Advancing mouse-tracking research : new solutions for study design, implementation and analysis

This thesis addresses the topic of mouse-tracking, that is, the recording and analysis of mouse movements in computerized experiments. Mouse-tracking is an increasingly popular process tracing method in many psychological disciplines as it allows capturing the temporal development of the relative attraction to and conflict between response alternatives. It thus provides the opportunity to test psychological theories about factors that influence the conflict involved in making a decision, and how this conflict develops over time. So far, researchers have faced a number of difficulties when conducting mouse-tracking studies: There has been no easy-to-use, flexible and open software for creating experiments and no general-purpose package for analysis. Researchers also have had to make many choices regarding the study setup, with no evidence-based guidelines to support their decisions. This thesis aims to provide solutions for these challenges. First, this thesis introduces free and open-source software packages for creating and analyzing mouse-tracking experiments. The mousetrap plugin enables researchers to implement mouse-tracking in their experiments without programming and, through integration with the experiment builder OpenSesame, offers a graphical user interface that makes it easy to create a variety of experiments and designs. The mousetrap R package provides extensive functionality for processing, analyzing, and visualizing mouse-tracking raw data of all major formats. It implements most of the commonly used preprocessing procedures and mouse-tracking indices, as well as a set of novel visualization and classification procedures for analyzing trajectory shapes. Second, this thesis presents results from a series of experiments that investigate how the methodological setup influences mouse-tracking data. In separate experiments, I manipulated the design factors starting procedure, mouse sensitivity, and response indication and investigated their impact on trajectory curvature and shape. An additional study investigated the effects of the starting procedure on movement consistency and also included dynamic analyses. While central cognitive effects on trajectory curvature were replicated in all setups, their size varied considerably between some of the setups. In addition, the setup strongly influenced the trajectory shapes and dynamic analyses. Based on this evidence, I discuss implications for interpreting mouse-tracking data and offer preliminary recommendations for conducting mouse-tracking experiments. In sum, I hope this thesis will contribute to advancing mouse-tracking research and making the method accessible to a broader audience

Cognitive conflict in social dilemmas: An analysis of response dynamics

Recently, it has been suggested that people are spontaneously inclined to cooperate in social dilemmas, whereas defection requires effortful deliberation. From this assumption, we derive that defection should entail more cognitive conflict than cooperation. To test this hypothesis, the current study presents a first application of the response dynamics paradigm (i.e., mouse-tracking) to social dilemmas. In a fully incentivized lab experiment, mouse movements were tracked while participants played simple two-person social dilemma games with two options (cooperation and defection). Building on previous research, curvature of mouse movements was taken as an indicator of cognitive conflict. In line with the hypothesis of less cognitive conflict in cooperation, response trajectories were more curved (towards the non-chosen option) when individuals defected than when they cooperated. In other words, the cooperative option exerted more “pull” on mouse movements in case of defection than the non-cooperative option (defection) did in case of cooperation. This effect was robust across different types of social dilemmas and occurred even in the prisoner’s dilemma, where defection was predominant on the choice level. Additionally, the effect was stronger for dispositional cooperators as measured by the Honesty-Humility factor of the HEXACO personality model. As such, variation in the effect across individuals could be accounted for through cooperativeness

Analisis Sentimen Masyarakat Tentang Provider Telekomunikasi Indonesia Pada Twitter Menggunakan Naïve Bayes Classifier

Perkembangan media sosial menjadikan media sosial sebagai salah satu media pengambilan data yang besar. Salah satu pemanfaatan dari data media sosial adalah untuk mengetahui pendapat atau sentimen pengguna media sosial terhadap suatu topik dan masalah. Topik penting yang dibicarakan oleh pengguna jejaring sosial adalah provider telekomunikasi. Masyarakat dapat mengungkapkan pendapat, pernyataan, maupun tanggapan tentang provider telekomunikasi melalui internet terkhusus jejaring sosial. Pendapat masyarakat melalui Twitter disampaikan dengan cara memposting tweets. Postingan tweets tersebut dapat dilakukan analisis sentimen terhadap topik provider telekomunikasi. Analisis sentimen adalah salah satu cabang penelitian dari Text Mining yang berguna untuk mengklasifikasikan dokumen teks berupa opini berdasarkan sentimen. Pendapat tentang provider telekomunikasi dikumpulkan dan dijadikan sebagai data untuk dianalisis dan diamati. Salah satu pembelajaran mesin untuk analisis sentimen adalah Naïve Bayes Classifier (NBC). Metode Naïve Bayes Classifier (NBC) terkenal dengan metode yang sederhana tetapi memiliki keakuratan yang tinggi dalam kasus analisis sentimen berupa dokumen dibandingkan SVM. Hasil analisis sentimen dengan metode Naïve Bayes Classifier (NBC) akan mendapatkan informasi dan pengetahuan yang dapat dijadikan pedoman dan masukan kepada pihak-pihak terkait untuk dapat meningkatkan layanan dan mempertahankan xii kepercayaan masyarakat terhadap provider telekomunikasi di Indonesia terutama Telkomsel

Lost to translation: How design factors of the mouse-tracking procedure impact the inference from action to cognition

From an embodiment perspective, action and cognition influence each other constantly. This interaction has been utilized in mouse-tracking studies to infer cognitive states from movements, assuming a continuous manifestation of cognitive processing into movement. However, it is mostly unknown how this manifestation is affected by the variety of possible design choices in mouse-tracking paradigms. Here we studied how three design factors impact the manifestation of cognition into movement in a Simon task with mouse tracking. We varied the response selection (i.e., with or without clicking), the ratio between hand and mouse cursor movement, and the location of the response boxes. The results show that all design factors can blur or even prevent the manifestation of cognition into movement, as reflected by a reduction in movement consistency and action dynamics, as well as by the adoption of unsuitable movement strategies. We conclude that deliberate and careful design choices in mouse-tracking experiments are crucial to ensuring a continuous manifestation of cognition in movement. We discuss the importance of developing a standard practice in the design of mouse-tracking experiments

Predicting question difficulty in web surveys: A machine learning approach based on mouse movement features

Survey research aims to collect robust and reliable data from respondents. However, despite researchers’ efforts in designing questionnaires, survey instruments may be imperfect, and question structure not as clear as could be, thus creating a burden for respondents. If it were possible to detect such problems, this knowledge could be used to predict problems in a questionnaire during pretesting, inform real-time interventions through responsive questionnaire design, or to indicate and correct measurement error after the fact. Previous research has used paradata, specifically response times, to detect difficulties and help improve user experience and data quality. Today, richer data sources are available, for example, movements respondents make with their mouse, as an additional detailed indicator for the respondent–survey interaction. This article uses machine learning techniques to explore the predictive value of mouse-tracking data regarding a question’s difficulty. We use data from a survey on respondents’ employment history and demographic information, in which we experimentally manipulate the difficulty of several questions. Using measures derived from mouse movements, we predict whether respondents have answered the easy or difficult version of a question, using and comparing several state-of-the-art supervised learning methods. We have also developed a personalization method that adjusts for respondents’ baseline mouse behavior and evaluate its performance. For all three manipulated survey questions, we find that including the full set of mouse movement measures and accounting for individual differences in these measures improve prediction performance over response-time-only models.German Research Foundation (DFG)Peer Reviewe

Tumor infiltration in enhancing and non-enhancing parts of glioblastoma: A correlation with histopathology

To correlate histopathologic findings from biopsy specimens with their corresponding location within enhancing areas, non-enhancing areas and necrotic areas on contrast enhanced T1-weighted MRI scans (cT1).In 37 patients with newly diagnosed glioblastoma who underwent stereotactic biopsy, we obtained a correlation of 561 1mm3 biopsy specimens with their corresponding position on the intraoperative cT1 image at 1.5 Tesla. Biopsy points were categorized as enhancing (CE), non-enhancing (NE) or necrotic (NEC) on cT1 and tissue samples were categorized as "viable tumor cells", "blood" or "necrotic tissue (with or without cellular component)". Cell counting was done semi-automatically.NE had the highest content of tissue categorized as viable tumor cells (89% vs. 60% in CE and 30% NEC, respectively). Besides, the average cell density for NE (3764 ± 2893 cells/mm2) was comparable to CE (3506 ± 3116 cells/mm2), while NEC had a lower cell density with 2713 ± 3239 cells/mm2. If necrotic parts and bleeds were excluded, cell density in biopsies categorized as "viable tumor tissue" decreased from the center of the tumor (NEC, 5804 ± 3480 cells/mm2) to CE (4495 ± 3209 cells/mm2) and NE (4130 ± 2817 cells/mm2).The appearance of a glioblastoma on a cT1 image (circular enhancement, central necrosis, peritumoral edema) does not correspond to its diffuse histopathological composition. Cell density is elevated in both CE and NE parts. Hence, our study suggests that NE contains considerable amounts of infiltrative tumor with a high cellularity which might be considered in resection planning

Cohen’s f in repeated measures ANOVAs

I wrote this R Markdown document back in April 2015 when I was running power analyses for repeated measures ANOVAs using G*Power (I believe with version 3.1.9.2 on Windows). This is a summary of my take-aways and questions (thanks a lot to Edgar Erdfelder for helpful discussions regarding some of my questions). The notes (and the R function in this document) were just internal and I never intended for them to be published, so I do not guarantee their accuracy. I am making this publicly available now, because I was asked about it by a group of researchers who would like to reference this document in a publication