Jurnal Online STTKD (Sekolah Tinggi Teknologi Kedirgantaraan)
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GORIC Evidence Aggregation: Combining Statistical Evidence for a Central Theory from Diverse Studies using an AIC-type Criterion
No full textIn social and behavioral science, the gold standard for scientific evidence is finding results that are consistent across independent studies. To summarize results from multiple studies, parameter estimates are conventionally aggregated with meta-analysis. However, this method is limited to studies that share the same context and design, which often means that a wealth of information remains unexploited. This paper proposes evidence aggregation using GORIC(A) weights: an alternative and/or complementary statistical tool for the aggregation of evidence across studies. Rather than aggregating parameter estimates to come to an overall estimate, GORIC(A) evidence aggregation combines support for a shared central theory and quantifies the overall support. It does so using GORIC(A), an information criterion that can evaluate both equality and inequality/order restrictions. GORIC(A) can be applied to a single study, and this GORIC(A) evidence can be aggregated over multiple studies, irrespective of context or design. The method is validated with a simulation study that shows that GORIC(A) evidence aggregation is not affected by study heterogeneity and can be used for evidence synthesis. This implies that GORIC(A) evidence aggregation can successfully combine evidence for a central theory over a widely diverse set of studies. This increases the available information to investigate a theory. Furthermore, GORIC(A) evidence aggregation aids in robustness and confidence of results because it can take into account the results of all type of studies that examine the central theory
Similarity is Associated With Where Repeated Event Memories Fall on the Semantic-Episodic Continuum
No full textMemories of repeated events are one form of memory thought to be intermediate on a proposed semantic-episodic continuum. However, it is not yet understood where repeated event memories fall on this continuum, and which factors may be associated with greater or lesser reliance on episodic and semantic memory during recall. We investigated similarity amongst instances of repeated events as one factor which may be associated with where repeated events fall on the semantic-episodic continuum. In two preregistered studies we asked participants to recall three repeated event memories from their own lives (N1 = 97 participants, 291 memories; N2 = 419 participants, 1257 memories) and report on the similarity amongst instances as well as the degree to which they relied on semantic memory, a single episode, and a mix of episodes in their recall of each event. In line with our predictions, similarity was positively correlated with reliance on semantic memory in both studies. In Study 2, similarity was negatively correlated with reliance on a single episode. We also conducted exploratory latent profile analyses using our three memory reliance variables, revealing three types of repeated event memories. In both studies, similarity of place and emotional arousal were each associated with different memory profiles. Our findings highlight the importance of considering similarity in basic and applied repeated event memory research, as different conditions of similarity (e.g., low versus high) can manifest in different patterns of reliance on episodic and semantic memory
Spotting false news and doubting true news: a systematic review and meta-analysis of news judgements
No full textHow good are people at judging the veracity of news? We conducted a systematic literature review and pre-registered meta-analysis of 303 effect sizes from 67 experimental articles evaluating accuracy ratings of true and fact-checked false news (N = 194,438 from 40 countries across 6 continents). We found that people rated true news as more accurate than false news (Cohen’s d = 1.12 [1.01, 1.22]) and were better at rating false news as false than at rating true news as true (Cohen’s d = 0.32 [0.24, 0.39]). In other words, participants were able to discern true from false news and erred on the side of skepticism rather than credulity. We found no evidence that the political concordance of the news had an effect on discernment, but participants were more skeptical of politically discordant news (Cohen’s d = 0.78 [0.62, 0.94]). These findings lend support to crowdsourced fact-checking initiatives and suggest that, to improve discernment, there is more room to increase the acceptance of true news than to reduce the acceptance of fact-checked false news
A Responsible Framework for Super-Alignment: Holistic Perspectives for Human-Machine Interaction are All You Need
No full textThis paper deals with the recently proposed topic of super-alignment in the context of superintelligence and Artificial Intelligence (AI) in general. In order to study this problem or task, the authors claim that there needs to be a specific framework or roadmap that tackle it in a holistic way, before conducting any experimental work. The framework proposed in this paper is based on Generalized Dynamical Systems, which are Dynamical Systems, but examined under the prism of the GUT-AI theory, which was also recently proposed. The paper draws analogies from the smallest particles in the universe to the largest celestial bodies, along with every physical, biophysical and biological system in-between (e.g., biological evolution, biological neurons in the human brain or humans as part of communities in a social network) in order to formulate the framework to study super-alignment, which is enabled by the interaction and exchange of information (i.e. communication) between humans and artificial forms of life. This includes the exploration of various metrics to measure super-alignment, along with all the accompanying computational models and dynamics that such a superintelligent system must possess. Since this is a necessarily interdisciplinary, multidisciplinary and transdisciplinary field, this paper considers approaches from various fields, such as Systems Theory, Complex Systems, Network Science, Machine Learning (ML), Signal Processing, Natural Language Processing, Information Theory, Game Theory, Control Theory, Optimization, Collective Intelligence and Self-Organization, and others. The research findings of this work are that (a) seven properties of all Generalized Dynamical Systems are explicitly systematized and standardized, and (b) one-sided approaches should be avoided, since every such property of Generalized Dynamical Systems must be considered and blended together for a crucial aspect such as super-alignment. Additionally, this work makes significant theoretical contributions by further expanding the GUT-AI theory
Media platforming and the normalisation of extreme right views
No full textAs extreme political views gain popularity and acceptability, the conditions under which media exposure to extreme right views contributes to this process, and strategies to counter media-induced persuasion and normalisation effects, remain unclear. Using population-based experiments leveraging real-world interviews with extreme right activists on Sky News UK and Australia, we test if media exposure leads to higher agreement with extreme right statements. We also test if exposure affects perceptions of how many others agree with these statements. Our findings are consistent across both countries: exposure to uncritical interviews increases agreement with extreme statements and perceptions of broader support in the population. Testing the media strategy in the UK, we find that critical interviewing tarnishes the activist’s image and reduces effects but still heightens perceived support for extreme statements. This study identifies a mechanism through which extreme political ideas spread and offers insights into media strategies to counteract persuasion and normalisation effects
Different functions of physical effort: A scoping review of the value of physical effort in physical activity and sports
No full textPsychologists have long been intrigued by effort and its underlying motivations. Effort serves multiple functions: a costly instrument, a reward, and adding value to outcomes. Although physical effort is a defining concept of sports, work on conceptualizing its functions in this context is lacking. Here, we aim to fill this gap by examining the existing sports and physical activity literature, especially focusing on how the value-generating functions have been addressed in previous research. To ensure comprehensive coverage, a machine-learning approach (ASReview) was employed for efficient paper screening, resulting in a total of N = 20 relevant articles from an initial pool of 28,079 identified articles. Several of these articles provided evidence that physical effort can be inherently rewarding and add value to an outcome. Moreover, several research focal points could be identified: the generalization of effort's value across domains, developmental aspects, neural correlates of effort valuation, and different measurement approaches used to assess the value of physical effort. There is still a dearth of research investigating the value of physical effort in sports and exercise. A coherent theoretical framework, such as the Expected Value of Control theory and standardized measurement methods, may aid future research in understanding effort’s different functions
Episode-contingent experience-sampling designs for accurate estimates of autoregressive dynamics
No full textAffect dynamics are often studied by means of first-order autoregressive (AR) modeling applied to intensive longitudinal data. A key target in these studies is the AR parameter, which is often tied conceptually to regulatory behavior in the affective process. The data is typically gathered using experience sampling methods, which are designed to pick up on fluctuations in affective variables as they evolve over time in naturalistic settings. In this manuscript, we compare classical time-contingent sampling designs to episode-contingent sampling designs, which initiate sampling when an emotional episode has been signalled. We define emotional episodes as periods where an affective process strays relatively far away from its mean. Compared to time-contingent designs, episode-contingent designs leverage on increased affective variability, which can have beneficial implications for the precision of the ordinary least squares AR effect estimator. Using an extensive simulation study, we attempt to delineate which characteristics of an episode-contingent design are important to consider, and how these characteristics are related to estimation benefits. We then turn to an empirical illustration, showing how an episode-contingent design can be implemented in practice. We also show that various patterns we expect based on the theoretical parts of the manuscript are recovered in the data. We conclude that episode-contingent designs can have marked benefits for the precision of the AR effect estimator, and discuss a number of challenges when it comes to implementing episode-contingent designs in practice
Personality & Morality 2
No full textFollow-up study to original Personality and Morality project