While some studies suggest cultural differences in visual processing, others do not, possibly because the complexity of their tasks draws upon high-level factors that could obscure such effects. To control for this, we examined cultural differences in visual search for geometric figures, a relatively simple task for which the underlying mechanisms are reasonably well known. We replicated earlier results showing that North Americans had a reliable search asymmetry for line length: Search for long among short lines was faster than vice versa. In contrast, Japanese participants showed no asymmetry. This difference did not appear to be affected by stimulus density. Other kinds of stimuli resulted in other patterns of asymmetry differences, suggesting that these are not due to factors such as analytic/holistic processing but are based instead on the target-detection process. In particular, our results indicate that at least some cultural differences reflect different ways of processing early-level features, possibly in response to environmental factors.

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Game-based training may have different characteristics than other forms of instruction. The independent validation of the Intelligence Advanced Research Projects Activity (IARPA) Sirius program evaluated game-based cognitive bias training across several games with a common set of control groups. Control groups included a professionally produced video that taught the same cognitive biases and an unrelated video that did not teach any biases. Knowledge was tested immediately after training and after a delay. This article presents the results from the two phases of the Sirius program. Gamebased training showed advantages in teaching bias mitigation skills (procedural knowledge) but had no advantage over video instruction in teaching people to answer explicit questions about biases (declarative knowledge). Overall, training effects persisted over time, and games performed as well as and in some cases better than the video-based instruction for knowledge retention. Our results suggest that serious games can be an effective training tool, particularly for teaching procedural knowledge.

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There is growing interest in using social networking sites such as Twitter to gather real-time data on the reactions and opinions of a region's population, including locations in the developing world where social media has played an important role in recent events, such as the 2011 Arab Spring. However, many interesting and important opinions and reactions may differ significantly within a given region depending on the demographics of the subpopulation, including such categories as gender and ethnicity. Unfortunately, the demographic characteristics of social media users are often unknown because such categories are not always captured in user metadata. Twitter, for example, does not capture a user’s gender in their profile, and inferring gender from first names is difficult since Twitter users are not required to give their real names. There is thus a need for automated methods that can infer such hidden attributes of users from other data sources. In this paper we describe a method to infer the gender of Twitter users from only the content of their tweets. Looking at Twitter users from the West African nation of Nigeria, we applied supervised machine learning using features derived from the content of user tweets to train a classifier. Using unigram features alone, we obtained an accuracy of 80% for predicting gender, suggesting that content alone can be a good predictor of gender. An analysis of the highest weighted features shows some interesting distinctions between men and women both topically and emotionally. We argue that approaches such as the one described here can give us a clearer picture of who is utilizing social media when certain user attributes are unreliable or not available.