No abstract

Decision making is an essential part of daily life, in which balancing reasons and calculating risks to reach a certain confidence are important to make reasonable choices. To investigate the EEG correlates of confidence during decision making a study involving a forced choice recognition memory task was implemented. Subjects were asked to distinguish old from new pictures and rate their decision with either high or low confidence. Event-related potential (ERP) analysis was performed in four different phases covering all stages of decision making, including the information encoding, retrieval, decision formation, and feedback processing during the recognition task. Additionally, a single trial support-vector machine (SVM) classification was performed on the ERPs of each phase to get a measure of differentiability of the two levels of confidence on a single subject level. It could be shown that the level of decision confidence is significantly reflected in all stages of decision making but most prominently during feedback presentation. The main differences between high and low confidence can be found in the ERPs during feedback presentation after a correct answer, whereas almost no differences can be found in ERPs from feedback to wrong answers. In the feedback phase the two levels of confidence can be separated with a classification accuracy of up to 70 % on average over all subjects, therefore showing potential as a control state in a brain-computer Interface (BCI) application.

No abstract

Abstract. Objective: According to current theoretical models of working memory (WM), executive functions (EFs) like updating, inhibition and shifting play an important role in WM functioning. The models state that EFs highly correlate with each other but also have some individual variance which makes them separable processes. Since this theory has mostly been substantiated with behavioral data like reaction time and the ability to execute a task correctly, the aim of this paper is to find evidence for diversity (unique properties) of the EFs updating and inhibition in neural correlates of EEG data by means of using brain-computer interface (BCI) methods as a research tool. To highlight the benefit of this approach we compare this new methodology to classical analysis approaches. Methods: An existing study has been reinvestigated by applying neurophysiological analysis in combination with support vector machine (SVM) classification on recorded electroenzephalography (EEG) data to determine the separability and variety of the two EFs updating and inhibition on a single trial basis. Results: The SVM weights reveal a set of distinct features as well as a set of shared features for the two EFs updating and inhibition in the theta and the alpha band power. Significance: In this paper we find evidence that correlates for unity and diversity of EFs can be found in neurophysiological data. Machine learning approaches reveal shared but also distinct properties for the EFs. This study shows that using methods from brain-computer interface (BCI) research, like machine learning, as a tool for the validation of psychological models and theoretical constructs is a new approach that is highly versatile and could lead to many new insights.

Objective: According to current theoretical models of working memory (WM), executive functions (EFs) like updating, inhibition and shifting play an important role in WM functioning. The models state that EFs highly correlate with each other but also have some individual variance which makes them separable processes. Since this theory has mostly been substantiated with behavioral data like reaction time and the ability to execute a task correctly, the aim of this paper is to find evidence for diversity (unique properties) of the EFs updating and inhibition in neural correlates of EEG data by means of using brain-computer interface (BCI) methods as a research tool. To highlight the benefit of this approach we compare this new methodology to classical analysis approaches. Methods: An existing study has been reinvestigated by applying neurophysiological analysis in combination with support vector machine (SVM) classification on recorded electroenzephalography (EEG) data to determine the separability and variety of the two EFs updating and inhibition on a single trial basis. Results: The SVM weights reveal a set of distinct features as well as a set of shared features for the two EFs updating and inhibition in the theta and the alpha band power. Significance: In this paper we find evidence that correlates for unity and diversity of EFs can be found in neurophysiological data. Machine learning approaches reveal shared but also distinct properties for the EFs. This study shows that using methods from brain-computer interface (BCI) research, like machine learning, as a tool for the validation of psychological models and theoretical constructs is a new approach that is highly versatile and could lead to many new insights.