scholarly journals No evidence for trait- and state-level urgency moderating the daily association between negative affect and subsequent alcohol use in two college samples

2022 ◽  
Author(s):  
Jonas Dora ◽  
Megan Elizabeth Schultz ◽  
Christine M Lee ◽  
Yuichi Shoda ◽  
Kevin Michael King
Keyword(s):  
Alcohol Use ◽  
Negative Affect ◽  
Model Comparison ◽  
Negative Reinforcement ◽  
Affective State ◽  
Secondary Data ◽  
State Level ◽  
Model Complexity ◽  
Previous Night ◽  
Bayesian Model Comparison

It remains unclear whether the negative reinforcement pathway to problematic drinking exists, and if so, for whom. One idea that has received some support recently is that people who tend to act impulsively in response to negative emotions (i.e., people high in negative urgency) may specifically respond to negative affect with increased alcohol consumption. We tested this idea in a preregistered secondary data analysis of two ecological momentary assessment studies using college samples. Participants (N = 226) reported on their current affective state multiple times per day and the following morning reported alcohol use the previous night. We assessed urgency both at baseline and during the momentary affect assessments. Results from our Bayesian model comparison procedure, which penalizes increasing model complexity, indicate that no combination of the variables of interest (negative affect, urgency, and the respective interactions) outperformed a baseline model that included two known demographic predictors of alcohol use. A non- preregistered exploratory analysis provided some evidence for the effect of daily positive affect, positive urgency, as well as their interaction on subsequent alcohol use. Taken together, our results suggest that college students’ drinking may be better described by a positive rather than negative reinforcement cycle.

scholarly journals Negative Affect Induces Rapid Learning of Counterfactual Representations: A Model-based Facial Expression Analysis Approach

2019 ◽  
Author(s):  
Nathaniel Haines ◽  
Olga Rass ◽  
Yong-Wook Shin ◽  
Joshua W. Brown ◽  
Woo-Young Ahn
Keyword(s):  
Facial Expression ◽  
Negative Affect ◽  
Expression Analysis ◽  
Model Comparison ◽  
Empirical Support ◽  
Functional Changes ◽  
Model Based ◽  
Facial Expression Analysis ◽  
Bayesian Model Comparison ◽  
Counterfactual Emotions

AbstractCounterfactual emotions including regret and disappointment play a crucial role in how people make decisions. For example, people often behave such that their decisions minimize potential regret or disappointment and therefore maximize subjective pleasure. Importantly, functional accounts of emotion suggest that the experience and future expectation of counterfactual emotions should promote goal-oriented behavioral change. Although many studies find empirical support for such functional theories, the cognitive-emotional mechanisms through which counterfactual thinking facilitates changes in behavior remain unclear. Here, we leverage computational models of risky decision-making that extend regret and disappointment theory to experience-based tasks, which we use to determine how people learn counterfactual representations of their decisions across time. Further, we use computer-vision to detect positive and negative affect (valence) intensity from participants’ faces in response to feedback, which we use to determine how experienced emotion may influence cognitive mechanisms of learning, reward sensitivity, or exploration/exploitation—any of which could result in functional changes in behavior. Using hierarchical Bayesian modeling and Bayesian model comparison methods, we found that: (1) people learn to explicitly represent and subjectively weight counterfactual outcomes with increasing experience, and (2) people update their counterfactual expectations more rapidly as they experience increasingly intense negative affect. Our findings support functional accounts of regret and disappointment and demonstrate the potential for computational modeling and model-based facial expression analysis to enhance our understanding of cognition-emotion interactions.

scholarly journals Balancing Accuracy and Parsimony in Genetic Programming

1995 ◽  
Vol 3 (1) ◽  
pp. 17-38 ◽  
Author(s):  
Byoung-Tak Zhang ◽  
Heinz Mühlenbein
Keyword(s):  
Genetic Programming ◽  
Adaptive Learning ◽  
Model Comparison ◽  
Model Complexity ◽  
Underlying Structure ◽  
Network Synthesis ◽  
Inference Problem ◽  
Bayesian Model Comparison ◽  
Fundamental Relationship ◽  
Representation Scheme

Genetic programming is distinguished from other evolutionary algorithms in that it uses tree representations of variable size instead of linear strings of fixed length. The flexible representation scheme is very important because it allows the underlying structure of the data to be discovered automatically. One primary difficulty, however, is that the solutions may grow too big without any improvement of their generalization ability. In this article we investigate the fundamental relationship between the performance and complexity of the evolved structures. The essence of the parsimony problem is demonstrated empirically by analyzing error landscapes of programs evolved for neural network synthesis. We consider genetic programming as a statistical inference problem and apply the Bayesian model-comparison framework to introduce a class of fitness functions with error and complexity terms. An adaptive learning method is then presented that automatically balances the model-complexity factor to evolve parsimonious programs without losing the diversity of the population needed for achieving the desired training accuracy. The effectiveness of this approach is empirically shown on the induction of sigma-pi neural networks for solving a real-world medical diagnosis problem as well as benchmark tasks.

scholarly journals Evaluation of quantitative models of the effect of insulin on lipolysis and glucose disposal

2008 ◽  
Vol 295 (4) ◽  
pp. R1089-R1096 ◽  
Author(s):  
Vipul Periwal ◽  
Carson C. Chow ◽  
Richard N. Bergman ◽  
Madia Ricks ◽  
Gloria L. Vega ◽  
...  
Keyword(s):  
Model Comparison ◽  
Tolerance Test ◽  
Intravenous Glucose Tolerance Test ◽  
Model Complexity ◽  
Glucose Disposal ◽  
Intravenous Glucose ◽  
Bayesian Model Comparison ◽  
Plasma Ffa ◽  
Second Category ◽  
Glucose Dynamics

The effects of insulin on the suppression of lipolysis are neither fully understood nor quantified. We examined a variety of mathematical models analogous to the minimal model of glucose disposal (MMG) to quantify the combined influence of insulin on lipolysis and glucose disposal during an insulin-modified frequently sampled intravenous glucose tolerance test. The tested models, which include two previously published ones, consisted of separate compartments for plasma free fatty acids (FFA), glucose, and insulin. They differed in the number of compartments and in the action of insulin to suppress lipolysis that decreased the plasma FFA level. In one category of models, a single insulin compartment acted on both glucose and FFA simultaneously. In a second category, there were two insulin compartments, each acting on FFA and glucose independently. For each of these two categories, we tested 11 variations of how insulin suppressed lipolysis. We also tested a model with an additional glucose compartment that acted on FFA. These 23 models were fit to the plasma FFA and glucose concentrations of 102 subjects individually. Using Bayesian model comparison methods, we selected the model that best balanced fit and minimized model complexity. In the best model, insulin suppressed lipolysis via a Hill function through a remote compartment that acted on both glucose and FFA simultaneously, and glucose dynamics obeyed the classic MMG.

Ambulatory assessment of physical pain, emotional distress, and alcohol use

2018 ◽  
Author(s):  
◽  
Ryan W. Carpenter
Keyword(s):  
Chronic Pain ◽  
Alcohol Use ◽  
Negative Affect ◽  
Negative Reinforcement ◽  
Strong Support ◽  
Ambulatory Assessment ◽  
Opioid Use ◽  
Analgesic Effects ◽  
Relationship Of ◽  
The Relationship

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] The available research suggests that many individuals with chronic pain drink alcohol to manage their pain. However, few studies have examined the association of alcohol and pain and none have done so in patients’ daily lives. The goal of this project was to test a theoretical framework for why individuals with chronic pain consume alcohol. The project used ambulatory assessment to investigate three interrelated reasons for using alcohol, namely that alcohol: 1) has analgesic effects similar to prescription opioids’, 2) is negatively reinforcing, and 3) is expected to relieve pain. Secondarily, we examined the relationship of pain and negative affect, and to what degree a baseline pain response obtained during a laboratory task was associated with pain in daily life. Eight-seven outpatients with chronic low back pain (CLBP) who drank alcohol at least twice per week (ALC; n =27), took daily or every-other-day prescribed opioids (OPI; n = 27), neither (NON; n = 26), or both (BOTH; n = 7) were recruited. Analyses focused on the ALC, OPI, and NON groups (n-observations = 6,973). Participants reported on their alcohol and opioid use and expectancies, pain, and negative affect (NA) multiple times daily for two weeks. Results supported the first two hypotheses. There was moderate support that greater pain was associated with later alcohol use, and strong support that alcohol use was associated with pain reductions. Support was also found for the association of opioid use and pain. Effects, though slightly more inconsistent, were also found for NA, suggesting a negative reinforcement process was involved in pain reductions. Contrary to predictions, pain-related expectancies largely did not moderate associations of alcohol and pain, though NA-related expectancies moderated associations of alcohol and NA. Expectancies also moderated the relationship of opioid use with both pain and NA. Thus, the findings suggest that alcohol has meaningful short-term effects in CLBP patients. These effects may put patients at risk for developing alcohol use problems.

scholarly journals Chronic Fentanyl Self-Administration Generates a Shift toward Negative Affect in Rats during Drug Use

2021 ◽  
Vol 11 (8) ◽  
pp. 1064
Author(s):  
Angela N. Dao ◽  
Nicholas J. Beacher ◽  
Vivian Mayr ◽  
Annalisa Montemarano ◽  
Sam Hammer ◽  
...  
Keyword(s):  
Drug Use ◽  
Negative Affect ◽  
Positive Affect ◽  
Chronic Exposure ◽  
Negative Reinforcement ◽  
Affective State ◽  
Sprague Dawley ◽  
Self Administration ◽  
Initial Exposure ◽  
Long Access

Drug addiction is thought to be driven by negative reinforcement, and it is thought that a shift from positive affect upon initial exposure to negative affect after chronic exposure to a drug is responsible for maintaining self-administration (SA) in addicted individuals. This can be modeled in rats by analyzing ultrasonic vocalizations (USVs), a type of intraspecies communication indicative of affective state based on the frequency of the emission: calls in the 22 kHz range indicate negative affect, whereas calls in the 50 kHz range indicate positive affect. We employed a voluntary chronic, long-access model of fentanyl SA to analyze affective changes in the response to chronic fentanyl exposure. Male Sprague-Dawley rats self-administered either fentanyl (N = 7) or saline (N = 6) for 30 consecutive days and USVs were recorded at four different time points: the day before first SA session (PRE), the first day of SA (T01), the last day of SA (T30), and the first day of abstinence (ABS). At T01, the ratio of 50 to 22 kHz calls was similar between the fentanyl and saline groups, but at T30, the ratio differed between groups, with the fentanyl group showing significantly fewer 50 kHz calls and more 22 kHz calls relative to saline animals. These results indicate a shift toward a negative affect during drug use after chronic exposure to fentanyl and support negative reinforcement as a main driving factor of opioid addiction.

scholarly journals Detecting episodes of star formation using Bayesian model selection

2021 ◽  
Vol 502 (3) ◽  
pp. 3993-4008
Author(s):  
Andrew J Lawler ◽  
Viviana Acquaviva
Keyword(s):  
Star Formation ◽  
Bayesian Model ◽  
Model Comparison ◽  
Density Ratio ◽  
Model Complexity ◽  
Spectral Energy ◽  
Analytic Approximation ◽  
Nested Models ◽  
Bayesian Model Comparison ◽  
Correct Model

ABSTRACT Bayesian model comparison frameworks can be used when fitting models to data in order to infer the appropriate model complexity in a data-driven manner. We aim to use them to detect the correct number of major episodes of star formation from the analysis of the spectral energy distributions (SEDs) of galaxies, modelled after 3D-HST galaxies at z ∼ 1. Starting from the published stellar population properties of these galaxies, we use kernel density estimates to build multivariate input parameter distributions to obtain realistic simulations. We create simulated sets of spectra of varying degrees of complexity (identified by the number of parameters), and derive SED fitting results and pieces of evidence for pairs of nested models, including the correct model as well as more simplistic ones, using the bagpipes codebase with nested sampling algorithm multinest. We then ask the question: is it true – as expected in Bayesian model comparison frameworks – that the correct model has larger evidence? Our results indicate that the ratio of pieces of evidence (the Bayes factor) is able to identify the correct underlying model in the vast majority of cases. The quality of the results improves primarily as a function of the total S/N in the SED. We also compare the Bayes factors obtained using the evidence to those obtained via the Savage–Dickey density ratio (SDDR), an analytic approximation that can be calculated using samples from regular Markov Chain Monte Carlo methods. We show that the SDDR ratio can satisfactorily replace a full evidence calculation provided that the sampling density is sufficient.

Positive and Negative Reinforcement of Alcohol Use Scales

2019 ◽  
Author(s):  
Seung Bin Cho ◽  
Jinni Su ◽  
Sally I-Chun Kuo ◽  
Kathleen K. Bucholz ◽  
Grace Chan ◽  
...  
Keyword(s):  
Alcohol Use ◽  
Negative Reinforcement

Bayesian model comparison

2014 ◽  
pp. 101-117
Author(s):  
Michael D. Lee ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Bayesian Model ◽  
Model Comparison ◽  
Bayesian Model Comparison

Variations in economic growth across states in Malaysia: an exploratory analysis

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yusniliyana Yusof ◽  
Kaliappa Kalirajan
Keyword(s):  
Economic Growth ◽  
Socioeconomic Development ◽  
Secondary Data ◽  
State Level ◽  
Bootstrap Sampling ◽  
Content Type ◽  
Estimation Techniques ◽  
Development Index ◽  
Three Stage Least Squares ◽  
Development Gap

PurposeThe study contributes to the aim of regional development policy in reducing regional disparities, by examining the spatial balance in socioeconomic development across the states of Malaysia based on composite development index (CDI). Besides, the study has attempted to understand the issues in the development gaps across Malaysian states by evaluating the factors that explain the variation in economic growthDesign/methodology/approachThis study uses three-stage least squares (3SLS) and bootstrap sampling and estimation techniques to examine the factors that explain the variations in the growth of development across the states in Malaysia. The analysis involves 13 states in Malaysia (Johor, Melaka, Negeri Sembilan, Pulau Pinang, Perak, Perlis, Selangor, Kedah, Kelantan, Pahang, Terengganu, Sabah and Sarawak) from 2005 to 2015.FindingsThe pattern in the spatial socioeconomic imbalance demonstrates a decreasing trend. However, the development index reveals that the performance of less developed states remained behind that of the developed states. The significant factors in explaining the variation in growth across the Malaysian states are relating to agriculture, manufacturing, human capital, population growth, Chinese ethnicity, institutional factors and natural resources.Research limitations/implicationsThe authors focused on Malaysian states over the period between 2005 and 2015. The authors encountered some limitations in obtaining relevant data such as international factors and technological change that might also explain the variation in economic growth as the data on these variables are not reported at the state level. Moreover, the data on GSDP by sector was only available from the year 2005. Second, the study is based on secondary data. Future studies might examine the factors that contribute to the development gap across Malaysian states through interviews or questionnaires and compare the findings with the existing results. Despite its limitations, this study contributes to the existing literature that emphasizes on spatial balance of socioeconomic in a developing country, focusing on Malaysian states.Practical implicationsThese findings provide guidance for policymakers by understanding key potential areas to reduce the disparity in economic growth across Malaysian states by understanding their impact on the growth.Originality/valueThis study employs different method of 3SLS and bootstrap sampling and estimation techniques in examining the factors that explain the variations in the growth of development across the states in Malaysia.

scholarly journals A complete search for redshift z ≳ 6.5 quasars in the VIKING survey

2020 ◽  
Vol 501 (2) ◽  
pp. 1663-1676
Author(s):  
R Barnett ◽  
S J Warren ◽  
N J G Cross ◽  
D J Mortlock ◽  
X Fan ◽  
...  
Keyword(s):  
Model Comparison ◽  
High Redshift ◽  
Detailed Comparison ◽  
Selection Function ◽  
Data Set ◽  
Bayesian Model Comparison ◽  
Fitting Method ◽  
Complete Search ◽  
Potential Contaminant ◽  
Ranked List

ABSTRACT We present the results of a new, deeper, and complete search for high-redshift 6.5 < z < 9.3 quasars over 977 deg2 of the VISTA Kilo-Degree Infrared Galaxy (VIKING) survey. This exploits a new list-driven data set providing photometry in all bands Z, Y, J, H, Ks, for all sources detected by VIKING in J. We use the Bayesian model comparison (BMC) selection method of Mortlock et al., producing a ranked list of just 21 candidates. The sources ranked 1, 2, 3, and 5 are the four known z > 6.5 quasars in this field. Additional observations of the other 17 candidates, primarily DESI Legacy Survey photometry and ESO FORS2 spectroscopy, confirm that none is a quasar. This is the first complete sample from the VIKING survey, and we provide the computed selection function. We include a detailed comparison of the BMC method against two other selection methods: colour cuts and minimum-χ2 SED fitting. We find that: (i) BMC produces eight times fewer false positives than colour cuts, while also reaching 0.3 mag deeper, (ii) the minimum-χ2 SED-fitting method is extremely efficient but reaches 0.7 mag less deep than the BMC method, and selects only one of the four known quasars. We show that BMC candidates, rejected because their photometric SEDs have high χ2 values, include bright examples of galaxies with very strong [O iii] λλ4959,5007 emission in the Y band, identified in fainter surveys by Matsuoka et al. This is a potential contaminant population in Euclid searches for faint z > 7 quasars, not previously accounted for, and that requires better characterization.

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