Eye Movement Evidence for Simultaneous Cognitive Processing in Reading.

Measuring simultaneous processing, a reliable predictor of reading development and reading difficulties (RDs), has traditionally involved cognitive tasks that test reaction or response time, which only capture the efficiency at the output processing stage and neglect the internal stages of information processing. However, with eye-tracking methodology, we can reveal the underlying temporal and spatial processes involved in simultaneous processing and investigate whether these processes are equivalent across chronological or reading age groups. This study used eye-tracking to investigate the simultaneous processing abilities of 15 Grade 6 and 15 Grade 3 children with RDs and their chronological-age controls (15 in each Grade). The Grade 3 typical readers were used as reading-level (RL) controls for the Grade 6 RD group. Participants were required to listen to a question and then point to a picture among four competing illustrations demonstrating the spatial relationship raised in the question. Two eye movements (fixations and saccades) were recorded using the EyeLink 1000 Plus eye-tracking system. The results showed that the Grade 3 RD group produced more and longer fixations than their CA controls, indicating that the pattern of eye movements of young children with RD is typically deficient compared to that of their typically developing counterparts when processing verbal and spatial stimuli simultaneously. However, no differences were observed between the Grade 6 groups in eye movement measures. Notably, the Grade 6 RD group outperformed the RL-matched Grade 3 group, yielding significantly fewer and shorter fixations. The discussion centers on the role of the eye-tracking method as a reliable means of deciphering the simultaneous cognitive processing involved in learning.

What are Romantic Relationships Good for? An Explorative Analysis of the Perceived Benefits of Being in a Relationship.

Forming long-term intimate relationships is a human universal, with most people across different times and cultures doing so. Such relationships should be associated with important benefits otherwise individuals would not engage in them, with the current research aiming to identify what people consider as beneficial in a long-term intimate relationship. More specifically, Study 1 employed qualitative research methods in a sample of 221 Greek-speaking participants, and identified 82 perceived benefits. Study 2 employed quantitative research methods in a sample of 545 Greek-speaking participants, and classified these benefits into 10 broad factors and two broader domains. Experiencing positive emotions, including love and passion, as well as having someone to provide support and do things together, were considered among the most important benefits. Although there were a few significant differences, the evaluations of the perceived benefits of intimate relationships were generally consistent across participants of different sex, age, and relationship status.

Phonological ability and neural congruency: Phonological loop or more?

OBJECTIVE: We explored neural components in Electroencephalography (EEG) signals during a phonological processing task to assess (a) the neural origins of Baddeley's working-memory components contributing to phonological processing, (b) the unitary structure of phonological processing and (c) the neural differences between children with dyslexia (DYS) and controls (CAC). METHODS: EEG data were collected from sixty children (half with dyslexia) while performing the initial- and final- phoneme elision task. We explored a novel machine-learning-based approach to identify the neural components in EEG elicited in response to the two conditions and capture differences between DYS and CAC. RESULTS: Our method identifies two sets of phoneme-related neural congruency components capturing neural activations distinguishing DYS and CAC across conditions. CONCLUSIONS: Neural congruency components capture the underlying neural mechanisms that drive the relationship between phonological deficits and dyslexia and provide insights into the phonological loop and visual-sketchpad dimensions in Baddeley's model at the neural level. They also confirm the unitary structure of phonological awareness with EEG data. SIGNIFICANCE: Our findings provide novel insights into the neural origins of the phonological processing differences in children with dyslexia, the unitary structure of phonological awareness, and further verify Baddeley's model as a theoretical framework for phonological processing and dyslexia.

RAN-related neural-congruency: a machine learning approach toward the study of the neural underpinnings of naming speed.

Objective: Naming speed, behaviorally measured via the serial Rapid automatized naming (RAN) test, is one of the most examined underlying cognitive factors of reading development and reading difficulties (RD). However, the unconstrained-reading format of serial RAN has made it challenging for traditional EEG analysis methods to extract neural components for studying the neural underpinnings of naming speed. The present study aims to explore a novel approach to isolate neural components during the serial RAN task that are (a) informative of group differences between children with dyslexia (DYS) and chronological age controls (CAC), (b) improve the power of analysis, and (c) are suitable for deciphering the neural underpinnings of naming speed. Methods: We propose a novel machine-learning-based algorithm that extracts spatiotemporal neural components during serial RAN, termed RAN-related neural-congruency components. We demonstrate our approach on EEG and eye-tracking recordings from 60 children (30 DYS and 30 CAC), under phonologically or visually similar, and dissimilar control tasks. Results: Results reveal significant differences in the RAN-related neural-congruency components between DYS and CAC groups in all four conditions. Conclusion: Rapid automatized naming-related neural-congruency components capture the neural activity of cognitive processes associated with naming speed and are informative of group differences between children with dyslexia and typically developing children. Significance: We propose the resulting RAN-related neural-components as a methodological framework to facilitate studying the neural underpinnings of naming speed and their association with reading performance and related difficulties.

Fixation-related potentials in naming speed: A combined EEG and eye-tracking study on children with dyslexia.

OBJECTIVE: We combined electroencephalography (EEG) and eye-tracking recordings to examine the underlying factors elicited during the serial Rapid-Automatized Naming (RAN) task that may differentiate between children with dyslexia (DYS) and chronological age controls (CAC). METHODS: Thirty children with DYS and 30 CAC (Mage = 9.79 years; age range 7.6 through 12.1 years) performed a set of serial RAN tasks. We extracted fixation-related potentials (FRPs) under phonologically similar (rime-confound) or visually similar (resembling lowercase letters) and dissimilar (non-confounding and discrete uppercase letters, respectively) control tasks. RESULTS: Results revealed significant differences in FRP amplitudes between DYS and CAC groups under the phonologically similar and phonologically non-confounding conditions. No differences were observed in the case of the visual conditions. Moreover, regression analysis showed that the average amplitude of the extracted components significantly predicted RAN performance. CONCLUSION: FRPs capture neural components during the serial RAN task informative of differences between DYS and CAC and establish a relationship between neurocognitive processes during serial RAN and dyslexia. SIGNIFICANCE: We suggest our approach as a methodological model for the concurrent analysis of neurophysiological and eye-gaze data to decipher the role of RAN in reading.

Type 2 diabetes and its correlates in a first nationwide study among Cypriot adults.

Obesity rates in Cyprus are very high and epidemiological information on type 2 diabetes mellitus is limited. The correlates of type 2 diabetes among adults remain unknown in the Cypriot population. Thus, the purpose of this study is to provide the first national estimate of the prevalence of type 2 diabetes and investigate its correlates. A randomly stratified nationally sample of 1001 adults aged 18-80 participated in the study. Only 950 subjects completed the study. All subjects were free of any diseases (known diabetes, kidney, liver), medication and supplementation. The overall prevalence of diabetes and pre-diabetes based on WHO criteria was 9.2% and 16.3%, respectively. After adjusting for age, energy intake, smoking and physical activity participants with obesity (BMI) (OR=2.00, P<0.001), waist circumference (WC) (OR=2.08, P<0.001), hypertension (HT) (OR=1.99, P<0.001) and hypercholesterolemia (HC) (OR=2.07, P<0.007) were most likely to develop T2DM compared with the normal ones. The odds of having diabetes were also found significant between subjects with high levels of triglycerides (TG) (OR=1.49, P<0.007), compared with the normal ones and between subjects with low levels of HDL (OR=1.44, P<0.008) compared with the ones with high levels of HDL. The prevalence of type 2 diabetes in Cyprus is relatively medium-high. However, the pre-diabetes rates are very high showing a promising increase toward total rates of type 2 diabetes. Obesity, HT, WC, TG, HC and low HDL are all strong correlates of type 2 diabetes. Healthy education programs should be initiated for young and older-aged people and those with described abnormal risk factors.

Your Brain on the Movies: A Computational Approach for Predicting Box-office Performance from Viewer's Brain Responses to Movie Trailers.

The ability to anticipate the population-wide response of a target audience to a new movie or TV series, before its release, is critical to the film industry. Equally important is the ability to understand the underlying factors that drive or characterize viewer's decision to watch a movie. Traditional approaches (which involve pilot test-screenings, questionnaires, and focus groups) have reached a plateau in their ability to predict the population-wide responses to new movies. In this study, we develop a novel computational approach for extracting neurophysiological electroencephalography (EEG) and eye-gaze based metrics to predict the population-wide behavior of movie goers. We further, explore the connection of the derived metrics to the underlying cognitive processes that might drive moviegoers' decision to watch a movie. Towards that, we recorded neural activity-through the use of EEG-and eye-gaze activity from a group of naive individuals while watching movie trailers of pre-selected movies for which the population-wide preference is captured by the movie's market performance (i.e., box-office ticket sales in the US). Our findings show that the neural based metrics, derived using the proposed methodology, carry predictive information about the broader audience decisions to watch a movie, above and beyond traditional methods. In particular, neural metrics are shown to predict up to 72% of the variance of the films' performance at their premiere and up to 67% of the variance at following weekends; which corresponds to a 23-fold increase in prediction accuracy compared to current neurophysiological or traditional methods. We discuss our findings in the context of existing literature and hypothesize on the possible connection of the derived neurophysiological metrics to cognitive states of focused attention, the encoding of long-term memory, and the synchronization of different components of the brain's rewards network. Beyond the practical implication in predicting and understanding the behavior of moviegoers, the proposed approach can facilitate the use of video stimuli in neuroscience research; such as the study of individual differences in attention-deficit disorders, and the study of desensitization to media violence.

From the eyes and the heart: a novel eye-gaze metric that predicts video preferences of a large audience.

Eye-tracking has been extensively used to quantify audience preferences in the context of marketing and advertising research, primarily in methodologies involving static images or stimuli (i.e., advertising, shelf testing, and website usability). However, these methodologies do not generalize to narrative-based video stimuli where a specific storyline is meant to be communicated to the audience. In this paper, a novel metric based on eye-gaze dispersion (both within and across viewings) that quantifies the impact of narrative-based video stimuli to the preferences of large audiences is presented. The metric is validated in predicting the performance of video advertisements aired during the 2014 Super Bowl final. In particular, the metric is shown to explain 70% of the variance in likeability scores of the 2014 Super Bowl ads as measured by the USA TODAY Ad-Meter. In addition, by comparing the proposed metric with Heart Rate Variability (HRV) indices, we have associated the metric with biological processes relating to attention allocation. The underlying idea behind the proposed metric suggests a shift in perspective when it comes to evaluating narrative-based video stimuli. In particular, it suggests that audience preferences on video are modulated by the level of viewers lack of attention allocation. The proposed metric can be calculated on any narrative-based video stimuli (i.e., movie, narrative content, emotional content, etc.), and thus has the potential to facilitate the use of such stimuli in several contexts: prediction of audience preferences of movies, quantitative assessment of entertainment pieces, prediction of the impact of movie trailers, identification of group, and individual differences in the study of attention-deficit disorders, and the study of desensitization to media violence.

Single-trial linear correlation analysis: application to characterization of stimulus modality effects.

A key objective in systems and cognitive neuroscience is to establish associations between behavioral measures and concurrent neuronal activity. Single-trial analysis has been proposed as a novel method for characterizing such correlates by first extracting neural components that maximally discriminate trials on a categorical variable, (e.g., hard vs. easy, correct vs. incorrect etc.), and then correlate those components to a continues dependent variable of interest, e.g., reaction time, difficulty Index, etc. However, often times in experiment design it is difficult to either define meaningful categorical variables, or to record enough trials for the method to extract the discriminant components. Experiments designed for the study of the effects of stimulus presentation modality in working memory provide such a scenario, as will be exemplified. In this paper, we proposed a new approach to single-trial analysis in which we directly extract neural activity that maximally correlates to single-trial manual response times; eliminating the need to define an arbitrary categorical variable. We demonstrate our method on real electroencephalography (EEG) data recordings from the study of stimulus presentation modality effect (SPME).

A systems analysis tool for construction and demolition wastes management.

Managing construction and demolition (C&D) wastes has challenged many municipalities with diminishing waste disposal capacity. Facing such challenges, the Massachusetts Department of Environmental Protection proposed a policy restricting the landfill disposal of certain C&D waste materials, if unprocessed. This research is to study the potential economic impact of such restriction on construction contractors and C&D waste processors. A spreadsheet-based systems analysis model has been developed to assist the cost-benefit evaluation for various C&D waste management scenarios. The model, developed based on the mass balance principle, is designed to track a C&D waste stream through the various stages of a waste management system, i.e. generation, source separation, processing, recycling, and final disposal. This model, by incorporating the material flow data with the cost/revenue data associated with each management activity, can then provide an economic analysis for a proposed C&D waste management scenario. A case study illustrating the application of this model for Massachusetts is also presented.