Brief Report: Patterns of Eye Movements in Face to Face Conversation are Associated with Autistic Traits: Evidence from a Student Sample

The current study investigated whether the amount of autistic traits shown by an individual is associated with viewing behaviour during a face-to-face interaction. The eye movements of 36 neurotypical university students were recorded using a mobile eye-tracking device. High amounts of autistic traits were neither associated with reduced looking to the social partner overall, nor with reduced looking to the face. However, individuals who were high in autistic traits exhibited reduced visual exploration during the face-to-face interaction overall, as demonstrated by shorter and less frequent saccades. Visual exploration was not related to social anxiety. This study suggests that there are systematic individual differences in visual exploration during social interactions and these are related to amount of autistic traits

Tutorial : applying machine learning in behavioral research

Machine-learning algorithms hold promise for revolutionizing how educators and clinicians make decisions. However, researchers in behavior analysis have been slow to adopt this methodology to further develop their understanding of human behavior and improve the application of the science to problems of applied significance. One potential explanation for the scarcity of research is that machine learning is not typically taught as part of training programs in behavior analysis. This tutorial aims to address this barrier by promoting increased research using machine learning in behavior analysis. We present how to apply the random forest, support vector machine, stochastic gradient descent, and k-nearest neighbors algorithms on a small dataset to better identify parents of children with autism who would benefit from a behavior analytic interactive web training. These step-by-step applications should allow researchers to implement machine-learning algorithms with novel research questions and datasets

Machine learning for genetic prediction of psychiatric disorders: a systematic review

Machine learning methods have been employed to make predictions in psychiatry from genotypes, with the potential to bring improved prediction of outcomes in psychiatric genetics; however, their current performance is unclear. We aim to systematically review machine learning methods for predicting psychiatric disorders from genetics alone and evaluate their discrimination, bias and implementation. Medline, PsycInfo, Web of Science and Scopus were searched for terms relating to genetics, psychiatric disorders and machine learning, including neural networks, random forests, support vector machines and boosting, on 10 September 2019. Following PRISMA guidelines, articles were screened for inclusion independently by two authors, extracted, and assessed for risk of bias. Overall, 63 full texts were assessed from a pool of 652 abstracts. Data were extracted for 77 models of schizophrenia, bipolar, autism or anorexia across 13 studies. Performance of machine learning methods was highly varied (0.48–0.95 AUC) and differed between schizophrenia (0.54–0.95 AUC), bipolar (0.48–0.65 AUC), autism (0.52–0.81 AUC) and anorexia (0.62–0.69 AUC). This is likely due to the high risk of bias identified in the study designs and analysis for reported results. Choices for predictor selection, hyperparameter search and validation methodology, and viewing of the test set during training were common causes of high risk of bias in analysis. Key steps in model development and validation were frequently not performed or unreported. Comparison of discrimination across studies was constrained by heterogeneity of predictors, outcome and measurement, in addition to sample overlap within and across studies. Given widespread high risk of bias and the small number of studies identified, it is important to ensure established analysis methods are adopted. We emphasise best practices in methodology and reporting for improving future studies

Kinematic and looking behaviour features of a movement imitation task

All features are continuous, raw (non-normalised). Missing values and outliers are replaced with class means. # of classes: 2 (1 – autistic / 0 – non-autistic) # of data samples: 44 (22-autistic / 22-non autistic) # of features: Kinematic dataset: 120 per instruction block, Looking behaviour dataset: 48 per instruction block. Feature descriptions are given in a title row in the files and fuller descriptions are given in the paper and it’s Supplementary Methods

Kinmatic and looking behaviour features of a movement imitation task

All features are continuous, raw (non-normalised). Missing values and outliers are replaced with class means. # of classes: 2 (1 – autistic / 0 – non-autistic) # of data samples: 44 (22-autistic / 22-non autistic) # of features: Kinematic dataset: 120 per instruction block, Looking behaviour dataset: 48 per instruction block. Feature descriptions are given in a title row in the files and fuller descriptions are given in the paper and it’s Supplementary Methods

Kinmatic and looking behaviour features of a movement imitation task

All features are continuous, raw (non-normalised). Missing values and outliers are replaced with class means. # of classes: 2 (1 – autistic / 0 – non-autistic) # of data samples: 44 (22-autistic / 22-non autistic) # of features: Kinematic dataset: 120 per instruction block, Looking behaviour dataset: 48 per instruction block. Feature descriptions are given in a title row in the files and fuller descriptions are given in the paper and it’s Supplementary Methods

Kinmatic and looking behaviour features of a movement imitation task

All features are continuous, raw (non-normalised). Missing values and outliers are replaced with class means. # of classes: 2 (1 – autistic / 0 – non-autistic) # of data samples: 44 (22-autistic / 22-non autistic) # of features: Kinematic dataset: 120 per instruction block, Looking behaviour dataset: 48 per instruction block. Feature descriptions are given in a title row in the files and fuller descriptions are given in the paper and it’s Supplementary Methods

Kinematic and looking behaviour features of a movement imitation task

All features are continuous, raw (non-normalised). Missing values and outliers are replaced with class means. # of classes: 2 (1 – autistic / 0 – non-autistic) # of data samples: 44 (22-autistic / 22-non autistic) # of features: Kinematic dataset: 120 per instruction block, Looking behaviour dataset: 48 per instruction block. Feature descriptions are given in a title row in the files and fuller descriptions are given in the paper and it’s Supplementary Methods