The Plausibility Problem: The Church And Same-Sex Attraction

Product Information

Abendroth and Richter (2020) presented an information processing account of plausibility. They considered the importance of plausibility judgments in identifying fake news–information on social media that is accidentally or, most commonly, deliberately false. The study suggested that when individuals are presented with a plausible explanation for an unfamiliar scientific phenomenon, their comprehension of the topic improves. Abendroth and Richter (2020) argued that people are more likely to accept and retain information that is presented in a plausible manner, as it aligns with their existing knowledge and beliefs. The results highlighted the importance of making scientific information accessible and plausible for the public to enhance their understanding and engagement with science. The positive predictive value of this study is reflected by the area of TP divided by the area of TP+FP. If we calculate the PPV this study we come up with a value of 0.46. So given a powerful study, and a VERY charitable prior probability for homeopathy, a positive clinical trial result for homeopathy has positive predictive value a little worse than a coin toss. If you lower the prior probability to more accurate .01 (1%) the positive predictive value becomes .14. Systems Engineering Laboratory, Department of Integrated Systems Engineering, College of Engineering, The Ohio State University, Columbus, OH, United States

Double dose of plausibility judgments. Plausibility judgments are embedded in story-building in two ways: in assessing the state transitions within the story, and in the activity of modifying the story in response to plausibility gaps. Lombardi et al. (2016b) hypothesized that information is judged as more plausible if it is less complex and requires less conjecture. They studied the relationship between evaluation, plausibility, and the refutation text effect–the phenomenon where exposure to a refuting text (i.e., a text that contradicts a previous belief) can lead to stronger beliefs in the refuted idea. The authors examined different models that aim to explain this effect, focusing on the role of evaluation (e.g., attitudes, emotions) and plausibility (i.e., the degree to which an idea seems believable). Lombardi et al. (2016b) showed the connection between evaluation, plausibility, and the refutation text effect. Existence of a stopping point. The sensemaking process that utilizes plausibility judgments does not go on forever. The stopping point occurs when the plausibility gap is reduced, and cognitive strain is minimized. This aspect of the Plausibility Transition model is consistent with the Klein et al. (2021) model of the process of explaining. Both of these models call out plausibility of transitions as a part of determining the stopping point.Matsuki et al. (2011) explored how plausibility affects language comprehension in real-time. The study found that people make judgments about the plausibility of events as they are reading, and that these judgments have a direct impact on their comprehension of the text. The results suggested that people use their prior knowledge and expectations to form beliefs about the likelihood of events, and that these beliefs guide their understanding of the text. When scientists design research studies, they hope to obtain a result that leads them to a better understanding of reality. In medicine, studies are frequently undertaken to investigate the effectiveness of a treatment strategy. S. Puntambekar and B. du Boulay. Design and development of MIST-a system to help students develop metacognition. Journal of Educational Computing Research, 16(1):1–35, 1997. Did you ask the right questions?'Friends have challenged me when buying some of the more expensive and important things in life, from a mobile phone contract to a car to a home. My problem is that I haven’t got a clue what the right questions are in those circumstances, so each time I’ve had to phone a friend to help me out. Only then did I get the questions – and answers – I needed.

By adding prior probability we have changed the width of the columns to reflect the difference in probability of the various outcomes. Remember, the columns on the left represent the possible outcomes of an effective treatment, and the columns on the right represent possible outcomes of an ineffecive treatment. The area of each cell represents the relative likelihood of that result, given the parameters specified. A study with the parameters shown above, if it is fair, and free of bias has a low chance of finding any positive result. If the result is positive, however, the areas of the TP and FP cells are approximately the same size, meaning that they have a similar likelihood. T. del Soldato and B. du Boulay. Implementation of motivational tactics in tutoring systems. Journal of Artificial Intelligence in Education, 6(4):337–378, 1996. GK, developed the plausibility transition model, selected and reviewed the cases, wrote the initial draft, and completed the final draft of the manuscript. MJ conducted the literature review, prepared the analysis of the articles that were selected for further examination, and assisted in the editing of the manuscript. RH provided critiques and suggestions for the plausibility transition model, identified additional literature for inclusion in the manuscript, and made numerous editorial recommendations on the manuscript. SM provided valuable recommendations on the plausibility transition model from the very inception of the model. All authors contributed to the article and approved the submitted version. Funding Lombardi et al. (2016a) drew on the work of Kahneman and Klein (2009) in claiming that plausibility judgments can be “automatic” and intuitive (System 1) as well as deliberative and analytical (System 2). Further, the authors argued that analogies can be useful in gauging plausibility. Ed Shaw writes with the insight of a pastor, the boldness of a prophet, the integrity of a true disciple, and the warmth of a friend. This is a hugely important book with a vital message for the whole church. - Sam AllberryWe must work as hard to support single Christians (whatever their sexual orientation) as we do in fighting for the truth of God’s word in the culture and the church. I know of no better resource to help us do both. Thank you Ed Shaw for your honesty, integrity, and Christ-like example to us all.

To identify leverage points for building a story a person has to draw on knowledge of the types of causes for events such as those that triggered the explanation process. This causal set is activated just-in-time in response to surprise or to ignorance. In the course of self-explaining, the causal set will be expanded and deepened, and a person’s mental model will become richer–the overall causal repertoire will be expanded. The leverage points people identify (the causes considered, and the cues noticed) will depend on the sophistication of the person’s mental model and the kinds of stories the person has considered in the past. So, stories determine the leverage points that are identified, and the leverage points identified will activate a set of causes and make certain cues more salient. The process of deepening

The Church and Same-Sex Attraction

In this blog post, Ed Shaw explains why when we talk about sexuality, it is so important to be asking the right questions. Connell (2004) and Connell and Keane (2004) proposed a Knowledge-Fitting Theory which identifies two stages of the plausibility judgment process, a comprehension stage (understanding the scenario) and an assessment stage (examining scenario fit to prior knowledge). To make a plausible judgment, people try to create a mental link between what the scenario describes and the previous knowledge they have about the scenario. The core of the KFT is the strength of relationship between the scenario and prior knowledge. Figure 2 presents the model as a cycle (a closed loop), beginning with the trigger that initiated the story-building process. This trigger is usually a surprise or a desire to overcome ignorance. The loop closes when the plausibility assessment is judged to be sufficient, and the insights gained resolve the triggering conditions. If people determine that the story is not adequately plausible, they try to build a different story and start the process again. Figure 2 emphasizes the function of Assessing plausibility during the story building process. It is the force driving the explanatory process, and is shown in the center as an oval, not a box because it is not a stage in the process. It is included in the figure to show which of the stages are involved in assessing plausibility. Surprise: triggering the explanation process Cognitive strain. These gaps pose problems for assessing the story as plausible versus implausible. The cognitive strain makes it more difficult to accept a story as plausible. This feature aligns with the concept of bridging inferences ( Foster and Keane, 2015).