Experiencing physical warmth promotes interpersonal warmth
Authors: Lawrence E Williams
John A Bargh
Journal: Science (2008)
Discipline: Multidisciplinary
Keywords: Warmth; Attachment theorists; Insula; Physical temperature; Interpersonal warmth
Affiliations: Leeds School of Business, University of Colorado at Boulder, UCB 419, Boulder, CO, 80309-0419, USA. 

Abstract:  "Warmth" is the most powerful personality trait in social judgment, and attachment theorists have stressed the importance of warm physical contact with caregivers during infancy for healthy relationships in adulthood. Intriguingly, recent research in humans points to the involvement of the insula in the processing of both physical temperature and interpersonal warmth (trust) information. Accordingly, we hypothesized that experiences of physical warmth (or coldness) would increase feelings of interpersonal warmth (or coldness), without the person's awareness of this influence. In study 1, participants who briefly held a cup of hot (versus iced) coffee judged a target person as having a "warmer" personality (generous, caring); in study 2, participants holding a hot (versus cold) therapeutic pad were more likely to choose a gift for a friend instead of for themselves. 

Retracted: No

PubPeer comments:
 I am sorry to reopen old wounds, but I heard Dr. Bargh on a recent podcast interview speaking of this study glowingly and sounding a bit perplexed that skeptics did not think it credible. In this comment, I clarify one reason why this study might be viewed with skepticism today: the reported p-values. In this comment, I calculate exact p-values to three decimal places, per modern APA style. Exact p-values are useful for modern meta-analytic tools like p-curve, which can help to assess the degree to which reported results may be influenced by publication bias or other causes of elevated Type I error rates (Simonsohn, Nelson, and Simmons, 2014). In Study 1, the exact p-value is just above .05. It may be as large as .0503 or as small as .0501 depending on rounding in the F value. In Study 2, the p-value obtained through logistic regression is .041, as best I can reproduce it with the reported statistics and details. It is very uncommon that two studies would yield two p-values that are so close to each other and so close to the p = .05 threshold (Simonsohn, Nelson, and Simmons, 2014), particularly if those studies have good statistical power. A possible alternative explanation is that there were other studies conducted but not reported, or that some process has elevated the Type I error rate above the intended 5%. To be clear, both of these processes are common within psychological science (John, Loewenstein, Prelec, 2014) and do not imply Stapel-style fraud. In the interview, Bargh sounded astonished that skeptics of these results were unwilling to listen to the data. It is not that we are unwilling to listen to or trust data, but rather, that we are able to listen to it closely enough to notice certain warning signs. Again, these problems are thought to be common in psychology, which has lead to enthusiasm for remedies like preregistration and registered reports. Works cited: John, L. K., Loewenstein, G., Prelec, D. (2012). Measuring the Prevalence of Questionable Research Practices With Incentives for Truth Telling. Psychological Science, 23(5), 524u2013532. doi:10.1177 0956797611430953u00a0 Simonsohn, U., Nelson, L. D., Simmons, J. P. (2014). P-curve: A key to the file-drawer. Journal of Experimental Psychology: General, 143(2), 534u2013547. https: doi.org 10.1037 a0033242 R code below: ```` # Williams Bargh, Study 1 ---- # Retrieve exact p-value given reported F test pf(4.08, 1, 39, lower.tail = F) # not significant, p = .0503 pf(4.08499999999, 1, 39, lower.tail = F) # still not significant, p = .0501 # Retrieve F-value that would be necessary for p = .05 qf(.95, 1, 39) # critical p-value at F = 4.09 # Williams Bargh, study 2 ---- # Reported N = 53, supplement reports 3 discarded. # I assume 24 in the cold pad condition and 26 in the hot pad condition, # because those permit the reported percentages without fractional participants # Not enough information on the framing manipulation to actually run it as an interaction, # but I think that this is equivalent. Happy to be corrected if I m wrong. # create dataset dat - data.frame(temp = c(rep( cold , 24), rep( hot , 26)), self = c(rep(0, 6), rep(1, 18), rep(0, 14), rep(1, 12)) ) # convert to table to double-check s2.tab - with(dat, table(temp, self)) s2.tab prop.table(s2.tab, margin = 1) # percentages match report # fit models for exact p values: # logistic regression summary(glm(self ~ temp, family =  binomial , data = dat)) # p = .0413 # chi-sq test chisq.test(s2.tab) # with continuity correction, p = .073 chisq.test(s2.tab, correct = F) # without continuity correction, p = .038 ```` , 
 This article was tested as part of the Reproducibility project and the conclusion was:  Despite using identical stimulus materials, dependent variables, and analysis strategies, the results of our study did not replicate the findings of Williams and Barghu2019s (2008) study on spatial priming and emotional closeness. , 
 The evidence for the claimed effect is quite easy to assess. While the reporting is patchy, one can still do a little testing on the combination of F-statistics, p-value, and sample size. For study 1, the reported F-statistic was 4.08, leading to a p value of exactly .05. [Let s assume that both experimental groups are roughly equally sized (21 and 20). Transforming the F statistic to a t statistic by taking its square root, we can easily calculate the Bayes Factor] from these statistics. The Bayes Factor (BF10) for the reported test is 1.50, which qualifies for nothing more but anecdotal evidence. A BF10 of 1 would imply that both the null and the alternative hypothesis explain the data equally well.  For study 2, I rely on the previous commenter s reproduction of the logistic regression model reported in the paper. The model gives us an r-squared of .068 when running a simple linear model and a McFadden s pseudo-r-squared of .67 when using the original logistic regression. With 53 subjects, an r-squared of .068 gives us a BF10 of 1.253. Again, that s barely anecdotal evidence.  Because some details were not reported (group sizes, experimental manipulation in study 2), the data are not fully reproducible. But I doubt that we missed anything serious here. If we would, like Dr. Bargh proposes,  listen to the data , the data would not tell us very much. , 
 Even if the statistics weren t a problem, the variable  interpersonal warmth is far too broad and far too vague to be warranted by the particular study procedures. There are a million ways one could choose to  operationalize it; do we expect all would yield  significance ? Similar things could be said of the independent variable,  physical warmth. How warm? To judge by the expression  hot under the collar, physical warmth is also associated with antisocial behavior. ,