Anxiety patients over-generalize fear, possibly because of an incapacity to discriminate threat and safety signals. Discrimination trainings are promising approaches for reducing such fear over-generalization. Here we investigated the efficacy of a fear-relevant vs. a fear-irrelevant discrimination training on fear generalization and whether the effects are increased with feedback during training. Eighty participants underwent two fear acquisition blocks, during which one face, but not another face, was associated with a female scream. During two generalization blocks, both (...) CSs plus four morphs were presented. Between these generalization blocks, half of the participants underwent a fear-relevant discrimination training with or without feedback and the other half a fear-irrelevant discrimination training with or without feedback. US expectancy, arousal, valence ratings, and skin conductance responses indicated successful fear acquisition. Importantly, fear-relevant vs. fear-irrelevant discrimination trainings and feedback vs. no feedback reduced generalization as reflected in US expectancy ratings independently from one another. No effects of training condition were found for arousal and valence ratings or SCR. In summary, this is a first indication that fear-relevant discrimination training and feedback can improve the discrimination between threat and safety signals in healthy individuals, at least for learning-related evaluations, but not evaluations of valence or arousal. (shrink)

While often presented as promising assistive technologies for motor-impaired users, electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) remain barely used outside laboratories due to low reliability in real-life conditions. There is thus a need to design long-term reliable BCIs that can be used outside-of-the-lab by end-users, e.g., severely motor-impaired ones. Therefore, we propose and evaluate the design of a multi-class Mental Task (MT)-based BCI for longitudinal training (20 sessions over 3 months) of a tetraplegic user for the CYBATHLON BCI series 2019. (...) In this BCI championship, tetraplegic pilots are mentally driving a virtual car in a racing video game. We aimed at combining a progressive user MT-BCI training with a newly designed machine learning pipeline based on adaptive Riemannian classifiers shown to be promising for real-life applications. We followed a two step training process: the first 11 sessions served to train the user to control a 2-class MT-BCI by performing either two cognitive tasks (REST and MENTAL SUBTRACTION) or two motor-imagery tasks (LEFT-HAND and RIGHT-HAND). The second training step (9 remaining sessions) applied an adaptive, session-independent Riemannian classifier that combined all 4 MT classes used before. Moreover, as our Riemannian classifier was incrementally updated in an unsupervised way it would capture both within and between-session non-stationarity. Experimental evidences confirm the effectiveness of this approach. Namely, the classification accuracy improved by about 30% at the end of the training compared to initial sessions. We also studied the neural correlates of this performance improvement. Using a newly proposed BCI user learning metric, we could show our user learned to improve his BCI control by producing EEG signals matching increasingly more the BCI classifier training data distribution, rather than by improving his EEG class discrimination. However, the resulting improvement was effective only on synchronous (cue-based) BCI and it did not translate into improved CYBATHLON BCI game performances. For the sake of overcoming this in the future, we unveil possible reasons for these limited gaming performances and identify a number of promising future research directions. Importantly, we also report on the evolution of the user's neurophysiological patterns and user experience throughout the BCI training and competition. (shrink)

Prior research shows that reasoners' confidence is poorly calibrated (Shynkaruk & Thompson, 2006). The goal of the current experiment was to increase calibration in syllogistic reasoning by training reasoners on (a) the concept of logical necessity and (b) the idea that more than one representation of the premises may be possible. Training improved accuracy and was also effective in remedying some systematic misunderstandings about the task: those in the training condition were better at estimating their overall performance (...) than those who were untrained. However, training was less successful in helping reasoners to discriminate which items are most likely to cause them difficulties. In addition we explored other variables that may affect confidence and accuracy, such as the number of models required to represent the problem and whether or not the presented conclusion was necessitated by the premises, possible given the premises, or impossible given the premises. These variables had systematically different relationships to confidence and accuracy. Thus, we propose that confidence in reasoning judgements is analogous to confidence in memory retrievals, in that they are inferentially derived from cues that are not diagnostic in terms of accuracy. (shrink)

Several authors have characterized a striking phenomenon of perceptual learning in visual discrimination tasks. This learning process is selective for the stimulus characteristics and location in the visual field. Since the human visual system exploits symmetry for object recognition we were interested in exploring how it learns to use preattentive symmetry cues for discriminating simple, meaningless, forms. In this study, similar to previous studies of perceptual learning, we asked whether the effects of practice acquired in the discrimination of (...) pairs of shape with a specific orientation of the symmetry axis would transfer to the discrimination of shapes with different orientation of symmetry axis, or to shapes presented in different areas of the visual field. We found that there was no learning transfer between forms with very different axes of symmetry (90° apart). Interestingly, however, we found a transfer of learning effect to horizontally oriented symmetry axis from a condition with an axis of symmetry differing by 45°. Also it appears that some subjects took a longer time to learn than the typical fast learning paradigm would predict. Data showed that when observers practice discrimination of meaningless symmetric forms, consistent improvement in the performance occurs. This improvement is lasting over days, and it tends to be specific for the area of the visual field trained. We will discuss results from some of the observers whose learning was not fast, but who actually improved with more practice and with large time intervals (1 day) between training sessions. (shrink)

Discriminative feature-based methods are widely used in natural language processing, but sentence parsing is still dominated by generative methods. While prior feature-based dynamic programming parsers have restricted training and evaluation to artificially short sentences, we present the first general, featurerich discriminative parser, based on a conditional random field model, which has been successfully scaled to the full WSJ parsing data. Our efficiency is primarily due to the use of stochastic optimization techniques, as well as parallelization and chart prefiltering. (...) On WSJ15, we attain a state-of-the-art F-score of 90.9%, a 14% relative reduction in error over previous models, while being two orders of magnitude faster. On sentences of length 40, our system achieves an F-score of 89.0%, a 36% relative reduction in error over a generative baseline. (shrink)

Zero-shot learning is dedicated to solving the classification problem of unseen categories, while generalized zero-shot learning aims to classify the samples selected from both seen classes and unseen classes, in which “seen” and “unseen” classes indicate whether they can be used in the training process, and if so, they indicate seen classes, and vice versa. Nowadays, with the promotion of deep learning technology, the performance of zero-shot learning has been greatly improved. Generalized zero-shot learning is a challenging topic that (...) has promising prospects in many realistic scenarios. Although the zero-shot learning task has made gratifying progress, there is still a strong deviation between seen classes and unseen classes in the existing methods. Recent methods focus on learning a unified semantic-aligned visual representation to transfer knowledge between two domains, while ignoring the intrinsic characteristics of visual features which are discriminative enough to be classified by itself. To solve the above problems, we propose a novel model that uses the discriminative information of visual features to optimize the generative module, in which the generative module is a dual generation network framework composed of conditional VAE and improved WGAN. Specifically, the model uses the discrimination information of visual features, according to the relevant semantic embedding, synthesizes the visual features of unseen categories by using the learned generator, and then trains the final softmax classifier by using the generated visual features, thus realizing the recognition of unseen categories. In addition, this paper also analyzes the effect of the additional classifiers with different structures on the transmission of discriminative information. We have conducted a lot of experiments on six commonly used benchmark datasets. The experimental results show that our model outperforms several state-of-the-art methods for both traditional as well as generalized zero-shot learning. (shrink)

Directive 2002/73 enacted by the Council and Parliament of the European Union introduces substantial and procedural amendments to the European Community's `old' Equal Treatment Directive 76/207, providing, in particular, clarification of the definitions of concepts such as direct and indirect discrimination and harassment. Yet, while the European Commission has praised the progressive nature of the new European legislation, a critical assessment of its provisions reveals some serious shortcomings and a host of missed opportunities. Although the new Directive generally reflects (...) the spirit of the case law of the E.C.J., it does so without any originality or pro-activism meaning that the European legislature has once again left the initiative of pursuing sex equality in the E.U. to the Court. (shrink)

Since the BBS article in which Premack and Woodruff (1978) asked “Does the chimpanzee have a theory of mind?,” it has been repeatedly claimed that there is observational and experimental evidence that apes have mental state concepts, such as “want” and “know.” Unlike research on the development of theory of mind in childhood, however, no substantial progress has been made through this work with nonhuman primates. A survey of empirical studies of imitation, self-recognition, social relationships, deception, role-taking, and perspective-taking suggests (...) that in every case where nonhuman primate behavior has been interpreted as a sign of theory of mind, it could instead have occurred by chance or as a product of nonmentalistic processes such as associative learning or inferences based on nonmental categories. Arguments to the effect that, in spite of this, the theory of mind hypothesis should be accepted because it is more parsimonious than alternatives or because it is supported by convergent evidence are not compelling. Such arguments are based on unsupportable assumptions about the role of parsimony in science and either ignore the requirement that convergent evidence proceed from independent assumptions, or fail to show that it supports the theory of mind hypothesis over nonmentalist alternatives. Progress in research on theory of mind requires experimental procedures that can distinguish the theory of mind hypothesis from nonmentalist alternatives. A procedure that may have this potential is proposed. It uses conditional discrimination training and transfer tests to determine whether chimpanzees have the concept “see.” Commentators are invited to identify flaws in the procedure and to suggest alternatives. Key Words: apes; associative learning; concepts; convergence; deception; evolution of intelligence; folk psychology; imitation; mental state attribution; monkeys; parsimony; perspective-taking; primates; role-taking; self-recognition; social cognition; social intelligence; theory of mind. (shrink)

This article examines the NEET (Not in Education, Employment, or Training) youth in Romania, employing the secondary analysis of quantitative data and focusing on the socio-economic, cultural and systemic factors contributing to this phenomenon. The analysis highlights how economic instability, poverty and limited access to education exacerbate the challenges faced by this demographic group. Gender disparities, driven by traditional roles and labour market discrimination, further limit opportunities for young women. Additionally, structural deficiencies in the labour market, such as (...) job shortages and mismatches between skills and demand, hinder employment prospects. Cultural expectations and societal norms also play a significant role in influencing youth participation in education and employment. Young citizens of Romania, particularly women and members of the Roma community, face significant barriers to employment, with low wages, poor working conditions and inadequate investment in education and vocational training contributing to high NEET youth rates. Patriarchal norms, especially within the Roma community, further exacerbate cycles of poverty and marginalisation by limiting educational and economic opportunities. (shrink)

Philip Reed (2023) argues that discrimination against (non-acutely) dying patients constitutes a unique kind—which he calls terminalism—because their status as persons with terminal illness marks them with a socially salient identity which, by means of direct and indirect discrimination, limits their sets of choices and resources, such as in hospice care or organ transplant policies. 1 Importantly, Reed also argues that while terminalism is an increasingly prevalent normative phenomenon, it has been overlooked in the literature, ‘hiding in plain (...) sight’ as even though we are ‘aware of it at some level’." 1 What could explain this paradox? Drawing from Jeffrey Bishop, Giorgio Agamben, and T. Kenny Fountain, I argue that an institutional episteme and operating assumption of medicine, from the training of medical students, discursively disciplines terminally ill persons as bare life, between medicine’s dichotomy of bodies: neither ‘biovaluable’ (the surplus value of separated human bodily material... (shrink)

The current study examined whether pigeons could learn to use abstract concepts as the basis for conditionally switching behavior as a function of time. Using a mid-session reversal task, experienced pigeons were trained to switch from matching-to-sample (MTS) to non-matching-to-sample (NMTS) conditional discriminations within a session. One group had prior training with MTS, while the other had prior training with NMTS. Over training, stimulus set size was progressively doubled from 3 to 6 to 12 stimuli to (...) promote abstract concept development. Prior experience had an effect on the initial learning at each of the set sizes but by the end of training there were no group differences, as both groups showed similar within-session linear matching functions. After acquiring the 12-item set, abstract-concept learning was tested by placing novel stimuli at the beginning and end of a test session. Prior matching and non-matching experience affected transfer behavior. The matching experienced group transferred to novel stimuli in both the matching and non-matching portion of the sessions using a matching rule. The non-matching experienced group transferred to novel stimuli in both portions of the session using a non-matching rule. The representations used as the basis for mid-session reversal of the conditional discrimination behaviors and subsequent transfer behavior appears to have different temporal sources. The implications for the flexibility and organization of complex behaviors are considered. (shrink)

This paper implements the continuous Hindi Automatic Speech Recognition (ASR) system using the proposed integrated features vector with Recurrent Neural Network (RNN) based Language Modeling (LM). The proposed system also implements the speaker adaptation using Maximum-Likelihood Linear Regression (MLLR) and Constrained Maximum likelihood Linear Regression (C-MLLR). This system is discriminatively trained by Maximum Mutual Information (MMI) and Minimum Phone Error (MPE) techniques with 256 Gaussian mixture per Hidden Markov Model(HMM) state. The training of the baseline system has been done (...) using a phonetically rich Hindi dataset. The results show that discriminative training enhances the baseline system performance by up to 3%. Further improvement of ~7% has been recorded by applying RNN LM. The proposed Hindi ASR system shows significant performance improvement over other current state-of-the-art techniques. (shrink)

The statistics-based method ignores the semantic constraints in the English grammar area branch training model and is unable to identify the orientation information effectively. This paper systematically discusses the close relationship between English grammar area branch training model filtering, English grammar area branch training model retrieval, and machine learning. By analyzing the role of the situation in the understanding of the English grammar area branch training model, the relationship between the English grammar area branch training (...) model and situation model and the correlation between the features of the English grammar area branch training model and situation model are determined, and then, a set of filtering methods for the English grammar area branch training model are proposed. At present, there are few research studies on bias filtering, and the method of thematic filtering is generally used, which has poor effect. This paper makes full use of the domain knowledge and adopts the semantic pattern analysis technology to establish a wealth of semantic analysis resources, including various dictionaries, rules, and weight representation, so as to effectively filter the inclined English grammar area branch training model. The introduction of semantic data sources solves the problem of data sparsity and cold start in the traditional collaborative filtering system. In addition, in order to improve the scalability and real-time performance of the recommendation system, the data mining method is used to perform fuzzy clustering for users and projects in the offline data preprocessing stage. This paper proposes a search and filter scheme based on the orientation of the training model in English grammar area, elaborates on the details, constructs a whole set of function structure from representation to weight, and gives the experimental results, which prove that the system has a good filtering effect and is fast. Compared with the traditional statistical methods, the results are satisfactory. (shrink)

In quantity discrimination tasks, adults, infants and animals have been sometimes observed to process number only after all continuous variables, such as area or density, have been controlled for. This has been taken as evidence that processing number may be more cognitively demanding than processing continuous variables. We tested this hypothesis by training mosquitofish to discriminate two items from three in three different conditions. In one condition, continuous variables were controlled while numerical information was available; in another, the (...) number was kept constant and information relating to continuous variables was available; in the third condition, stimuli differed for both number and continuous quantities. Fish learned to discriminate more quickly when both number and continuous information were available compared to when they could use continuous information only or number only; there was no difference in the learning rate between the two latter conditions. Our results do not support the hypothesis that processing numbers imposes a higher cognitive load than processing continuous variables. Rather, they suggest that availability of multiple information sources may facilitate discrimination learning. (shrink)

Implicit bias training (IBT) is now frequently provided by employers, in order to raise awareness of the problems related to implicit biases, and of how to safeguard against discrimination that may result. However, as Atewologun et al (2018) have noted, there is very little systematicity in IBT, and there are many unknowns about what constitutes good IBT. One important issue concerns the tone of information provided regarding implicit bias. This paper engages this question, focusing in particular on the (...) observation that much bias training is delivered in exculpatory tone, emphasising that individuals are not to blame for possessing implicit biases. Normative guidance around IBT exhorts practitioners to adopt this strategy (Moss-Racusin et al 2014). However, existing evidence about the effects of moralized feedback about implicit bias is equivocal (Legault et al 2011; Czopp et al 2006). Through a series of studies, culminating in an experiment with a pre-registered analysis plan, we develop a paradigm for evaluating the impact of moralized feedback on participants’ implicit racial bias scores. We also conducted exploratory analyses of the impact on their moods, and behavioural intentions. Our results indicated that an exculpatory tone, rather than a blaming or neutral tone, did not make participants less resistant to changing their attitudes and behaviours. In fact, participants in the blame condition had significantly stronger explicit intentions to change future behaviour than those in the ‘no feedback’ condition (see experiment 3). These results indicate that considerations of efficacy do not support the need for implicit bias feedback to be exculpatory. We tease out the implications of these findings, and directions for future research. (shrink)