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 fo...
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|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.