SciCombinator

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Concept: Constant false alarm rate

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OBJECTIVES: Combining behavioral and neurophysiological measurements inevitably implies mutual constraints, such as when the neurophysiological measurement requires fast-paced stimulus presentation and hence the attribution of a behavioral response to a particular preceding stimulus becomes ambiguous. We develop and test a method for validly assessing behavioral detection performance in spite of this ambiguity. METHODS: We examine four approaches taken in the literature to treat such situations. We analytically derive a new variant of computing the classical parameters of signal detection theory, hit and false alarm rates, adapted to fast-paced paradigms. RESULTS: Each of the previous approaches shows specific shortcomings (susceptibility towards response window choice, biased estimates of behavioral detection performance). Superior performance of our new approach is demonstrated for both simulated and empirical behavioral data. Further evidence is provided by reliable correspondence between behavioral performance and the N2b component as an electrophysiological indicator of target detection. CONCLUSIONS: The appropriateness of our approach is substantiated by both theoretical and empirical arguments. SIGNIFICANCE: We demonstrate an easy-to-implement solution for measuring target detection performance independent of the rate of event presentation. Thus overcoming the measurement bias of previous approaches, our method will help to clarify the behavioral relevance of different measures of cortical activation.

Concepts: Scientific method, Critical thinking, Measurement, Signal processing, Detection theory, Test method, Systems of measurement, Constant false alarm rate

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Decision making is one of the most vital processes we use every day, ranging from mundane decisions about what to eat to life-threatening choices such as how to avoid a car collision. Thus, the context in which our decisions are made is critical, and our physiology enables adaptive responses that account for how environmental stress influences our performance. The relationship between stress and decision making can additionally be affected by one’s expertise in making decisions in high-threat environments, where experts can develop an adaptive response that mitigates the negative impacts of stress. In the present study, 26 male military personnel made friend/foe discriminations in an environment where we manipulated the level of stress. In the high-stress condition, participants received a shock when they incorrectly shot a friend or missed shooting a foe; in the low-stress condition, participants received a vibration for an incorrect decision. We characterized performance using signal detection theory to investigate whether a participant changed their decision criterion to avoid making an error. Results showed that under high-stress, participants made more false alarms, mistaking friends as foes, and this co-occurred with increased high frequency heart rate variability. Finally, we examined the relationship between decision making and physiology, and found that participants exhibited adaptive behavioral and physiological profiles under different stress levels. We interpret this adaptive profile as a marker of an expert’s ingrained training that does not require top down control, suggesting a way that expert training in high-stress environments helps to buffer negative impacts of stress on performance.

Concepts: Decision making, Critical thinking, Decision theory, Signal processing, Decision making software, Detection theory, Expert, Constant false alarm rate

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Target detection is one of the important applications in the field of remote sensing. The Gaofen-3 (GF-3) Synthetic Aperture Radar (SAR) satellite launched by China is a powerful tool for maritime monitoring. This work aims at detecting ships in GF-3 SAR images using a new land masking strategy, the appropriate model for sea clutter and a neural network as the discrimination scheme. Firstly, the fully convolutional network (FCN) is applied to separate the sea from the land. Then, by analyzing the sea clutter distribution in GF-3 SAR images, we choose the probability distribution model of Constant False Alarm Rate (CFAR) detector from K-distribution, Gamma distribution and Rayleigh distribution based on a tradeoff between the sea clutter modeling accuracy and the computational complexity. Furthermore, in order to better implement CFAR detection, we also use truncated statistic (TS) as a preprocessing scheme and iterative censoring scheme (ICS) for boosting the performance of detector. Finally, we employ a neural network to re-examine the results as the discrimination stage. Experiment results on three GF-3 SAR images verify the effectiveness and efficiency of this approach.

Concepts: Detection theory, Normal distribution, False alarm, Computational complexity theory, Radar, Constant false alarm rate, Synthetic aperture radar, Interferometric synthetic aperture radar

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People easily recognize a familiar melody in a previously unheard key, but they also retain some key-specific information. Does the recognition of a transposed melody depend on either pitch distance or harmonic distance from the initially learned instances? Previous research has shown a stronger effect of pitch closeness than of harmonic similarity, but did not directly test for an additional effect of the latter variable. In the present experiment, we familiarized participants with a simple eight-note melody in two different keys (C and D) and then tested their ability to discriminate the target melody from foils in other keys. The transpositions included were to the keys of C# (close in pitch height, but harmonically distant), G (more distant in pitch, but harmonically close), and F# (more distant in pitch and harmonically distant). Across participants, the transpositions to F# and G were either higher or lower than the initially trained melodies, so that their average pitch distances from C and D were equated. A signal detection theory analysis confirmed that discriminability (d') was better for targets and foils that were close in pitch distance to the studied exemplars. Harmonic similarity had no effect on discriminability, but it did affect response bias ©, in that harmonic similarity to the studied exemplars increased both hits and false alarms. Thus, both pitch distance and harmonic distance affect the recognition of transposed melodies, but with dissociable effects on discrimination and response bias.

Concepts: Effect, Affect, Detection theory, Length, Melody, Harmony, Psychophysics, Constant false alarm rate

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This study aims to detect vessels with lengths ranging from about 70 to 300 m, in Gaofen-3 (GF-3) SAR images with ultrafine strip-map (UFS) mode as fast as possible. Based on the analysis of the characteristics of vessels in GF-3 SAR imagery, an effective vessel detection method is proposed in this paper. Firstly, the iterative constant false alarm rate (CFAR) method is employed to detect the potential ship pixels. Secondly, the mean-shift operation is applied on each potential ship pixel to identify the candidate target region. During the mean-shift process, we maintain a selection matrix recording which pixels can be taken, and these pixels are called as the valid points of the candidate target. The l 1 norm regression is used to extract the principal axis and detect the valid points. Finally, two kinds of false alarms, the bright line and the azimuth ambiguity, are removed by comparing the valid area of the candidate target with a pre-defined value and computing the displacement between the true target and the corresponding replicas respectively. Experimental results on three GF-3 SAR images with UFS mode demonstrate the effectiveness and efficiency of the proposed method.

Concepts: Effectiveness, Detection theory, Alarm, False alarm, Car alarm, Vessels, Pixel, Constant false alarm rate

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Feeling, or the subjective emotional experience, is a fundamental element of the emotional reaction, yet past attempts to understand the mechanisms of feeling generation remain limited. The current study presents a signal detection theory (SDT) conceptualization of feeling generation. Accordingly, feeling, like other sensations, reflects an outcome of an inner decision regarding the emotional evidence, and, therefore, can be evaluated via 2 processes: evidence differentiation (d')-the ability to emotionally differentiate between external stimuli, given the essentially noisy evidence-and criterion ©-the report threshold, or amount of evidence needed to have an intense reportable feeling. According to the model, feelings can be disproportionally intense (false alarms; e.g., emotional overreaction) or disproportionally weak (misses; e.g., failing to detect danger), with the criterion controlling the relative proportion of these “errors.” Results from a novel task indicate that our conceptualization provides a suitable model for valence (pleasant-unpleasant) feeling generation, as reflected in superior model fit relative to plausible alternative models, nonsignificant lack of fit, and by successful experimental tests of a novel prediction regarding contextual influences and related uncertainty. Additional evidence for the external validity of the model shows that SDT parameters, especially the criterion, were meaningfully correlated with relevant emotion regulation and affective style constructs. Implications for the understanding of feeling generation, in general, and in psychopathology, in particular, are discussed. (PsycINFO Database Record

Concepts: Psychology, Affect, Signal processing, Detection theory, Emotion, Receiver operating characteristic, Feeling, Constant false alarm rate

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Traditional constant false alarm rate (CFAR) detectors only use the contrast information between ship targets and clutter, and they suffer probability of detection (PD) degradation in multiple target situations. This paper proposes a correlation-based joint CFAR detector using adaptively-truncated statistics (hereafter called TS-2DLNCFAR) in SAR images. The proposed joint CFAR detector exploits the gray intensity correlation characteristics by building a two-dimensional (2D) joint log-normal model as the joint distribution (JPDF) of the clutter, so joint CFAR detection is realized. Inspired by the CFAR detection methodology, we design an adaptive threshold-based clutter truncation method to eliminate the high-intensity outliers, such as interfering ship targets, side-lobes, and ghosts in the background window, whereas the real clutter samples are preserved to the largest degree. A 2D joint log-normal model is accurately built using the adaptively-truncated clutter through simple parameter estimation, so the joint CFAR detection performance is greatly improved. Compared with traditional CFAR detectors, the proposed TS-2DLNCFAR detector achieves a high PD and a low false alarm rate (FAR) in multiple target situations. The superiority of the proposed TS-2DLNCFAR detector is validated on the multi-look Envisat-ASAR and TerraSAR-X data.

Concepts: Statistics, Type I and type II errors, Signal processing, Detection theory, False alarm, 2D computer graphics, Radar, Constant false alarm rate

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In this paper, we present a statistical model of an indirect path generated in an ultra-wideband (UWB) human tracking scenario. When performing moving target detection, an indirect path signal can generate ghost targets that may cause a false alarm. For this purpose, we performed radar measurements in an indoor environment and established a statistical model of an indirect path based on the measurement data. The proposed model takes the form of a modified Saleh-Valenzuela model, which is used in a UWB channel model. An application example of the proposed model for mitigating false alarms is also presented.

Concepts: Mathematics, Detection theory, Alarm, False alarm, Car alarm, Statistical theory, Scientific modeling, Constant false alarm rate

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Auditory hallucinations constitute an important symptom component in 70-80% of schizophrenia patients. These hallucinations are proposed to occur due to an imbalance between perceptual expectation and external input, resulting in attachment of meaning to abstract noises; signal detection theory has been proposed to explain these phenomena. In this study, we describe the development of an auditory signal detection task using a carefully chosen set of English words that could be tested successfully in schizophrenia patients coming from varying linguistic, cultural and social backgrounds. Schizophrenia patients with significant auditory hallucinations (N=15) and healthy controls (N=15) performed the auditory signal detection task wherein they were instructed to differentiate between a 5-s burst of plain white noise and voiced-noise. The analysis showed that false alarms (p=0.02), discriminability index (p=0.001) and decision bias (p=0.004) were significantly different between the two groups. There was a significant negative correlation between false alarm rate and decision bias. These findings extend further support for impaired perceptual expectation system in schizophrenia patients.

Concepts: Signal processing, Detection theory, Schizophrenia, Hallucination, Alarm, False alarm, Constant false alarm rate, D'

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Remarkable progress has been achieved in the detection of single stationary human. However, restricted by the mutual interference of multiple humans (e.g., strong sidelobes of the torsos and the shadow effect), detection and localization of the multiple stationary humans remains a huge challenge. In this paper, ultra-wideband (UWB) multiple-input and multiple-output (MIMO) radar is exploited to improve the detection performance of multiple stationary humans for its multiple sight angles and high-resolution two-dimensional imaging capacity. A signal model of the vital sign considering both bi-static angles and attitude angle of the human body is firstly developed, and then a novel detection method is proposed to detect and localize multiple stationary humans. In this method, preprocessing is firstly implemented to improve the signal-to-noise ratio (SNR) of the vital signs, and then a vital-sign-enhanced imaging algorithm is presented to suppress the environmental clutters and mutual affection of multiple humans. Finally, an automatic detection algorithm including constant false alarm rate (CFAR), morphological filtering and clustering is implemented to improve the detection performance of weak human targets affected by heavy clutters and shadow effect. The simulation and experimental results show that the proposed method can get a high-quality image of multiple humans and we can use it to discriminate and localize multiple adjacent human targets behind brick walls.

Concepts: Blood pressure, Vital signs, Detection theory, Human body, Human anatomy, Signal-to-noise ratio, Radar, Constant false alarm rate