Data privacy is typically particularly difficult to achieve in medical applications of machine learning, despite its importance in this area. The datasets are often small, which is why machine learning models such as neural networks tend to memorize information about the training data. This allows confidential and sensitive information about patients to be extracted from the model. Further challenging achieving data privacy is that the best possible utility must be ensured. In this work, we aim to detect tics based on video data of patients with Gilles de la Tourette syndrome. Facial landmarks were used as a lower-dimensional representation of the video data. Through membership inference attacks, we show that training a simple neural network directly on sensitive training data leaks information about the training data, and that this can be prevented by suitable pretraining on a large amount of unlabeled public data of healthy subjects. The proposed approach can not only reduce the attack accuracy to 52.15 %, but also achieves a high tic detection accuracy of 86.53 %.

Information overload resulting from the ever faster-growing mass of digital data makes knowledge work more and more complex. Being able to not get distracted and focus on what is currently relevant consumes valuable cognitive resources. Support by intelligent assistance software might alleviate this problem. We report two experiments that addressed this challenge by examining how context-based assistance may provide more available cognitive resources. Experiment 1 focused on work within a single context. Results indicate that external relevance classification can improve memory for content classified as currently more relevant. Experiment 2 focused on switching between two different contexts and shows that cognitive performance after context switches can be enhanced by context-specific structuring and saving a previous task status. Taken together, these results clearly demonstrate that automatic external information structuring by intelligent assistance software can protect knowledge workers from information overload by lightening their cognitive load and, thus, help improve cognitive performance.

As digital gaming has grown from a leisure activity into a competitive endeavor with college scholarships, celebrity, and large prize pools at stake, players search for ways to enhance their performance, including through coaching, training, and employing tools that yield a performance advantage. Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique that is presently being explored by esports athletes and competitive gamers. Although shown to modulate cognitive processing in standard laboratory tasks, there is little scientific evidence that tDCS improves performance in digital games, which are visually complex and attentionally demanding environments. We applied tDCS between two sessions of the Stop-Signal Game (SSG; Friehs, Dechant, Vedress, Frings, & Mandryk, 2020). The SSG is a custom-built infinite runner that is based on the Stop-Signal Task (SST; Verbruggen et al., 2019). Consequently, the SSG can be used to evaluate response inhibition as measured by Stop-Signal Reaction Time (SSRT), but in an enjoyable 3D game experience. We used anodal, offline tDCS to stimulate the right dorsolateral prefrontal cortex (rDLPFC); a 9 cm² anode was always positioned over the rDLPFC while the 35 cm² cathode was placed over the left deltoid. We hypothesized that anodal tDCS would enhance neural processing (as measured by a decrease in SSRT) and improve performance, while sham stimulation (i.e., the control condition with a faked stimulation) should lead to no significant change. In a sample of N = 45 healthy adults a significant session x tDCS-condition interaction emerged in the expected direction. Subsequent analysis confirmed that the statistically significant decrease in SSRT after anodal tDCS to the rDLPFC was not due to a general change in reaction times. These results provide initial evidence that tDCS can influence performance in digital games.

Abstract Inhibition is one of the core concepts in Cognitive Psychology. The idea of inhibitory mechanisms actively weakening representations in the human mind has inspired a great number of studies in various research domains. In contrast, Computer Science only recently has begun to consider concepts such as digital forgetting or suppression of irrelevant information to complement activation and highlighting of relevant information. Here, we review psychological research on inhibition in memory and link the gained insights with the current efforts and opportunities in Computer Science of incorporating inhibitory principles for reducing information overload and improving information retrieval in Personal Information Management. Four common aspects guide this review in both domains: (i) the purpose of inhibition to increase processing efficiency; (ii) its relation to activation; (iii) its links to contexts; (iv) its temporariness. In summary, the principle of suppressing information has been used by Computer Science for enhancing software in some ways already. Yet, we consider how novel methods for reducing information overload can be inspired by a more systematic involvement of the inhibition concept.