Biology subjects

Krumpe, T.

Publications and source records attributed to Krumpe, T..

3 recordsLinked to original sources

bioRxiv2018

Using a machine learning approach to complement group level statistics in experimental psychology: A case study to reveal different levels of inhibition in a modified Flanker Task

In this paper, we demonstrate how machine learning (ML) can be used to beneficially complement the traditional analysis of behavioral and physiological data to provide new insights into the structure of mental states, in this case, executive functions (EFs) with a focus on inhibitory control. We used a modified Flanker task with the aim to distinguish three levels of inhibitory control: no inhibition, readiness for inhibition and the actual execution of inhibitory control. A simultaneously presented n-back task was used to additionally induce demands on a second executive function. This design enabled us to investigate how the overlap of resources influences the distinction between three levels of inhibitory control. A support vector machine (SVM) based classification approach has been used on EEG data to predict the level of inhibitory control on single-subject and single-trial level. The SVM classification is a subject-specific and single-trial based approach which will be compared to standard group-level statistical approaches to reveal that both methodologies access different properties of the data. We show that considering both methods can give new insights into mental states which cannot be discovered when only using group-level statistics alone. Machine learning results indicate that three different levels of inhibitory control can be distinguished, while the group-average analysis does not give rise to this assumption. In addition, we highlight one other important benefit of the ML approach. We are able to define specific properties of the executive function inhibition by investigating the neural activation patterns that were used during the classification process.

neuroscience↗

bioRxiv2018

Decision confidence: EEG correlates of confidence in different phases of a decision task

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.

neuroscience↗

bioRxiv2018

Unity and diversity in working memory load: Evidence for the separability of the executive functions updating and inhibition using machine learning

AbstractO_ST_ABSObjectiveC_ST_ABSAccording 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.\n\nMethodsAn 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.\n\nResultsThe 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.\n\nSignificanceIn 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.

neuroscience↗