ANTISTRUCTURE AND THE ROOTS OF RELIGIOUS EXPERIENCE

The cognitive and evolutionary sciences of religion offer a standard model of religious representations, but no equivalent paradigm for investigating religiously interpreted altered states of consciousness (religious ASCs). Here, I describe a neo-Durkheimian framework for studying religious ASCs that centralizes social predictive cognition. Within a processual model of ritual, ritual behaviors toggle between reinforcing normative social structures and downplaying them. Specifically, antistructural ritual shifts cognitive focus away from conventional affordances, collective intentionality, and social prediction, and toward physical affordances and behavioral motivations that make few references to others’ intentional states. Using synchrony and dance as paradigmatic examples of antistructural ritual that stimulate religious ASCs, I assemble literature from anthropology, cognitive neuroscience, and philosophy of language to offer fruitful empirical predictions and opportunities for testing based on this framework. Among the empirical predictions is that antistructural ritual may provide for cultural change in religions when religions are construed as complex adaptive systems.     

FNDNet – A deep convolutional neural network for fake news detection

With the increasing popularity of social media and web-based forums, the distribution of fake news has become a major threat to various sectors and agencies. This has abated trust in the media, leaving readers in a state of perplexity. There exists an enormous assemblage of research on the theme of Artificial Intelligence (AI) strategies for fake news detection. In the past, much of the focus has been given on classifying online reviews and freely accessible online social networking-based posts. In this work, we propose a deep convolutional neural network (FNDNet) for fake news detection. Instead of relying on hand-crafted features, our model (FNDNet) is designed to automatically learn the discriminatory features for fake news classification through multiple hidden layers built in the deep neural network. We create a deep Convolutional Neural Network (CNN) to extract several features at each layer. We compare the performance of the proposed approach with several baseline models. Benchmarked datasets were used to train and test the model, and the proposed model achieved state-of-the-art results with an accuracy of 98.36% on the test data. Various performance evaluation parameters such as Wilcoxon, false positive, true negative, precision, recall, F1, and accuracy, etc. were used to validate the results. These results demonstrate significant improvements in the area of fake news detection as compared to existing state-of-the-art results and affirm the potential of our approach for classifying fake news on social media. This research will assist researchers in broadening the understanding of the applicability of CNN-based deep models for fake news detection.     

PSO-GA based hybrid with Adam Optimization for ANN training with application in Medical Diagnosis

This paper introduces a novel PSO-GA based hybrid training algorithm with Adam Optimization and contrasts performance with the generic Gradient Descent based Backpropagation algorithm with Adam Optimization for training Artificial Neural Networks. We aim to overcome the shortcomings of the traditional algorithm, such as slower convergence rate and frequent convergence to local minima, by employing the characteristics of evolutionary algorithms. PSO has a property of faster convergence rate, which can be exploited to account for the slower pace of convergence of the traditional BP (which is due to low values of gradients). In contrast, the integration with GA complements the drawback of convergence to local minima as GA, possesses the capability of efficient global search. So by this integration of these algorithms, we propose our new hybrid algorithm for training ANNs. We compare both the algorithms for the application of medical diagnosis. Results display that the proposed hybrid training algorithm, significantly outperforms the traditional training algorithm, by enhancing the accuracies of the ANNs with an increase of 20% in the average testing accuracy and 0.7% increase in the best testing accuracy.     

Integrating the Automatic and the Controlled: Strategies in Semantic Priming in an Attractor Network With Latching Dynamics

Semantic priming has long been recognized to reflect, along with automatic semantic mechanisms, the contribution of controlled strategies. However, previous theories of controlled priming were mostly qualitative, lacking common grounds with modern mathematical models of automatic priming based on neural networks. Recently, we introduced a novel attractor network model of automatic semantic priming with latching dynamics. Here, we extend this work to show how the same model can also account for important findings regarding controlled processes. Assuming the rate of semantic transitions in the network can be adapted using simple reinforcement learning, we show how basic findings attributed to controlled processes in priming can be achieved, including their dependency on stimulus onset asynchrony and relatedness proportion and their unique effect on associative, category‐exemplar, mediated and backward prime‐target relations. We discuss how our mechanism relates to the classic expectancy theory and how it can be further extended in future developments of the model.     

An Evolutionary Analysis of Learned Attention

Humans and many other species selectively attend to stimuli or stimulus dimensions—but why should an animal constrain information input in this way? To investigate the adaptive functions of attention, we used a genetic algorithm to evolve simple connectionist networks that had to make categorization decisions in a variety of environmental structures. The results of these simulations show that while learned attention is not universally adaptive, its benefit is not restricted to the reduction of input complexity in order to keep it within an organism's processing capacity limitations. Instead, being able to shift attention provides adaptive benefit by allowing faster learning with fewer errors in a range of ecologically plausible environments.     

Is the Effect of Behavioral Synchrony on Cooperative Behavior Mediated by Pain Threshold?

Synchronized behavior results in a variety of prosocial behaviors. Research has also implicated that interpersonal synchrony affects pain thresholds, inferred as indicative of endorphin levels. The current study was designed to see if these pain threshold effects mediated the effect of synchrony on interpersonal cooperation. Twenty six individuals were randomly assigned to complete a 30 minute run on a treadmill in either a synchronized or nonsynchronized condition. Pain threshold was measured both before and after exercise as an indicator of endorphin activity. A postrun social investment game measured interpersonal cooperation. Analyses showed that there was a significant direct relationship between condition and cooperation but that this effect was not mediated by pain threshold.     

Neural synchronization as a hypothetical explanation of the psychoanalytic unconscious

Cognitive scientists have tried to explain the neural mechanisms of unconscious mental states such as coma, epileptic seizures, and anesthesia-induced unconsciousness. However these types of unconscious states are different from the psychoanalytic unconscious. In this review, we aim to present our hypothesis about the neural correlates underlying psychoanalytic unconscious. To fulfill this aim, we firstly review the previous explanations about the neural correlates of conscious and unconscious mental states, such as brain oscillations, synchronicity of neural networks, and cognitive binding. By doing so, we hope to lay a neuroscientific ground for our hypothesis about neural correlates of psychoanalytic unconscious; parallel but unsynchronized neural networks between different layers of consciousness and unconsciousness. Next, we propose a neuroscientific mechanism about how the repressed mental events reach the conscious awareness; the lock of neural synchronization between two mental layers of conscious and unconscious. At the last section, we will discuss the data about schizophrenia as a clinical example of our proposed hypothesis.     

The moment‐to‐moment pitch dynamics of child‐directed speech shape toddlers’ attention and learning

Young children have an overall preference for child‐directed speech (CDS) over adult‐directed speech (ADS), and its structural features are thought to facilitate language learning. Many studies have supported these findings, but less is known about processing of CDS at short, sub‐second timescales. How do the moment‐to‐moment dynamics of CDS influence young children's attention and learning? In Study 1, we used hierarchical clustering to characterize patterns of pitch variability in a natural CDS corpus, which uncovered four main word‐level contour shapes: ‘fall’, ‘rise’, ‘hill’, and ‘valley’. In Study 2, we adapted a measure from adult attention research—pupil size synchrony—to quantify real‐time attention to speech across participants, and found that toddlers showed higher synchrony to the dynamics of CDS than to ADS. Importantly, there were consistent differences in toddlers’ attention when listening to the four word‐level contour types. In Study 3, we found that pupil size synchrony during exposure to novel words predicted toddlers’ learning at test. This suggests that the dynamics of pitch in CDS not only shape toddlers’ attention but guide their learning of new words. By revealing a physiological response to the real‐time dynamics of CDS, this investigation yields a new sub‐second framework for understanding young children's engagement with one of the most important signals in their environment.     

Word Senses as Clusters of Meaning Modulations: A Computational Model of Polysemy

Most words in natural languages are polysemous; that is, they have related but different meanings in different contexts. This one-to-many mapping of form to meaning presents a challenge to understanding how word meanings are learned, represented, and processed. Previous work has focused on solutions in which multiple static semantic representations are linked to a single word form, which fails to capture important generalizations about how polysemous words are used; in particular, the graded nature of polysemous senses, and the flexibility and regularity of polysemy use. We provide a novel view of how polysemous words are represented and processed, focusing on how meaning is modulated by context. Our theory is implemented within a recurrent neural network that learns distributional information through exposure to a large and representative corpus of English. Clusters of meaning emerge from how the model processes individual word forms. In keeping with distributional theories of semantics, we suggest word meanings are generalized from contexts of different word tokens, with polysemy emerging as multiple clusters of contextually modulated meanings. We validate our results against a human-annotated corpus of polysemy focusing on the gradedness, flexibility, and regularity of polysemous sense individuation, as well as behavioral findings of offline sense relatedness ratings and online sentence processing. The results provide novel insights into how polysemy emerges from contextual processing of word meaning from both a theoretical and computational point of view.     

Emotional Valence Precedes Semantic Maturation of Words: A Longitudinal Computational Study of Early Verbal Emotional Anchoring

We present a longitudinal computational study on the connection between emotional and amodal word representations from a developmental perspective. In this study, children's and adult word representations were generated using the latent semantic analysis (LSA) vector space model and Word Maturity methodology. Some children's word representations were used to set a mapping function between amodal and emotional word representations with a neural network model using ratings from 9-year-old children. The neural network was trained and validated in the child semantic space. Then, the resulting neural network was tested with adult word representations using ratings from an adult data set. Samples of 1210 and 5315 words were used in the child and the adult semantic spaces, respectively. Results suggested that the emotional valence of words can be predicted from amodal vector representations even at the child stage, and accurate emotional propagation was found in the adult word vector representations. In this way, different propagative processes were observed in the adult semantic space. These findings highlight a potential mechanism for early verbal emotional anchoring. Moreover, different multiple linear regression and mixed-effect models revealed moderation effects for the performance of the longitudinal computational model. First, words with early maturation and subsequent semantic definition promoted emotional propagation. Second, an interaction effect between age of acquisition and abstractness was found to explain model performance. The theoretical and methodological implications are discussed.