Suprathreshold perceptual decisions constrain models of confidence

Perceptual confidence is an important internal signal about the certainty of our decisions and there is a substantial debate on how it is computed. We highlight three confidence metric types from the literature: observers either use 1) the full probability distribution to compute probability correct (Probability metrics), 2) point estimates from the perceptual decision process to estimate uncertainty (Evidence-Strength metrics), or 3) heuristic confidence from stimulus-based cues to uncertainty (Heuristic metrics). These metrics are rarely tested against one another, so we examined models of all three types on a suprathreshold spatial discrimination task. Observers were shown a cloud of dots sampled from a dot generating distribution and judged if the mean of the distribution was left or right of centre. In addition to varying the horizontal position of the mean, there were two sensory uncertainty manipulations: the number of dots sampled and the spread of the generating distribution. After every two perceptual decisions, observers made a confidence forced-choice judgement whether they were more confident in the first or second decision. Model results showed that observers were on average best-fit by a Heuristic model that used dot cloud position, spread, and number of dots as cues. However, almost half of the observers were best-fit by an Evidence-Strength model that uses the distance between the discrimination criterion and a point estimate, scaled according to sensory uncertainty, to compute confidence. This signal-to-noise ratio model outperformed the standard unscaled distance from criterion model favoured by many researchers and suggests that this latter simple model may not be suitable for mixed-difficulty designs. An accidental repetition of some sessions also allowed for the measurement of confidence agreement for identical pairs of stimuli. This N-pass analysis revealed that human observers were more consistent than their best-fitting model would predict, indicating there are still aspects of confidence that are not captured by our model. As such, we propose confidence agreement as a useful technique for computational studies of confidence. Taken together, these findings highlight the idiosyncratic nature of confidence computations for complex decision contexts and the need to consider different potential metrics and transformations in the confidence computation.

Face recognition using fractional coefficients and discrete cosine transform tool

Face recognition is a computer vision application based on biometric information for automatic person identification or verification from image sequence or a video frame. In this context DCT is the easy technique to determine significant parameters. Until now the main object is selection of the coefficients to obtain the best recognition. Many techniques rely on premasking windows to discard the high and low coefficients to enhance performance. However, the problem resides in the shape and size of premask. To improve discriminator ability in discrete cosine transform domain, we used fractional coefficients of the transformed images with discrete cosine transform to limit the coefficients area for a better performance system. Then from the selected bands, we use the discrimination power analysis to search for the coefficients having the highest power to discriminate different classes from each other. Feature selection algorithm is a key issue in all pattern recognition system, in fact this algorithm is utilized to define features vector among several ones, where these features are selected according a specified discrimination criterion. Many classifiers are used to evaluate our approach like, support vector machine and random forests. The proposed approach is validated with Yale and ORL Face databases. Experimental results prove the sufficiency of this method in face and facial expression recognition field.

Jointly Learning the Discriminative Dictionary and Projection for Face Recognition

Recently, dictionary learning has become an active topic. However, the majority of dictionary learning methods directly employs original or predefined handcrafted features to describe the data, which ignores the intrinsic relationship between the dictionary and features. In this study, we present a method called jointly learning the discriminative dictionary and projection (JLDDP) that can simultaneously learn the discriminative dictionary and projection for both image-based and video-based face recognition. The dictionary can realize a tight correspondence between atoms and class labels. Simultaneously, the projection matrix can extract discriminative information from the original samples. Through adopting the Fisher discrimination criterion, the proposed framework enables a better fit between the learned dictionary and projection. With the representation error and coding coefficients, the classification scheme further improves the discriminative ability of our method. An iterative optimization algorithm is proposed, and the convergence is proved mathematically. Extensive experimental results on seven image-based and video-based face databases demonstrate the validity of JLDDP.

An Optimal Design Criterion for Within-Individual Covariance Matrices Discrimination and Parameter Estimation in Nonlinear Mixed Effects Models

In this paper, we consider the problem of nding optimal populationdesigns for within-individual covariance matrices discrimination andparameter estimation in nonlinear mixed eects models. A compound optimality criterion is provided, which combines an estimation criterion and a discrimination criterion. We used the D-optimality criterion for parameter estimation, which maximizes the determinant of the Fisher information matrix. For discrimination, we propose a generalization of the T-optimality criterion for xed-eects models. Equivalence theorems are provided for these criteria. We illustrated the application of compound criteria with an example in a pharmacokinetic experiment.

Method for measuring distortions of a speech signal during its transmission over a communication channel to a biometric identification system

This paper is devoted to the presence of distortions in a speech signal transmitted over a communication channel to a biometric system during voice-based remote identification. We propose to preliminary correct the frequency spectrum of the received signal based on the pre-distortion principle. Taking into account a priori uncertainty, a new information indicator of speech signal distortions and a method for measuring it in conditions of small samples of observations are proposed. An example of fast practical implementation of the method based on a parametric spectral analysis algorithm is considered. Experimental results of our approach are provided for three different versions of communication channel. It is shown that the usage of the proposed method makes it possible to transform the initially distorted speech signal into compliance on the registered voice template by using acceptable information discrimination criterion. It is demonstrated that our approach may be used in existing biometric systems and technologies of speaker identification.

Priors and Payoffs in Confidence Judgments

1AbstractPriors and payoffs are known to affect perceptual decision-making, but little is understood about how they influence confidence judgments. For optimal perceptual decision-making, both priors and payoffs should be considered when selecting a response. However, for confidence to reflect the probability of being correct in a perceptual decision, priors should affect confidence but payoffs should not. To experimentally test whether human observers follow this normative behavior, we conducted an orientation-discrimination task with varied priors and payoffs, probing both perceptual and metacognitive decision-making. We then examined the placement of discrimination and confidence criteria according to several plausible Signal Detection Theory models. In the normative model, observers use the optimal discrimination criterion (i.e., the criterion that maximizes expected gain) and confidence criteria that shift with the discrimination criterion that maximizes accuracy (i.e., are not affected by payoffs). No observer was consistent with this model, with the majority exhibiting non-normative confidence behavior. One subset of observers ignored both priors and payoffs for confidence, always fixing the confidence criteria around the neutral discrimination criterion. The other group of observers incorrectly incorporated payoffs into their confidence by always shifting their confidence criteria with the same gains-maximizing criterion used for discrimination. Such metacognitive mistakes could have negative consequences outside the laboratory setting, particularly when priors or payoffs are not matched for all the possible decision alternatives.

A Piezoelectric Sensor Signal Analysis Method for Identifying Persons Groups

The is an increasing number of elderly single-person households causing lonely deaths and it is a social problem. We study a watching system for elderly families by laying the piezoelectric sensors inside the house. There are few privacy issues of this system because piezoelectric sensor detects only a person’s vibration signal. Furthermore, it has a benefit of sensing the ability for a bio-signal including the respiration cycle and cardiac cycle. We propose a method of identifying the person who is on the sensor by analyzing the frequency spectrum of the bio-signal. Multiple peaks of harmonics originating from the heartbeat appear in the graph of the frequency spectrum. We propose a method to identify people by using the peak shape as a discrimination criterion.

Strategy for Fault Diagnosis on Train Plug Doors Using Audio Sensors

As the only entry/exit for passengers getting on and off a train, the train plug door is of great importance to keep train operation safe and reliable. As signal processing technologies develop rapidly, taking the easy acquisition advantages of sound signals, a novel fault diagnosis method for train plug doors using multi-scale normalized permutation entropy (MNPE) and an improved particle swarm optimization based multi-class support vector machine (IPSO-MSVM) is proposed. Firstly, sound samples are collected using high-precision audio sensor. In the features extraction process, a hybrid method blending empirical mode decomposition (EMD), multi-scale permutation entropy (MNPE) with Fisher discrimination criterion is utilized. First, EMD is used to decompose each sound signal into several intrinsic mode functions (IMFs) and a residue for stationary processing. Then, MNPE features are extracted from the IMFs. To obtain the most significant features, the Fisher discrimination criterion is further applied. To address the time-consuming defects of traditional grid based method for selecting the optimal parameters of multi-class SVM, an improved PSO (IPSO) is proposed. The superiority of the IPSO-MSVM model and the hybrid feature extraction method was tested on the collected sound samples by comparing to commonly applied methods. Results indicate the identification accuracy of the proposed method is highest, which reaches 90.54%, demonstrating its feasibility.