How strongly do word reading times and lexical decision times correlate? Combining data from eye movement corpora and megastudies

We assess the amount of shared variance between three measures of visual word recognition latencies: eye movement latencies, lexical decision times and naming times. After partialling out the effects of word frequency and word length, two well-documented predictors of word recognition latencies, we see that 7-44% of the variance is uniquely shared between lexical decision times and naming times, depending on the frequency range of the words used. A similar analysis of eye movement latencies shows that the percentage of variance they uniquely share either with lexical decision times or with naming times is much lower. It is 5 – 17% for gaze durations and lexical decision times in studies with target words presented in neutral sentences, but drops to .2% for corpus studies in which eye movements to all words are analysed. Correlations between gaze durations and naming latencies are lower still. These findings suggest that processing times in isolated word processing and continuous text reading are affected by specific task demands and presentation format, and that lexical decision times and naming times are not very informative in predicting eye movement latencies in text reading once the effect of word frequency and word length are taken into account. The difference between controlled experiments and natural reading suggests that reading strategies and stimulus materials may determine the degree to which the immediacy-of-processing assumption and the eye-mind assumption apply. Fixation times are more likely to exclusively reflect the lexical processing of the currently fixated word in controlled studies with unpredictable target words rather than in natural reading of sentences or texts

A Chatbot Framework for Yioop

Over the past few years, messaging applications have become more popular than Social networking sites. Instead of using a specific application or website to access some service, chatbots are created on messaging platforms to allow users to interact with companies’ products and also give assistance as needed. In this project, we designed and implemented a chatbot Framework for Yioop. The goal of the Chatbot Framework for Yioop project is to provide a platform for developers in Yioop to build and deploy chatbot applications. A chatbot is a web service that can converse with users using artificial intelligence in messaging platforms. Chatbots feel more like a human and it changes the interaction between people and computers. The Chatbot Framework enables developers to create chatbots and allows users to connect with them in the user chosen Yioop discussion channel. A developer can incorporate language skills within a chatbot by creating a knowledge base so that the chatbot understands user messages and reacts to them like a human. A knowledge base is created by using a language understanding web interface in Yioop

Using Machine Learning and Natural Language Processing to Review and Classify the Medical Literature on Cancer Susceptibility Genes

PURPOSE: The medical literature relevant to germline genetics is growing exponentially. Clinicians need tools monitoring and prioritizing the literature to understand the clinical implications of the pathogenic genetic variants. We developed and evaluated two machine learning models to classify abstracts as relevant to the penetrance (risk of cancer for germline mutation carriers) or prevalence of germline genetic mutations. METHODS: We conducted literature searches in PubMed and retrieved paper titles and abstracts to create an annotated dataset for training and evaluating the two machine learning classification models. Our first model is a support vector machine (SVM) which learns a linear decision rule based on the bag-of-ngrams representation of each title and abstract. Our second model is a convolutional neural network (CNN) which learns a complex nonlinear decision rule based on the raw title and abstract. We evaluated the performance of the two models on the classification of papers as relevant to penetrance or prevalence. RESULTS: For penetrance classification, we annotated 3740 paper titles and abstracts and used 60% for training the model, 20% for tuning the model, and 20% for evaluating the model. The SVM model achieves 89.53% accuracy (percentage of papers that were correctly classified) while the CNN model achieves 88.95 % accuracy. For prevalence classification, we annotated 3753 paper titles and abstracts. The SVM model achieves 89.14% accuracy while the CNN model achieves 89.13 % accuracy. CONCLUSION: Our models achieve high accuracy in classifying abstracts as relevant to penetrance or prevalence. By facilitating literature review, this tool could help clinicians and researchers keep abreast of the burgeoning knowledge of gene-cancer associations and keep the knowledge bases for clinical decision support tools up to date

Statistical Inferences for Polarity Identification in Natural Language

Information forms the basis for all human behavior, including the ubiquitous decision-making that people constantly perform in their every day lives. It is thus the mission of researchers to understand how humans process information to reach decisions. In order to facilitate this task, this work proposes a novel method of studying the reception of granular expressions in natural language. The approach utilizes LASSO regularization as a statistical tool to extract decisive words from textual content and draw statistical inferences based on the correspondence between the occurrences of words and an exogenous response variable. Accordingly, the method immediately suggests significant implications for social sciences and Information Systems research: everyone can now identify text segments and word choices that are statistically relevant to authors or readers and, based on this knowledge, test hypotheses from behavioral research. We demonstrate the contribution of our method by examining how authors communicate subjective information through narrative materials. This allows us to answer the question of which words to choose when communicating negative information. On the other hand, we show that investors trade not only upon facts in financial disclosures but are distracted by filler words and non-informative language. Practitioners - for example those in the fields of investor communications or marketing - can exploit our insights to enhance their writings based on the true perception of word choice

Spectators’ aesthetic experiences of sound and movement in dance performance

In this paper we present a study of spectators’ aesthetic experiences of sound and movement in live dance performance. A multidisciplinary team comprising a choreographer, neuroscientists and qualitative researchers investigated the effects of different sound scores on dance spectators. What would be the impact of auditory stimulation on kinesthetic experience and/or aesthetic appreciation of the dance? What would be the effect of removing music altogether, so that spectators watched dance while hearing only the performers’ breathing and footfalls? We investigated audience experience through qualitative research, using post-performance focus groups, while a separately conducted functional brain imaging (fMRI) study measured the synchrony in brain activity across spectators when they watched dance with sound or breathing only. When audiences watched dance accompanied by music the fMRI data revealed evidence of greater intersubject synchronisation in a brain region consistent with complex auditory processing. The audience research found that some spectators derived pleasure from finding convergences between two complex stimuli (dance and music). The removal of music and the resulting audibility of the performers’ breathing had a significant impact on spectators’ aesthetic experience. The fMRI analysis showed increased synchronisation among observers, suggesting greater influence of the body when interpreting the dance stimuli. The audience research found evidence of similar corporeally focused experience. The paper discusses possible connections between the findings of our different approaches, and considers the implications of this study for interdisciplinary research collaborations between arts and sciences

Comparing the E-Z Reader Model to Other Models of Eye Movement Control in Reading

The E-Z Reader model provides a theoretical framework for understanding how word identification, visual processing, attention, and oculomotor control jointly determine when and where the eyes move during reading. Thus, in contrast to other reading models reviewed in this article, E-Z Reader can simultaneously account for many of the known effects of linguistic, visual, and oculomotor factors on eye movement control during reading. Furthermore, the core principles of the model have been generalized to other task domains (e.g., equation solving, visual search), and are broadly consistent with what is known about the architecture of the neural systems that support reading