Following Health Measures in the Pandemic: A Matter of Values?

Three studies (N = 887) tested the hypothesis that value consistency predicts intended coronavirus disease-2019 (COVID-19) health behaviors and overrides other utility-based motivational factors. Accordingly, Study 1 showed that intentions of social distancing were higher if it was perceived as more value-consistent. The higher value consistency, the less self-interest inconsistency, and the perceived efficacy of social distancing mattered for intentions. On the other hand, Study 2 failed to induce value consistency experimentally. However, correlative results show a moderation pattern similar to Study 1 regarding social distancing intentions, policy support, and devaluation of transgressors. In Study 3, higher value consistency of vaccination reduced the experimental effect of prosocial efficacy but not the effect of self-interest efficacy of the vaccine. The findings are discussed regarding theoretical implications for the interplay of values and utility in motivation. In addition, implications for the potentially ambivalent effects of appealing to values to increase compliance are discussed.

ORGAN WEIGHT INDICES AS INDICATORS FOR ESTIMATING THE EXPERIMENTAL EFFECT OF METAL NITRIDE COATED IMPLANTS ON ANIMAL ORGANISMS

The obtained results confirm the safety of steel 12ХI8Н9Т implants and titanium plus hafnium nitride TiN+NHf coated steel 12ХI8Н9Т implants. Experimental data analysis shows the toxic properties of copper supported by the earlier research on blood morphology of lab rats.

Effect of a hypothetical modified risk tobacco product claim on heated tobacco product use intention and perceptions in young adults

IntroductionModified risk tobacco product (MRTP) claims for heated tobacco products (HTPs) that convey reduced exposure compared with conventional cigarettes may promote product initiation and transition among young people. We assessed the effects of a hypothetical MRTP claim for HTPs on young adults’ intention and perceptions of using HTPs and whether these effects differed by their current cigarette and e-cigarette use.MethodsWe embedded a randomised between-subjects experiment into a web-based survey administered among a cohort of 2354 Southern California young adults (aged 20–23) in 2020. Participants viewed depictions of HTPs with an MRTP claim (n=1190) or no claim (n=1164). HTP use intention and HTP-related harm and use perceptions relative to cigarettes and e-cigarettes were assessed.ResultsOverall, participants who viewed versus did not view the claim did not differ in HTP use intention (28.5% vs 28.7%) but were more likely to perceive HTPs as less harmful than cigarettes (11.4% vs 7.0%; p<0.001). The experimental effect on HTP use intention did not differ among past 30-day cigarette smokers versus non-smokers (interaction adjusted OR (AOR)=0.78, 95% CI 0.36 to 1.76) but differed among past 30-day e-cigarette users versus non-users (interaction AOR=1.67, 95% CI 1.02 to 2.68).DiscussionThe hypothetical MRTP claim may lower young adults’ HTP harm perceptions compared with cigarettes but may not change HTP use intention overall or differentially for cigarette smokers. The larger effect on HTP use intention among e-cigarette users than non-users raises the question of whether MRTP claims may promote HTP use or HTP and e-cigarette dual use among young e-cigarette users.

On the measurement and estimation of cognitive processes with electrophysiological recordings and reaction time modeling

A primary goal in cognitive psychology is to describe the latent information processingunits that operate between the onset of a stimulus and a measured behavior. Mathe-matical models of cognition aim at decomposing behavior into such processing unitsby formalizing an assumed generative model. Unfortunately, a generative model mayexplain the behavioral data while not necessarily reflecting the underlying processes.Obtaining measurements between the stimulus and the responses could provideadditional information that fruitfully constrains the processing assumptions.The present thesis explores this issue by focusing on models of perceptual deci-sion making, a field with a long tradition of cognitive modeling. These models areconstructed to account for decision choices and their durations (reaction time inthe range of a second) on the basis of a decomposition into encoding, decision andresponse execution stages. We used electrophysiological measures (electromyographyand electroencephalography) to decompose each reaction time into different intervals,presumed to contain these stages. Simultaneously, we manipulated time-honoredexperimental factors to compare the cognitive locus of experimental effects inferredfrom both electrophysiological recordings and from model fitting procedures.Throughout four empirical chapters, we show that the inferences drawn from cogni-tive models conflict with the electrophysiological decomposition when: 1) the model’score assumption of independence between decision and non-decision processes isproven to be false; 2) standard modeling strategies are inadequate to capture thelocus of an experimental effect revealed by the electrophysiological decomposition;3) opposite experimental effects are revealed in decision vs. encoding and responseexecution processes.This thorough assessment of a generative model of decision making delineates itsvalidity, merits and limitations to account for the latent cognitive processes. Newinsights are thus provided on the information processes that allow humans to decidebetween alternatives.

Research on the Application of User Recommendation Based on the Fusion Method of Spatially Complex Location Similarity

Since the user recommendation complex matrix is characterized by strong sparsity, it is difficult to correctly recommend relevant services for users by using the recommendation method based on location and collaborative filtering. The similarity measure between users is low. This paper proposes a fusion method based on KL divergence and cosine similarity. KL divergence and cosine similarity have advantages by comparing three similar metrics at different K values. Using the fusion method of the two, the user’s similarity with the preference is reused. By comparing the location-based collaborative filtering (LCF) algorithm, user-based collaborative filtering (UCF) algorithm, and user recommendation algorithm (F2F), the proposed method has the preparation rate, recall rate, and experimental effect advantage. In different median values, the proposed method also has an advantage in experimental results.

Bayes Factors for Mixed Models

Although Bayesian mixed models are increasingly popular for data analysis in psychology and other fields, there remains considerable ambiguity on the most appropriate Bayes factor hypothesis test to quantify the degree to which the data support the presence or absence of an experimental effect. Specifically, different choices for both the null model and the alternative model are possible, and each choice constitutes a different definition of an effect resulting in a different test outcome. We outline the common approaches and focus on the impact of aggregation, the effect of measurement error, the choice of prior distribution, and the detection of interactions. For concreteness, three example scenarios showcase how seemingly innocuous choices can lead to dramatic differences in statistical evidence. We hope this work will facilitate a more explicit discussion about best practices in Bayes factor hypothesis testing in mixed models.

Image Semantic Segmentation Use Multiple-Threshold Probabilistic R-CNN with Feature Fusion

With continuous developments in deep learning, image semantic segmentation technology has also undergone great advancements and been widely used in many fields with higher segmentation accuracy. This paper proposes an image semantic segmentation algorithm based on a deep neural network. Based on the Mask Scoring R-CNN, this algorithm uses a symmetrical feature pyramid network and adds a multiple-threshold architecture to improve the sample screening precision. We employ a probability model to optimize the mask branch of the model further to improve the algorithm accuracy for the segmentation of image edges. In addition, we adjust the loss function so that the experimental effect can be optimized. The experiments reveal that the algorithm improves the results.

The Experimental Effect of Parent Versus Peer Influence on Children’s Physical Activity and Sedentary Behavior

Purpose: To assess children’s physical activity, sedentary behavior, liking, and motivation during 3 separate simulated recess conditions: playing alone, with their parent participating, and with their peer participating. Methods: Children participated in the 3 separate conditions. During each condition, the children had access to an outdoor playground and sedentary activity options for 30 minutes. Accelerometry recorded the physical activity. Time allocated to sedentary options was monitored via a stopwatch. A visual analog scale was used to assess liking, and motivation was assessed as the children’s willingness to participate in an additional 10 minutes of each condition. Results: The children sat 88% less and were 33% more physically active with their peer versus playing alone. The children also sat 65% less during the parent condition than alone. Lastly, the children reported ≥34% liking and were ≥2-fold more likely to participate in the additional 10-minute activity bout during the parent and peer conditions than alone. The differences were significant (P ≤ .05) except for the children’s decision to participate in the additional 10 minutes in the parent versus the alone condition (P = .058). Conclusions: Relative to the alone condition, the presence of a peer or parent reduced sedentary behavior and increased liking and the motivation to participate in that condition. However, only the presence of a peer increased physical activity versus alone.