How Reliably Do Eye Parameters Indicate Internal Versus External Attentional Focus?

Eye behavior is increasingly used as an indicator of internal versus external focus of attention both in research and application. However, available findings are partly inconsistent, which might be attributed to the different nature of the employed types of internal and external cognition tasks. The present study, therefore, investigated how consistently different eye parameters respond to internal versus external attentional focus across three task modalities: numerical, verbal, and visuo-spatial. Three eye parameters robustly differentiated between internal and external attentional focus across all tasks. Blinks, pupil diameter variance, and fixation disparity variance were consistently increased during internally directed attention. We also observed substantial attentional focus effects on other parameters (pupil diameter, fixation disparity, saccades, and microsaccades), but they were moderated by task type. Single-trial analysis of our data using machine learning techniques further confirmed our results: Classifying the focus of attention by means of eye tracking works well across participants, but generalizing across tasks proves to be challenging. Based on the effects of task type on eye parameters, we discuss what eye parameters are best suited as indicators of internal versus external attentional focus in different settings.     

An effective daily box office prediction model based on deep neural networks

The task of the daily box office prediction model is to build a dynamic prediction model to rolling forecast daily box office. It is a complex task as the movie box office has a short life cycle, and the static data and dynamic data that affect the trend of box office are heterogeneous. This paper proposes an end-to-end deep learning model for daily box office prediction, called Deep-DBP which consists of temporal component and static characteristics component. The temporal component is the main component which uses LSTM to learn the temporal dependencies between data points. The static characteristics component is an auxiliary component and it integrates static characteristics to improve prediction effect. The Deep-DBP can overcome the problems that the ARIMA and traditional ANN model cannot solve. The structure of input and output proposed in the model can well handle short time series prediction problem. It is a successful case in dealing with multi-source and multi-view data, addition of static characteristics component reduces the prediction error by 7%. The prediction error of Deep-DBP is 30.1%, which is better than that of the previous model. The experiment proved that the more training data collected, the better the prediction effect.