Timing Attention: from Reaction Time to Models of Visual Attention

Models of visual attention have been widely proposed over the last two decades. Researchers in different disciplines, such as psychology and engineering, are interested in these models in order to understand human perceptual mechanisms and/or build algorithms which mimic the attentional processes for some applications (e.g. robotics). In this dissertation I modeled the effect of learning experiences on attentional guidance. The presented model is an algorithmic-level model which links display inputs to the participants' reaction times. This dissertation consists of three studies. In the first study the role of selection history -as the effect of learning from the practice phase of the experiment on the main phase- is investigated. I also tested dimension-level (e.g. color and shape) and feature-level (e.g. blue and red) selection histories. The results showed the version of the model which includes selection history (on feature-level), beside stimulus-driven (bottom-up) and goal-driven (top-down) control mechanisms, is best suited for a quantitative description of the participants' reaction times. In the second study, I investigated the importance of intertrial priming -the effect of a previous trial on the current one- as well as the importance of each feature map (color, shape or orientation) in the model predictions. It was shown that by including the effect of intertrial priming a better description of the behavioral database can be achieved. Additionally, excluding any of the feature maps deteriorates the model predictions. In the third study, I proposed a model to decompose reaction times -into decision and sensorimotor components- as a prerequisite of RT modeling. This study will help us introduce more accurate attention models. Furthermore, it can support cognitive studies to better investigate the effect of certain factors (e.g. age and mental disorders) on motor system vs. decision making. The proposed attention model (in the first and the second study) is one of the first models that includes the selection history effect on guiding attention. This model can capture the between-group differences where each group of participants had a different learning experience. The model considers total reaction times of each participant. But attention can influence reaction times by affecting different cognitive processes. The third study introduces a method which helps us look at each process (and its relevant reaction time component) independently

Sensorimotor processes are not a source of much noise: Sensory-motor and decision components of reaction times

Statistical descriptions of reaction times are central components of quantitative attention models. It is often assumed that total reaction time is comprised of various components, e.g. sensory delays, decision making and motor execution contributions. We use machine learning to decompose observed total reaction times into sensorimotor and decision components, and evaluate which model assumptions maximize approximate Bayesian model evidence (free energy or evidence lower bound). We find that an inverse Gaussian decision time distribution combined with a very narrow Gaussian sensorimotor distribution can best explain human reaction time data. We also model outliers explicitly by a uniform background distribution. We find that the model assigns a small fraction of datapoints to this outlier distribution

Distracted by Previous Experience: Integrating Selection History, Current Task Demands and Saliency in an Algorithmic Model

Attention can be biased by previous learning and experience. We present an algorithmic-level model of this selection history bias in visual attention that predicts quantitatively how stimulus-driven processes, goal-driven control and selection history compete to control attention. In the model, the output of saliency maps as stimulus-driven guidance interacts with a history map that encodes learning effects and a goal-driven task control to prioritize visual features. The model works on coded features rather than image pixels which is common in many traditional saliency models. We test the model on a reaction time (RT) data from a psychophysical experiment. The model accurately predicts parameters of reaction time distributions from an integrated priority map that is comprised of an optimal, weighted combination of separate maps. Analysis of the weights confirms selection history effects on attention guidance. The model is able to capture individual differences between participants’ RTs and response probabilities per group. Moreover, we demonstrate that a model with a reduced set of maps performs worse, indicating that integrating history, saliency and task information are required for a quantitative description of human attention. Besides, we show that adding intertrial effect to the model (as another lingering bias) improves the model’s predictive performance

A model of selection history in visual attention

Attention can be biased by the previous learning and experience. We present an algorithmic-level model of this bias in visual attention that predicts quantitatively how bottom-up, top-down and selection history compete to control attention. In the model, the output of saliency maps as bottom-up guidance interacts with a history map that encodes learning effects and a top-down task control to prioritize visual features. We test the model on a reaction-time (RT) data set from the experiment presented in (Feldmann-Wustefeld, Uengoer, & Schubö, 2015). The model accurately predicts parameters of reaction time distributions from an integrated priority map that is comprised of an optimal, weighted combination of separate maps. Analysis of the weights confirms learning history effects on attention guidance

Incidentally diagnosed multiple intradural extramedullary spinal hydatidosis in a young adult: A case report and review of the literature

Key Clinical Message Although quite rare, vertebral hydatidosis should always be considered as a differential diagnosis for spinal presentations, particularly in endemic areas for echinococcosis. Abstract In this paper, we report a rare case of asymptomatic multiple intradural, extramedullary spinal hydatidosis, incidentally diagnosed in a patient with signs and symptoms of a true protruded disc. Although quite rare, vertebral hydatidosis should always be considered as a differential diagnosis for spinal presentations, particularly in endemic areas for echinococcosis