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Software is created by people who think, feel, and express themselves to one another and their computers. For a long time, researchers have investigated how people read and write code on their computers and talk about code with one another. This way, researchers identified skills, education, and practices necessary to acquire expertise and perform software development duties. While these investigations are valuable, we have yet to devise and validate a scientific theory of program comprehension, which would be an important step in designing support for developers that is tailored to their cognitive needs. To succeed, we need techniques to shed more light on how programmers think. To this end, we need to look beyond computer science research. Specifically, in the field of psychology and cognitive neuroscience, considerable progress has been made in building theories of cognitive processes. Important enabling technologies include eye tracking, functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and functional near infrared spectroscopy (fNIRS). These methods have revolutionized the understanding of cognitive processes and are routinely used in non-computing disciplines. Such techniques have the potential to also modernize classic approaches to program comprehension research by informing new experimental designs. However, the use of such technologies to study program comprehension is recent, and many of the challenges of this interdisciplinary field remain unexplored. This report documents the program and the outcomes of Dagstuhl Seminar 22402, "Foundations for a New Perspective of Understanding Programming", which explores these challenges. In total, 23 on-site participants attended the seminar along with two virtual keynote speakers. Participants engaged in intensive collaboration, including discussing past and current research, identifying gaps in the literature, and proposing future directions for improving the state of the art in program comprehension research., DagRep, Volume 12, Issue 10, pages 61-83

Distractibility is one of the key features of attention deficit hyperactivity disorder (ADHD) and has been associated with alterations in the neural orienting and alerting networks. Task-irrelevant stimuli are thus expected to have detrimental effects on the performance of patients with ADHD. However, task-irrelevant presentation of novel sounds seems to have the opposite effect and improve subsequent attentional performance particularly in patients with ADHD. Here, we aimed to understand the neural modulations of the attention networks underlying these improvements. Fifty boys (25 with ADHD) participated in a functional magnetic resonance imaging (fMRI) study in which unique (novel) or repeatedly presented (familiar) sounds were placed before a visual flanker task in 2/3 of the trials. We found that presenting any sound improved task performance in all participants, but the underlying neural mechanisms differed for the type of sound. Familiar sounds led to a stronger increase in activity in the left posterior insula in patients with ADHD compared to typically developing peers. Novel sounds led to activations of the fronto-temporoparietal ventral attention network, likewise in ADHD and TD. These changes in signaling by novelty in the right inferior frontal gyrus were directly related to improved response speed showing that neural orienting network activity following novel sounds facilitated subsequent attentional performance. This mechanism of behavioral enhancement by short distractions could potentially be useful for cognitive trainings or homework situations.

We are considering the statistical analysis of functional magnetic resonance imaging (fMRI) data. As demonstrated in previous work, grouping voxels into regions (of interest) and carrying out a multiple test for signal detection on the basis of these regions typically leads to a higher sensitivity when compared with voxel-wise multiple testing approaches. In the case of a multi-subject study, we propose to define the regions for each subject separately based on their individual brain anatomy, represented, e.g., by so-called Aparc labels. The aggregation of the subject-specific evidence for the presence of signals in the different regions is then performed by means of a combination function for p-values. We apply the proposed methodology to real fMRI data and demonstrate that our approach can perform comparably to a two-stage approach for which two independent experiments are needed, one for defining the regions and one for actual signal detection.

Transcranial direct current stimulation (tDCS) is one of the most prominent non-invasive electrical brain stimulation method to alter neuronal activity as well as behavioral processes in cognitive and perceptual domains. However, the exact mode of action of tDCS-related cortical alterations is still unclear as the results of tDCS studies often do not comply with the somatic doctrine assuming that anodal tDCS enhances while cathodal tDCS decreases neuronal excitability. Changes in the regional cortical neurotransmitter balance within the stimulated cortex, measured by excitatory and inhibitory neurotransmitter levels, have the potential to provide direct neurochemical underpinnings of tDCS effects. Here we assessed tDCS-induced modulations of the neurotransmitter concentrations in the human auditory cortex (AC) by using magnetic resonance spectroscopy (MRS) at ultra-high-field (7 T). We quantified inhibitory gamma-amino butyric (GABA) concentration and excitatory glutamate (Glu) and compared changes in the relative concentration of GABA to Glu before and after tDCS application. We found that both, anodal and cathodal tDCS significantly increased the relative concentration of GABA to Glu with individual temporal specificity. Our results offer novel insights for a potential neurochemical mechanism that underlies tDCS-induced alterations of AC processing.

Human learning is one of the main topics in psychology and cognitive neuroscience. The analysis of experimental data, e.g. from category learning experiments, is a major challenge due to confounding factors related to perceptual processing, feedback value, response selection, as well as inter-individual differences in learning progress due to differing strategies or skills. We use machine learning to investigate (Q1) how participants of an auditory category-learning experiment evolve towards learning, (Q2) how participant performance saturates and (Q3) how early we can differentiate whether a participant has learned the categories or not. We found that a Gaussian Mixture Model describes well the evolution of participant performance and serves as basis for identifying influencing factors of task configuration (Q1). We found early saturation trends (Q2) and that CatBoost, an advanced classification algorithm, can separate between participants who learned the categories and those who did not, well before the end of the learning session, without much degradation of separation quality (Q3). Our results show that machine learning can model participant dynamics, identify influencing factors of task design and performance trends. This will help to improve computational models of auditory category learning and define suitable time points for interventions into learning, e.g. by tutorial systems.