Violent Attitudes and Antisocial Behavior: Examining the Code of the Street’s Generalizability among a College Sample

Drawing on Elijah Anderson’s (1999) Code of the Street thesis, this study assesses the generalizability of street code attitudes. Using a sample of college students from a large Midwest university, the current study contributes to the literature by examining: (1) the generalizability of street code attitudes; (2) the correlates of street code attitudes, including various forms of strain; and (3) the predictive power of street code attitudes on criminal and non-criminal behavior. Our results reveal that street code attitudes are generalizable to college students, strain constructs are important correlates of street code attitudes, and street code attitudes are associated with criminal behavior. However, the effect of street code attitudes on criminal behavior becomes non-significant once measures of strain are included. Further, street code attitudes do not predict non-criminal behavior.     

Effects of alcohol,rumination, and gender on the time course of negative affect

This study modelled associations between gender, ruminative cognitive style, alcohol use, and the time course of negative affect over the course of 43,111 random assessments in the natural environment. Participants (N?=?263) completed 49 days of experience sampling over 1.3 years. The data indicated that rumination at baseline was positively associated with alcohol dependence symptoms at baseline as well as higher negative affect over the course of the study. Consistent with negative reinforcement models, drinking served to decrease the persistence of negative affect from moment to moment. However, this ameliorative effect of drinking was evident only among women, suggesting an increased risk for negative reinforcement driven drinking behaviour. In addition, rumination appeared to counteract the desired effects of alcohol on mood among women. This suggests that women who ruminate more may be motivated to consume larger amounts of alcohol to achieve the desired effects. Overall, the results indicate that ruminative cognitive style and the persistence of negative affect from moment to moment may reflect an individual vulnerability for the development of alcohol use disorder especially among women.     

Explanation can cause Forgetting: Memory Dynamics in the Generation of New Arguments

Retrieval-induced forgetting is observed when the retrieval of target information causes the forgetting of nontarget information. The present study investigated whether similar dynamics occur in the context of generating arguments in the process of explanation. Participants studied arguments associated with several issues before attempting to think of new arguments pertaining to a subset of those issues. When given a later memory test, participants were less likely to recall the studied arguments if they had attempted to think of new arguments than if they had not. This argument-induced forgetting effect was observed regardless of whether participants attempted to generate arguments that either agreed or disagreed with the position of the arguments they studied. The effect was significantly reduced, however, and even numerically reversed, when participants generated arguments that were highly related to the studied arguments. This finding fits well with previous research on retrieval-induced forgetting, which has shown that the retrieval or generation of new information fails to cause the forgetting of old information when the two types of information are well integrated or semantically associated.     

Unpacking buyer-seller differences in valuation from experience: A cognitive modeling approach

People often indicate a higher price for an object when they own it (i.e., as sellers) than when they do not (i.e., as buyers)—a phenomenon known as the endowment effect. We develop a cognitive modeling approach to formalize, disentangle, and compare alternative psychological accounts (e.g., loss aversion, loss attention, strategic misrepresentation) of such buyer-seller differences in pricing decisions of monetary lotteries. To also be able to test possible buyer-seller differences in memory and learning, we study pricing decisions from experience, obtained with the sampling paradigm, where people learn about a lottery’s payoff distribution from sequential sampling. We first formalize different accounts as models within three computational frameworks (reinforcement learning, instance-based learning theory, and cumulative prospect theory), and then fit the models to empirical selling and buying prices. In Study 1 (a reanalysis of published data with hypothetical decisions), models assuming buyer-seller differences in response bias (implementing a strategic-misrepresentation account) performed best; models assuming buyer-seller differences in choice sensitivity or memory (implementing a loss-attention account) generally fared worst. In a new experiment involving incentivized decisions (Study 2), models assuming buyer-seller differences in both outcome sensitivity (as proposed by a loss-aversion account) and response bias performed best. In both Study 1 and 2, the models implemented in cumulative prospect theory performed best. Model recovery studies validated our cognitive modeling approach, showing that the models can be distinguished rather well. In summary, our analysis supports a loss-aversion account of the endowment effect, but also reveals a substantial contribution of simple response bias.     

Teaching children with autism to follow gaze shift: A brief report on three cases

This study evaluated the extent to which prompting and reinforcement increased three participants' correct selections of items following a therapist's gaze shift using a non‐concurrent multiple baseline design for two participants and an ABCD design for one participant. Results show that each participant learned to discriminate an adult's gaze direction to make a correct selection and each participant's responding generalized to selections with non‐reinforcing stimuli. Two participants also displayed the skill during probe sessions with their parents; however, the third participant required tangible reinforcement for correct selections to demonstrate the skill with a parent.     

Cumulative burden of comorbid mental disorders,substance use disorders,chronic medical conditions,and poverty on health among adults in the U.S.A.

The health of individuals in the U.S.A. is increasingly being defined by complexity and multimorbidity. We examined the patterns of co-occurrence of mental illness, substance abuse/dependence, and chronic medical conditions and the cumulative burden of these conditions and living in poverty on self-rated health. We conducted a secondary data analysis using publically-available data from the National Survey on Drug Use and Health (NSDUH), which is an annual nationally-representative survey. Pooled data from the 2010–2012 NSDUH surveys included 115,921 adults 18 years of age or older. The majority of adults (52.2%) had at least one type of condition (mental illness, substance abuse/dependence, or chronic medical conditions), with substantial overlap across the conditions. 1.2%, or 2.2 million people, reported all three conditions. Generally, as the number of conditions increased, the odds of reporting worse health also increased. The likelihood of reporting fair/poor health was greatest for people who reported AMI, chronic medical conditions, and poverty (AOR = 9.41; 95% CI: 7.53–11.76), followed by all three conditions and poverty (AOR = 9.32; 95% CI: 6.67–13.02). For each combination of conditions, the addition of poverty increased the likelihood of reporting fair/poor health. Traditional conceptualizations of multimorbidity should be expanded to take into account the complexities of co-occurrence between mental illnesses, chronic medical conditions, and socioeconomic factors.     

Cognitive and neural consequences of memory suppression in major depressive disorder

Negative biases in cognition have been documented consistently in major depressive disorder (MDD), including difficulties in the ability to control the processing of negative material. Although negative information-processing biases have been studied using both behavioral and neuroimaging paradigms, relatively little research has been conducted examining the difficulties of depressed persons with inhibiting the retrieval of negative information from long-term memory. In this study, we used the think/no-think paradigm and functional magnetic resonance imaging to assess the cognitive and neural consequences of memory suppression in individuals diagnosed with depression and in healthy controls. The participants showed typical behavioral forgetting effects, but contrary to our hypotheses, there were no differences between the depressed and nondepressed participants or between neutral and negative memories. Relative to controls, depressed individuals exhibited greater activity in right middle frontal gyrus during memory suppression, regardless of the valence of the suppressed stimuli, and differential activity in the amygdala and hippocampus during memory suppression involving negatively valenced stimuli. These findings indicate that depressed individuals are characterized by neural anomalies during the suppression of long-term memories, increasing our understanding of the brain bases of negative cognitive biases in MDD.     

Speaking two “Languages” in America: A semantic space analysis of how presidential candidates and their supporters represent abstract political concepts differently

In this article we report a computational semantic analysis of the presidential candidates’ speeches in the two major political parties in the USA. In Study One, we modeled the political semantic spaces as a function of party, candidate, and time of election, and findings revealed patterns of differences in the semantic representation of key political concepts and the changing landscapes in which the presidential candidates align or misalign with their parties in terms of the representation and organization of politically central concepts. Our models further showed that the 2016 US presidential nominees had distinct conceptual representations from those of previous election years, and these patterns did not necessarily align with their respective political parties’ average representation of the key political concepts. In Study Two, structural equation modeling demonstrated that reported political engagement among voters differentially predicted reported likelihoods of voting for Clinton versus Trump in the 2016 presidential election. Study Three indicated that Republicans and Democrats showed distinct, systematic word association patterns for the same concepts/terms, which could be reliably distinguished using machine learning methods. These studies suggest that given an individual’s political beliefs, we can make reliable predictions about how they understand words, and given how an individual understands those same words, we can also predict an individual’s political beliefs. Our study provides a bridge between semantic space models and abstract representations of political concepts on the one hand, and the representations of political concepts and citizens’ voting behavior on the other.