 ± 11.1 during the resting and IQ sessions, respectively. The mean of the α exponents was 0.80 ± 0.23 and 0.62 ± 0.16 for the resting and IQ-test sessions, respectively.
ANOVA determined that the α distributions of the BR during the resting and IQ-test sessions were significantly different, with F (1, 46) = 9.43, p = 0.036 and F (1, 46) = 12.99, p. Subjects were divided into two groups based upon their IQ scores.
The first group consisted of 9 subjects with scores above the median (= 4), and the remaining 15 subjects formed the second group, with scores below or equal to the median. We refer to them as group IQ + and IQ −, respectively. We used one way ANOVA to analyze the BR and α exponents of the two groups of subjects during the resting and IQ-test sessions (see Table ). During the resting session, the BR of the IQ + group was 16.14 ± 5.72, while the subjects in the IQ − group had an average BR of 21.24 ± 14.65. We tested the hypothesis H 0 (i.e., the null hypothesis) that the BR is not an indicator of cognitive performance; the ANOVA results for the resting and IQ sessions were F (1, 22) = 0.139, p = 0.713 and F (1, 22) = 0.001, p = 0.981, which was insufficient evidence for rejecting H 0. During the resting session, the exponent α for the IQ + group was 0.94 ± 0.25, whereas for the IQ − group it was 0.72 ± 0.18.
Looking at the IQ-test session, we see the BR for the IQ + was 9.59 ± 5.49, whereas for the IQ −, it was 9.54 ± 5.92. During the IQ-test session, the exponent α for the IQ + was 0.64 ± 0.10, whereas for the IQ − group, it was 0.61 ± 0.19. The α exponents during the IQ test did not indicate any difference between the two groups during the IQ-test session F (1, 22) = 0.178, p = 0.677, but there was a group difference in the exponents estimated for the resting session F (1, 22) = 6.456, p = 0.019. Hence, we accepted the hypothesis that the population means of the α exponents of both groups were different during the resting session. Therefore, the scale exponent estimated during rest may indicate the cognitive performance of the subjects. In summary, we hypothesized that the dynamics of eye blink rate variability are influenced by the mental workload associated with solving IQ problems. This appears to be true, since there was a difference between α while resting and solving IQ-related problems ( p = 0.004).
We also showed that scores on an IQ test were positively correlated with the scale exponent of BRV during rest with r (22) = 0.43, p = 0.035, R 2 = 0.185, which can be observed in Figure. Additionally, we found that the group with higher IQ scores ( IQ +) had significantly higher α BRV while resting than the group with lower IQ scores did, IQ − ( p = 0.019) (see Figure ). However, the α of BR did not reveal a difference between those two groups, which suggests BRV might be applied where BR fails. Discussion The present study investigated eye blinks during rest and in the presence of mental workload. Mental workload was manipulated by stimulating cognitive activity in response to answering IQ-test items that focused on mathematical problems, recognition of geometrical patterns, and visual problems; hence, in terms of MI, logical-mathematical and visual-spatial intelligence were tested. Such intelligence requires the ability to stay focused and efficiently use working memory, a system that is driven by dopamine (e.g., ). By analyzing the α of BRV with respect to IQ scores, we tested whether the dynamics of BRV is a predictive indicator of intelligence.
Convergent thinking (the ability to give a correct answer) brings executive functions into play, which entail a set of cognitive processes that are necessary for the cognitive control of behavior. These functions keep a person focused until a solution is found. It has been shown that functions related to the frontal lobe, including working memory, are responsible for maintaining a high level of focus on a task. This phenomenon aligns and binds other cognitive processes and keep individuals focused on a task.
On the other hand, it has been shown that dopamine regulates blinking. The basal ganglia, which are interconnected with the cerebral cortex and play a key role in memory, attention, and consciousness, modulate the release of dopamine in the striatum, thereby influencing the eye-blink reflex.
The basal ganglia also control the input of working memory (WM), and have the capacity to manipulate information in short-term memory and use it to guide action. It has been proposed that one of the functions of the basal ganglia is to filter what enters into working memory and modulate its focus by modifying dopamine levels.
This phenomenon has also been used to support eye-blink rate as a measure to track changes in WM during task performance and as a possible measure of striatal dopamine activity. In our research, the phenomena of gating information and exciting basal ganglia circuits on a given task can be observed in the difference ( p = 0.0036) between the α values during the IQ-test and the resting session. The change in BRV dynamics might suggest a change in dopamine levels, although further research is required to explore this possibility. A conceptually similar phenomenon has been observed by, indicating a relationship between cognitive abilities and pupil size during a passive baseline condition, with r (60) = 0.34, p. Reviewed by:, Universidad Pablo de Olavide, Spain, University of Houston, United States Copyright © 2017 Paprocki and Lenskiy. This is an open-access article distributed under the terms of the.
The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.Correspondence: Rafal Paprocki.