The Neurodynamics of Cognition: A Tutorial on Computational Cognitive Neuroscience

Computational Cognitive Neuroscience (CCN) is a new field that lies at the intersection of computational neuroscience, machine learning, and neural network theory (i.e., connectionism). The ideal CCN model should not make any assumptions that are known to contradict the current neuroscience literature and at the same time provide good accounts of behavior and at least some neuroscience data (e.g., single-neuron activity, fMRI data). Furthermore, once set, the architecture of the CCN network and the models of each individual unit should remain fixed throughout all applications. Because of the greater weight they place on biological accuracy, CCN models differ substantially from traditional neural network models in how each individual unit is modeled, how learning is modeled, and how behavior is generated from the network. A variety of CCN solutions to these three problems are described. A real example of this approach is described, and some advantages and limitations of the CCN approach are discussed.     

Using habituation in machine learning

Habituation, a decrement in response to a stimulus that is presented repeatedly without ill effect, can be identified in almost all animals. It can also be used in machine learning to provide a variety of different applications, such as novelty detection, recency encoding, and temporal signal pre-processing. This paper examines how habituation can be mathematically modelled, and discusses how well these models fit the revised characteristics of habituation. It then demonstrates how the models can be combined with neural networks in order to realise the various applications. Finally, some simple experimental results are presented that demonstrate the effectiveness of the methods.     

A neural implementation of multi-adjoint logic programming

A neural net based implementation of propositional [0,1]-valued multi-adjoint logic programming is presented, which is an extension of earlier work on representing logic programs in neural networks carried out in [A.S. d'Avila Garcez et al., Neural-Symbolic Learning Systems: Foundations and Applications, Springer, 2002; S. Hölldobler et al., Appl. Intelligence 11 (1) (1999) 45–58]. Proofs of preservation of semantics are given, this makes the extension to be well-founded.The implementation needs some preprocessing of the initial program to transform it into a homogeneous program; then, transformation rules carry programs into neural networks, where truth-values of rules relate to output of neurons, truth-values of facts represent input, and network functions are determined by a set of general operators; the net outputs the values of propositional variables under its minimal model.     

Comparing different kinds of words and word-word relations to test an habituation model of priming

Huber and O'Reilly (2003) proposed that neural habituation exists to solve a temporal parsing problem, minimizing blending between one word and the next when words are visually presented in rapid succession. They developed a neural dynamics habituation model, explaining the finding that short duration primes produce positive priming whereas long duration primes produce negative repetition priming. The model contains three layers of processing, including a visual input layer, an orthographic layer, and a lexical-semantic layer. The predicted effect of prime duration depends both on this assumed representational hierarchy and the assumption that synaptic depression underlies habituation. The current study tested these assumptions by comparing different kinds of words (e.g., words versus non-words) and different kinds of word-word relations (e.g., associative versus repetition). For each experiment, the predictions of the original model were compared to an alternative model with different representational assumptions. Experiment 1 confirmed the prediction that non-words and inverted words require longer prime durations to eliminate positive repetition priming (i.e., a slower transition from positive to negative priming). Experiment 2 confirmed the prediction that associative priming increases and then decreases with increasing prime duration, but remains positive even with long duration primes. Experiment 3 replicated the effects of repetition and associative priming using a within-subjects design and combined these effects by examining target words that were expected to repeat (e.g., viewing the target word ‘BACK' after the prime phrase ‘back to'). These results support the originally assumed representational hierarchy and more generally the role of habituation in temporal parsing and priming.     

The Effects Of Recruitment Message Specificity On Applicant Attraction To Organizations

We used the elaboration likelihood model (ELM) from marketing research to explain and examine how recruitment message specificity influences job seeker attraction to organizations. Using an experimental design and data from 171 college-level job seekers, the results showed that detailed recruitment messages led to enhanced perceptions of organization attributes and person–organization (P–O) fit. Perceptions of fit were found to mediate the relationship between message specificity and intention to apply to the organization. In addition, perceptions of organization attributes and P–O fit were found to influence intentions to apply under circumstances of explicit recruitment information while attractiveness and fit perceptions were shown to influence application intentions under conditions of implicit recruitment information. The theoretical and practical implications of these findings are discussed.     

Adaptations to Ovulation

ABSTRACT— In socially monogamous species in which males heavily invest in offspring, there arises an inevitable genetic conflict between partners over whether investing males become biological fathers of their partners' offspring. Humans are such a species. The ovulatory-shift hypothesis proposes that changes in women's mate preferences and sexual interests across the cycle are footprints of this conflict. When fertile (mid-cycle), women find masculine bodily and behavioral features particularly sexy and report increased attraction to men other than current partners. Men are more vigilant of partners when the latter are fertile, which may reflect evolved counteradaptations. This adaptationist hypothesis has already generated several fruitful research programs, but many questions remain.     

Beer Goggles: Blood Alcohol Concentration in Relation to Attractiveness Ratings for Unfamiliar Opposite Sex Faces in Naturalistic Settings

ABSTRACT

The popular notion that alcohol intoxication enhances perceptions of the physical attractiveness of the opposite sex has been inconsistently supported. The current study tested intoxicated and non-intoxicated persons of both genders in naturalistic settings after measuring their blood alcohol concentration (BAC) by a breath test. A sample of 80 heterosexual university student social drinkers was recruited at a campus pub and campus parties over a 3-month period to take a survey rating the attractiveness of unfamiliar faces of the opposite gender presented in photographs. Attractiveness ratings were positively correlated with BAC. Analysis of covariance (ANCOVA) was conducted on attractiveness ratings with independent variables of gender and BAC group, with three levels of the latter: non-intoxicated (BAC = 0), moderately intoxicated (BAC .01%–.09%), and highly intoxicated (BAC .10%–.19%). Both intoxicated groups gave significantly higher attractiveness ratings than non-intoxicated controls. The findings confirm the “beer goggles” phenomenon of folk psychology for both genders, although the mechanism remains unclear.     

The Power of Change: Interpersonal Attraction as a Function of Attitude Similarity and Attitude Alignment

Abstract

In an experimental simulation with Israeli participants, the author examined the influence of two aspects of pre-employment screening (duration of screening and type of testing) on applicants' attitudes toward a recruitment effort and toward a potential job. Testing that lasted longer led to more favorable attitudes. The participants considered knowledge testing, compared with personality testing, more job related, less invasive of privacy, and less sensitive to the amount of time spent testing.     

Voice and interpersonal attraction

The present study examined the effects of voice and physical appearance on inter-personal attraction. Furthermore, the attributes of voice that enhance interpersonal attraction were investigated. In the first study the subjects were 25 female students from one university and the target persons were four male students from another university. The subjects rated attractiveness of voice and physical appearance, and the overall interpersonal attraction of the target persons. The attractiveness of voice and physical appearance had independent effects on interpersonal attraction. In the second study the subjects were 62 students (20 males, 42 females) from one university and the target persons were 16 students from another university (eight of each sex). The results of the first study were replicated. Bright, generous voices, low vocal pitch and a small range of vocal pitch increased interpersonal attraction.     

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.     

Intelligence is differentially related to neural effort in the task-positive and the task-negative brain network

Previous studies on individual differences in intelligence and brain activation during cognitive processing focused on brain regions where activation increases with task demands (task-positive network, TPN). Our study additionally considers brain regions where activation decreases with task demands (task-negative network, TNN) and compares effects of intelligence on neural effort in the TPN and the TNN. In a sample of 52 healthy subjects, functional magnetic resonance imaging was used to determine changes in neural effort associated with the processing of a working memory task. The task comprised three conditions of increasing difficulty: (a) maintenance, (b) manipulation, and (c) updating of a four-letter memory set. Neural effort was defined as signal increase in the TPN and signal decrease in the TNN, respectively. In both functional networks, TPN and TNN, neural effort increased with task difficulty. However, intelligence, as assessed with Raven's Matrices, was differentially associated with neural effort in the TPN and TNN. In the TPN, we observed a positive association, while we observed a negative association in the TNN. In terms of neural efficiency (i.e., task performance in relation to neural effort expended on task processing), more intelligent subjects (as compared to less intelligent subjects) displayed lower neural efficiency in the TPN, while they displayed higher neural efficiency in the TNN. The results illustrate the importance of differentiating between TPN and TNN when interpreting correlations between intelligence and fMRI measures of brain activation. Importantly, this implies the risk of misinterpreting whole brain correlations when ignoring the functional differences between TPN and TNN.