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Individual differences in brain anatomy can be used to predict variations in cognitive ability. Most studies to date have focused on broad population-level trends, but the extent to which the observed predictive features are shared across sexes and age groups remains to be established. While it is standard practice to account for intracranial volume (ICV) using proportion correction in both regional and whole-brain morphometric analyses, in the context of brain-behavior predictions the possible differential impact of ICV correction on anatomical features and subgroups within the population has yet to be systematically investigated. In this work, we evaluate the effect of proportional ICV correction on sex-independent and sex-specific predictive models of individual cognitive abilities across multiple anatomical properties (surface area, gray matter volume, and cortical thickness) in healthy young adults (Human Connectome Project; n = 1013, 548 females) and typically developing children (Adolescent Brain Cognitive Development study; n = 1823, 979 females). We demonstrate that ICV correction generally reduces predictive accuracies derived from surface area and gray matter volume, while increasing predictive accuracies based on cortical thickness in both adults and children. Furthermore, the extent to which predictive models generalize across sexes and age groups depends on ICV correction: models based on surface area and gray matter volume are more generalizable without ICV correction, while models based on cortical thickness are more generalizable with ICV correction. Finally, the observed neuroanatomical features predictive of cognitive abilities are unique across age groups regardless of ICV correction, but whether they are shared or unique across sexes (within age groups) depends on ICV correction. These findings highlight the importance of considering individual differences in ICV, and show that proportional ICV correction does not remove the effects of cranial volume from anatomical measurements and can introduce ICV bias where previously there was none. ICV correction choices affect not just the strength of the relationships captured, but also the conclusions drawn regarding the neuroanatomical features that underlie those relationships.

Elderly adults hold different beliefs regarding vaccination and are at higher risks for COVID-19 related illnesses and deaths. The current study aims to explore elderly (aged 65 or above) Chinese adults’ intentions to get vaccinated against COVID-19 and the facilitators and barriers to vaccination intentions. We conducted in-depth interviews with 35 elderly adults in China through the lens of the integrative model of behavioral prediction. The results identified a number of facilitators, including convenience (both individual and collective), psychological and physiological wellbeing, collective wellbeing, supportive normative referents, and trust in the government, and some barriers, including vaccine ineffectiveness, side effects, safety, unsupportive normative referents, and the accessibility, affordability, and availability of COVID-19 vaccines. In addition, the results revealed participants’ decision-making process: collective wellbeing and trust in the government overrode perceived barriers and perceived individual-level risks, which eventually overwhelmingly led to a high level of intentions to get vaccinated. Practical implications related to vaccine promotion and trust in the government were discussed.

The connectome provides the structural substrate facilitating communication between brain regions. We aimed to establish whether accounting for polysynaptic communication in structural connectomes would improve prediction of interindividual variation in behavior as well as increase structure-function coupling strength. Connectomes were mapped for 889 healthy adults participating in the Human Connectome Project. To account for polysynaptic signaling, connectomes were transformed into communication matrices for each of 15 different network communication models. Communication matrices were (a) used to perform predictions of five data-driven behavioral dimensions and (b) correlated to resting-state functional connectivity (FC). While FC was the most accurate predictor of behavior, communication models, in particular communicability and navigation, improved the performance of structural connectomes. Communication also strengthened structure-function coupling, with the navigation and shortest paths models leading to 35–65% increases in association strength with FC. We combined behavioral and functional results into a single ranking that provides insight into which communication models may more faithfully recapitulate underlying neural signaling patterns. Comparing results across multiple connectome mapping pipelines suggested that modeling polysynaptic communication is particularly beneficial in sparse high-resolution connectomes. We conclude that network communication models can augment the functional and behavioral predictive utility of the human structural connectome. Brain network communication models aim to describe the patterns of large-scale neural signaling that facilitate functional interactions between brain regions. While information can be directly communicated between anatomically connected regions, signaling between disconnected areas must occur via a sequence of intermediate regions. We investigated a number of candidate models of connectome communication and found that they improved structure-function coupling and the extent to which structural connectomes can predict interindividual variation in behavior. Comparing the behavioral and functional predictive utility of different models provided initial insight into which conceptualizations of network communication may more faithfully recapitulate biological neural signaling. Our results suggest network communication models as a promising avenue to unite our understanding of brain structure, brain function, and human behavior.

Decision-making and planning are the core aspects of autonomous driving systems. These factors are crucial for improving the safety, driving experience, and travel efficiency of autonomous vehicles. Intersections are crucial nodes in urban road traffic networks. The objective of this study is to comprehensively review the latest issues and research progress in decision-making and planning for autonomous vehicles in intersection environments. This paper reviews the research progress in the behavioral prediction of traffic participants in terms of machine learning-based behavioral prediction, probabilistic model behavioral prediction, and mixed-method behavioral prediction. Then, behavioral decision-making is divided into reactive decision-making, learning decision-making, and interactive decision-making, each of which is analyzed. Finally, a comparative analysis of motion planning and its applications is performed from a methodological viewpoint, including search, sampling, and numerical methods. First, key issues and major research progress related to end-to-end decision-making and path planning are summarized and analyzed. Second, the impact of decision-making and path planning on the intelligence level of autonomous vehicles in intersecting environments is discussed. Finally, future development trends and technical challenges are outlined.

Behavioral prediction modeling applies statistical techniques for classifying, recognizing, and predicting behavior using various data. However, performance deterioration and data bias problems occur in behavioral prediction. This study proposed that researchers conduct behavioral prediction using text-to-numeric generative adversarial network (TN-GAN)-based multidimensional time-series augmentation to minimize the data bias problem. The prediction model dataset in this study used nine-axis sensor data (accelerometer, gyroscope, and geomagnetic sensors). The ODROID N2+, a wearable pet device, collected and stored data on a web server. The interquartile range removed outliers, and data processing constructed a sequence as an input value for the predictive model. After using the z-score as a normalization method for sensor values, cubic spline interpolation was performed to identify the missing values. The experimental group assessed 10 dogs to identify nine behaviors. The behavioral prediction model used a hybrid convolutional neural network model to extract features and applied long short-term memory techniques to reflect time-series features. The actual and predicted values were evaluated using the performance evaluation index. The results of this study can assist in recognizing and predicting behavior and detecting abnormal behavior, capacities which can be applied to various pet monitoring systems.

A patient player privately observes a persistent state and interacts with an infinite sequence of myopic uninformed players. The patient player is either a strategic type who maximizes his payoff or one of several commitment types who mechanically play the same action in every period. I focus on situations in which the uninformed player’s best reply to a commitment action depends on the state and where the total probability of commitment types is sufficiently small. I show that the patient player’s equilibrium payoff is bounded below his commitment payoff in some equilibria under some of his payoff functions. This is because he faces a trade-off between building his reputation for commitment and signaling favorable information about the state. When players’ stage-game payoff functions are monotone-supermodular, the patient player receives high payoffs in all states and in all equilibria. Under an additional condition on the state distribution, my reputation model yields a unique prediction on the patient player’s equilibrium payoff and on-path behavior.

PrEP is an effective daily prevention medicine used to reduce risks of HIV infections. Previous research has pointed out the intention–behavior gap on PrEP uptake among gay men. The current study built on research examining how the integrative model of behavioral prediction (IMBP) factors influences PrEP uptake among gay men to explore how actual control variables, namely skills and environmental constraints, moderated the intention–behavior relationship. We used results from formative interviews to construct a survey and collected data from 420 gay men. Results showed several factors that were associated with PrEP uptake among gay men. Specifically, the lack of access to an LGBTQ-friendly healthcare provider(s) and lack of English fluency had significant main effects on PrEP uptake behavior, while lack of access to a healthcare provider and lack of healthcare system knowledge were significant moderators of the intention–behavior relationship. This study advances our understanding of the IMBP and offers practical implications for PrEP promotion. Limitations and suggestions for future studies are discussed.

Predicting user purchase behavior using shopping history data on e-commerce platforms helps to improve user experience and marketing effect. Our paper uses the time-sliding window method to construct features that mine users’ interest preferences in different periods based on the real interaction records between users and products in e-commerce scenarios. Then, a model for predicting user purchase behavior based on CNN-LSTM is proposed. By automatically extracting and selecting user attributes, product attributes, and user behavioral features, the model is used to predict user purchasing behavior. An online retail platform implements precision marketing using this model. The results show that the calculated values of the marketing effect in the Attention Stage, Interest Stage and Active Participation Stage are between [0.8-1.0], and the effect of Precision Marketing is “Excellent”. The calculated value of the marketing effect in the action stage and repeat purchase stage is between [0.6-0.8], and the effect of precision marketing is “good”. After the implementation of precision marketing, the operating income of e-commerce platform A is increasing, while the operating expense ratio remains stable. This paper’s model can effectively improve consumers’ purchase intention, as evidenced by its findings.

Lack of physical activity (PA) has become an increasingly worrying public health issue among Chinese adolescents. Drawing upon an integrative model of behavioral prediction, this study investigated how adolescents’ favorability and identification of their sporty prototypes were associated with their PA behavior. A sample of 640 Chinese adolescents aged 11–15 years old participated in the survey. The results showed that adolescents’ prototype evaluation and prototype similarity were linked to their PA behavior through injunctive norms, perceived behavioral control, and PA intentions. Moreover, prototype evaluation, but not prototype similarity, was directly linked to PA behavior. These results were similar among boys and girls. The findings highlight the importance of emphasizing health images to campaigns aimed at promoting adolescents’ PA behavior.

A large amount of user data on network platforms contains rich treasures, and the mining and development of user consumption behavior in combination with big data provide new possibilities for enterprise precision marketing. In this paper, a consumption behavior prediction model is constructed based on context-aware data and support vector machine classification algorithm, and interactive information collection is used to collect contextual data of user consumption behavior and consumption behavior cycle data. Combined with the support vector machine classification algorithm, the collected behavioral consumption data is divided into hyperplanes, and for nonlinear data, the method of placing the corresponding behavioral data in hyperplanes in the feature space is proposed. The selection of consumption behavior features is obtained using the min-max normalization process, and behavior prediction is carried out on this basis. The results show that the context-aware behavioral prediction model in this paper has the highest R-value, F1 value and NDCG among all models in Top-10, which are 0.796, 0.645 and 0.878, respectively. The two-stage prediction method in the behavioral prediction model can achieve a 98.15% data capture rate, which can accurately obtain the number of these users so as to formulate an accurate marketing strategy.