Learning is an essential skill that humans and animals need to interact successfully with stimuli in their surrounding environment. Discovering the learning motifs applied by humans and animals is a highly complex task. Indeed, identifying significant motifs in behavioural actions by analyzing how actions are affected by stimuli and prior actions enables us to understand how a learning motif is formed by a subject. Granger causality (GC) is a statistical tool used to check whether the past of a variable X is predictive of the future of another variable Y, in this case, we say that X Granger causes Y. Furthermore, subjects may also change the learning motif followed over time due to the learning process. In this study, we propose a method that (1) models a ‘learning motif’ as a set of Granger causality relationships involving past stimuli and past actions, (2) employs a hidden Markov model (HMM) to capture the change in the followed learning motifs and (3) identifies the salient learning motifs that are common to many subjects. The evaluation of our proposed method is not a trivial task due to the absence of the ground truth of the learning motifs. In general, it is difficult to acquire the ground truth of the learning motifs because for example, animals cannot articulate these learning motifs and this also applies to humans in complex learning tasks. Therefore, we also propose a solution that does not require ground truth to validate the derived learning motifs. We evaluate our proposed method on behavioural data collected from two groups of mice, a group of healthy mice and a group of mice with induced cognitive impairment. We show that our model is appropriate for identifying the learning motifs followed by these two groups of animals during a learning experiment in which animals are expected to maximize the reward gain.

Background: Researchers and practitioners have been using code complexity metrics for decades to predict how developers comprehend a program. While it is plausible and tempting to use code metrics for this purpose, their validity is debated, since they rely on simple code properties and rarely consider particularities of human cognition. Aims: We investigate whether and how code complexity metrics reflect difficulty of program comprehension. Method: We have conducted a functional magnetic resonance imaging (fMRI) study with 19 participants observing program comprehension of short code snippets at varying complexity levels. We dissected four classes of code complexity metrics and their relationship to neuronal, behavioral, and subjective correlates of program comprehension, overall analyzing more than 41 metrics. Results: While our data corroborate that complexity metrics can-to a limited degree-explain programmers' cognition in program comprehension, fMRI allowed us to gain insights into why some code properties are difficult to process. In particular, a code's textual size drives programmers' attention, and vocabulary size burdens programmers' working memory. Conclusion: Our results provide neuro-scientific evidence supporting warnings of prior research questioning the validity of code complexity metrics and pin down factors relevant to program comprehension. Future Work: We outline several follow-up experiments investigating fine-grained effects of code complexity and describe possible refinements to code complexity metrics.

Program comprehension is an important, but hard to measure cognitive process. This makes it difficult to provide suitable programming languages, tools, or coding conventions to support developers in their everyday work. Here, we explore whether functional magnetic resonance imaging (fMRI) is feasible for soundly measuring program comprehension. To this end, we observed 17 participants inside an fMRI scanner while they were comprehending source code. The results show a clear, distinct activation of five brain regions, which are related to working memory, attention, and language processing, which all fit well to our understanding of program comprehension. Furthermore, we found reduced activity in the default mode network, indicating the cognitive effort necessary for program comprehension. We also observed that familiarity with Java as underlying programming language reduced cognitive effort during program comprehension. To gain confidence in the results and the method, we replicated the study with 11 new participants and largely confirmed our findings. Our results encourage us and, hopefully, others to use fMRI to observe programmers and, in the long run, answer questions, such as: How should we train programmers? Can we train someone to become an excellent programmer? How effective are new languages and tools for program comprehension?

Program comprehension is a central cognitive process in programming. It has been in the focus of researchers for decades, but is still not thoroughly unraveled. Multi-modal psycho-physiological and neurobiological measurement methods have proved successful to gain a more holistic understanding of program comprehension. However, there is no proper tool support that lets researchers explore synchronized, conjoint multi-modal data, specifically designed for the needs in program-comprehension research. In this paper, we present CodersMUSE, a prototype implementation that aims to satisfy this crucial need.

Background Researchers have recently started to validate decades-old program-comprehension models using functional magnetic resonance imaging (fMRI). While fMRI helps us to understand neural correlates of cognitive processes during program comprehension, its comparatively low temporal resolution (i.e., seconds) cannot capture fast cognitive subprocesses (i.e., milliseconds).

Intention-based, anticipatory, interactive systems (IAIS) represent a new class of user-centered assistance systems. IAIS uses actions and system intentions derived from signal data, and the affective state of the user. By anticipating the further action of the user, solutions are interactively negotiated. The active roles of humans and systems change strategically, which requires behavioral models, which in turn can be specified by life sciences' results. Deployed human-machine-systems and lab tests have the goal of understanding of the situated interaction. This supports integration of assistance systems for Industry 4.0 and in the context of demographic change. We provide a definition of IAIS, discuss the underlying requirements, goals and challenges and provide a brief review of the state-of-the-art in the involved research areas.

Exploration in large multimedia collections is challenging because the user often navigates into misleading directions or information areas. The vision of our project is to develop an assistive technology that is able to support the individual user and enhance the efficiency of an ongoing exploratory search. Such a technical search aid should be able to find out about the user's current interests and goals. Respective parameters can be found in the central and in the peripheral nervous system as well as in overt behavior. Therefore, we aim at using eye movements, pupillometry and EEG to assess respective information. Here, we describe the set-up and the first results of a preliminary user study investigating the effects of searching an image collection on eye movements and pupil dilations. First data show that numbers of fixation, fixation durations as well as pupil dilations differ systematically when looking at a subsequently selected target as compared with not selected items. These results support our vision that further research additionally investigating EEG can in fact result in better predicting the searchers goals and next choices.

Most modern software programs cannot be understood in their entirety by a single programmer. Instead, programmers must rely on a set of cognitive processes that aid in seeking, filtering, and shaping relevant information for a given programming task. Several theories have been proposed to explain these processes, such as ``beacons,' for locating relevant code, and ``plans,'' for encoding cognitive models. However, these theories are decades old and lack validation with modern cognitive-neuroscience methods. In this paper, we report on a study using functional magnetic resonance imaging (fMRI) with 11 participants who performed program comprehension tasks. We manipulated experimental conditions related to beacons and layout to isolate specific cognitive processes related to bottom-up comprehension and comprehension based on semantic cues. We found evidence of semantic chunking during bottom-up comprehension and lower activation of brain areas during comprehension based on semantic cues, confirming that beacons ease comprehension.

Program comprehension is an important cognitive process that inherently eludes direct measurement. Thus, researchers are struggling with providing suitable programming languages, tools, or coding conventions to support developers in their everyday work. In this paper, we explore whether functional magnetic resonance imaging (fMRI), which is well established in cognitive neuroscience, is feasible to soundly measure program comprehension. In a controlled experiment, we observed 17 participants inside an fMRI scanner while they were comprehending short source-code snippets, which we contrasted with locating syntax errors. We found a clear, distinct activation pattern of five brain regions, which are related to working memory, attention, and language processing---all processes that fit well to our understanding of program comprehension. Our results encourage us and, hopefully, other researchers to use fMRI in future studies to measure program comprehension and, in the long run, answer questions, such as: Can we predict whether someone will be an excellent programmer? How effective are new languages and tools for program understanding? How should we train programmers?

Program comprehension is an often evaluated, internal cognitive process. In neuroscience, functional magnetic resonance imaging (fMRI) is used to visualize such internal cognitive processes. We propose an experimental design to measure program comprehension based on fMRI. In the long run, we hope to answer questions like What distinguishes good programmers from bad programmers? or What makes a good programmer?

This paper gives both general linear model (GLM) and support vector machine (SVM) analyses of an experiment concerned with tonality in music. The two forms of analysis are both contrasted and used to complement each other, and a new technique employing the GLM as a pre-processing step for the SVM is presented. The SVM is given the task of classifying the stimulus conditions (tonal or atonal) on the basis of the blood oxygen level-dependent signal of novel data, and the prediction performance is evaluated. In addition, a more detailed assessment of the SVM performance is given in a comparison of the similarity in the identification of voxels relevant to the classification of the SVM and a GLM. A high level of similarity between SVM weight and GLM t-maps demonstrate that the SVM is successfully identifying relevant voxels, and it is this that allows it to perform well in the classification task in spite of very noisy data and stimuli that involve higher-order cognitive functions and considerably inter-subject variation in neural response.

In the above article, published in Connection Science, Volume 21, Issues 2–3, pp.161–175 (DOI: 10.1080/09540090902733863), the author affiliations appeared as follows: Simon Durranta*, David R. Har...

We developed a hierarchical framework for the representation of variable compound objects in 2-dimensional images in terms of an adaptive two-level shape model that can be used for recognition and classification tasks. A control instance guides the local search for shapes by managing knowledge about variable spatial relations between single deformable shapes that is uniformly represented through explicit dynamic models. Temporary and permanent memory of past successful searches are used to accomplish a purposeful search, and to improve the a-priori model using generated training data. The framework is applied to the labeling of Heschl's gyrus in flattened parametric representations of the human cortex (flat maps), which is of great interest with respect to multi-subject fMRI studies. Results indicate the models' potential for recognition and classification without the need for prior training.