Investigations for Ergonomic Presentation of AIS Symbols for ECDIS

Empirical investigations were carried out in a research project for the German Federal Ministry of Transport, Building, and Housing to evaluate the presentation of AIS target information on ECDIS. The investigations were performed at three international simulation centres. The features, colour and fillingjsize of AIS symbols, as well as the influence of the ECDIS display category on the detection of AIS targets were the main issues of the investigations. Results show that blue (5-52 colour token RE5BL) is the most suitable colour of the tested colours for the presentation of AIS targets under all ambient light conditions on the tested IHO S-52 colour tables

Investigations for Ergonomic Presentation of AIS Symbols on ECDIS

Empirical investigations were carried out in a research project for the German Federal Ministry of Transport, Building, and Housing to evaluate the presentation of AIS target information on ECDIS. The investigations were performed at three international simulation centres. The features, colour and fillingjsize of AIS symbols, as well as the influence of the ECDIS display category on the detection of AIS targets were the main issues of the investigations. Results show that blue (5-52 colour token RE5BL) is the most suitable colour of the tested colours for the presentation of AIS targets under all ambient light conditions on the tested IHO S-52 colour tables.Las investigaciones empfricas fueron llevadas a cabo en un proyecto de investigacion para el Ministerio Federal Aleman de Transporte, Construccion y Vivienda para evaluar la presentacion de la informacion de objetos del AIS en el ECDIS. Se emprendieron investigaciones en tres centros de simulacion intemacionales. Las caracterfsticas, color y tamaino de los simbolos del AIS, asi como la influencia de la categoria de la presentacion ECDIS en la deteccion de objetos del AIS fueron los temas principales de las investigaciones. Los resultados muestran que el azul (muestras del color RESBL de la S-52) es el color mas apropiado de entre los colores puestos a prueba, para la presentacion de los objetos del AIS bajo todas las condiciones de luz ambiente en las tablas de colores puestos a prueba de la S-52 de la OHI.Des investigations empiriques ant ete effectuees dans le cadre d'un projet de recherche du Ministere federal allemand des Transports, de la Construction et du logement, en vue d'evaluer la presentation des informations cibles des systemes AIS sur les ECDIS. Ces investigations ont ete effectuees dans trois centres de simulation intemationaux. La couleur des elements et la teinte /la taille des signes conventionnels des AIS, ainsi que l'influence de la categorie d'affichage de l'ECDIS sur la detection des cibles AIS ant constitue les principaux themes des investigations. Les resultats ant montre que le bleu (code de couleur RE5BL dans la S52) est la couleur la plus appropriee parmi celles testees pour la presentation des cibles AIS sous toutes les conditions ambiantes de lumiliere dans le cadre des tables de couleur testees de la S52 de l'OHI

Relevance Prediction from Eye-movements Using Semi-interpretable Convolutional Neural Networks

We propose an image-classification method to predict the perceived-relevance of text documents from eye-movements. An eye-tracking study was conducted where participants read short news articles, and rated them as relevant or irrelevant for answering a trigger question. We encode participants' eye-movement scanpaths as images, and then train a convolutional neural network classifier using these scanpath images. The trained classifier is used to predict participants' perceived-relevance of news articles from the corresponding scanpath images. This method is content-independent, as the classifier does not require knowledge of the screen-content, or the user's information-task. Even with little data, the image classifier can predict perceived-relevance with up to 80% accuracy. When compared to similar eye-tracking studies from the literature, this scanpath image classification method outperforms previously reported metrics by appreciable margins. We also attempt to interpret how the image classifier differentiates between scanpaths on relevant and irrelevant documents