scholarly journals Assessing tACS-induced phosphene perception using closed-loop Bayesian optimization

2017 ◽  
Author(s):  
Romy Lorenz ◽  
Laura E. Simmons ◽  
Ricardo P. Monti ◽  
Joy L. Arthur ◽  
Severin Limal ◽  
...  
Keyword(s):  
Relative Phase ◽  
Rating Scale ◽  
Computational Modelling ◽  
Human Perception ◽  
Past Research ◽  
Bayesian Optimization ◽  
Optimization Approach ◽  
Experimental Conditions ◽  
Combinatorial Explosion ◽  
Transcranial Alternating Current Stimulation

AbstractTranscranial alternating current stimulation (tACS) can evoke illusory flash-like visual percepts known as phosphenes. The perception of phosphenes represents a major experimental challenge when studying tACS-induced effects on cognitive performance. Besides growing concerns that retinal phosphenes themselves could potentially have neuromodulatory effects, the perception of phosphenes may also modify the alertness of participants. Past research has shown that stimulation intensity, frequency and electrode montage affect phosphene perception. However, to date, the effect of an additional tACS parameter on phosphene perception has been completely overlooked: the relative phase difference between stimulation electrodes. This is a crucial and timely topic given the confounding nature of phosphene perception and the increasing number of studies reporting changes in cognitive function following tACS phase manipulations. However, studying phosphene perception for different frequencies and phases simultaneously is not tractable using standard approaches, as the physiologically plausible range of parameters results in a combinatorial explosion of experimental conditions, yielding impracticable experiment durations. To overcome this limitation, here we applied a Bayesian optimization approach to efficiently sample an exhaustive tACS parameter space. Moreover, unlike conventional methodology, which involves subjects judging the perceived phosphene intensity on a rating scale, our study leveraged the strength of human perception by having the optimization driven based on a subject’s relative judgement. Applying Bayesian optimization for two different montages, we found that phosphene perception was affected by differences in the relative phase between cortical electrodes. The results were replicated in a second study involving new participants and validated using computational modelling. In summary, our results have important implications for the experimental design and conclusions drawn from future tACS studies investigating the effects of phase on cognition.

scholarly journals Strategies for Improving Optimal Positioning of Quality Sensors in Urban Drainage Systems for Non-Conservative Contaminants

Water ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 934
Author(s):  
Mariacrocetta Sambito ◽  
Gabriele Freni
Keyword(s):  
Treatment Plant ◽  
Optimal Solution ◽  
Monitoring Network ◽  
Computational Effort ◽  
Bayesian Optimization ◽  
Urban Drainage ◽  
Optimization Approach ◽  
Sensor Location ◽  
Contamination Source ◽  
Monitoring Point

In the urban drainage sector, the problem of polluting discharges in sewers may act on the proper functioning of the sewer system, on the wastewater treatment plant reliability and on the receiving water body preservation. Therefore, the implementation of a chemical monitoring network is necessary to promptly detect and contain the event of contamination. Sensor location is usually an optimization exercise that is based on probabilistic or black-box methods and their efficiency is usually dependent on the initial assumption made on possible eligibility of nodes to become a monitoring point. It is a common practice to establish an initial non-informative assumption by considering all network nodes to have equal possibilities to allocate a sensor. In the present study, such a common approach is compared with different initial strategies to pre-screen eligible nodes as a function of topological and hydraulic information, and non-formal 'grey' information on the most probable locations of the contamination source. Such strategies were previously compared for conservative xenobiotic contaminations and now they are compared for a more difficult identification exercise: the detection of nonconservative immanent contaminants. The strategies are applied to a Bayesian optimization approach that demonstrated to be efficient in contamination source location. The case study is the literature network of the Storm Water Management Model (SWMM) manual, Example 8. The results show that the pre-screening and ‘grey’ information are able to reduce the computational effort needed to obtain the optimal solution or, with equal computational effort, to improve location efficiency. The nature of the contamination is highly relevant, affecting monitoring efficiency, sensor location and computational efforts to reach optimality.

scholarly journals How Downward and Upward Comparisons on Facebook Influence Grandiose and Vulnerable Narcissists’ Self-Esteem—A Priming Study

2021 ◽  
Vol 11 (3) ◽  
pp. 39
Author(s):  
Phillip Ozimek ◽  
Hans-Werner Bierhoff ◽  
Elke Rohmann
Keyword(s):  
Social Networking Sites ◽  
Online Survey ◽  
Social Comparisons ◽  
Past Research ◽  
Self Esteem ◽  
Self Report ◽  
Control Group ◽  
Experimental Conditions ◽  
Vulnerable Narcissism ◽  
And Control

Past research showed that social networking sites represent perfect platforms to satisfy narcissistic needs. The present study aimed to investigate how grandiose (GN) and vulnerable narcissism (VN) as well as social comparisons are associated with Facebook activity, which was measured with a self-report on three activity dimensions: Acting, Impressing, and Watching. In addition, the state self-esteem (SSE) was measured with respect to performance, social behavior, and appearance. One hundred and ten participants completed an online survey containing measures of SSE and Facebook activity and a priming procedure with three experimental conditions embedded in a social media context (upward comparison, downward comparison, and control group). Results indicated, as expected, that high VN was negatively associated with SSE on each subscale and the overall score. In addition, it was found that VN, but not GN, displayed positive associations with frequency of Facebook activities. Finally, it was proposed and confirmed that VN in interaction with the priming of downward comparisons negatively affected SSE. The conclusion drawn is that VN represents a key variable for the prediction of self-esteem as well as for the frequency of Facebook activity.

Morpheme Learning of Children With Specific Language Impairment Under Controlled Instructional Conditions

1992 ◽  
Vol 35 (4) ◽  
pp. 844-852 ◽  
Author(s):  
Phil J. Connell ◽  
C. Addison Stone
Keyword(s):  
Language Impairment ◽  
Specific Language Impairment ◽  
Specific Effect ◽  
Past Research ◽  
Specific Response ◽  
Experimental Conditions ◽  
Phonological Representations ◽  
Specific Language ◽  
Response To Instruction ◽  
Nonverbal Ability

Three groups of children were exposed to instances of a novel morpheme under controlled experimental conditions. The performance of 32 children with specific language impairment (SLI), aged 5:0 to 7:0 years (years:months), was compared to that of 24 normally developing children matched for age and nonverbal ability and 20 younger normally developing children matched for language development and nonverbal ability. The children were taught under two instructional conditions that differed only in whether the child was asked to imitate the new language form after each instance imitation) or just to observe its use (modeling). Consistent with past research (Connell, 1987b), the children with SLI performed significantly better under the imitation condition than under modeling, but the age-matched controls showed no difference in response to instruction. The performance of the language-matched controls was similar to that of the age-matched controls, suggesting that the instruction-specific effect for the children with SLI is not merely a function of general language immaturity. Although the superiority of the imitation condition for the children with SLI was evident for test trials requiring production of the new morpheme (as in past research), no such effect was evident for comprehension trials. This differing effect of output demands suggests that the SLI-specific response to instruction is not a matter of different mastery of the new rule but rather is specific to the need to access the newly induced rule on production trials. The accessing of phonological representations as a possible explanation for the effect is discussed.

Application of Psychometric Theory to the Measurement of Voice Quality Using Rating Scales

2005 ◽  
Vol 48 (2) ◽  
pp. 323-335 ◽  
Author(s):  
Rahul Shrivastav ◽  
Christine M. Sapienza ◽  
Vuday Nandur
Keyword(s):  
Measurement Errors ◽  
Rating Scales ◽  
Rating Scale ◽  
Voice Quality ◽  
Random Order ◽  
Past Research ◽  
Poor Agreement ◽  
Psychometric Theory ◽  
Point Rating Scale ◽  
Exact Agreement

Rating scales are commonly used to study voice quality. However, recent research has demonstrated that perceptual measures of voice quality obtained using rating scales suffer from poor interjudge agreement and reliability, especially in the midrange of the scale. These findings, along with those obtained using multidimensional scaling (MDS), have been interpreted to show that listeners perceive voice quality in an idiosyncratic manner. Based on psychometric theory, the present research explored an alternative explanation for the poor interlistener agreement observed in previous research. This approach suggests that poor agreement between listeners may result, in part, from measurement errors related to a variety of factors rather than true differences in the perception of voice quality. In this study, 10 listeners rated breathiness for 27 vowel stimuli using a 5-point rating scale. Each stimulus was presented to the listeners 10 times in random order. Interlistener agreement and reliability were calculated from these ratings. Agreement and reliability were observed to improve when multiple ratings of each stimulus from each listener were averaged and when standardized scores were used instead of absolute ratings. The probability of exact agreement was found to be approximately .9 when using averaged ratings and standardized scores. In contrast, the probability of exact agreement was only .4 when a single rating from each listener was used to measure agreement. These findings support the hypothesis that poor agreement reported in past research partly arises from errors in measurement rather than individual differences in the perception of voice quality.

Monitoring Water Resources through a Bayesian Optimization-based Approach using Multiple Surface Vehicles: The Ypacarai Lake Case Study

2021 ◽  
Author(s):  
Federico Peralta Samaniego ◽  
Sergio Toral Marín ◽  
Daniel Gutierrez Reina
Keyword(s):  
Water Quality ◽  
Mean Squared Error ◽  
Bayesian Optimization ◽  
Water Quality Parameter ◽  
Optimization Approach ◽  
Decision Strategy ◽  
Sequential Decision ◽  
Partition System ◽  
Autonomous Surface Vehicle ◽  
Voronoi Partition

<div>Bayesian optimization is a popular sequential decision strategy that can be used for environmental monitoring. In this work, we propose an efficient multi-Autonomous Surface Vehicle system capable of monitoring the Ypacarai Lake (San Bernardino, Paraguay) (60 km<sup>2</sup>) using the Bayesian optimization approach with a Voronoi Partition system. The system manages to quickly approximate the real unknown distribution map of a water quality parameter using Gaussian Processes as surrogate models. Furthermore, to select new water quality measurement locations, an acquisition function adapted to vehicle energy constraints is used. Moreover, a Voronoi Partition system helps to distributing the workload with all the available vehicles, so that robustness and scalability is assured. For evaluation purposes, we use both the mean squared error and computational efficiency. The results showed that our method manages to efficiently monitor the Ypacarai Lake, and also provides confident approximate models of water quality parameters. It has been observed that, for every vehicle, the resulting surrogate model improves by 38%.</div>

scholarly journals Unsupervised Parameterization for Optimal Segmentation of Agricultural Parcels from Satellite Images in Different Agricultural Landscapes

2020 ◽  
Vol 12 (18) ◽  
pp. 3096
Author(s):  
Gideon Okpoti Tetteh ◽  
Alexander Gocht ◽  
Marcel Schwieder ◽  
Stefan Erasmi ◽  
Christopher Conrad
Keyword(s):  
Evaluation System ◽  
Reference Data ◽  
Cost Effective ◽  
Agricultural Landscapes ◽  
Bayesian Optimization ◽  
Absolute Difference ◽  
Optimization Approach ◽  
Lower Saxony ◽  
Optimal Segmentation ◽  
Unsupervised Methods

Image segmentation is a cost-effective way to obtain information about the sizes and structural composition of agricultural parcels in an area. To accurately obtain such information, the parameters of the segmentation algorithm ought to be optimized using supervised or unsupervised methods. The difficulty in obtaining reference data makes unsupervised methods indispensable. In this study, we evaluated an existing unsupervised evaluation metric that minimizes a global score (GS), which is computed by summing up the intra-segment uniformity and inter-segment dissimilarity within a segmentation output. We modified this metric and proposed a new metric that uses absolute difference to compute the GS. We compared this proposed metric with the existing metric in two optimization approaches based on the Multiresolution Segmentation (MRS) algorithm to optimally delineate agricultural parcels from Sentinel-2 images in Lower Saxony, Germany. The first approach searches for optimal scale while keeping shape and compactness constant, while the second approach uses Bayesian optimization to optimize the three main parameters of the MRS algorithm. Based on a reference data of agricultural parcels, the optimal segmentation result of each optimization approach was evaluated by calculating the quality rate, over-segmentation, and under-segmentation. For both approaches, our proposed metric outperformed the existing metric in different agricultural landscapes. The proposed metric identified optimal segmentations that were less under-segmented compared to the existing metric. A comparison of the optimal segmentation results obtained in this study to existing benchmark results generated via supervised optimization showed that the unsupervised Bayesian optimization approach based on our proposed metric can potentially be used as an alternative to supervised optimization, particularly in geographic regions where reference data is unavailable or an automated evaluation system is sought.

scholarly journals Neural theta oscillations support semantic memory retrieval

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Martin Marko ◽  
Barbora Cimrová ◽  
Igor Riečanský
Keyword(s):  
Memory Retrieval ◽  
Relative Phase ◽  
Brain Activity ◽  
Theta Oscillations ◽  
Semantic Retrieval ◽  
Theta Band ◽  
Retrieval Performance ◽  
Automatic Retrieval ◽  
Transcranial Alternating Current Stimulation ◽  
Oscillatory Brain Activity

AbstractLexical–semantic retrieval emerges through the interactions of distributed prefrontal and perisylvian brain networks. Growing evidence suggests that synchronous theta band neural oscillations might play a role in this process, yet, their functional significance remains elusive. Here, we used transcranial alternating current stimulation to induce exogenous theta oscillations at 6 Hz (θ-tACS) over left prefrontal and posterior perisylvian cortex with a 180° (anti-phase) and 0° (in-phase) relative phase difference while participants performed automatic and controlled retrieval tasks. We demonstrate that θ-tACS significantly modulated the retrieval performance and its effects were both task- and phase-specific: the in-phase tACS impaired controlled retrieval, whereas the anti-phase tACS improved controlled but impaired automatic retrieval. These findings indicate that theta band oscillatory brain activity supports binding of semantically related representations via a phase-dependent modulation of semantic activation or maintenance.

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