Perceived risk vs actual driving performance during distracted driving: A comparative analysis of phone use and other secondary distractions

The present study attempts to explore the association of drivers’ risk perception towards phone usage as well as other everyday distractions (operating a music player and eating during driving), and their driving performance observed during these distracted conditions. For this purpose, driving simulator experiments were conducted with 90 participants to collect their driving performance data and a questionnaire was conducted to obtain their basic details along with their risk perceptions. Firstly, the driving performance was divided into clusters using hierarchical clustering and the clustered subgroups were compared for crash and non-crash cases to identify the groups having significant performance degradation. Based on this comparison, the driving performance subgroups were then divided into the following crash risk probabilities: High risk, Moderate risk and Low risk. Further, the associations of perceived risk with these performance subgroups and other potential factors were analyzed using association rules mining technique. Most of the drivers (72.06%) reported texting as an extremely risky task. But, surprisingly none of them considered conversation as an extremely risky task. However, in case of conversation, it was found that even though the professional drivers reported the task to be not at all risky, the observed crash risk was high for them (S = 5.21%, C = 67.86%), indicating an underestimation of the associated risk by the drivers. Similarly, the results revealed that for music player and eating tasks, drivers reported the distracting tasks to be less risky, but, in some instances, their driving performance was associated with higher chances of crash occurrence. Many interesting associations of risk perception and driving performance with respect to demographic and driving characteristics were also obtained. The findings can be useful while designing the awareness programs related to distracted driving with an aim to reduce such practices.     

Psychological predictors of texting while driving among university students

Although text messaging while driving is illegal in Spain previous research has shown that a substantial proportion of drivers, particularly young drivers, engage in this risky behaviour. The present study set out to investigate the psychological predictors of this behaviour using the Theory of Planned Behaviour (TPB). This study also measured the drivers’ perceptions regarding the effectiveness of the ban on mobile phone use while driving, their perceived crash risk, the risk of being fined and the drivers perceived ability to compensate for the distraction caused by reading or writing text messages while driving. Data were collected using an online questionnaire from 1082 university students who were drivers and owned a mobile phone. Attitude and perceived behavioural control significantly predicted the intention to send and read text messages while driving, even after controlling for exposure and demographic variables. Furthermore, intention was found to be a significant predictor of retrospective measures of both sending and reading text messages while driving, as was perceived behavioural control for several of the outcome measures. The present findings provide support for the TPB and also demonstrate the additional contributions that the mobile phone ban and perceived ability to compensate for the distraction had in predicting intentions. In addition, perceived crash risk was positively related to the prediction of intentions to send text messages and the number of messages read in the last week. The implications of these findings are discussed.     

EEG decoding of semantic category reveals distributed representations for single concepts

Achieving a clearer picture of categorial distinctions in the brain is essential for our understanding of the conceptual lexicon, but much more fine-grained investigations are required in order for this evidence to contribute to lexical research. Here we present a collection of advanced data-mining techniques that allows the category of individual concepts to be decoded from single trials of EEG data. Neural activity was recorded while participants silently named images of mammals and tools, and category could be detected in single trials with an accuracy well above chance, both when considering data from single participants, and when group-training across participants. By aggregating across all trials, single concepts could be correctly assigned to their category with an accuracy of 98%. The pattern of classifications made by the algorithm confirmed that the neural patterns identified are due to conceptual category, and not any of a series of processing-related confounds. The time intervals, frequency bands and scalp locations that proved most informative for prediction permit physiological interpretation: the widespread activation shortly after appearance of the stimulus (from 100 ms) is consistent both with accounts of multi-pass processing, and distributed representations of categories. These methods provide an alternative to fMRI for fine-grained, large-scale investigations of the conceptual lexicon.     

Generating keywords improves metacomprehension and self-regulation in elementary and middle school children

The ability to monitor understanding of texts, usually referred to as metacomprehension accuracy, is typically quite poor in adult learners; however, recently interventions have been developed to improve accuracy. In two experiments, we evaluated whether generating delayed keywords prior to judging comprehension improved metacomprehension accuracy for children. For sixth and seventh graders, metacomprehension accuracy was greater when generating keywords. By contrast, for fourth graders, metacomprehension accuracy did not differ across conditions. Improved metacomprehension accuracy led to improved regulation of study. The delayed keyword effect in children reported here is discussed in terms of situation model activation.     

Sequential sampling models of human text classification

Text classification involves deciding whether or not a document is about a given topic. It is an important problem in machine learning, because automated text classifiers have enormous potential for application in information retrieval systems. It is also an interesting problem for cognitive science, because it involves real world human decision making with complicated stimuli. This paper develops two models of human text document classification based on random walk and accumulator sequential sampling processes. The models are evaluated using data from an experiment where participants classify text documents presented one word at a time under task instructions that emphasize either speed or accuracy, and rate their confidence in their decisions. Fitting the random walk and accumulator models to these data shows that the accumulator provides a better account of the decisions made, and a “balance of evidence” measure provides the best account of confidence. Both models are also evaluated in the applied information retrieval context, by comparing their performance to established machine learning techniques on the standard Reuters‐21578 corpus. It is found that they are almost as accurate as the benchmarks, and make decisions much more quickly because they only need to examine a small proportion of the words in the document. In addition, the ability of the accumulator model to produce useful confidence measures is shown to have application in prioritizing the results of classification decisions.     

再论退溪易学

文章辨析了退溪与朱熹在对待《周易》“经传关系”问题上的差异,特别强调:退溪并不尊崇朱子 易为卜筮而作的观点,他坚定的实行经传合观,也与朱子在经传分合上犹疑不定的态度大相径庭。朱子 易为卜筮而作的观点,在当时或以后并未得到很多人的认同。     

试论程颐对今本《周易》古经分篇的义理阐释

通行本<周易>古经分为上下两篇.对于其何以如此分篇,生当北宋的易学家程颐,在承继、整合<易传·序卦>和<易纬·乾凿度>等的观点的基础上,作<上下篇义>,明确提出以阴阳为基准分篇的原则,指明"阳盛者居上篇,阴盛者居下篇",并逐卦作了分析,丰富了人们在此领域的识见.     

Twitter sentiment analysis with a deep neural network: An enhanced approach using user behavioral information

Sentiment analysis on social media such as Twitter has become a very important and challenging task. Due to the characteristics of such data—tweet length, spelling errors, abbreviations, and special characters—the sentiment analysis task in such an environment requires a non-traditional approach. Moreover, social media sentiment analysis is a fundamental problem with many interesting applications. Most current social media sentiment classification methods judge the sentiment polarity primarily according to textual content and neglect other information on these platforms. In this paper, we propose a neural network model that also incorporates user behavioral information within a given document (tweet). The neural network used in this paper is a Convolutional Neural Network (CNN). The system is evaluated on two datasets provided by the SemEval-2016 Workshop. The proposed model outperforms current baseline models (including Naive Bayes and Support Vector Machines), which shows that going beyond the content of a document (tweet) is beneficial in sentiment classification, because it provides the classifier with a deep understanding of the task.     

A novel machine learning approach for early detection of hepatocellular carcinoma patients

Liver cancer is quite common type of cancer among individuals worldwide. Hepatocellular carcinoma (HCC) is the malignancy of liver cancer. It has high impact on individual’s life and investigating it early can decline the number of annual deaths. This study proposes a new machine learning approach to detect HCC using 165 patients. Ten well-known machine learning algorithms are employed. In the preprocessing step, the normalization approach is used. The genetic algorithm coupled with stratified 5-fold cross-validation method is applied twice, first for parameter optimization and then for feature selection. In this work, support vector machine (SVM) (type C-SVC) with new 2level genetic optimizer (genetic training) and feature selection yielded the highest accuracy and F₁-Score of 0.8849 and 0.8762 respectively. Our proposed model can be used to test the performance with huge database and aid the clinicians.     

Evidence for dissociable neural mechanisms underlying inference generation in familiar and less-familiar scenarios

In this study, we investigated whether the left and right hemispheres are differentially involved in causal inference generation. Participants read short inference-promoting texts that described either familiar or less-familiar scenarios. After each text, they performed a lexical decision on a letter string (which sometimes constituted an inference-related word) presented directly to the left or right hemisphere. Response-time results indicated that hemisphere of direct presentation interacted with type of inference scenario. When test stimuli were presented directly to the left hemisphere, lexical decisions were facilitated following familiar but not following less-familiar inference scenarios, whereas when test stimuli were presented directly to the right hemisphere, facilitation was observed in both familiar and less-familiar conditions. Thus, inferences may be generated in different ways depending on which of two dissociable neural subsystems underlies the activation of background information.