At present, it is largely unclear how the human brain optimally learns foreign languages. We investigated teaching strategies that utilize complementary information ("enrichment"), such as pictures or gestures, to optimize vocabulary learning outcome. We found that learning while performing gestures was more efficient than the common practice of learning with pictures and that both enrichment strategies were better than learning without enrichment ("verbal learning"). We tested the prediction of an influential cognitive neuroscience theory that provides explanations for the beneficial behavioral effects of enrichment: the "multisensory learning theory" attributes the benefits of enrichment to recruitment of brain areas specialized in processing the enrichment. To test this prediction, we asked participants to translate auditorily presented foreign words during fMRI. Multivariate pattern classification allowed us to decode from the brain activity under which enrichment condition the vocabulary had been learned. The visual-object-sensitive lateral occipital complex (LOC) represented auditory words that had been learned with pictures. The biological motion superior temporal sulcus (bmSTS) and motor areas represented auditory words that had been learned with gestures. Importantly, brain activity in these specialized visual and motor brain areas correlated with behavioral performance. The cortical activation pattern found in the present study strongly supports the multisensory learning theory in contrast to alternative explanations. In addition, the results highlight the importance of learning foreign language vocabulary with enrichment, particularly with self-performed gestures.

Social media data is widely analyzed in computational social science. Twitter, one of the largest social media platforms, is used for research, journalism, business, and government to analyze human behavior at scale. Twitter offers data via three different Application Programming Interfaces (APIs). One of which, Twitter's Sample API, provides a freely available 1% and a costly 10% sample of all Tweets. These data are supposedly random samples of all platform activity. However, we demonstrate that, due to the nature of Twitter's sampling mechanism, it is possible to deliberately influence these samples, the extent and content of any topic, and consequently to manipulate the analyses of researchers, journalists, as well as market and political analysts trusting these data sources. Our analysis also reveals that technical artifacts can accidentally skew Twitter's samples. Samples should therefore not be regarded as random. Our findings illustrate the critical limitations and general issues of big data sampling, especially in the context of proprietary data and undisclosed details about data handling.

The human body is a highly familiar and socially very important object. Does this mean that the human body has a special status with respect to visual attention? In the current paper we tested whether people in natural scenes attract attention and “pop out” or, alternatively, are at least searched for more efficiently than targets of another category (machines). Observers in our study searched a visual array for dynamic or static scenes containing humans amidst scenes containing machines and vice versa. The arrays consisted of 2, 4, 6 or 8 scenes arranged in a circular array, with targets being present or absent. Search times increased with set size for dynamic and static human and machine targets, arguing against pop out. However, search for human targets was more efficient than for machine targets as indicated by shallower search slopes for human targets. Eye tracking further revealed that observers made more first fixations to human than to machine targets and that their on-target fixation durations were shorter for human compared to machine targets. In summary, our results suggest that searching for people in natural scenes is more efficient than searching for other categories even though people do not pop out.