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Mashup Score: 2Psychological reactance to system-level policies before and after their implementation - 20 hour(s) ago
Governments need to develop and implement effective policies to address pressing societal problems of our time, such as climate change and global pandemics. While some policies focus on changing individual thoughts and behaviors (e.g., informational interventions, behavioral nudges), others involve systemic changes (e.g., car bans, vaccination mandates). Policymakers may use system-level policies to achieve socially desirable outcomes, yet often refrain from doing so because they anticipate public opposition. We propose that people’s psychological reactance driving this opposition is a transient phenomenon that dissipates once system-level policies are in place. Using secondary survey data (N = 53,227) and experimental data (six studies; N = 4,628; all preregistered), we show that psychological reactance to system-level policies is greater when they are planned (ex ante implementation) than when they are already implemented (ex post implementation). We further demonstrate that this eff
Source: osf.ioCategories: General Medicine News, Hem/OncsTweet
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Mashup Score: 3The Causal Cookbook: Recipes for Propensity Scores, G-Computation, and Doubly Robust Standardization - 4 day(s) ago
Recent developments in the causal inference literature have renewed psychologists’ interest in how to improve causal conclusions based on observational data. A lot of the recent writing has focused on concerns of causal identification (under which conditions is it, in principle, possible to recover causal effects?); in this primer, we turn to causal estimation (how do we actually turn the data into an effect estimate?) and modern approaches to it that are commonly used in epidemiology. First, we explain how causal estimands can be defined rigorously with the help of the potential outcomes framework, and we highlight four crucial assumptions necessary for causal inference to succeed (exchangeability, positivity, consistency, and non-interference). Next, we present three types of approaches to causal estimation and compare their strengths and weaknesses: propensity score methods (in which the independent variable is modeled as a function of controls), g-computation methods (in which the
Source: osf.ioCategories: General Medicine News, Hem/OncsTweet
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Mashup Score: 3The Causal Cookbook: Recipes for Propensity Scores, G-Computation, and Doubly Robust Standardization - 10 day(s) ago
Recent developments in the causal inference literature have renewed psychologists’ interest in how to improve causal conclusions based on observational data. A lot of the recent writing has focused on concerns of causal identification (under which conditions is it, in principle, possible to recover causal effects?); in this primer, we turn to causal estimation (how do we actually turn the data into an effect estimate?) and modern approaches to it that are commonly used in epidemiology. First, we explain how causal estimands can be defined rigorously with the help of the potential outcomes framework, and we highlight four crucial assumptions necessary for causal inference to succeed (exchangeability, positivity, consistency, and non-interference). Next, we present three types of approaches to causal estimation and compare their strengths and weaknesses: propensity score methods (in which the independent variable is modeled as a function of controls), g-computation methods (in which the
Source: osf.ioCategories: General Medicine News, Hem/OncsTweet
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Mashup Score: 0
Although morality is often characterized as a set of stable values that are deeply held, we argue that moral expressions are highly malleable and sensitive to social norms. For instance, norms can either lead people to exaggerate their expressions of morality (such as on social media) or restrain them (such as in professional settings). In this paper, we discuss why moral expressions are subject to social influence by considering two goals that govern social influence: affiliation goals (the desire to affiliate with one’s group) and accuracy goals (the desire to be accurate in ambiguous situations). Different from other domains of social influence, we argue that moral expressions often satisfy both affiliation goals (“I want to fit in with the group”) and accuracy goals (“I want to do the right thing”). As such, the fundamental question governing moral expressions is: “what does my group consider moral?” We argue that this central consideration achieves both goals underlying social inf
Source: osf.ioCategories: General Medicine News, Hem/OncsTweet
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Mashup Score: 7
According to various sources the world is likely to witness another pandemic on the scale of COVID-19 in the future. How can the social and behavioral sciences contribute to a successful response? Here we conduct a cost-effectiveness analysis of an under-evaluated yet promising tool from modern social and behavioral science: the randomized controlled trial conducted in an online survey environment (“in-survey RCT”). Specifically, we analyze whether, in a pandemic context, a public health campaign that uses an in-survey RCT to pre-test two or more different message interventions — and then selects the top-performing one for their public outreach — has greater impact in expectation than a campaign which does not use this strategy. Our results are threefold. First, in-survey RCT pre-testing is plausibly cost-effective for public health campaigns with typical resources. Second, in-survey RCT pre-testing has potentially powerful returns to scale: for well-resourced campaigns, it looks highl
Source: osf.ioCategories: General Medicine News, Hem/OncsTweet
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Mashup Score: 2That’s a lot to Process! Pitfalls of Popular Path Models - 16 day(s) ago
Path models to test claims about mediation and moderation are a staple of psychology. But applied researchers may sometimes not understand the underlying causal inference problems and thus endorse conclusions that rest on unrealistic assumptions. In this article, we aim to provide a clear explanation for the limited conditions under which standard procedures for mediation and moderation analysis can succeed. We discuss why reversing arrows or comparing model fit indices cannot tell us which model is the right one, and how tests of conditional independence can at least tell us where our model goes wrong. Causal modeling practices in psychology are far from optimal but may be kept alive by domain norms which demand that every article makes some novel claim about processes and boundary conditions. We end with a vision for a different research culture in which causal inference is pursued in a much slower, more deliberate, and collaborative manner.
Source: osf.ioCategories: General Medicine News, Hem/OncsTweet
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Mashup Score: 1
We examined the extent to which constructs and measures have proliferated in psychological sci-ence. We integrated two large databases obtained from the American Psychology Association (APA) that they have used to keep track of constructs, measures, and research in the psychological science literature for the past 30 years. Our descriptive analyses finds that (i) thousands of new constructs and measures are published each year, (ii) most measures are used very few times, and (iii) there is no trend towards consensus or standardization in the use of constructs and measures; in fact, there is a slight trend towards even greater fragmentation over time. That is, constructs and measures are proliferating. We conclude that measurement in the psychological science litera-ture is fragmented, creating problems such as redundancy and confusion, and stifling cumulative scientific progress. We conclude by providing suggestions for what researchers can do about this problem.
Source: osf.ioCategories: General Medicine News, Hem/OncsTweet
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Mashup Score: 0
People seem willing to censor disagreeable political and moral ideas. Five studies explore why people engage in political censorship and test a potential route to decreasing censorship. While Americans report being generally supportive of free speech and against censorship (Study 1), we find that people censor material that seems harmful and false (Study 2)—which are often ideas from political opponents (Study 3). Building on work demonstrating the perceived truth of experiences, we test an experience-sharing intervention that, among college students, decreases the perception that controversial campus speakers are sharing harmful and false ideas related to gun policy, thereby reducing students’ willingness to censor their ideas (Study 4). We also find benefits of experience sharing in the abortion debate—with Americans less willing to censor and report the social media posts of opponents who base their views on lived experiences rather than scientific findings (Study 5).
Source: osf.ioCategories: General Medicine News, Hem/OncsTweet
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Mashup Score: 32
The concept of representations is widely used across the cognitive sciences, but its meaning is highly contested. Representations are often thought of as “vehicles” with “content” – that is, internal physical patterns that are correlated with some state of affairs and that usefully convey that state of affairs to the rest of the neural system or the cognitive economy at large. This raises a number of problems: how does an internal pattern come to be correlated with something else? How does the rest of the system know what the pattern means? How does the system know what to do with that information? I suggest that thinking of representations as vehicles with content presents a stumbling block to answering these questions. Instead, thinking of them simply as meaningful patterns offers a more naturalistic framework for understanding their roles in perception, behavioural control, and cognition. Meaning is not contained within a vehicle – it is relational, contextual, and interpretive. Her
Source: osf.ioCategories: General Medicine News, Future of MedicineTweet
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Mashup Score: 0Skill but not Effort Drive GPT Overperformance over Humans in Cognitive Reframing of Negative Scenarios - 28 day(s) ago
Recent advancements in large language models (LLMs), such as GPT, have led to their implementation in tasks involving emotional support. However, LLM performance has not been compared to humans in both quality and the type of content produced. We examined this question by focusing on the skill of reframing negative situations to reduce negative emotions, also known as cognitive reappraisal. We trained both humans (N= 601) and GPT-4 to reframe negative vignettes (Nreappraisals = 4195) and compared their performance using human raters (N = 1744). GPT-4 outperformed humans on three of the four examined metrics. We investigated whether the gap was driven by effort or skill by incentivizing participants to produce better reappraisals, which led to increased time spent on reappraisals but did not decrease the gap between humans and GPT-4. Content analysis suggested that high-quality reappraisals produced by GPT-4 were associated with being more semantically similar to the emotional scenarios
Source: osf.ioCategories: General Medicine News, Hem/OncsTweet
Psychological reactance to system-level policies before and after their implementation https://t.co/Wht2FHAPTD via @robert_bohm et al https://t.co/V0ZdagrPYg