Using Twitter to Explore the Frontiers of Psychological Research

This is a crosspost from the Good Science, Bad Science blog, where I am writing these days as part of a course I am taking at the University of Amsterdam.

Twitter is a great tool to keep up with developments in psychological research good and bad. In this post, I will make a case for using Twitter as a student or researcher. For those yet uninitiated, I will give a simple and easy-to-follow introduction on how the platform works and how to use it. Finally, I will recommend a few advocates of good science to follow on Twitter. All in all, this should enable you to dive right into the debates on post-publication peer review and pre-registered replication reports (and all the other things that make good science).

Why Use Twitter?

Some social media platform – notably Facebook – have been characterised in the literature as ‘semi-public spheres’. The public sphere, of course, was developed as a concept by social theorist Jürgen Habermas, who evoked the coffeehouses of yore as places in which the public could meet in free and non-violent discourse. In the evening, after work is done (the wonderful German word Feierabend unfortunately does not have an adequate English translation), the citizens would come together and discuss the urgent and not-so-urgent matters concerning their community. Twitter, in a sense, is a hyper-public sphere: it is a place not only to meet with friends and acquaintances; but also one in which it is perfectly acceptable to eavesdrop on and even butt into the conversations of others.

Such low barriers to entering a conversation are particularly great when you are a student. Not only do many conversations normally take place behind doors that are, for you, still closed; you will surely feel uncomfortable butting into a chat between your intellectual heroes should you find them, say, at a conference (and they might not appreciate it either). On Twitter, social conventions are more relaxed, and the whole platform is designed around the idea that conversations take place in public, and it’s o.k. to listen. And there sure is a lot to listen to! Many, many researchers and advocates use Twitter, and have meaningful and interesting conversations between each other. Later in this post, I will show you how to find them.


And then, of course, there is also the old wisdom that on the Internet, nobody knows you’re a dog (or a lowly master’s student, for the matter). Yet, Twitter is also a great place to make a name for oneself. If you have something smart to say, it’ll be the content more than who you are that counts; and through its features the platform allows anybody who follows you to quickly share your work with others. What could be better for an evangelist (and let us be honest, all committed researchers, and advocates of good science all the more, are missionaries of their cause)? So, Twitter allows you to connect easily with people all over the world, to follow the conversations of thought leaders in your field, and to quickly get your own work and ideas to those who care.

How to Use Twitter

In this section, I will give an accessible introduction to the platform. If you already know how Twitter works – or feel comfortable with social media in general – you’ll probably want to skip this section. Also, if you don’t trust me, there’s a great Twitter primer by the Society for Personality and Social Psychology. All others, here we go!

Although you do not need an account yourself to read content on Twitter, getting one is a great place to start. Not only does it allow you to post tweets yourself; it also enables you to select other users to follow and regularly receive their updates, as well as a few other useful things. Signing up is easy! All you need is an email address and a username you want to go by on Twitter (choose something short and not too cryptic). The site also asks for your full name, but any pseudonym will work. A lot of people do not twitter under their real name! Once you’ve created an account, also upload a profile picture and write a short description so people know what you care about.

Updates on Twitter (‘tweets’) can be up to 140 characters long – being eloquent under these restrictions is a whole art unto itself! So, try to be concise (although it’s customary to split longer messages into multiple tweets. Just indicate that there’s something to follow, e.g. by going 1/2, 2/2). There’s no need to worry about the length of links, though! Twitter has a built-in link-shortener, so all links will be of the same length. Ready to send out a ‘hello world’?


Next, you will want to follow some people. Following somebody establishes a one-way link (i.e., they can follow you back, but they do not have to – there is no obligation there): you will see all posts written by that user on your wall (the feed on the home screen). I will recommend a few interesting advocates of good science to follow later, but perhaps you want to go explore on your own first. Twitter offers a host of recommendations, for instance under ‘Who to follow’ and ‘Popular accounts’ on the left-hand side.

Finally, there are three basic, but important features of Twitter. One is the hashtag (although used all over these days, it originated from Twitter): #word. It’s a way of assigning a label to a tweet, and users can search for tweets containing a particular hashtag. A lot of conferences, for instance, have an official hashtag to make it easier to find tweets from participants (go and type ‘#easp2014’ into the search field in the upper right corner – it’s the official hashtag of the conference of the European Association of Social Psychology that was hosted at the UvA over the summer).

Second, there is the retweet option. When you hover over a tweet with your mouse, you will see three option – ‘reply’ (we will get to that), ‘retweet’, and ‘favorite’. When you click on retweet, the post will appear on your wall (and on that of people you follow). It will still be attributed to the original author, but also show that you retweeted it. This is the key feature behind Twitter’s power for spreading ideas – including those linked to in a tweet – at rapid speed and with great reach.


The last, and most important, feature is the ‘mention’. Prefixing another Twitter user’s handle by an ‘@’ will make that user aware of your message (you can find messages directed at you under ‘Notifications’ in the upper left corner). When you start a tweet with an @ and a handle, this message will only appear on the wall of the person you are addressing and contacts you have in common. So nobody will be bothered by your conversation with somebody they don’t know! When you click on the ‘reply’ button underneath each tweet that I mentioned earlier, it’ll set up an @-message that is linked to the original tweet. What’s so great about that? People can see what you are replying to! When a conversation goes back and forth dozens of times, that can be very handy. Also, clicking on a tweet will show you the thread of tweets it (may have) replied to, and all replies it has received. Go find some reply-tweets and try it out!

Now you should be ready to delve right into Twitter. Perhaps you’ll still want to be a passive reader for a while to see what people are talking about, but first you’ll have to find some interesting people to follow. Let’s go do that in the next section!

Finding Conversations

Who exactly you’ll want to follow on Twitter will depend on your own interests. After all, it’s not just good science advocates on there, but literally people from all walks of life. Here, I’ll just recommend a few people whose conversations about good science I’ve found particularly exciting to follow.

Chris Chamber / @chrisdc77 – cognitive neuroscientist and as editor of Cortex one of the driving forces in establishing pre-registered reports in journals.

Brian Nosek / @BrianNosek – social psychologist and director of the Center for Open Science (@OSFramework). Leader in the replication movement.

Neuroskeptic / @Neuro_Skeptic – anonymous blogger and quite certainly the snarkiest critic of neuroscience on the Internet.

Daniël Lakens / @lakens – experimental psychologist and methodologist at TU Eindhoven; open science advocate.

Erika Salomon / @ecsalomon – social psychology Ph.D. student and blogger, among others for the SPSP blog.

Ben Goldacre / @bengoldacre – Science journalist and author of the bestselling books “Bad Science” and “Bad Pharma”; leader of the AllTrials campaign to require registration and publication of clinical trials.

Ed Yong / @edyong209 – science journalist and blogger for the National Geographic. Seemingly never sleeps, and so while not a psychologist, still engaged in many conversations.

Sanjay Srivastava / @hardsci – personality and social psychologist at the University of Oregon and author of the excellent blog The Hardest Science.

Betsy Levy Paluck / @betsylevyp – Princeton professor of psychology and public policy and an outspoken defender of good research practices.

Rolf Zwaan / @RolfZwaan – psychologist at Erasmus University Rotterdam and prolific blogger.

Uri Simonsohn / @uri_sohn – methodologist and leader of the replication movement; recent inventions include the p-curve as a measure of publication bias. Also author (with his colleague Joe Simmons (@jpsimmons)) of DataColada.

Heather Coates / @landPangurBan – data librarian at Indiana University and research transparency advocate.

Jelte Wicherts / @JelteWicherts – Han’s former Ph.D. student; now methodologist at Tilburg University. Speaker at the ‘Human Factors’ conference!

Kai Jonas / @KaiJJonas – hipster. Also social psychologist at the UvA and editor-in-chief of Comprehensive Results in Social Psychology, a journal based on pre-registration.


Matt Wall / @m_wall – neuroscientist and occasional author of the rather useful blog Computing for Psychologists.

Simine Vazire / @siminevazire – personality psychologist and regular blogger on good science.

Dorothy Bishop / @deevybee – developmental neuropsychologist and blogger; advocate for replication.

Michael Eisen / @mbeisen – biologist and co-founder of PLoS; open access advocate.

Alex Holcombe / @ceptional – cognitive neuroscientist and advocate of registered replication reports; runs PsychFileDrawer, a platform for sharing replications.

Dale Barr / @dalejbarr – social scientist and methodologist at the University of Glasgow.

… and last but not least:

Lego Academics / @LegoAcademics


I hope I’ve been able to convince you of the value of Twitter for you as an advocate of good science. If you want to follow me, I’m @simoncolumbus. See you there.

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EHBEA Day Three: Punishment, Parochial Altruism, and Cooperative Institutions

The third day of the EHBEA conference in Amsterdam brought several highly interesting talk on the evolution of cooperation, altruism, and punishment, my main areas of interest. Unfortunately, I am a bit strapped of time, so I will only summarise the talks most important to me.

Altruistic Punishment in Public Goods Games

The day commenced with a keynote by Simon Gächter of the University of Nottingham. Gächter, together with Ernst Fehr, pioneered the use of punishment in Public Goods Games1 and has been a major proponent of altruistic punishment as a solution to free-rider problems.

Gächter recapped his research program of the last ten years, showing that altruistic punishment induces cooperation, occurs widely,2 and increases total pay-offs from cooperation in the long term.3 In particular, he argued that, though abstract, Public Goods Games are psychologically rich, indicated by the anger trigger by free-riding.

A major challenge to the ecological validity of Gächter’s experiments comes from the fact that they are conducted under conditions of full anonymity, which some have argued opens the door to unrealistically harsh punishment. New data from ongoing experiments which he presented counter this criticism. When participants meet each other before playing the games, even for a short moment, this increases cooperation even in the absence of punishment (but does not stop its gradual decline).

When identifiability and punishment are combined, cooperation increases to near-perfection. At the same time, punishment becomes less frequent than expected; nevertheless it successfully sustains cooperation that would otherwise decline (or so does the threat; some groups never actually punish). This is quite fascinating, because it indicates that in prehistoric small-scale societies, in which members of small communities frequently interacted, punishment would have been a highly cost-effective way of enforcing cooperation.

Social and Individual Information; Prospect Theory

Ulf Tölch of the Humboldt University of Berlin presented findings from experiments on the integration of social and individual information. In a two-phase experiment, individuals first learned about their own accuracy in indicating a target location on a circle. Subsequently, they were presented with a combination of their own guess and either more or less accurate social information (or a combination of the latter two).

Tölch found that when integrating two bits of social information, players made bayes-optimal decisions, i.e. weighted the integration of information for source reliability. When integrating their own information with other sources, however, failed to do this. In particular, it appeared that people who were very accurate themselves overestimated their own accuracy. Using fMRI scans, the researchers found evidence that some people – who acted Bayes-optimal – were able to overwrite individual information.

Dave Mallpress presented a model for the evolution of the fourfold pattern of risk preference described by prospect theory4 In a variable, but autocorrelated environment, agents dependent on energy levels were offered the choice between a (more or less risky) gamble and a safe option. Whether agents in the model chose to gamble depended on the state of the environment in a fashion similar to the fourfold pattern of prospect theory: gamble in extremely bad environments, but play it safe in extremely good ones; mostly gamble in quite good environments, but mostly play it safe in quite bad ones.

Parochial Altruism

Antonio Silva of the University College London presented two experiments out of a larger research program on parochial altruism and inter-group conflict in Northern Ireland. This research program is particularly interesting because it aims to maximise the ecological validity of experiments. The methods Silva described look very promising to me.

Parochial altruism is the idea that inter-group conflict gives rise to increased in-group altruism and decreased out-group altruism. Northern Ireland with its long-lasting conflict between Catholics and Protestants naturally lends itself to studying this phenomenon. Silva and his colleagues used several methods, including donations (of endowments to neutral, Catholic, or Protestant charities) and a lost-letter paradigm, in which letters were addressed to either Catholic or Protestant neighborhoods and ‘lost’.

The lost-letter paradigm found evidence for reduced out-group altruism – Catholic letters ‘lost’ in Protestant neighbourhoods were returned less often than Protestant and neutral letters (and vice-versa). Donations to in-group, out-group, and neutral charities were predicted mostly by socio-economic variables; a sectarian threat variable was only negatively correlated with out-group donations. Hence both measures found evidence for reduced out-group altruism, but not increased in-group altruism, and thus not for parochial altruism.

From Small-Scale to Large-Scale Societies

Simon Powers of the University of Lausanne presented a model for the evolution of punishment institutions. He argued that while most theories (such as Gächter’s) assume that social interactions are uncoordinated, “in real groups [they] tend to be regulated by institutions.” Powers explicitly bases his models for the bottom-up creation of institutions on the work of Elinor Ostrom, who also pioneered research into altruistic punishment.5

Power’s model is based on a modified Public Goods Game. Instead of making punishment decisions individually, agents first decide on the share of the public good they would like to see used for punishment (vs. investment), and then play the PGG. Institutional rules are formed by taking the mean preference of cooperators and defectors for sanctioning. The dynamics of the model are thus governed by individual preference for punishment and propensity to cooperate, defect, or not participate in the PGG.

When most of the public good is used for investment, cooperators can invade, but when investment gets too high, asocials take over, thus leading to cycling dynamics. When spatial structure is introduced in the model, however, where migration is dependent on the level of cooperation within a group, cooperators can take over a group. The group size then expands and cooperation as well as institutional sanctions stabilise at high levels.

Power’s model is interesting for multiple reasons. I was particularly intrigued that it considers migration rate as a variable dependent on cooperation levels (rather than as a constant, which I’ve seen in many group-level selection models). I’d also be curious to see how such institutional sanctions would fare in behavioural experiments (while being aware that as an evolutionary model, this does not make predictions about contemporary behaviour).

  1. Fehr, E. & Gächter, S. (2000). Cooperation and Punishment in Public Goods Experiments. The American Economic Review, 90(4), 980-94. []
  2. Herrmann, B., Gächter, S., & Thöni, C. (2008). Antisocial punishment across societies. Science, 319(5868), 1362-7. DOI: 10.1126/science.1153808. []
  3. Gächter, S., Renner, E., & Sefton, M. (2008). The long-run benefits of punishment. Science, 322(5907), 1510. DOI: 10.1126/science.1153808 []
  4. Kahneman, D. & Tversky, A. (1979). Prospect Theory: An Analysis of Decision Under Risk. Econometrica, 47, 263-91. []
  5. Ostrom, E., Walker, J., & Gardner, R. (1992). Covenants With and Without a Sword: Self-Governance is Possible. The American Political Science Review, 86(2), 404-17. []
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EHBEA Day Two: Competitive Altruism and Competition for Partners; also a lot of masculinity.

Today was the second day of the EHBEA conference in Amsterdam. Unfortunately, I was forced to miss the keynote, but there were some very interesting presentations. In particular the work by Gilbert Roberts on competitive altruism is very promising, and it was great to hear about it. Arno Riedl also reported some very interesting (and related) work on competition for partners as a driver of cooperation.

Gilbert Roberts on Competitive Altruism

Gilbert Roberts of Newcastle University presented models and empirical evidence on competitive altruism, which is the idea that by behaving altruistically, individuals provide information to potential cooperation partners. Competition for more appealing cooperation partners then drives reputation-enhancing (altruistic) behaviour.van Vugt, M., Roberts, G., & Hardy, C. L. (2005). Competitive Altruism: Development of Reputation-based Cooperation in Groups. In R. I. M. Dunbar & Louise Barrett, Handbook of Evolutionary Psychology (pp. 1-28). Oxford, UK: Oxford University Press.

Competitive altruism has been tested in two-stage economic games. The first stage allows for building a reputation as a good cooperator (e.g. Public Goods Games, PGG). Then, during the second stage players can choose partners to play paired games with. As such, competitive altruism is in particular a rival theory to indirect reciprocity. Roberts reported findings that in the PGG, competitive altruism drives contributions more than indirect reciprocity does.

Based on these findings, Roberts proposed an agent-based model of reputation building with three decision points:

  • Stage 1: Build reputation or not?
  • – choose partner according to reputation or not –
  • Stage 2: Cooperate or defect on partners? (or don’t play)

In simulations, reputation and cooperation can evolve (as can choosing, but at lower levels), at least for some parameters. As the number of meetings increases, higher levels of reputation building are observed.

Child Mortality and Reproduction

Caroline Uggla of the University College London proposed to investigate why health technologies are not adopted in developing countries even when they are available based on parental investment theory, and bringing together human behavioural ecology and public health. Using data from the Demographic Health Surveys, she showed that parental investment theory predicts how several mother- and child-status variables predict whether children will receive curative and preventative treatments. For example, older mothers invest more in their children. However, some puzzles remain. The positive effect of mothers’ age, for instance, appears contradictory with the negative effect of birth order. Also, sicker children are more likely to get curative, but less likely to get preventative treatments, but it is not clear why.

Paul Mathews of the London School of Hygiene and Tropical Medicine (LSHTM) presented evidence from two studies on reproductive plasticity, i.e. the idea that reproduction is dependent on environmental factors. In two experiments, he found that priming subjects with their own mortality increases the ideal number of children in men, but not women; and increases the desire for childlessness in women, but not men (furthermore, priming subject with dental health increases their desire to remain childless – a control condition gone hilariously wrong).

Susan Schaffnit, also of the LSHTM, presented findings on parent-child proximity and women’s reproductive fitness in Europe. She set out to test hypotheses about two conflicting theories. On the one hand, cooperative breeding theory predicts that living with parents increases reproductive fitness. On the other hand, local resource competition theory predicts that being around relatives (although more often siblings than parents) decreases reproductive fitness. Schaffnit’s analysis of data from Europe found support for both hypotheses: living parents are associated with younger age at first birth, but living with them has a negative effect. Women who move away from home later also have a higher probability of remaining childless.

Facial Characteristics and Ultimatum Game Behaviour; Female Economic Dependence

I missed the first talk of this session, by Daniel Tayler on the excludability of public goods, and was unfortunately distracted during the third by Hannah Cornish on systematicity of culture.

Poppy Mulvaney of the University of Bristol reported on two studies investigating the effect of receiver’s facial characteristics on proposer behaviour in the Ultimatum Game. In a UK sample, formidability – associated with physical dominance – predicted offers, but trustworthiness did not. However, in a second sample in the United States, trustworthiness, but not formidability predicted fair offers. Notably, the two samples did not differ in their personality ratings for the faces, thus leaving open the question as to why different facial characteristics would predict proposer behaviour.

Michael Price of Brunel University proposed a model of morality judgements on promiscuity that includes female economic dependence. This hypothesis is consistent with the idea that opposition to promiscuity is to promote paternity certainty. Indeed, female economic dependence somewhat predicted attitudes towards both male and female promiscuity in a model also including sex, religiosity, and conservativism. Price argues that religiousity and conservatism are only proximate predictors, i.e. that their anti-promiscuous stance requires explanation, and sees evolution in an environment of high female economic dependence as an answer.

Arno Riedl on Competition for Partners as a Driver of Cooperation

Arno Riedl of Maastricht University presented experimental findings that competition for partners can drive cooperation among strangers. The underlying question of this study is what it needs to enforce the social norms of cooperation in societies with infrequent interactions. This was tested using a very interesting modification of the classic Prisoners’ Dilemma that allowed Riedl his collaborator Aljaz Ule (of the University of Amsterdam) to tease apart the effects of exclusion and partner choice.

Subjects played repeated PD games, paired up in groups of three. They all learn about the most recent interactin of each subject in the triplet. In each triplet, at most one pair of subjects can play the PD (which is strictly preferable over not playing), thus inducing partner competition. Riedl and Ule then varied the condition under which pairs are formed:

  • baseline: formed randomly (no partner refusal, no competition)
  • refusal: formed randomly, but each subject can refuse to play
  • competition: subjects indicate acceptable partner, if feasible a pair is formed

Cooperation rates in round one were very similar (43/43/38%), but diverged strongly across 60 iterations: While baseline and refusal condition cooperation fell to around ten percent in a monotonic decrease, it steadily increased to almost fifty percent in the competition condition. Refusal was rare throughout.

Interestingly, in the competition condition, two types of groups emerge: non-cooperative and fully cooperative groups. This difference appears to be driven by the exclusion of previous defectors. In the refusal condition, refusal was rare (2%). Non-cooperative groups in the competition condition also showed rather low levels of exclusion of previous defectors (15%), which markedly set them apart from cooperative groups, whose members refused to cooperate with defectors 50 percent of the time. Riedl concluded that community enforcement of cooperation norms is possible, but requires competition. The mechanism at work is the exclusion of revealed defectors.

Recalibration Hypothesis; Evolution of Masculine Faces

Lars Penke of the University of Edinburgh tested the recalibration hypothesis, which argues that the effects of genetic variation on personality (or behaviour more broadly) is mediated by morphology (e.g., a proposal that narcicissm is founded in objective attractiveness). Using several anthropomorphic measures and third-party rating, Penke found only scattered associations with personality traits. He concludes that the recalibration theory explains only little variation in social personality traits, and only in men. Previous studies were likely confounded, e.g. by using self-reported attractiveness (which might itself correlate with traits like narcicissm).

Iris Holzleitner of the University of St. Andrews presented a model linking morphological masculinity and attractiveness. Two contrasting hypotheses have been put forward to explain the evolution of masculine faces. One is that they are a handicap to signal health, and evolved due to intersexual selection (i.e. choosy women looking for healthy males). In contrast, masculine faces might have evolved under pressure of intrasexual competition as a cue to dominance. Holzleitner’s model, which takes into account facial masculinity (as well as height and weight), finds that masculinity has a significant effect on social dominance, but not health; and an effect on attractiveness only via social dominance. This suggests that masculinity evolved as an intrasexually selected trait.

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