The Human Penguin Project: Climate, Social Integration, and Core Body Temperature

IJzerman, Hans; Lindenberg, Siegwart; Dalğar, İlker; Weißgerber, Sophia Christin; Vergara, Rodrigo Clemente; Cairo, Athena H.; Čolić, Marija V.; Dursun, Pınar; Frankowska, Natalia; Hadi, Rhonda; Hall, Calvin S.; Hong, Yeh-Sun; Chuan-Peng, Hu; Joy-Gaba, Jennifer A.; Lazarevic, Dusanka A.; Lazarević, Ljiljana B.; Parzuchowski, Michał; Ratner, Kyle G.; Rothman, David J.; Sim, Samantha; Simão, Cláudia; Song, Mengdi; Stojilović, Darko; Blomster, Johanna Katarina; Brito, Rodrigo; Hennecke, Marie; Jaume-Guazzini, Francisco; Schubert, Thomas W.; Schütz, Astrid; Seibt, Beate; Zickfeld, Janis

doi:10.31234/osf.io/6b7ne

Cited by 13 publications

(22 citation statements)

References 23 publications

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“…Thus, temperature-dependent social behavior is not solely applicable for huddling with one another, but it extends to what kind of predictions we make of others. Perhaps the most sophisticated example is a recent finding that people living further from the equator (typically taken as a proxy for a colder climate) have more diverse social networks, and these diverse social networks protect people's core temperatures from the cold (IJzerman et al, 2016b). And only those who have a romantic relationship get access to those more diverse social networks.…”

Section: Human Cognition: Meet Our Brain the Prediction Machinementioning

confidence: 99%

People as Penguins: Thermoregulation as Part of the Human Essence

IJzerman¹,

Hogerzeil²

2017

Preprint

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In this chapter we provide an overview of the domain of social thermoregulation. Our overview suggests that humans have always relied on, and still rely on, thermoregulation to navigate their social environment. In outlining social thermoregulation as a crucial feature of the human essence, we focus on the continuity of thermoregulatory mechanisms from other animals to humans. We then provide a number of different effects from the social psychological literature that helps us understand how thermoregulation is implicated in social behavior. From there on, we seek to understand our brain as a “hierarchically organized prediction machine” that helps us keep temperature at optimal levels. By then, it should be clear how thermoregulation is part of our human essence, how it changes our thoughts about others, and alters our motivations to be with others. We end by pointing to the implications of understanding thermoregulation as human essence.

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Section: Human Cognition: Meet Our Brain the Prediction Machinementioning

confidence: 99%

People as Penguins: Thermoregulation as Part of the Human Essence

IJzerman¹,

Hogerzeil²

2017

Preprint

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“…Our analyses simply provide a starting point to know with which variables to proceed (for one example of an approach starting with machine learning and proceeding into a mediation model, see IJzerman et al, 2017). What random forests do show, however, is whether a variable predicts the outcome measure -Stroop performance in this case -or not in a given dataset.…”

Section: Discussionmentioning

confidence: 99%

What Predicts Stroop Performance? A Conditional Random Forest Approach

et al. 2016

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An experimental science relies on solid and replicable results. The last few years have seen a rich discussion on the reliability and validity of psychological science and whether our experimental findings can falsify our existing theoretical models. Yet, concerns have also arisen that this movement may impede new theoretical developments. In this article, we reanalyze the data from a crowdsourced replication project that concluded that lab site did not matter as predictor for Stroop performance, and, therefore, that there were no "hidden moderators" (i.e., context was likely to matter little in predicting the outcome of the Stroop task). The authors challenge this conclusion via a new analytical method-supervised machine learning -that "allows the data to speak." The authors apply this approach to the results from a Stroop task to illustrate the utility of machine learning and to propose moderators for future (confirmatory) testing. The authors discuss differences with some conclusions of the original article, which variables need to be controlled for in future inhibitory control tasks, and why psychologists can use machine learning to find surprising, yet solid, results in their own data. What Predicts Stroop Performance? A Conditional Random Forest ApproachAn experimental science relies on solid and replicable results. The last few years have seen a rich discussion on the reliability and validity of psychological science and whether our experimental findings can falsify our existing theoretical models (Coyne, 2016). Arguably, one of the most important events calling into question the solidity of psychological science was the publication on precognition by Daryl Bem (2011) followed by a paper on data contingent analyses by Simmons, Nelson, and Simonsohn (2011). Following these papers, the times, they have been a-changing: Extensive discussions have been held on power, replicability, pre-registration, and context sensitivity (Bakker, Hartgertink, Wicherts, & Van der Maas, in press;Brandt, IJzerman et al., 2014, Van Bavel, Mende-Siedlecki, Brady, & Reinero, 2016IJzerman et al., 2016). Though Bem's (2011) paper was perhaps the most prominent, problems seem to be widespread, and these problems are often blamed on the fact that novelty has been championed over solid and replicable findings (Coyne, 2016;Nosek, Spies, & Motyl, 2012). Yet, this begs the question: How can we retain creative and innovative exploration in service of finding out "truths" about human functioning? Currently, the most popular way to do so is to conduct an experiment, explore the data, write a paper, replicate this study, and, if it fails, update one's theoretical assumptions (and perhaps argue on social media for a while). This approach is incredibly sensitive to post hoc interpretation and is inefficient. Indeed, there are near infinite reasons why effects replicate or not and failures to replicate often do little to inform theoretical predictions (but, see Brandt, IJzerman, et al., 2014). A number of recent articles were publishe...

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“…Perhaps the most compelling evidence on attaching in a variety of contexts from recent work on social integration and climatic variation. IJzerman et al (2017b) found in a relatively large sample in 12 different countries that the lower people’s core temperatures, the more they engage in complex social integration (i.e., engage in contact with different people in different social contexts); they also found that this integration buffers their core against distance from the equator (as a proxy for colder climates). In short, the available evidence is strongly in favor of the idea that people’s social networks – even the more complex ones – protect them from the cold, and that humans adapt their social behaviors and cognitions to temperature changes.…”

Section: Why Social Thermoregulation Is Vital For Co-regulation: the mentioning

confidence: 99%

“…Applying this approach to psychological science, IJzerman et al (2017b) classified complex social integration as one of the most important predictors of core body temperature. Using a similar approach, researchers and therapists can easily identify whether responsive co-thermoregulation is one of the most important predictors of relationship quality (or not), and which types of oscillation patterns are ideal for high quality relationships.…”

Section: Getting From Here To There: Research Therapists Can Domentioning

confidence: 99%

Modernizing Relationship Therapy through Social Thermoregulation Theory: Evidence, Hypotheses, and Explorations

et al. 2017

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In the present article the authors propose to modernize relationship therapy by integrating novel sensor and actuator technologies that can help optimize people’s thermoregulation, especially as they pertain to social contexts. Specifically, they propose to integrate Social Thermoregulation Theory ( IJzerman et al., 2015a ; IJzerman and Hogerzeil, 2017 ) into Emotionally Focused Therapy by first doing exploratory research during couples’ therapy, followed by Randomized Clinical Trials (RCTs). The authors thus suggest crafting a Social Thermoregulation Therapy (STT) as enhancement to existing relationship therapies. The authors outline what is known and not known in terms of social thermoregulatory mechanisms, what kind of data collection and analyses are necessary to better understand social thermoregulatory mechanisms to craft interventions, and stress the need to conduct RCTs prior to implementation. They further warn against too hastily applying these theoretical perspectives. The article concludes by outlining why STT is the way forward in improving relationship functioning.

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The Human Penguin Project: Climate, Social Integration, and Core Body Temperature

Cited by 13 publications

References 23 publications

People as Penguins: Thermoregulation as Part of the Human Essence

People as Penguins: Thermoregulation as Part of the Human Essence

What Predicts Stroop Performance? A Conditional Random Forest Approach

Modernizing Relationship Therapy through Social Thermoregulation Theory: Evidence, Hypotheses, and Explorations

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