Decision from Experience Behavior Modeling (DEBM): an open-source Python package for developing, evaluating, and visualizing behavioral models.

The last decade was characterized by an emphasis on enhancing reproducibility and replicability in the social sciences. To contribute to these efforts within the decision-making research field, we introduce DEBM (Decision from Experience Behavior Modeling) – an open-source Python package. The main goal of DEBM is to serve as a central colloberative pool of models and methods in the decision from experience domain. Specifically, it provides a convenient “playground” for developing models or experimenting with existing ones. DEBM includes many features such as multiprocessing, parameter estimation, visualization, and more. In this paper we cover the basic functionality of DEBM by simulating behavior using an existing model and given parameters, and recovering these parameters using grid search.

The Effect of Behavioral Therapy with Modeling Techniques on Changes in the Adherence of Diabetes Mellitus Patients

Diabetes Mellitus (DM) is a disease whose prevalence is not infectious increases with changes in lifestyle. If not managed properly, it will cause various complications that reduce quality of life, increase morbidity and mortality, and harm the economy. The success of DM management is strongly influenced by the patient's adherence to medication and diet. One of the interventions to improve the adherence of DM sufferers is Behavior Therapy with modeling techniques. The purpose of this study was to analyze the effect of behavioral therapy with modeling techniques on changes in adherence of DM patients. The design of this study was quasy-experimental with pretest posttest and control group, on a sample of 40 DM patients with consecutive sampling technique. The intervention was carried out in 4 sessions for 2 weeks. The results of the statistical test paired sample t-test and independent sample t-test obtained p <0.5 (0.000), which meant that there was an effect of behavioral therapy with modeling techniques on changes in adherence DM patient. Changes in compliance that occurred between before and after the intervention was 16.95 points. Modeling technique is behavioral learning through observation of a model who has successfully controlled his illness to emphasize changes in mindset, beliefs, and commitment to a person's new positive behavior. Modeling has an impact not only on imitating, but also adding or subtracting the observed behavior, so that it can be applied to obtain new behavior, leave old negative behavior, and maintain the desired behavior.

The modelling and application of cross-scale human behavior in realizing the shop-floor digital twin

The digital twin shop-floor has received much attention from the manufacturing industry as it is an important way to upgrade the shop-floor digitally and intelligently. As a key part of the shop-floor, humans' high autonomy and uncertainty leads to the difficulty in digital twin modeling of human behavior. Therefore, the modeling system for cross-scale human behavior in digital twin shop-floors was developed, powered by the data fusion of macro-behavior and micro-behavior virtual models. Shop-floor human macro-behavior mainly refers to the role of the human and their real-time position. Shop-floor micro-behavior mainly refers to real-time human limb posture and production behavior at their workstation. In this study, we reviewed and summarized a set of theoretical systems for cross-scale human behavior modeling in digital twin shop-floors. Based on this theoretical system, we then reviewed modeling theory and technology from macro-behavior and micro-behavior aspects to analyze the research status of shop-floor human behavior modeling. Lastly, we discuss and offer opinion on the application of cross-scale human behavior modeling in digital twin shop-floors. Cross-scale human behavior modeling is the key for realizing closed-loop interactive drive of human behavior in digital twin shop-floors.

Influence of the Height in a Colombian Multi-Tunnel Greenhouse on Natural Ventilation and Thermal Behavior: Modeling Approach

The dimensions of a passive greenhouse are one of the decisions made by producers or builders based on characteristics of the available land and the economic cost of building the structure per unit of covered area. In few cases, the design criteria are reviewed and the dimensions are established based on the type of crop and local climate conditions. One of the dimensions that is generally exposed to greater manipulation is the height above the gutter and the general height of the structure, since a greenhouse with a lower height has a lower economic cost. This has led some countries in the tropical region to build greenhouses that, due to their architectural characteristics, have inadequate microclimatic conditions for agricultural production. The objective of this study was to analyze the effect on air flows and thermal distribution generated by the increase of the height over gutter of a Colombian multi-tunnel greenhouse using a successfully two-dimensional computational fluid dynamics (CFD) model. The simulated numerical results showed that increasing the height of the greenhouse allows obtaining temperature reductions from 0.1 to 11.7 °C depending on the ventilation configuration used and the external wind speed. Likewise, it was identified that the combined side and roof ventilation configuration (RS) allows obtaining higher renovation indexes (RI) in values between 144 and 449% with respect to the side ventilation (S) and roof ventilation (R) configurations. Finally, the numerical results were successfully fitted within the surface regression models responses.

Trial-by-trial fluctuations in amygdala activity track motivational enhancement of desirable sensory evidence during perceptual decision-making

People are biased towards seeing outcomes that they are motivated to see. For example, sports fans of opposing teams often perceive the same ambiguous foul in favor of the team they support. Here, we test the hypothesis that amygdala-dependent allocation of visual attention facilitates motivational biases in perceptual decision-making. Human participants were rewarded for correctly categorizing an ambiguous image into one of two categories while undergoing fMRI. On each trial, we used a financial bonus to motivate participants to see one category over another. The reward maximizing strategy was to perform the categorization task accurately, but participants were biased towards categorizing the images as the category we motivated them to see. Heightened amygdala activity preceded motivation consistent categorizations, and participants with higher amygdala activation exhibited stronger motivational biases in their perceptual reports. Trial-by-trial amygdala activity was associated with stronger enhancement of neural activity encoding desirable percepts in sensory cortices, suggesting that amygdala-dependent effects on perceptual decisions arose from biased sensory processing. Analyses using a drift diffusion model provide converging evidence that trial-by-trial amygdala activity was associated with stronger motivational biases in the accumulation of sensory evidence. Prior work examining biases in perceptual decision-making have focused on the role of frontoparietal regions. Our work highlights an important contribution of the amygdala. When people are motivated to see one outcome over another, the amygdala biases perceptual decisions towards those outcomes.Significance StatementForming accurate perceptions of the environment is essential for adaptive behavior. People however are biased towards seeing what they want to see, giving rise to inaccurate perceptions and erroneous decisions. Here, we combined behavior, modeling, and fMRI to show that the bias towards seeing desirable percepts is related to trial-by-trial fluctuations in amygdala activity. In particular, during moments with higher amygdala activity, sensory processing is biased in favor of desirable percepts, such that participants are more likely to see what they want to see. These findings highlight the role of the amygdala in biasing visual perception, and shed light on the neural mechanisms underlying the influence of motivation and reward on how people decide what they see.