This work is concerned with gunshot detection and recognition. Contains overview of published works and methods used in this field. Furthermore, it lists several commercial gunshot detectors. Binary gunshot detection and recognition algorithm is proposed which discriminates between gunshot and non-gunshot sounds occuring in nature. Algorithm is tested in Matlab. Proposed algorithm is implemented in TMS320C6713 digital signal processor, achieved results are compared.

Our brains handle vast amounts of information incoming through our senses. Continuously exposed to sensory input, the sense of touch, however, may miss tactile stimuli, no matter how much attention we pay to them. In four empirical studies, this thesis tested (1) the feasibility of investigating undetectable stimulation by electrical finger nerve pulses, (2) how its neural correlates dissociate from detectable stimulation and (3) whether and how selective somatosensory attention nevertheless affects the neural representation of undetectable stimuli. The first two studies showed that there is a natural range of electrical stimulation intensities that cannot be detected. A rigorous statistical evaluation with Bayes factor analysis indicated that the evidence of chance performance after undetectable stimulation reliably outweighed evidence of above-chance performance. A subsequent study applying electroencephalography (EEG) revealed qualitative differences between the processing of detectable and undetectable stimulation, which is evident in altered event-related potentials (ERP). Specifically, undetectable stimulation evokes a single component that is not predictive of stimulus detectability but lacks a subsequent component, which correlates with upcoming stimulus detection. The final study showed that attention nevertheless affects neural processing of undetectable stimuli in a top-down manner as it does for detectable stimuli and fosters the view of attention and awareness being two separate and mostly independent mechanisms. The influence of the pre-stimulus oscillatory (~10 Hz) alpha amplitude—a putative marker of attentional deployment—on the ERP depended on the current attentional state and indicates that both processes are interacting but not functionally matching.:1 Touch, Consciousness, And Attention – Theoretical Considerations ........ 1-11 1.1 A Neural Account To (Un-) Consciousness ............................................ 1-12 1.2 Controlling detectability of external stimulation ...................................... 1-14 1.3 Thresholds in the light of signal detection theory ................................... 1-17 1.4 Selective attention in touch .................................................................... 1-19 1.5 Research questions ............................................................................... 1-21 2 Empirical Evidence .................................................................................... 2-25 2.1 General methods .................................................................................... 2-25 2.1.1 Stimulation ........................................................................................... 2-25 2.1.2 Threshold assessment procedure ....................................................... 2-25 2.1.3 Behavioral analysis .............................................................................. 2-26 2.1.4 Electrophysiological measurement ...................................................... 2-28 2.1.5 Analysis of event-related potentials ..................................................... 2-30 2.1.6 Spectral Analysis resolved over time ................................................... 2-30 2.2 Psychophysical assessment of subthreshold stimulation ........................ 2-33 2.2.1 A method for assessing the individual absolute detection threshold (ADTH) ......................................................................................................... 2-33 2.2.2 Validation of absolute detection threshold assessment by signal detection theory measures and Bayesian Null-Hypothesis testing ................ 2-39 2.3 Non-invasive neural markers of unconscious perception ....................... 2-47 2.3.1 Neural Correlates of Undetectable Somatosensory Stimulation in EEG and fMRI ...................................................................................................... 2-47 2.3.2 Prediction of stimulus perception by features of the evoked potential for different stimulation intensities along the psychometric function ................. 2-51 2.4 The role of Rolandic Alpha Activity in Somatosensation and its Relation to Attention ................................................................................................. 2-75 3 General Discussion and Conclusions ...................................................... 3-101 3.1 Summary of empirical results ................................................................ 3-101 3.2 Neural processing of undetectable stimulation ..................................... 3-102 3.3 Attention, awareness and neural oscillatory activity ............................. 3-104 3.4 Limits of the current studies and future perspectives ........................... 3-109 References .................................................................................................... 113 Summary ....................................................................................................... 137 Zusammenfassung ........................................................................................ 143 Curriculum Vitae ............................................................................................ 151 Selbständigkeitserklärung ............................................................................. 155 Nachweis über die Anteile der Co-Autoren .................................................... 157

Un des défis majeurs en traitement radar consiste à identifier une cible cachée dans un environnement bruité. Pour ce faire, il est nécessaire de caractériser finement les propriétés statistiques du bruit, en particulier sa matrice de covariance. Sous l'hypothèse gaussienne, cette dernière est estimée par la matrice de covariance empirique (SCM) dont le comportement est parfaitement connu. Cependant, dans de nombreuses applications actuelles, tels les systèmes radar modernes à haute résolution par exemple, les données collectées sont de nature hétérogène, et ne peuvent être proprement décrites par un processus gaussien. Pour pallier ce problème, les distributions symétriques elliptiques complexes, caractérisant mieux ces phénomènes physiques complexes, ont été proposées. Dans ce cas, les performances de la SCM sont très médiocres et les M-estimateurs apparaissent comme une bonne alternative, principalement en raison de leur flexibilité par rapport au modèle statistique et de leur robustesse aux données aberrantes et/ou aux données manquantes. Cependant, le comportement de tels estimateurs reste encore mal compris. Dans ce contexte, les contributions de cette thèse sont multiples.D'abord, une approche originale pour analyser les propriétés statistiques des M-estimateurs est proposée, révélant que les propriétés statistiques des M-estimateurs peuvent être bien approximées par une distribution de Wishart. Grâce à ces résultats, nous analysons la décomposition de la matrice de covariance en éléments propres. Selon l'application, la matrice de covariance peut posséder une structure particulière impliquant valeurs propres multiples contenant les informations d'intérêt. Nous abordons ainsi divers scénarios rencontrés dans la pratique et proposons des procédures robustes basées sur des M-estimateurs. De plus, nous étudions le problème de la détection robuste du signal. Les propriétés statistiques de diverses statistiques de détection adaptative construites avec des M-estimateurs sont analysées. Enfin, la dernière partie de ces travaux est consacrée au traitement des images radar à synthèse d'ouverture polarimétriques (PolSAR). En imagerie PolSAR, un effet particulier appelé speckle dégrade considérablement la qualité de l'image. Dans cette thèse, nous montrons comment les nouvelles propriétés statistiques des M-estimateurs peuvent être exploitées afin de construire de nouvelles techniques pour la réduction du speckle. / One of the main challenges in radar processing is to identify a target hidden in a disturbance environment. To this end, the noise statistical properties, especially the ones of the disturbance covariance matrix, need to be determined. Under the Gaussian assumption, the latter is estimated by the sample covariance matrix (SCM) whose behavior is perfectly known. However, in many applications, such as, for instance, the modern high resolution radar systems, collected data exhibit a heterogeneous nature that cannot be adequately described by a Gaussian process. To overcome this problem, Complex Elliptically Symmetric distributions have been proposed since they can correctly model these data behavior. In this case, the SCM performs very poorly and M-estimators appear as a good alternative, mainly due to their flexibility to the statistical model and their robustness to outliers and/or missing data. However, the behavior of such estimators still remains unclear and not well understood. In this context, the contributions of this thesis are multiple.First, an original approach to analyze the statistical properties of M-estimators is proposed, revealing that the statistical properties of M-estimators can be approximately well-described by a Wishart distribution. Thanks to these results, we go further and analyze the eigendecomposition of the covariance matrix. Depending on the application, the covariance matrix can exhibit a particular structure involving multiple eigenvalues containing the information of interest. We thus address various scenarios met in practice and propose robust procedures based on M-estimators. Furthermore, we study the robust signal detection problem. The statistical properties of various adaptive detection statistics built with M-estimators are analyzed. Finally, the last part deals with polarimetric synthetic aperture radar (PolSAR) image processing. In PolSAR imaging, a particular effect called speckle significantly degrades the image quality. In this thesis, we demonstrate how the new statistical properties of M-estimators can be exploited in order to build new despeckling techniques.

In 2015, Federal Communications Commission (FCC) enabled shared commercial use of the 3.550-3.700 GHz band. A framework was developed to enable this spectrum-sharing capa- bility which included an automated frequency coordinator called Spectrum Access System (SAS). This work extends the open source SAS based on the aforementioned FCC SAS framework developed by researchers at Virginia Tech Wireless group, with real-time envi- ronment sensing capability along with intelligent incumbent detection using Software-defined Radios (SDRs) and a real-time graphical user interface. This extended version is called the OpenSAS. Furthermore, the SAS client and OpenSAS are extended to be compliant with the Wireless Innovation Forum (WINNF) specifications by testing the SAS-CBRS Base Station Device (CBSD) interface with the Google SAS Test Environment. The Environment Sensing Capability (ESC) functionality is evaluated and tested in our xG Testbed to verify its ability to detect the presence of users in the CBRS band. An ML-based feedforward neural net- work model is employed and trained using simulated radar waveforms as incumbent signals and captured 5G New Radio (NR) signals as a non-incumbent signal to predict whether the detected user is a radar incumbent or an unknown user. If the presence of incumbent radar is detected with an 85% or above certainty, incumbent protection is activated, terminating CBSD grants causing damaging interference to the detected incumbent. A 5G NR signal is used as a non-incumbent user and added to the training dataset to better the ability of the model to reject non-incumbent signals. The model achieves a maximum validation accuracy of 95.83% for signals in the 40-50 dB Signal-to-Noise Ratio (SNR) range. It achieves an 85.35% accuracy for Over the air (OTA) real-time tests. The non-incumbent 5G NR signal rejection accuracy is 91.30% for a calculated SNR range of 10-20 dB. In conclusion, this work advances state of the art in spectrum sharing systems by presenting an enhanced open source SAS and evaluating the newly added functionalities. / Master of Science / In 2015, Federal Communications Commission (FCC) enabled shared commercial use of the 3.550-3.700 GHz band. A framework was developed to enable this spectrum-sharing capability which included an automated frequency coordinator called Spectrum Access System (SAS). The task of the SAS is to make sure no two users use the same spectrum in the same location causing damaging interference to each other. The SAS is also responsible for prioritizing the higher tier users and protecting them from interference from lower tier users. This work extends the open source SAS based on the aforementioned FCC SAS framework developed by researchers at Virginia Tech Wireless group, with real-time environment sensing capability along with intelligent incumbent detection using Software-defined Radios (SDRs) and a real-time graphical user interface. This extended version is called the OpenSAS. Furthermore, the SAS client and OpenSAS are extended to be compliant with the Wireless Innovation Forum (WINNF) specifications by testing the SAS-CBRS Base Station Device (CBSD) interface with the Google SAS Test Environment. The Environment Sensing Capability (ESC) functionality is evaluated and tested in our xG Testbed to verify its ability to detect the presence of users in the CBRS band. The ESC is used to detect incumbent users (the highest tier) that do not inform the SAS about their use of the spectrum. An ML-based feedforward neural net- work model is employed and trained using simulated radar waveforms as incumbent signals and captured 5G New Radio (NR) signals as a non-incumbent signal to predict whether the detected user is a radar incumbent or an unknown user. If the presence of incumbent radar is detected with an 85% or above certainty, incumbent protection is activated, terminating CBSD grants causing damaging interference to the detected incumbent. A 5G NR signal is used as a non-incumbent user and added to the training dataset to better the ability of the model to reject non-incumbent signals. The model achieves a maximum validation accuracy of 95.83% for signals in the 40-50 dB Signal to-Noise Ratio (SNR) range. It achieves an 85.35% accuracy for Over the air (OTA) real-time tests. The non-incumbent 5G NR signal rejection accuracy is 91.30% for a calculated SNR range of 10-20 dB. In conclusion, this work advances state of the art in spectrum sharing systems by presenting an enhanced open source SAS and evaluating the newly added functionalities.

The tremendous ongoing growth of wireless digital communications has raised spectrum shortage and security issues. In particular, the need for new spectrum is the main obstacle in continuing this growth. Recent studies on radio spectrum usage have shown that pre-allocation of spectrum bands to specific wireless communication applications leads to poor utilization of those allocated bands. Therefore, research into new techniques for efficient spectrum utilization is being aggressively pursued by academia, industry, and government. Such research efforts have given birth to two concepts: Cognitive Radio (CR) and Dynamic Spectrum Access (DSA) network. CR is believed to be the key enabling technology for DSA network implementation. CR based DSA (cDSA) networks utilizes white spectrum for its operational frequency bands. White spectrum is the set of frequency bands which are unoccupied temporarily by the users having first rights to the spectrum (called primary users). The main goal of cDSA networks is to access of white spectrum. For proper access, CR nodes must identify the right cDSA network and the absence of primary users before initiating radio transmission. To solve the cDSA network access problem, methods are proposed to design unique second-order cyclic features using Orthogonal Frequency Division Multiplexing (OFDM) pilots. By generating distinct OFDM pilot patterns and measuring spectral correlation characteristics of the cyclostationary OFDM signal, CR nodes can detect and uniquely identify cDSA networks. For this purpose, the second-order cyclic features of OFDM pilots are investigated analytically and through computer simulation. Based on analysis results, a general formula for estimating the dominant cycle frequencies is developed. This general formula is used extensively in cDSA network identification and OFDM signal detection, as well as pilot pattern estimation. CR spectrum awareness capability can be enhanced when it can classify the modulation type of incoming signals at low and varying signal-to-noise ratio. Signal classification allows CR to select a suitable demodulation process at the receiver and to establish a communication link. For this purpose, a threshold-based technique is proposed which utilizes cycle-frequency domain profile for signal detection and feature extraction. Hidden Markov Models (HMMs) are proposed for the signal classifier. The spectrum awareness capability of CR can be undermined by spoofing radio nodes. Automatic identification of malicious or malfunctioning radio signal transmitters is a major concern for CR information assurance. To minimize the threat from spoofing radio devices, radio signal fingerprinting using second-order cyclic features is proposed as an approach for Specific Emitter Identification (SEI). The feasibility of this approach is demonstrated through the identification of IEEE 802.11a/g OFDM signals from different Wireless Local Area Network (WLAN) card manufactures using HMMs. / Ph. D.

Dynamic spectrum access (DSA) wireless networks focus on using RF spectrum more efficiently and dynamically. Significant progress has been made during the past few years. For example, many measurements of current spectrum utilization are available. Theoretical analyses and computational simulations of DSA networks also abound. In sharp contrast, few network systems, particularly those with a decentralized structure, have been built even at a small scale to investigate the performance, behavior, and dynamics of DSA networks under different scenarios. This dissertation provides the theory, design, and implementation of a software radio-based decentralized DSA network prototype, and its enabling technologies: software radio, signal detection and classification, and distributed cooperative spectrum sensing. By moving physical layer functions into the software domain, software radio offers an unprecedented level of flexibility in radio development and operation, which can facilitate research and development of cognitive radio (CR) and DSA networks. However, state-of-the-art software radio systems still have serious performance limitations. Therefore, a performance study of software radio is needed before applying it in any development. This dissertation investigates three practical issues governing software radio performance that are critical in DSA network development: RF front end nonlinearity, dynamic computing resource allocation, and execution latency. It provides detailed explanations and quantitative results on SDR performance. Signal detection is the most popular method used in DSA networks to guarantee non-interference to primary users. Quickly and accurately detecting signals under all possible conditions is challenging. The cyclostationary feature detection method is attractive for detecting primary users because of its ability to distinguish between modulated signals, interference, and noise at a low signal-to-noise ratio (SNR). However, a key issue of cyclostationary signal analysis is the high computational cost. To tackle this challenge, parallel computing is applied to develop a cyclostationary feature based signal detection method. This dissertation presents the method's performance on multiple signal types in noisy and multi-path fading environments. Distributed cooperative spectrum sensing is widely endorsed to monitor the radio environment so as to guarantee non-interference to incumbent users even at a low SNR and under hostile conditions like shadowing, fading, interference, and multi-path. However, such networks impose strict performance requirements on data latency and reliability. Delayed or faulty data may cause secondary users to interfere with incumbent users because secondary users could not be informed quickly or reliably. To support such network performance, this dissertation presents a set of data process and management schemes in both sensors and data fusion nodes. Further, a distributed cooperative sensor network is built from multiple sensors; together, the network compiles a coherent semantic radio environment map for DSA networks to exploit available frequencies opportunistically. Finally, this dissertation presents the complete design of a decentralized and asynchronous DSA network across the PHY layer, MAC layer, network layer, and application layer. A ten-node prototype is built based on software radio technologies, signal detection and classification methods, distributed cooperative spectrum sensing systems, dynamic wireless protocols, and a multi-channel allocation algorithm. Systematic experiments are carried out to identify several performance determining factors for decentralized DSA networks. / Ph. D.

Master of Science / Department of Psychology / James C. Shanteau / Situation Awareness (SA) measurement takes on many forms: subjective, direct, and implicit performance, each with limitations. Subjective measures are based on self and peer reports, which allow biases to enter the measurement. Direct measures, such as SA Global Assessment Technique (SAGAT), interrupt SA in order to probe the participants’ SA level using questions. Implicit performance measures are based on participants’ ability to complete SA tasks, which must be created for each domain. A new approach, Dynamic – SA Task (D-SAT), was developed using a microworld wildfire fighting simulation, Networked Fire Chief (NFC). D-SAT is an implicit performance measure that can be adapted to multiple domains, for example inattentional blindness. Scenarios were developed during study one by tracking participant performance and scenario situations. Study two used the scenarios developed during study one to test D-SAT’s ability to evaluate SA by comparing D-SAT performance to an established SA performance measure, situation awareness global assessment technique (SAGAT). While the manipulation used to create had an effect on D-SAT performance, it was not associated with the established SA performance measure. However, a signal detection theory (SDT) analysis showed additional promise for D-SAT being a useful SA measure.

The aim of this dissertation was to address a theoretical debate on belief bias. Belief bias is the tendency for people to be influenced by their prior beliefs when engaged in deductive reasoning. Deduction is the act of drawing necessary conclusions from premises which are meant to be assumed as true. Given that the logical validity of an argument is independent of its content, being influenced by your prior beliefs in such content is considered a bias. Traditional theories posit there are two belief bias components. Motivated reasoning is the tendency to reason better for arguments with unbelievable conclusions relative to arguments with believable conclusions. Response bias is the tendency to accept believable arguments and to reject unbelievable arguments. Dube et al. (2010) pointed out critical methodological problems that undermine evidence for traditional theories. Using signal detection theory (SDT), they found evidence for response bias only. We adopted the SDT method to compare the viability of the traditional and the response bias accounts. In Chapter 1 the relevant literature is reviewed. In Chapter 2 four experiments which employed a novel SDT-based forced choice reasoning method are presented, showing evidence compatible with motivated reasoning. In Chapter 3 four experiments which used the receiver operating characteristic (ROC) method are presented. Crucially, cognitive ability turned out to be linked to motivated reasoning. In Chapter 4 three experiments are presented in which we investigated the impact of cognitive ability and analytic cognitive style on belief bias, concluding that cognitive style mediated the effects of cognitive ability on motivated reasoning. In Chapter 5 we discuss our findings in light of a novel individual differences account of belief bias. We conclude that using the appropriate measurement method and taking individual differences into account are two key elements to furthering our understanding of belief bias, human reasoning, and cognitive psychology in general.

Stimuli that cannot be perceived (i.e., that are subliminal) can still elicit neural responses in an observer, but can such stimuli influence behavior and higher-order cognition? Empirical evidence for such effects has periodically been accepted and rejected over the last six decades. Today, many psychologists seem to consider such effects well-established and recent studies have extended the power of subliminal processing to new limits. In this thesis, I examine whether this shift in zeitgeist is matched by a shift in evidential strength for the phenomenon. This thesis consists of three empirical studies involving more than 250 participants, a simulation study, and a quantitative review. The conclusion based on these efforts is that several methodological, statistical, and theoretical issues remain in studies of subliminal processing. These issues mean that claimed subliminal effects might be caused by occasional or weak percepts (given the experimenters’ own definitions of perception) and that it is still unclear what evidence there is for the cognitive processing of subliminal stimuli. New data are presented suggesting that even in conditions traditionally claimed as “subliminal”, occasional or weak percepts may in fact influence cognitive processing more strongly than do the physical stimuli, possibly leading to reversed priming effects. I also summarize and provide methodological, statistical, and theoretical recommendations that could benefit future research aspiring to provide solid evidence for subliminal cognitive processing. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 1: Manuscript. Paper 4: Manuscript.</p><p> </p>

O uso lesivo de bebidas alcoólicas é o terceiro maior fator mundial de risco de doenças e incapacitação. O álcool pode causar várias alterações no Sistema Nervoso Central (SNC), dentre elas, a diminuição do processamento de informações visuais. Alguns estudos avaliam as alterações nos processos perceptuais durante a Síndrome de Abstinência do Álcool (SAA) por meio da ilusão visual da máscara côncava. Essa ilusão exemplifica o fenômeno ilusório de inversão visual da profundidade de uma máscara humana oca, que é percebida como convexa. Foi encontrado um possível desequilíbrio entre os componentes bottom-up e top-down da percepção visual provocado pelo álcool, capaz de prejudicar a capacidade do indivíduo de perceber essa ilusão. Entretanto, a presente pesquisa investiga uma hipótese alternativa, e sugere que a redução na frequência de respostas de inversão pode resultar de mudanças de critérios para emitir as repostas e não em alterações perceptuais. Para isso, foram utilizados os parâmetros da Teoria da Detecção de Sinal (TDS) aplicada à psicofísica. Participaram da pesquisa 20 indivíduos saudáveis, 20 com SAA leve e 20 com SAA moderada. Eles realizaram duas tarefas experimentais de observação monocular dos lados côncavo e convexo, alternadamente, de uma máscara da face humana de tamanho reduzido. Na tarefa de confidence rating, foram julgadas a concavidade ou convexidade da máscara e o grau de certeza nas respostas, com certeza ou com dúvida. Na tarefa de escolha forçada entre duas alternativas (2AFC), as máscaras foram apresentadas aos pares alternados e o participante identificava o lado côncavo. A análise dos resultados revelou que os indivíduos com SAA moderada apresentaram maiores escores para os índices de sensibilidade R-index, Az, e da, na tarefa de confidence rating, apontando para uma capacidade significativamente maior de identificação e discriminação dos lados côncavo e convexo da máscara. Isto reflete um prejuízo significativo na capacidade destes indivíduos em perceber a ilusão da máscara côncava. Não houve diferença entre os grupos de indivíduos saudáveis e com SAA leve. Entretanto, os grupos com SAA apresentaram um maior grau de certeza em seus julgamentos comparativamente ao grupo controle. Resultados semelhantes para o grupo com SAA moderada foram encontrados na tarefa de escolha forçada para os índices d e taxa de acerto, indicando maior capacidade desses indivíduos em discriminar os dois lados da máscara. Todos os participantes apresentaram critérios de decisão moderados na tarefa de confidence rating. Os indivíduos saudáveis, na tarefa de escolha forçada, se mostraram tão capazes quanto os com SAA moderada na discriminação dos dois lados da máscara. Os indivíduos saudáveis, na tarefa de escolha forçada, se mostraram tão capazes quanto os com SAA moderada na discriminação dos dois lados da máscara. Entretanto, supõe-se que esses resultados foram provenientes de estratégias inesperadas por esses indivíduos em seus julgamentos, prejudicando a validade interna dos resultados; e representam um viés de pesquisa importante. As análises apontam para um possível desequilíbrio, já relatado em estudos anteriores, entre os componentes bottom-up e top-down da percepção visual, provocado pelo álcool, que impede o SNC de corrigir hipóteses perceptuais ambíguas. / The harmful use of alcoholic beverages is the third largest worldwide risk factor for illness and disability. Alcohol can cause several changes in the Central Nervous System (CNS), among them, the decrease in the processing of visual information. Some studies evaluate changes in perceptual processes during Alcohol Withdrawal Syndrome (AWS) through the hollow face illusion. Its exemplifies the illusory visual depth inversion of a hollow human mask, which is perceived as convex. A possible imbalance was found between the bottomup and top-down components of visual perception caused by alcohol, which could impair the individual\'s ability to perceive the illusion. However, the present research investigates an alternative hypothesis, and suggests that the reduction in the frequency of inversion responses may result from changes in the criteria to issue responses rather than on perceptual changes. The parameters of the Signal Detection Theory (SDT) applied to psychophysics allow this analysis. Twenty healthy subjects, 20 with mild AWS and 20 with moderate AWS participated in the study. They performed two experimental tasks of monocular observation of concave and convex sides, alternately, of a reduced size human face mask. In the task of confidence rating, the concavity or convexity of the mask and the degree of certainty in the answers were judged: certainly or with doubt. In the task of forced choice between two alternatives (2AFC), the masks were presented in alternating pairs and the participant identified the concave side. The analysis of the results revealed that individuals with moderate AWS presented higher scores for the sensitivity index scores R-index, Az, and da, pointing to a significantly greater capacity of identification and discrimination of the concave and convex sides of the mask. This reflects a significant impairment in the ability of these individuals to perceive the hollow face illusion. There was no difference between healthy individuals and with mild AWS. However, all groups with AWS presented a greater degree of certainty in their judgments compared to the control group. Similar results for the group with moderate AWS were found in the task of forced choice to the indices d and hit rate, indicating a greater ability of these individuals to discriminate both sides of the mask. Healthy subjects, on the task of forced choice, showed themselves to be as capable as those with moderate AWS in discriminating both sides of the mask. However, it supposed that these results were from unexpected strategies used by these individuals in their judgments, impairing the internal validity of the results, and represented an important research bias. The analyzes point to a possible imbalance, already reported in previous studies, between the bottom-up and top-down components of visual perception, caused by alcohol, which inhibits the CNS from correcting ambiguous perceptual hypotheses.