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Self-control can be defined as making choices in accordance with long-term, rather than short-term, patterns of behavior. Rachlin (2016) suggested a novel technique to enhance self-control, by which individual choices carry the weight of a larger pattern of choices. This report describes a study of 169 college students who made repeated choices between two gambles. The better of the two gambles had a greater win probability but required waiting an uncertain amount of time. Some "patterned" subjects were forced to repeat their previous choices according to a schedule, while control subjects could choose freely on every trial. It was found that on free-choice trials, the patterned subjects chose the better gamble more often than controls. There was stronger evidence for such an effect appearing immediately than for it developing gradually from a process of learning over the course of the task. An additional condition in which subjects were forced to choose the better gamble yielded inconsistent results. Overall, the results support the use of pattern-setting as a strategy to improve decision-making.

Background Machine-learning algorithms are becoming popular techniques to predict ambient air PM2.5 concentrations at high spatial resolutions (1 × 1 km) using satellite-based aerosol optical depth (AOD). Most machine-learning models have aimed to predict 24 h-averaged PM2.5 concentrations (mean PM2.5) in high-income regions. Over Mexico, none have been developed to predict subdaily peak levels, such as the maximum daily 1-h concentration (max PM2.5). Objective Our goal was to develop a machine-learning model to predict mean PM2.5 and max PM2.5 concentrations in the Mexico City Metropolitan Area from 2004 through 2019. Methods We present a new modeling approach based on extreme gradient boosting (XGBoost) and inverse-distance weighting that uses AOD, meteorology, and land-use variables. We also investigated applications of our mean PM2.5 predictions that can aid local authorities in air-quality management and public-health surveillance, such as the co-occurrence of high PM2.5 and heat, compliance with local air-quality standards, and the relationship of PM2.5 exposure with social marginalization. Results Our models for mean and max PM2.5 exhibited good performance, with overall cross-validated mean absolute errors (MAE) of 3.68 and 9.20 μg/m3, respectively, compared to mean absolute deviations from the median (MAD) of 8.55 and 15.64 μg/m3. In 2010, everybody in the study region was exposed to unhealthy levels of PM2.5. Hotter days had greater PM2.5 concentrations. Finally, we found similar exposure to PM2.5 across levels of social marginalization. Significance Machine learning algorithms can be used to predict highly spatiotemporally resolved PM2.5 concentrations even in regions with sparse monitoring. Impact Our PM2.5 predictions can aid local authorities in air-quality management and public-health surveillance, and they can advance epidemiological research in Central Mexico with state-of-the-art exposure assessment methods.

The challenge of reconstructing air temperature for environmental applications is to accurately estimate past exposures even where monitoring is sparse. We present XGBoost-IDW Synthesis for air temperature (XIS-Temperature), a high-resolution machine-learning model for daily minimum, mean, and maximum air temperature, covering the contiguous US from 2003 through 2021. XIS uses remote sensing (land surface temperature and vegetation) along with a parsimonious set of additional predictors to make predictions at arbitrary points, allowing the estimation of address-level exposures. We built XIS with a computationally tractable workflow for extensibility to future years, and we used weighted evaluation to fairly assess performance in sparsely monitored regions. The weighted root mean square error (RMSE) of predictions in site-level cross-validation for 2021 was 1.89 K for the minimum daily temperature, 1.27 K for the mean, and 1.72 K for the maximum. We obtained higher RMSEs in earlier years with fewer ground monitors. Comparing to three leading gridded temperature models in 2021 at thousands of private weather stations not used in model training, XIS had at most 49% of the mean square error for the minimum temperature and 87% for the maximum. In a national application, we report a stronger relationship between minimum temperature in a heatwave and social vulnerability with XIS than with the other models. Thus, XIS-Temperature has potential for reconstructing important environmental exposures, and its predictions have applications in environmental justice and human health.

The goal of this study was to examine social feedback processing among emerging adults with borderline personality features (BPF). Participants (N = 118; 66.9% female) completed ratings of BPF and a computerized peer interaction task designed to measure processing of rejection and acceptance cues at the neurophysiological (i.e., electroencephalogram [EEG]), behavioral, and self-report levels. When covarying symptoms of depression and social anxiety, greater BPF were associated with heightened neural processing of social acceptance cues, accounting for reactivity to neutral and rejection cues, as demonstrated by an enhanced reward positivity (RewP) component. Additionally, BPF were associated with less adaptive voting in response to peer acceptance, such that emerging adults with higher BPF made fewer votes to keep peers in the game who had provided acceptance feedback to participants. These neural and behavioral patterns associated with BPF highlight the potential role of social reward processing in borderline personality. Specifically, emerging adults high in BPF show a hyper-responsiveness to social acceptance at the neural level but difficulty modulating behavioral responses in an adaptive way to obtain more social rewards. Future research replicating these effects across development may guide efforts to address and prevent the profound social dysfunction associated with BPF.

Machine-learning algorithms are becoming popular techniques to predict ambient air PMOur goal was to develop a machine-learning model to predict mean PMWe present a new modeling approach based on extreme gradient boosting (XGBoost) and inverse-distance weighting that uses AOD, meteorology, and land-use variables. We also investigated applications of our mean PMOur models for mean and max PMMachine learning algorithms can be used to predict highly spatiotemporally resolved PMOur PM

Measurement of social processes is of interest across areas of research. Event-related potentials (ERPs) measured using electroencephalography (EEG) offer high temporal resolution, but little work has leveraged this strength to characterize the time course of social feedback processing. The present study aimed to replicate and extend previous research by systematically identifying the temporal dynamics of responses to both social acceptance and rejection feedback relative to neutral feedback, examining internal consistency of ERPs, and exploring correspondence with behavioral measures elicited during a peer interaction task. Emerging adults (N = 118) completed a computerized peer interaction task in which they made decisions to accept or reject peers and received rejection, acceptance, and neutral feedback while EEG data were recorded. Principal component analysis was used to derive temporally and spatially distinct ERP components sensitive to positive and negative social feedback. Participant voting patterns and post-task liking ratings for computer-controlled peers were also examined. Replicating prior work, components consistent with N1, P2, reward positivity (RewP), and P3 emerged, but distinct patterns of modulation by acceptance and rejection relative to neutral feedback were observed. Most components showed acceptable internal consistency. Sensitivity to peer feedback assessed through participant voting patterns and liking ratings for peers was correlated with RewP and P3 components. Results highlight the complexity of social feedback processing observable in a computerized peer interaction task and offer promising neural and behavioral measures that can be used to examine individual differences in sensitivity to both social acceptance and rejection feedback.