EDA Data Research Articles

EDA-Copilot: A RAG-Powered Intelligent Assistant for EDA Tools

With the rise of Large Language Models (LLMs), researchers have become increasingly interested in their applications in EDA flows, particularly in specific subdomains like serving as knowledge assistants and generating RTL code. In this study, we present a Retrieval-Augmented Generation (RAG) framework tailored to EDA task processing, named EDA-Adaptive RAG. This framework addresses the implicit semantics of EDA data and facilitates efficient knowledge acquisition through classification and enhanced retrieval, significantly enhancing LLMs ability to acquire EDA knowledge. Furthermore, we aim to integrate RAG into the design process as an EDA assistant application. Using RTL code generation as a case study, we demonstrate that the performance of RTL code generation can be enhanced through highly relevant retrievals provided by our RAG. The experimental analysis involves EDA Q&A tasks and RTL code generation evaluation. It is shown that our method outperforms the latest works in terms of both answer stability and code quality.

Electrodermal Activity for Quantitative Assessment of Dental Anxiety

In spite of the development in technology and the recent innovations in dentistry, dental anxiety remains a common issue, and accurately assessing it is challenging due to reliance on patients’ self-reports, which are often biased. Hence, this study was undertaken to determine whether dental anxiety can be quantified objectively using the EDA parameters. EDA (skin conductance (SC), skin susceptance (SS), and skin potential (SP)) parameters and heart rate (HR) were recorded from 40 participants during two different sessions (baseline and anxiety). In addition, the Modified Dental Anxiety Scale (MDAS) scale was also used to record the level of anxiety. The physiological data from EDA and HR were compared with the subjective self-reports of anxiety provided on the MDAS to show whether higher EDA and HR readings correspond to higher scores on the MDAS. To elicit dental anxiety, participants were exposed to several film clips associated with dental treatment. EDA signals were compared between the two sessions for all clips and all EDA scores. SC and HR significantly (p < 0.05) increased during the anxiety session compared to the baseline session. The number of fluctuations per minute in the SC, SS, and SP notably increased during the anxiety session. The MDAS results revealed that the participants had dental anxiety when they were exposed to video clips of dental procedures. The study results imply that EDA parameters could be used as a useful tool to monitor dental anxiety, in particular in young children and non-verbal patients or those with intellectual disabilities, which may aid the dentist in the successful management of dental anxiety during treatment. The moment-to-moment EDA data on a patient’s anxiety levels throughout particular dental operations provides a clearer picture of anxiety variations than pre- or post-appointment surveys alone, in addition to offering unbiased tracking of dental anxiety levels over self-reports. This study seeks to encourage further research into the most effective EDA parameters for improving the management of dental anxiety.

Predicting Driver's mental workload using physiological signals: A functional data analysis approach

This study investigates the impact of advanced driver-assistance systems on drivers' mental workload. Using a combination of physiological signals including ECG, EMG, EDA, EEG (af4 and fc6 channels from the theta band), and eye diameter data, this study aims to predict and categorize drivers’ mental workload into low, adequate, and high levels. Data were collected from five different driving situations with varying cognitive demands. A functional linear regression model was employed for prediction, and the accuracy rate was calculated. Among the 31 tested combinations of physiological variables, 9 combinations achieved the highest accuracy result of 90%. These results highlight the potential benefits of utilizing raw physiological signal data and employing functional data analysis methods to understand and assess driver mental workload. The findings of this study have implications for the design and improvement of driver-assistance systems to optimize safety and performance.

Assessment of a Coordinated Arrangement of Wearable Physiological Sensors for Stress Checking in Work Spaces by Utilizing Organic Markers

The objectives of this investigation were to create and evaluate a wearable physiological sensor system for detecting human stress using ECG, EDA, and EEG data, as well as to see if the changes in physiological indicators were correlated levels. The main elements of the study are listed below. This study included 15 healthy volunteers with an average age of 40.8 years, comprising seven men and six women. They underwent the well-known Maastricht Intense Stress Test, a procedure that is known to cause significant physical and psychological stress, while wearing three commercial sensors have been employed to keep track of their physiologic responses.Throughout salivary samples were taken at various points. The use of a Salivary concentrations of cortisol were correlated with the obtained physiological indicators using an algorithm called the Support Vector Machine (SVM) segmentation technique were both part of the statistical analysis. A significant ability to discriminate between stress and relaxation was shown by fifteen features obtained from measurements including heart rate variability, electro dermal activity, and electroencephalography signals. Based on these important features, the classification system produced results with an accuracy rate of 86% that was satisfactory.Furthermore, the correlation analysis revealed, with an R-squared value of 0.714, a considerable consistency between changes in physiological characteristics and the trends in salivary cortisol levels. The study successfully showed that the wearable sensor system's implementation caught human stress levels and measured the state of stress with accuracy. The design of adaptable and controllable systems, such as medical devices, targeted at inducing interventions to stop stress-related effects, is particularly affected by these findings.

ADABase: A Multimodal Dataset for Cognitive Load Estimation.

Driver monitoring systems play an important role in lower to mid-level autonomous vehicles. Our work focuses on the detection of cognitive load as a component of driver-state estimation to improve traffic safety. By inducing single and dual-task workloads of increasing intensity on 51 subjects, while continuously measuring signals from multiple modalities, based on physiological measurements such as ECG, EDA, EMG, PPG, respiration rate, skin temperature and eye tracker data, as well as behavioral measurements such as action units extracted from facial videos, performance metrics like reaction time and subjective feedback using questionnaires, we create ADABase (Autonomous Driving Cognitive Load Assessment Database) As a reference method to induce cognitive load onto subjects, we use the well-established n-back test, in addition to our novel simulator-based k-drive test, motivated by real-world semi-autonomously vehicles. We extract expert features of all measurements and find significant changes in multiple modalities. Ultimately we train and evaluate machine learning algorithms using single and multimodal inputs to distinguish cognitive load levels. We carefully evaluate model behavior and study feature importance. In summary, we introduce a novel cognitive load test, create a cognitive load database, validate changes using statistical tests, introduce novel classification and regression tasks for machine learning and train and evaluate machine learning models.

Affective states and regulation of learning during socio-emotional interactions in secondary school collaborative groups.

Group affective states for learning are constantly formed through socio-emotional interactions. However, it remains unclear how the affective states vary during collaboration and how they occur with regulation of learning. Appropriate methods are needed to track both group affective states and these interaction processes. The present study identifies different socio-emotional interaction episodes during groups' collaborative learning and examines how group affective states fluctuate with regulation of learning during these episodes. The participants were 54 secondary school students working in groups across four science learning sessions. Multichannel process data (video, electrodermal activity [EDA]) were collected in an authentic classroom. Groups' affective states were measured with emotional valence captured from video data, and activation captured as sympathetic arousal from EDA data. Regulation of learning was observed from the videotaped interactions. The study disclosed four clusters of socio-emotional interaction episodes (positive, negative, occasional regulation, frequent regulation), which differed in terms of fluctuation of affective states and activated regulation of learning. These clustered episodes confirm how affective states are constantly reset by socio-emotional interactions and regulation of learning. The results also show that states requiring regulation do not automatically lead to its activation. By advancing existing understanding of how group level socio-emotional processes contribute to regulation of learning, the study has implications for educational design and psychological practice. Methodologically, it contributes to collaborative learning research by employing multiple data channels (including biophysiological measures) to explore the various dimensions of socio-emotional processes in groups.

Analysis of the Effectiveness of Online Learning Using Eda Data Science and Machine Learning

In educational institutions, it is important to evaluate the performance of lecturers. One aspect that can be used as a reference in assessing the performance of lecturers is the assessment in terms of teaching (learning - teaching process). Value data in the learning evaluation process can be further processed to classify or map the results of the learning evaluation. In this study, the authors use Data Science and Exploratory Data Analysis (EDA) techniques and apply the K-Means++ clustering algorithm using the Java programming language to determine the value of the learning process. group learning evaluation data. To determine the number of clusters to be formed, the elbow method is applied. Meanwhile, to measure the quality of each cluster formed, the silhouette coefficient method is applied. The elbow graph can visualize the Sum of Square Error (SSE) value as a way to determine the best k value in the clustering process. From the tests carried out 12 times, using 6 datasets with variations in 4 areas of assessment, 10 tests resulted in a value of k = 3, 10 tests produced a value of k = 4, and 1 test resulted in a value of k = 5 as the best number of clusters. In the cluster quality measurement process, 13 tests have good clustering quality, because on average they produce clusters that have a good structure with a Global Silhouette Coefficient > 0.65.

Automatic motion artifact detection in electrodermal activity data using machine learning

Background and objectiveElectrodermal activity (EDA) has gained popularity in recent years for diverse applications such as emotion and stress recognition; assessment of pain, fatigue, and sleepiness; and diagnosis of depression and epilepsy. However, presence of motion artifacts (MA) hinders accurate analysis of EDA signals. This study presents a machine learning framework for automatic motion artifact detection on electrodermal activity signals. MethodsWe extracted several statistical and time frequency features from EDA and investigated machine learning algorithms to automatically detect noisy EDA segments. To avoid incorrect adjudication due to the aperiodic nature of EDA signals, we collected both clean and MA-corrupted EDA from immobile and moving hands, respectively. The MA-corrupted EDA data were annotated by three experts as either MA-corrupted or clean using the criteria recommended in the literature, as well as the correlation between MA and the reference EDA. ResultsWe performed a subject-independent validation strategy to evaluate the performance of the machine learning models. The best-performing model classified the MA and clean EDA segments with 94.7% accuracy. A comparison of our motion artifact detection approach with two previously published methods showed that our best performing method outperformed them and retained its accuracy on entirely different, unseen data from a separate study, indicating the method’s generalizability. ConclusionsThe current work can provide accurate and autonomous adjudication of MA-corrupted EDA signals. Given the lack of accurate MA detection methods for EDA signals, this work may lead to more applications of EDA as a physio-marker.

Intelligent Prescription-Generating Models of Traditional Chinese Medicine Based on Deep Learning

Objective: This study aimed to construct an intelligent prescription-generating (IPG) model based on deep-learning natural language processing (NLP) technology for multiple prescriptions in Chinese medicine. Materials and Methods: We selected the Treatise on Febrile Diseases and the Synopsis of Golden Chamber as basic datasets with EDA data augmentation, and the Yellow Emperor's Canon of Internal Medicine, the Classic of the Miraculous Pivot, and the Classic on Medical Problems as supplementary datasets for fine-tuning. We selected the word-embedding model based on the Imperial Collection of Four, the bidirectional encoder representations from transformers (BERT) model based on the Chinese Wikipedia, and the robustly optimized BERT approach (RoBERTa) model based on the Chinese Wikipedia and a general database. In addition, the BERT model was fine-tuned using the supplementary datasets to generate a Traditional Chinese Medicine-BERT model. Multiple IPG models were constructed based on the pretraining strategy and experiments were performed. Metrics of precision, recall, and F1-score were used to assess the model performance. Based on the trained models, we extracted and visualized the semantic features of some typical texts from treatise on febrile diseases and investigated the patterns. Results: Among all the trained models, the RoBERTa-large model performed the best, with a test set precision of 92.22%, recall of 86.71%, and F1-score of 89.38% and 10-fold cross-validation precision of 94.5% ± 2.5%, recall of 90.47% ± 4.1%, and F1-score of 92.38% ± 2.8%. The semantic feature extraction results based on this model showed that the model was intelligently stratified based on different meanings such that the within-layer's patterns showed the associations of symptom–symptoms, disease–symptoms, and symptom–punctuations, while the between-layer's patterns showed a progressive or dynamic symptom and disease transformation. Conclusions: Deep-learning-based NLP technology significantly improves the performance of IPG model. In addition, NLP-based semantic feature extraction may be vital to further investigate the ancient Chinese medicine texts.

A novel approach to measuring student engagement while using an augmented reality sandbox

ABSTRACT Augmented reality (AR) sandboxes are useful tools in helping students develop a three-dimensional understanding of topographic maps and landforms. Research on the AR sandbox indicates that students enjoy it, but evidence about student learning gains is inconclusive. This study aims to understand how students engage with these tools as a precedent to understanding learning gains. Technology in the form of skin biosensors allows researchers to measure skin conductance (or electrodermal activity – EDA) as a proxy for student engagement. This study used wrist biosensors to monitor the engagement of students exposed to different instructional treatments of an AR sandbox in an introductory lab course. The data were supplemented by dynamic video of the students interacting with the sandbox. Statistical analysis of the EDA data suggests that the use of an AR sandbox, particularly during structured lab activities, increases student engagement. Regression modeling supports a relationship between spatial reasoning skills. Based on video data, students’ collaborative and social interactions also showed a significant relationship with EDA. These results suggest that instructors focus on developing and using structured and collaborative activities when incorporating AR sandboxes in their courses in order to best engage students.

Seeking Accessible Physiological Metrics to Detect Cybersickness in VR

Virtual reality is predicted to change the way we use technologies in education. However, currently restricting the lack of integration is high cost, unconvincing learning data, complexity of the technologies, and persistently, cybersickness. There are a number of theories as to the causes of cybersickness, but none are infallible. Moreover, many of the evaluation methods and empirical studies are highly specialized physiological analyses requiring sophisticated measuring equipment. Such studies can be difficult to prepare and present unnatural conditions for users engaged in a VR experience. In this research the Empatica E4 wearable device and its ecosystem were utilized to record physiological metrics of heart rate variability and electrodermal activity during customized computer-based and VR tasks to detect the onset of cybersickness. Although inconclusive, the metrics of NNMean, SDNN, RMSSD in HRV data, and SCR width and Peak EDA in EDA data are proposed for further analysis as potential indicators of cybersickness.

Effectiveness of Noise-Attenuating Headphones on Physiological Responses for Children With Autism Spectrum Disorders.

Objective: The purpose of this study was to evaluate the proof of concept of an intervention to decrease sympathetic activation as measured by skin conductivity (electrodermal activity, EDA) in children with an autism spectrum disorder (ASD) and auditory hypersensitivity (hyperacusis). In addition, researchers examined if the intervention provided protection against the negative effects of decibel level of environmental noises on electrodermal measures between interventions. The feasibility of implementation and outcome measures within natural environments were evaluated.Method: A single-subject multi-treatment design was used with six children, aged 8–16 years, with a form of Autism (i.e., Autism, PDD-NOS). Participants used in-ear (IE) and over-ear (OE) headphones for two randomly sequenced treatment phases. Each child completed four phases: (1) a week of baseline data collection; (2) a week of an intervention; (3) a week of no intervention; and (4) a week of the other intervention. Empatica E4 wristbands collected EDA data. Data was collected on 16–20 occasions per participant, with five measurements per phase.Results: Separated tests for paired study phases suggested that regardless of intervention type, noise attenuating headphones led to a significance difference in both skin conductance levels (SCL) and frequency of non-specific conductance responses (NS-SCRs) between the baseline measurement and subsequent phases. Overall, SCL and NS-SCR frequency significantly decreased between baseline and the first intervention phase. A protective effect of the intervention was tested by collapsing intervention results into three phases. Slope correlation suggested constant SCL and NS-SCR frequency after initial use of the headphones regardless of the increase in environmental noises. A subsequent analysis of the quality of EDA data identified that later phases of data collection were associated with better data quality.Conclusion: Many children with ASD have hypersensitivities to sound resulting in high levels of sympathetic nervous system reactivity, which is associated with problematic behaviors and distress. The findings of this study suggest that the use of noise attenuating headphones for individuals with ASD and hyperacusis may reduce sympathetic activation. Additionally, results suggest that the use of wearable sensors to collect physiological data in natural environments is feasible with established protocols and training procedures.

A wearables-based approach to detect and identify momentary engagement in afterschool Makerspace programs

Educational activities and programs associated with the Maker movement, which emphasizes creation of physical and digital artifacts as part of the learning experience, are presumed to be highly engaging for youth. However, there has been limited research examining what features of Maker learning activities are associated with youth engagement. We describe a research approach using wearable electrodermal activity sensors and wearable cameras to obtain data from two afterschool programs at a community Makerspace for adolescent girls (N = 12, 13). Using data obtained from these two sources along with daily survey data, we compare what is revealed from these different data sources. We observe a moderate correlation between electrodermal activity and engagement from survey responses. We also observe that activities emphasizing personal expression elicited engagement from many youth. From the EDA data and first-person video, we also identify 23 moments when groups of participants had several concurrent EDA responses suggesting high levels momentary engagement. Key features associated with those moments were opportunities for peer socialization, interactive instructional discourse, and physical making activities when objects were being assembled and manipulated.

Computational study of An-X bonding (An = Th, U; X = p-block-based ligands) in pyrrolic macrocycle-supported complexes from the quantum theory of atoms in molecules and bond energy decomposition analysis.

A systematic computational study of organoactinide complexes of the form [LAnX]n+ has been carried out using density functional theory, the quantum theory of atoms in molecules (QTAIM) and Ziegler-Rauk energy decomposition analysis (EDA) methods. The systems studied feature L = trans-calix[2]benzene[2]pyrrolide, An = Th(iv), Th(iii), U(iii) and X = BH4, BO2C2H4, Me, N(SiH3)2, OPh, CH3, NH2, OH, F, SiH3, PH2, SH, Cl, CH2Ph, NHPh, OPh, SiH2Ph, PHPh2, SPh, CPh3, NPh2, OPh, SiPh3 PPh2, SPh. The PBE0 hybrid functional proved most suitable for geometry optimisations based on comparisons with available experimental data. An-X bond critical point electron densities, energy densities and An-X delocalisation indices, calculated with the PBE functional at the PBE0 geometries, are correlated with An-X bond energies, enthalpies and with the terms in the EDA. Good correlations are found between energies and QTAIM metrics, particularly for the orbital interaction term, provided the X ligand is part of an isoelectronic series and the number of open shell electrons is low (i.e. for the present Th(iv) and Th(iii) systems).

Emotional reaction in nursing home residents with dementia-associated apathy: A pilot study

IntroductionConventionally apathy is defined as a symptom primarily characterized by loss of feelings and emotional display and is the most common behavioural symptom in dementia patients. Neuronal network of emotions has ceased to function in patients suffering from dementia associated apathy. Objective and methodsWe measured changes in skin conductance and heart rate from n=12 demented nursing home residents with clinical significant apathy (sumscore>40 in the Apathy Evaluation Scale, AES) to images taken from the International Affective Picture System (IAPS, Lang et al., 2008). Additionally we used autobiographical material to study the responsiveness of the neuronal network of emotions and the adequacy of somatic indicators to demonstrate emotional reactions in individuals diagnosed with dementia and apathy. ResultsAnalysis of the EDA data revealed a significant difference in the change of skin conductance between the different image categories. The autobiographical material generated the largest change in skin conductance. Analysis of the heart rate did not yield any significant difference between the image categories. ConclusionsOur findings demonstrate that the neuronal network underlying emotion is still responsive in patients diagnosed with dementia associated apathy, although residents don׳t seem to be involved emotionally from an extraneous visual focus. Non pharmacological therapy approaches should deal with individual, familiar, autobiographic material to ensure a high level of emotional response and therefore reduce apathy severity.

Antiepileptic action of c-Jun N-terminal kinase (JNK) inhibition

tonic–clonic seizures (GTCSs) and 19 out of 22 (86%) complex partial seizures (CPS) were accompanied by significant increases in EDA, more than 2 standard deviations above the preictal baseline [2]. While responses for GTCSs are usually higher than those for CPS, the size of the EDA response was not associatedwith the length of the seizure nor was it associated with the duration of the motor component in GTCSs [2]. Combining EDA data with motion data improved the accuracy of automated GTCS detection [3]. We have examined the relationship between EDA response during GTCSs and postictal generalized EEG suppression (PGES). The duration of PGES is a potential biomarker of SUDEP. Postictal generalized EEG suppression was found in all monitored cases of SUDEP in the MORTEMUS study [4]. Our studies in a pediatric population with treatment-resistant epilepsy showed that longer PGES correlates with larger EDA response during GTCSs [2]. This finding was replicated in a larger cohort, including children and adults, at another hospital [5]. Thus, EDA on the wrist provides a strong correlate to PGES duration. Altered autonomic function is considered by many to be among the mechanisms that lead to SUDEP [6]. In addition to the EDAfindings, our studies showed decreased heart rate variability associated with longer duration of PGES, which is also consistent with autonomic disruption. Respiratory arrest is inducible in humans by electrical stimulation of the amygdala during the expiratory phase of the respiratory cycle [7]. Stimulation of the amygdala in patients with epilepsy can produce a very strong ipsilateral EDA response [1]. We encourage more clinical studies to collect and examine autonomic data in epilepsy, especially since it can nowbe easily collected.

Analysis of the metal–ligand bonds in [Mo(X)(NH2)3] (X = P, N, PO, and NO), [Mo(CO)5(NO)]+, and [Mo(CO)5(PO)

Quantum chemical calculations at the DFT level have been carried out for model complexes [Mo(P)(NH2)3] (1), [Mo(N)(NH2)3] (2), [Mo(PO)(NH2)3] (3), [Mo(NO)(NH2)3] (4), [Mo(CO)5(PO)]+ (5), and [Mo(CO)5(NO)]+ (6). The equilibrium geometries and the vibration frequencies are in good agreement with experimental and previous theoretical results. The nature of the Mo–PO, Mo–NO, Mo–PO+, Mo–NO+, Mo–P, and Mo–N bond has been investigated by means of the AIM, NBO and EDA methods. The NBO and EDA data complement each other in the interpretation of the interatomic interactions while the numerical AIM results must be interpreted with caution. The terminal Mo–P and Mo–N bonds in 1 and 2 are clearly electron-sharing triple bonds. The terminal Mo–PO and Mo–NO bonds in 3 and 4 have also three bonding contributions from a σ and a degenerate π orbital where the σ components are more polarized toward the ligand end and the π orbitals are more polarized toward the metal end than in 1 and 2. The EDA calculations show that the π bonding contributions to the Mo–PO and Mo–NO bonds in 3 and 4 are much more important than the σ contributions while σ and π bonding have nearly equal strength in the terminal Mo–P and Mo–N bonds in 1 and 2. The total (NH2)3Mo–PO binding interactions are stronger than for (NH2)3Mo–P which is in agreement with the shorter Mo–PO bond. The calculated bond orders suggest that there are only (NH2)3Mo–PO and (NH2)3Mo–NO double bonds which comes from the larger polarization of the σ and π contributions but a closer inspection of the bonding shows that these bonds should also be considered as electron-sharing triple bonds. The bonding situation in the positively charged complexes [(CO)5Mo–(PO)]+ and [(CO)5Mo–(NO)]+ is best described in terms of (CO)5Mo → XO+ donation and (CO)5Mo ← XO+ backdonation (X = P, N) using the Dewar–Chatt–Duncanson model. The latter bonds are stronger and have a larger π character than the Mo-CO bonds.

What is OpenAccess?

OpenAccess is a community effort to enable interoperability among IC design tools through an open source standard data access interface (API) and Reference Implementation of that API. Today, many design flows use common data formats (Verilog, DEF, GDSII, SPEF) to exchange information between tools. Unfortunately, this collection of data files often represents an incomplete and inconsistent view of the design. In contrast, the OpenAccess information model is more complete and unambiguous. The API is object-oriented, written in C++. It was built from the start for open community use. Consistency was emphasized during the design of the API, in order to make it easier to understand and use. It also has extensive online documentation.The OpenAccess information model covers a large portion of the EDA domain. It represents both logical and physical hierarchy and connectivity, as well as an occurrence model which relates the two. It includes custom geometry, routing topology and floor planning information, parameterized cells, and technology information. It also provides a parasitic network API which enables access to parasitics on a per-net or even per-subnet basis, to minimize memory usage. Finally, the API supports definition of extensions to most objects, which can optionally be saved.The API has been designed to support typical application access to EDA data. Various collections are supported, with filters for different types of usage. The API supports efficient searches, utilities such as Region Query, and name mapping capabilities. Multiple Design Management (DM) implementations are possible via a plug-in mechanism. The Reference Implementation has been tuned for improved performance and memory efficiency. Information and relationships are cached on the fly, depending on application access. An initial version of a Tcl binding to the C++ API is also available for use.The OpenAccess Coalition of companies interested in promoting interoperability through OA now numbers more than thirty. The current version of OpenAccess, OA 2.2, is supported by a number of products from both large and small EDA companies, and usage at IC design companies is growing. Universities such as UC Berkeley, CMU and University of Michigan are using OA to build tools and perform research in Design Automation.For more information on the OpenAccess Coalition, see http://www.si2.org/?page=69, and for how to download OpenAccess code and documentation, go to http://www.openeda.si2.org. To learn about the OA Gear project coordinated by Cadence Berkeley Labs visit http://openedatools.si2.org/projects/oagear.

Augmenting–reducing paradox lost? A test of Davis et al.'s (1983) hypothesis

Abstract The aim of the experiment was to test Davis et al.'s [Davis, C., Cowles, M., & Kohn, P. (1983). Strength of the nervous system and augmenting–reducing: paradox lost. Personality and Individual Differences, 4, 491–498.] hypothesis, that Petrie-style reducers become evoked potential (EP) augmenters at high intensities. Central, autonomic, and subjective responses to auditory stimuli of five intensities from 65 to 105 dB(A) were recorded in subjects classified as augmenters/reducers according to the Vando reducer–augmenter scale (RAS). Forty-five white noise stimuli of each intensity were presented. EEG, ECG, EDA, subjective and behavioral data were recorded. It was hypothezised that augmenters would show a zero- or negative-slope of the evoked potential and a defensive-response pattern in peripheral-physiological variables at higher intensities whereas reducers would exhibit a positive slope and an orienting response pattern. In addition, the relationship between augmenting–reducing as assessed by the RAS and EP-method and Sensation Seeking was investigated. Contrary to expectation, reducers exhibited a lower reactivity than augmenters. This applied to the N1-P2-component as well as to the electrodermal response and partially also to the heart rate response. Reducers rated the noises as less loud and less uncomfortable compared with augmenters. Moreover, reducers scored higher on the sensation seeking scale, but there were no significant correlations between the EP-method and sensation seeking. Additionally, reducers responded faster in a reaction time task than augmenters. Overall, results indicate an enhanced reactivity of augmenters and a diminished reactivity of reducers thereby confirming the construct validity of the RAS.