Advances.in’s Post

💡 Kyuri Park, Lourens J. Waldorp, and Oisín Ryan recently published a groundbreaking paper in advances.in/psychology, renowned for its equitable publishing model that compensates reviewers. Their research focuses on cyclic causal models, which are pivotal for understanding complex psychological phenomena involving feedback loops. By exploring these models, the authors critically advance the methodology used in psychological research, offering a more robust framework for interpreting the dynamic interplay of psychological variables. https://lnkd.in/e_GcNuRm

It's out in Psychological Review! In this paper, we propose a method for testing whether theories explain empirical phenomena. Thanks Riet van Bork, Adam Finnemann, Jonas Haslbeck, Han L.J. van der Maas, Jill de Ron, jan sprenger, and Denny Borsboom! The paper is open-access and available here: https://t.co/JX0bpqOL9W 𝗔 𝗯𝗿𝗶𝗲𝗳 𝘀𝘂𝗺𝗺𝗮𝗿𝘆 𝗼𝗳 𝘁𝗵𝗲 𝗽𝗮𝗽𝗲𝗿: Our paper introduces a framework for evaluating explanations in psychological science. By representing theories as formal models and capturing phenomena as statistical patterns that are observed across studies, we aim to clarify how theories explain these phenomena. Productive explanation involves three steps: a) Explicate the verbal theory as a mathematical or computational model b) Represent the phenomenon as a statistical pattern, c) Simulate data from the model and test if it matches the phenomenon's pattern. We propose three criteria to evaluate explanations: Precision: How much of the formalization is determined by the theory Robustness: How well the statistical pattern is produced under parameter variations Empirical Relevance: The necessity of theory components in the model. We demonstrate our framework on the regulatory resource theory of ego depletion. This theory is much debated and replication studies failed to observe the effect of ego depletion under rigorous conditions. We draw some interesting conclusions which I'm not going to spoil here ;-)

🚀 Excited to share our latest publication in Scientific Reports research! 🚀 Together with Gökhan Aydogan, Gene Brewer, and Samuel M. McClure, I co-authored a paper titled "Decoding the Influence of Emotional and Attentional States on Self-Control Using Facial Analysis." Our study investigates how changes in attention and emotion impact self-control—a key factor in achieving long-term goals related to health and financial well-being. Using emotion recognition software, we analyzed facial expressions to understand how attentional and emotional states influence self-control. Our findings show a fascinating dissociation: Cognitive tasks are primarily affected by changes in attention. Social preference tasks, however, are more influenced by emotional valence. These insights not only advance psychological and economic models of self-control but also have real-world implications for policies aimed at reducing self-control lapses and their potential costs. If you're interested in the intersection of economics, psychology, and behavioral science, I’d love to connect and discuss further! #SelfControl #Attention #FacialAnalysis #UltimatumGame

This is a clarifying piece about the nature of research itself, one that goes beyond its implications for psychology: "On one hand, it is a serious error to repeatedly expect dramatically larger x-y relationships than what we typically find... On the other hand, overestimating the importance of a given variable while we are thinking about it may be the only way we can think about it in the first place. Put differently, the mental acuity afforded by zooming in on one x-y relationship may be possible only while ignoring myriad other causes and moderators that likely diminish this relationship."

Have you ever felt discouraged by small effect sizes in your research? 🤔 Gandhi, Benjamin, Manning, and Duckworth (2014) offer a good perspective in their article. They suggest comparing findings to similar studies rather than using standard thresholds (e.g., ES < 0.3 = small). For instance, an effect size of 0.09 may not be as small if past research indicates 0.2. Conversely, an effect size of 0.31 could actually be substantial! 💡 The authors emphasize that small effect sizes shouldn't surprise us. In fields like psychology (and education), our variable of interest "x" is unlikely only to affect "y" or has the largest effect. Instead, multiple factors often contribute to the outcome. So, we shouldn't be surprised if the variable we're studying shows a small effect size when there are many contextual influences at play. Interesting, don’t you think? #Research #EffectSize #Psychology #Education #ComplexSystem Emma Naslund-Hadley, Mel Hyeri Yang, Ana Medina, Victor Saavedra Mercado, Sebastian Montano, Carlos Felipe Balcazar, Hugo Ñopo, Lesbia Maris, Mariana Pinzon-Caicedo, OECD Education and Skills, David DuBois, Ting Dai, Dario Maldonado Carrizosa

This is very interesting (and relevant to graduate students 😅). The paper also notes how the focusing illusion leads to underestimating non-focal variables, which reminds me of a recent study showing AI alone outperformed human + AI teams in diagnoses. This makes me wonder if AI could also better identify research gaps, avoiding this particular cognitive bias.

I really enjoyed this article! The author has a fantastic and unique perspective on how we problem solve and test solutions. A few tastes: "In anything involving psychology, and, you might argue, certain things involving complex systems, actually trying the same thing again and again and again with the expectation that it might work one time is not necessarily a definition of insanity; it might be a definition of complexity." "That’s one of my final creative lessons for behavioral science. Don’t just test the things that make sense. Test the things that don’t make any sense. Then, if you find that they work, you’ve learned something valuable. Actually, you’ve learned something mega valuable, because it’s something that nobody else knows, because the odds are nobody else has been wacko enough to test it."

Statistics in human sciences often focus on measures like the mean and median, which can fail to capture the full complexity of individual experiences and societal dynamics, especially in fields like modern psychology. It seems to me that the truth often lies in the outliers (though I'm neither a mathematician nor a psychologist; this is just my intuition). For instance, consider the genetic basis of depression in identical twins. Studies show that if one twin develops clinical depression, there is a 50% chance the other twin will also develop it. However, this raises a crucial question: is this correlation due to genetics or the shared environment? In my opinion, there are no measures can answer that.

We've (very) carefully cherry-picked the most valid and reliable set of items for the short form of the Sensory Processing Sensitivity Questionnaire. Our report includes a 2x3 grid of fancy colored item information curves & full-width table - now available on European Journal of Psychological Assessment with Veronique De Gucht #SensoryProcessingSensitivity #MultidimensionalGradedResponseModel

This Psychology Journal BANNED p-values (Should we too?) BASP banned p values in 2015 and it's not the only one. Now I agree with their claim that crossing the 0.05 threshold isn’t difficult (especially with low power). The threshold of 0.05 is relatively arbitrary since Ronald Fisher wrote about it and there’s been quite some criticism against that specific value too along with other critiques, you can read the statement by the American Statistical Association on the same (Wasserstein & Lazar, 2016). Their reasoning does follow to some extent that the p value may not be the best option but I personally don’t think it needs to be entirely removed or replaced, p values are still pretty cool if used correctly (Lakens, 2021)— that’s the most I will compliment the frequentist position 🙃. Some, myself included, advocate for other values like the Bayes Factor (BF). Greenland (2019) offers a solution in the form of s-values or suprisal values in which they use Shannon transform (no, not me 😭) where S = –log2(p). But this too has been criticised for simply presenting the p value in a different way. The journal above is also against the approach I like - Bayesian statistics. I was a little shocked but once I read their position I felt they weren’t wrong. Basically Bayes requires a prior usually which, when there is little data, typically ends up following the Laplace rule (uniform probability assigned to each possibility). So they state “with respect to Bayesian procedures, we reserve the right to make case-by-case judgments, and thus Bayesian procedures are neither required nor banned from BASP”. They conclude “The null hypothesis significance testing procedure (NHSTP) has dominated psychology for decades; we hope that by instituting the first NHSTP ban, we demonstrate that psychology does not need the crutch of the NHSTP”. What do you think? #psychology #statistics #psychologyjournal #psychologyresearch