Political implications of bilingual cognition

Though more than two-thirds of all children around the world grow up in bilingual environments (Crystal 1997) and more than half of the world's population speak more than one language in everyday life (Grosjean 2010), political science continues to operate under a Chomskian scheme in which language is characterized as "a parsimonious symbol system, [or] a type of mental algebra" (Caldwell-Harris 2014, 2). Simply put, the prevailing assumptions are that language is transparent and trivial, and that no special evaluation of its impact independent of the plain meaning is required. Taken further, this paradigm implies that phrases from different languages will be understood exactly the same way, as long as they are faithful translational equivalents. However, recent research in cognitive psychology has demonstrated otherwise. Affect research experiments have shown that the emotional impact of negative and taboo stimuli is significantly blunted when a bilingual receives them in a second language (Caldwell-Harris and Ayçiçeği-Dinn 2009, Eilola and Havelka 2010, Hsu, Jacobs and Conrad 2015). Concurrently, researchers studying implicit social cognition have found that whether an ethnic group was viewed favorably by bilingual respondents depended on the language in which they were prompted to express an opinion (Danzinger and Ward 2010, Ogunnaike, Dunham, and Banaji 2010). More recently, decision research has found that working language affected how bilinguals made moral judgments (Costa et al. 2014a) and perceived causality (Geipel, Hadjichristidis, and Surian 2015a). These findings suggest that language may carry enormous ramifications for the study of political behavior. Decreased affective response in second languages may mean that bilinguals respond less intensely to messages broadcast in their second language. Political advertisements, perhaps especially attack ads, may lose their efficacy if their keywords and symbols do not provoke a bilingual citizen as intended, or if the bilingual forgets them too quickly. Citizens who must evaluate candidates, issue platforms, or even ballot initiatives in their second language may come to very different conclusions and vote choices than they would in their native language. In bargaining, second language negotiations may enable parties working in a second language to be more objective and willing to take risks in achieving a consensus, as they may be less likely to be provoked by topics that are controversial or hold deep emotional resonance. From a methodological perspective, if a respondent’s response in his second language differs from in his native language, then the investigator will fail in his goal of accurately capturing the opinions and attitudes of the target populations, especially those of minority constituents. Researchers who should be alert to possible effects of language for multilingual respondents must recognize the challenges that lie ahead, either of discerning which attitudes and opinions are "true" ones, or of being able to measure language-specific attitudes. This dissertation investigates whether a native speaker and a non-native speaker process and react to a language in the same manner. I incorporate recent findings from psychology to explain why speaking native and non-native languages may prompt different modes of cognition, and subsequently, result in observable differences in attitudes and behaviors. I use data drawn from an original survey experiment that I designed and conducted in the People's Republic of China (PRC) in 2013 to draw larger conclusions about the potential impact of bilingualism in the political realm. This survey experiment, the first in political science to vary the working language as an experimental condition, asked university students at Capital Normal University to respond on a battery of questions including both commonly-used as well as original attitudinal and behavioral questions. The balance of this dissertation comprises three chapters, each organized thematically around findings from a different cluster of psychology research into bilingualism. The first substantive chapter provides a basic primer on the terminology used in bilingualism research and investigates the L1 affective advantage, in which one's native language (L1) usually evokes greater affect that one's second language (L2). The L1 affective advantage influences how bilinguals view choices and outcomes in a hypothetical situation, which in turn affects their decision-making. I extend this research further to examine how bilinguals assess the fairness of money sharing proposals in an Ultimatum Game. The second substantive chapter examines language in regard to the encoding specificity principle, which Tulving and Thomson (1973) defined as the improvement in recall when conditions at the time of encoding match those at the time of retrieval. Its corollary in bilingualism, the language specificity effect, asserts that memories are "more likely to be activated by the language in which the original events took place" (Pavlenko 2012, 410). I examine whether self-reported patterns of political discussion and media exposure changed as a function of the working language, and their methodological implications for political behavior research. The last substantive chapter examines cultural frame switching, defined (by Hong et al. 2000) as the process through which a bilingual accesses networks of knowledge that are associated with different cultures. Cross-cultural psychologists have demonstrated that changing the working language can change a bilingual's self-perception and influence how he views and relates to different groups of people (Hong et al., Ross, Xun, and Wilson 2002). I investigate whether monocultural bilinguals, a group commonly assumed not to be capable of displaying cultural frame switching, profess different core values, such as prioritizing group harmony over individual rights and deference toward authority, or different political judgments when the working language changed

A General Framework of Learning Multi-Vehicle Interaction Patterns from Videos

Semantic learning and understanding of multi-vehicle interaction patterns in a cluttered driving environment are essential but challenging for autonomous vehicles to make proper decisions. This paper presents a general framework to gain insights into intricate multi-vehicle interaction patterns from bird's-eye view traffic videos. We adopt a Gaussian velocity field to describe the time-varying multi-vehicle interaction behaviors and then use deep autoencoders to learn associated latent representations for each temporal frame. Then, we utilize a hidden semi-Markov model with a hierarchical Dirichlet process as a prior to segment these sequential representations into granular components, also called traffic primitives, corresponding to interaction patterns. Experimental results demonstrate that our proposed framework can extract traffic primitives from videos, thus providing a semantic way to analyze multi-vehicle interaction patterns, even for cluttered driving scenarios that are far messier than human beings can cope with.Comment: 2019 IEEE Intelligent Transportation Systems Conference (ITSC

EGTA, a calcium chelator, affects cell cycle and increases DNA methylation in root tips of Triticum aestivum L.

In this study, when germinated Triticum aestivum L. seeds were treated with 0, 2, 4 and 6 mM ethyl glycol tetraacetic acid (EGTA), root growth was suppressed and the mitotic index decreased. These inhibitory effects were positively correlated with EGTA concentration. RT-PCR analysis revealed that the expression of several gene markers related to the G1/S transition of the cell cycle were significantly downregulated. Confocal microscopy of Fluo-3/AM-stained roots showed chelation of nearly all of the Ca2+ within the root meristematic regions. Both random amplified polymorphic DNA (RAPD) and coupled restriction enzyme digestion-random amplification (CRED-RA) techniques showed significant increases in the levels of genomic DNA polymorphisms and degree of DNA methylation. The study provides information concerning the impact of Ca2+ chelator, EGTA, on the growth, expression of cell cycle transition marker genes, and changes in DNA structure and methylation in the wheat roots

Spatiotemporal Learning of Multivehicle Interaction Patterns in Lane-Change Scenarios

Interpretation of common-yet-challenging interaction scenarios can benefit well-founded decisions for autonomous vehicles. Previous research achieved this using their prior knowledge of specific scenarios with predefined models, limiting their adaptive capabilities. This paper describes a Bayesian nonparametric approach that leverages continuous (i.e., Gaussian processes) and discrete (i.e., Dirichlet processes) stochastic processes to reveal underlying interaction patterns of the ego vehicle with other nearby vehicles. Our model relaxes dependency on the number of surrounding vehicles by developing an acceleration-sensitive velocity field based on Gaussian processes. The experiment results demonstrate that the velocity field can represent the spatial interactions between the ego vehicle and its surroundings. Then, a discrete Bayesian nonparametric model, integrating Dirichlet processes and hidden Markov models, is developed to learn the interaction patterns over the temporal space by segmenting and clustering the sequential interaction data into interpretable granular patterns automatically. We then evaluate our approach in the highway lane-change scenarios using the highD dataset collected from real-world settings. Results demonstrate that our proposed Bayesian nonparametric approach provides an insight into the complicated lane-change interactions of the ego vehicle with multiple surrounding traffic participants based on the interpretable interaction patterns and their transition properties in temporal relationships. Our proposed approach sheds light on efficiently analyzing other kinds of multi-agent interactions, such as vehicle-pedestrian interactions. View the demos via https://youtu.be/z_vf9UHtdAM.Comment: for the supplements, see https://chengyuan-zhang.github.io/Multivehicle-Interaction

Shareable Driving Style Learning and Analysis with a Hierarchical Latent Model

Driving style is usually used to characterize driving behavior for a driver or a group of drivers. However, it remains unclear how one individual's driving style shares certain common grounds with other drivers. Our insight is that driving behavior is a sequence of responses to the weighted mixture of latent driving styles that are shareable within and between individuals. To this end, this paper develops a hierarchical latent model to learn the relationship between driving behavior and driving styles. We first propose a fragment-based approach to represent complex sequential driving behavior, allowing for sufficiently representing driving behavior in a low-dimension feature space. Then, we provide an analytical formulation for the interaction of driving behavior and shareable driving style with a hierarchical latent model by introducing the mechanism of Dirichlet allocation. Our developed model is finally validated and verified with 100 drivers in naturalistic driving settings with urban and highways. Experimental results reveal that individuals share driving styles within and between them. We also analyzed the influence of personalities (e.g., age, gender, and driving experience) on driving styles and found that a naturally aggressive driver would not always keep driving aggressively (i.e., could behave calmly sometimes) but with a higher proportion of aggressiveness than other types of drivers