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Determining priority in visual selection

Everyday visual scenes are complex and dynamic, containing more objects than the human visual system is able to process. This project examines how we prioritize objects in a scene and how attentional control mechanisms are coordinated to guide our attention to the information we need in a particular action context.

We use behavioral measures like response times and accuracy, and record neural correlates of attentional selection with electroencephalography (EEG). 

In this area, we are currently conducting research in these topics:

Selection history and attentional selection

Principal investigators: Dion Henare, Hossein Abbasi, Anna Schubö

Recent studies have shown that an observer’s previous experience and selection history play a crucial role in visuals selection. Stimuli that were associated with reward or other attributes in the past (e.g., have proven to be valuable or informative) bias the deployment of attention towards them, even when they are no longer relevant or valuable. Even with maximizing top-down control via a fully predictably task sequence it is not possible to override a bias induced by prior knowledge (Kadel, Feldmann-Wüstefeld & Schubö, 2017).

One objective of this project is to determine to what extent and for how long attention can be biased towards stimuli. To this end, we induce learning in the lab and manipulate the value of several stimuli in learning. We then measure how attention is deployed to these stimuli, and how well they can be suppressed when no longer relevant. We use behavioral performance measures, EEG parameters, and ERP components associated with selective attention (N2pc, NT, PD and ND) and also alpha oscillation to investigate how selection history modulates attentional control.

Figure 1: Here, we use an additional singleton paradigm with a color singleton distractor (red circle) to examine to attention capture in participants with different learning experiences.

Related literature:

Feldmann-Wüstefeld, T., Uengoer, M. & Schubö, A. (2015). You see what you have learned. Evidence for an interrelation of associative learning and visual selective attention. Psychophysiology, 52, 1483–1497.

Kadel, H., Feldmann-Wüstefeld, T., & Schubö, A. (2017). Selection history alters attentional filter settings persistently and beyond top-down control. Psychophysiology, 54, 736-754.

Explicit goals and attentional selection

Principal investigators: Sizhu Han, Anna Schubö

Studies have shown that prior knowledge of target information can bias attention, making target selection more efficient. Recent research presents mixed results on whether knowing distractor information enhances search performance. This project aims to resolve these contradictions by investigating how explicit goals affect 1) the preparation for the upcoming search and 2) attentional deployment after the search begins. 

Figure 2: An example of the cued search paradigm. The task is to find the target and report its gap location (up or down). Prior to the search, a cue appears indicating the color of the target or the distractor.

To achieve this, we will present target or distractor information prior to search (see Fig. 2) to investigate the mechanisms underlying the cue-related effects. The insights will enhance our understanding of attentional control mechanisms and have broader implications for improving cognitive performance in everyday tasks.

Related literature:

Han, S., & Schubö, A. (2024). Cue-guided search facilitates attentional selection: Evidence from an EEG study. Journal of Vision, 24(10), 826.

Han, S., & Schubö, A. (2023). How robust are negative attentional templates? Journal of Vision, 23(9), 5350.

Heuer, A., & Schubö, A. (2020). Cueing distraction: Electrophysiological evidence for anticipatory active suppression of distractor location. Psychological research, 84(8), 2111–2121.

Categorization learning and selective attention

Principal investigators: Yunyun Mu, Geun Hyun Kim, Anna Schubö

Categorization learning helps to structure everyday life because it allows organisms to identify and understand novel objects and situations based on prior knowledge and experience. This enables us to respond quickly and efficiently and to preserve our limited cognitive resources for other tasks.

This project examines whether selective attention can be tuned to object semantics in a similar way as it can be tuned to visual dimensions. Prior work has shown that in visual categorization, pre-selection task settings can transfer to an unrelated task and bias selective attention systematically towards formerly relevant stimuli. Here, we examine whether semantic categorization can yield a similar attention bias.

Related literature:

Feldmann-Wüstefeld, T., Uengoer, M. & Schubö, A. (2015). You see what you have learned. Evidence for an interrelation of associative learning and visual selective attention. Psychophysiology, 52, 1483–1497.

Peelen, M.V., & Kastner, S. (2014). Attention in the real world: toward understanding its neural basis. Trends in Cognitive Sciences, 18, 242-250.

Interaction between informational and motivational value

Principal investigators: Dion Henare, Anna Schubö

There are many different ways that selection history can influence whether an object grabs our attention. Some objects attract our attention because of their motivational value, that is, because we have learned that they give us rewards like food or money. Other objects attract our attention because of their informational value; we have learned that they tell us something reliable about what will happen next.

The purpose of this project is to understand how motivational and informational value interact with each other, and how this affects attention. We use displays where participants have to search for a colored object and learn which button to push in response. When people respond correctly, they have a chance to win some money, however, the probability that they will win and the amount that they win can vary across the task. We record EEG while people perform this task, allowing us to measure both their behavioral responses as well as lateralized neural components that index attention processing (N2pc, Nt, Pd). These measures provide detailed insight into the performance of cognitive functions like attention, and help us understand how they are impacted by factors like motivational and informational value.

Related literature:

Feldmann-Wüstefeld, T., Uengoer, M. & Schubö, A. (2015). You see what you have learned. Evidence for an interrelation of associative learning and visual selective attention. Psychophysiology, 52, 1483–1497.

Koenig, S., Kadel, H., Uengoer, M., Schubö, A., & Lachnit, H. (2017). Reward draws the eye, uncertainty holds the eye: Associative learning modulates distractor interference in visual search. Frontiers in behavioral neuroscience, 11, 128.