How well do we understand how people make choices? Place a bet on your favorite theory

Put your money where your mouth is, as they say:

The goal of this competition is to facilitate the derivation of models that can capture the classical choice anomalies (including Allais, St. Petersburg, and Ellsberg paradoxes, and loss aversion) and provide useful forecasts of decisions under risk and ambiguity (with and without feedback).

The rules of the competition are described in http://departments.agri.huji.ac.il/cpc2015. The submission deadline is May17, 2015. The prize for the winners is an invitation to be a co-author of the paper that summarizes the competition (the first part can be downloaded fromhttp://departments.agri.huji.ac.il/economics/teachers/ert_eyal/CPC2015.pdf)…

Our analysis of these 90 problems (see http://departments.agri.huji.ac.il/cpc2015) shows that the classical anomalies are robust, and that the popular descriptive models (e.g., prospect theory) cannot capture all the phenomena with one set of parameters. We present one model (a baseline model) that can capture all the results, and challenge you to propose a better model.

There was a competition recently that asked people to predict seizures from EEG activity; the public blew the neuroscientists out of the water. How will the economists do?

Register” by April 1. The submission deadline is May 17!

 

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Learning, Bounded Rationality, and Decisions

I just thought I’d pass on this set of workshops that would be of interest to people in this blog. If only I were a wee bit closer to Israel…

Learning, Bounded Rationality, and Decisions

Three Related Workshops, and Winter School

Israel, January 23-29, 2014

Basic purpose: To facilitate communication between scholars and students of learning and bounded rationality from different disciplines. The focus is on understanding and predicting decisions when the rationality assumption does not yield sharp predictions.  The speakers in the different workshops are encouraged to provide non-technical reviews of key results from their research.

See the schedule here.

Reinforcement Learning and Decision Making (RLDM) 2013

I have just returned from the Reinforcement Learning and Decision Making (RLDM) conference and it was…quite the learning experience. As a neuroscientist, I tend to only read other scientific papers written by neuroscientists so it is easy to forget how big the Reinforcement Learning community really is. The talks were split pretty evenly between the neuroscience/psychology community and the machine learning/CS community, with a smattering of other people (including someone who works on forestry problems to find the optimal response to forest fires!). All in all, it was incredibly productive and I learned a ton about the machine learning side of things while meeting great people.

I think my favorite fact that I learned was from Tom Stafford, which is that there is a color called the ‘tritan line’ which is visible to visual cortex but not to certain other visual areas (specifically the superior colliculus). Just the idea that there is a color invisible to certain visual area is…bizarre and awesome. The paper he presented is discussed on his blog here.

There were a few standout talks.

Joe Kable gave a discussion of the infamous marshmallow task, where a young child is asked to not eat a marshmallow while the adult leaves the room for some indeterminate amount of time. It turns out that if the child believes the adult’s returning time is distributed in a Gaussian fashion then it makes sense to wait but if the returning time follows a heavy-tailed distribution then it makes sense to eat the marshmallow. This is because the predicted amount of time until the adult returns increases as time passes for a heavy-tailed function. And indeed, if you ask subjects to do a delay task they act as if the distribution of delay times are heavy-tailed. See his paper here.

Yin Li used monkeys to ask how an animal’s learning rate changes depending on the situation. There is no one optimal learning rate: it depends on the situation. If you are in an environment where you a tracking a target with little noise until sudden dramatic changes (small variance in between sudden changes in mean), then you want a high learning rate; you are not at risk of being overly responsive to the internal variability of the signal while it is stationary On the other hand, if there is a very noisy signal whose mean does not change much, then you want a low learning rate. When a monkey is given a task like this, it does about as well as a Bayesian-optimal model. I’m not sure which one he used, though I think this is a problem that has gotten attention in vision (see Wark et al and DeWeese & Zador). Anyway, when they try to fit a bog-standard Reinforcement Learning model it cannot fit the data. This riled up the CS people in the audience who suggested that something called “adaptive learning RL” could have fit the data, a technique I am not aware of? Although Li’s point was that the basic RL algorithm is insufficient to explain behavior, it also highlights the lack of crosstalk between the two RL kingdoms.

Michael Littman gave an absolutely fantastic talk asking how multiple agents should coordinate their behavior. If you use RL, one possibility is just to treat other agents as randomly moving objects…but “that’s a bit autistic”, as Littman put it. Instead, you can do something like minimax or maximin. Then you just need to find the Nash equilibrium! Unfortunately this doesn’t always converge to the correct answer, there can be multiple equilibria, and it requires access to the other agent’s value. Littman suggested that side payments can solve a lot of these problems (I think someone was paging Coase).

Finally, the amazing Alex Kacelnik gave a fascinating talk about parasitism in birds, particularly cuckoos. It turns out that when you take into account costs of eggs and such, it might actually be beneficial to the host to raise 1-2 parasite eggs; at least, it’s not straight forward that killing the parasites is the optimal decision. Anne Churchland asked whether neurons in the posterior parietal cortex of rats show mixed sensory and decision signals, and then showed that they are orthogonal on the level of the population. Paul Phillips gave a very lucid talk detailing the history of dopamine and TD learning. Tom Dietterich showed how reinforcement learning is being used by the government to make decisions for fire and invasive-species control. And Pieter Abbeel showed robots! See, for instance, the PR2 Willow Garage fetching beer (other videos):

Here are some other robots he mentioned.

Some final thoughts:

1. CS people are interested in convergence proofs, etc. But in the end, a lot of their talks were really just them acting as engineers trying to get things to work in the real world. That’s not that far from what psychologists and neuroscientists are doing: trying to figure out why things are working the way that they are.

2. In that spirit, someone in psych/neuro needs to take the leading-edge of what CS people are doing and apply it to human/animal models of decision-making. I’ve never heard of Adaptive Reinforcement Learning; what else is there?

3. At the outset, it would help if they could make it clear what are the open research questions for each field. At the end, maybe there could be some discussion on how to get the fields to collaborate more.

4. Invite some economists! They have this whole thing called Decision Theory… and would have a lot to contribute.

 

Cosyne: Decision-making

I spent a week recently in Salt Lake City at the Cosyne (COmputational and SYstems NEuroscience); people had told me that it was their favorite conference, and now I understand why.  Other attendees have put up their reactions, so I figure it’s about time I got off the couch and did the same.

Probably the biggest effect this meeting had on me is that I started using twitter a bit more seriously – follow me at @neuroecology – and participated in my first “tweet-up” (is that really a thing?).  There are lots of great neuroscientists tweeting though there should be more!

For simplicity of organization, there will be three posts on Cosyne: one on a few decision-making talks, one on The Big Talks, and one on neural correlates of foraging.

Carlos Brody

On decision-making, the first (and longest) talk was by Carlos Brody.  His talk was focused on the question of how we make decisions in noisy environments.  In this case, rats had to sit around listening to two speakers emit clicks at random (poisson) intervals and decide which speaker, left or right, was clicking more.  We typically think of the way animals make these types of decisions is as a ‘noisy integrator‘: each point of information – each click – is added up with some noise thrown in there because we’re imperfect, forgetful, and the environment (and our minds!) are full of noise.  The decision is then made when enough information has been accumulated that the animal can be confident in going one direction or another.

One small problem with this is that there are a lot of models that are consistent with the behavioral data.  How noisy is the internal mind?  Is it noisy at all?  How forgetful are we?  That sort of thing.  The Brody lab fit the data to many models and found that the one that most accurately describes the observed behavior is a slow accumulator that is leaky (ie a bit forgetful) but where the only noise is from the sensory input!  Actually, I have in my notes that is ‘lossless’ but also that it is ‘leaky’ so I’m not sure which of the two is accurate, but the important detail is that once the information is in the brain it gets computed on perfectly and our integration is noiseless; all the noise in the system comes from the sensory world.

They also recorded from two areas in the rat brain, the posterior parietal cortex (PPC) and the frontal orienting fields (FOF).  The PPC is an area analogous to LIP where neural activity looks like it is integrating information; you can even look at the neural activity in response to every click from a speaker and see the information (activity) go up and down!  The rational expectation is that you’d need this information to make a decision, right?  Well, when he goes and inactivates the region there is no effect on the behavior.  The other region he records from is the FOF which is responsible orienting the head (say, in the direction of the right decision).  The neural activity here looks like a binary signal of ‘turn left’ or ‘turn right’.  Inactivating this area just prior to the decision interferes with the ability to make a proper decision so the information is certainly being used here, though only as an output.  Where the information is being integrated and sent from, though, is not clear; it’s apparently not the PPC (and then maybe not LIP)!

Kenway Louie

The second good talk was from a member of Paul Glimcher’s lab, Kenway Louie.  He was interested in the question of why we make worse decisions when given more choices.  Although he wanted to talk about value, he used a visual task as a proxy and explainer.  Let’s say you have two noisy options where you weren’t certain which option was better; if the options are noisy but very distinct, it is easy to decide which one you want. However, if they are noisy and closer together in value it becomes harder and harder to distinguish them both behaviorally and as a matter of signal detection.

But now let’s add in a third object.  It also has some noisy value, but you only have to make a decision between the first two.  Should be easy right?  Let’s add in some neuroscience: in the brain, one common way to represent the world is ‘divisive normalization’.  Basically, the firing of a neuron is normalized by the activity of the other neurons in the region.  So now that we’ve added in the third option, the firing of the neurons representing the value of the other two objects goes down.  My notes were unfortunately…not great… so this is where I get a bit confused, because what I remember thinking doesn’t make total sense on reflection.  But anyway: this normalization interferes with the probability distributions of the two options making it more difficult to make the right choice, although it is nonlinear and the human behavior matches nicely (I think).  The paper is in press so hopefully I can report on it soon…

Paul Schrater

Paul Schrater gave a talk that was a mix of decision-making and foraging.  His first and main point was that many of the things that we refer to having value are in fact secondary sources for value; money only has value inasmuch as it can buy things that are first-order needs such as food or movies or such.  However, the same amount of money cannot always buy the same amount of goods so value is really an inference problem, and he claims it can of course be explained through Bayesian inference.

His next important point is that we really need to think about decision-making as a process.  We are in a location, in which we must perform actions which have values and must fulfill some need states which, of course, influence our choice of location.  Thinking about the decision-process as this loop makes us realize we need to have an answer to the stopping problem or, how long should I stay in a location before I leave to another location?  The answer in economics tends to come from the answer to the Secretary Problem (how many secretaries should I interview before I just hire one?) and the answer in ecology comes from Optimal Foraging; in fact, both of these rely on measuring the expected mean value of the next option and both of these are wrong.  We can instead think of the whole question as a question of learning and get an answer by reinforcement learning.  Then when we stop relies not just on the mean expected reward but also the variance and other higher-order statistics.  And how do humans do when tested in these situations?  They rely on not just mean but also variance!  And they fit quite closely to the reinforcement learning approach.

He also talked about the distractor problem that Kenway Louie discussed, but my notes here don’t make much sense and I’m afraid I don’t remember what his answer was…

Is neuroscience useful? (Updated)

I recently got a quadcopter and in pockets of my spare time I’ve been attempting to make it an autonomous drone. Yet reading this article on unmanned drones has me returning to some thoughts I’ve had while working on the project.  Basically: is neuroscience useful?  Much of the utility from drones comes from their autonomy and adaptability.  In my naive fantasies, I think that the work we do to understand the nervous system should inspire drone makers, hiring neuroscientists left and right to implant the lessons we’ve learned from the nervous system into these machines.

And yet – and yet I’m not aware of anyone doing this.  There are whispers and rumors emanating from the Brain Corporation that this is their mission but I have yet to see anything concrete come out of that (to be fair, they’re a relatively new company).  But even more we should be asking ourselves: are we going to be leap-frogged by those who are working in computer sciences – artificial intelligence, machine learning, vision processing?

That the drones are living in a newly created ecosystem, interacting and invading new niches, is undeniable.  Presumably an enterprising young scientist in ecology, neuroscience, (economic) decision-making should be perfectly suited to at least consulting on these projects.  I guess the question is: does that actually happen?  Outside of ‘explaining the brain’ for ‘medicine’, do we do anything that’s actually useful?  Or is that up to the engineers?

Update: Well here’s a good example of using animal behavior/reflexes to improve robotics.

Never make a decision on an empty stomach… or a full stomach…

You are hungry already and dinner is hours away.  You’re getting irritable and making stupid decisions that you normally wouldn’t.  Or maybe you just had a big meal and you’re sated.  Your friend who is seated next to you turns and asks for a favor; you pleasantly agree and sink into your chair sleepily.  What’s going on?

An underappreciated fact about the neuromodulatory system is that release of these molecules can have diffuse and widespread effects all across the brain.  Take dopamine and leptin. Dopamine is a chemical that drives decision-making – among other things, but it really does have an important role in this – while leptin is generally thought to signal satiety.  Leptin is released from the fat cells of the body and we typically think of it acting on the hypothalamus, an area responsible for many metabolic behaviors.  When more leptin is circulating in the blood stream, you will eat less food and increase more energy which makes it a natural candidate for yet another failed diet pill.  Since leptin interacts with motivation to eat food, an alternative set of areas it could interact with are the dopamine regions .  And in those regions, in the striatum in particular, the response to food and food pictures will be reduced when there is increased leptin.

It would be nice to know mechanistically how the two systems interact.  One method of going about this is to activate dopamine release through a stress pathway: by keeping pain at a constant self-reported score, a robust and constant amount of dopamine will be released.  Yes, for some reason people actually volunteer for these experiments.  Now we can exploit the fact that there are known variants in the gene responsible for leptin, LEP.  If you look at how people with these variants respond, you get large differences in dopamine release, which seems to preferentially effect the D2/3 receptors.  Although different researchers seem to disagree on which specific regions of the striatum are modified by leptin, a good guess it that this is highly dependent on the task and leptin will change the amount of dopamine available to the areas.

What affect might this have on behavior?  One behavior that these D2/3 receptors are involved in is risky decision-making.  We all have our own preferences for risky bets.  Some people prefer small bets that they are guaranteed whereas others prefer the risky option (these are the compulsive gamblers).  But it’s a bit more complicated than that.  Sure, you’d take a risky bet when the option was between a sure 5 cents and a “risky” $1.  But maybe you wouldn’t if you were guaranteed $100 with a risky option of $2000 or nothing.  How sensitive you are to these bets turns out to rely on the concentration of D2/3 receptors in the dorsal striatum.  Putting two and two together, we can bet that the leptin that has an effect on dopamine levels also has an effect on how willing you are to take a risk as the stakes get larger.

This means that all of our body is linked, together, with the state of the world.  Periods of hunger or bounty will cause people to behave in very different ways, with behavior linked to the body’s hormone signaling.  Particularly prevalent here is that hormones that are generally thought of as responding purely to food may have a broader role in signaling to the body how to properly respond to all sorts of situations.

References

Burghardt, P., Love, T., Stohler, C., Hodgkinson, C., Shen, P., Enoch, M., Goldman, D., & Zubieta, J. (2012). Leptin Regulates Dopamine Responses to Sustained Stress in Humans Journal of Neuroscience, 32 (44), 15369-15376 DOI: 10.1523/JNEUROSCI.2521-12.2012

Cocker, P., Dinelle, K., Kornelson, R., Sossi, V., & Winstanley, C. (2012). Irrational Choice under Uncertainty Correlates with Lower Striatal D2/3 Receptor Binding in Rats Journal of Neuroscience, 32 (44), 15450-15457 DOI: 10.1523/JNEUROSCI.0626-12.2012

Dunn, J., Kessler, R., Feurer, I., Volkow, N., Patterson, B., Ansari, M., Li, R., Marks-Shulman, P., & Abumrad, N. (2012). Relationship of Dopamine Type 2 Receptor Binding Potential With Fasting Neuroendocrine Hormones and Insulin Sensitivity in Human Obesity Diabetes Care, 35 (5), 1105-1111 DOI: 10.2337/dc11-2250

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Papers of the week, 6/10 – 6/17

Joint attention, social-cognition, and recognition memory in adults

“The early emerging capacity for Joint Attention (JA), or socially coordinated visual attention, is thought to be integral to the development of social-cognition in childhood…We tested the validity of the differentiation of IJA [initiating joint attention] and RJA [responding to joint attention] in our paradigm in two studies of picture recognition memory in undergraduate students. Study 1 indicated that young adults correctly identified more pictures they had previously viewed in an IJA condition (67%) than in a RJA (58%) condition, η2 = 0.57. Study 2 controlled for IJA and RJA stimulus viewing time differences, and replicated the findings of Study 1.”

The biological bases of conformity

“We review the relevant literature considering the causation, function, history, and ontogeny of conformity, and describe a computer-based experiment on human subjects that we carried out in order to resolve ambiguities. We found that only when many demonstrators were available and subjects were uncertain was subject behavior conformist. A further analysis found that the underlying response to social information alone was generally conformist. Thus, our data are consistent with a conformist use of social information, but as subjects’ behavior is the result of both social and asocial influences, the resultant behavior may not be conformist.”

Effects of age, sex, and  neuropsychological performance on financial decision-making

“Results indicated that Older participants significantly outperformed Younger participants on a multiple-choice test of acquired financial knowledge. However, after controlling for such pre-existing knowledge, several age effects were observed. For example, Older participants were more likely to make immediate investment decisions, whereas Younger participants exhibited a preference for delaying decision-making pending additional information…In terms of sex differences, Older Males were more likely to pay credit card bills and utilize savings accounts than were Older Females. Multiple positive correlations were observed between Older participants’ financial decision-making ability and performance on neuropsychological measures of non-verbal intellect and executive functioning. Lastly, the ability to justify one’s financial decisions declined with age, among the Older participants.”

Efficient coding and the neural representation of value

“Although normative theories of choice have outlined the theoretical structure of these valuations, recent experiments have begun to reveal how value is instantiated in the activity of neurons and neural circuits. Here, we review the various forms of value coding that have been observed in different brain systems and examine the implications of these value representations for both neural circuits and behavior. In particular, we focus on emerging evidence that value coding in a number of brain areas is context dependent, varying as a function of both the current choice set and previously experienced values. Similar contextual modulation occurs widely in the sensory system, and efficient coding principles derived in the sensory domain suggest a new framework for understanding the neural coding of value.”  By Paul Glimcher, so of course you should read this.

Orbitofrontal cortical activity during repeated free choice

“OFC neurons encode important features of the choice behavior. These features include activity selective for exceptionally long runs of a given choice (stay selectivity) as well as activity selective for switches between choices (switch selectivity). These results suggest that OFC neural activity, in addition to encoding subjective values on a long timescale that is sensitive to satiety, also encodes a signal that fluctuates on a shorter timescale and thereby reflects some of the statistically improbable aspects of free-choice behavior.”

Physical competition increases testosterone among Amazonian forager-horticulturalists: a test of the ‘challenge-hypothesis’

“We tested whether the Tsimane, pathogenically stressed forager-horticulturalists of the Bolivian Amazon, would express acute T increases in response to physical competition…Linear mixed-effects models were used to establish that T increased significantly immediately following competition (β = 0.23, p < 0.001), remaining high 1 h later (β = 0.09, p = 0.007); equivalent to 30.1 and 15.5 per cent increases in T, respectively. We did not find larger increases in T among winners (p = 0.412), although T increases were positively associated with self-rated performance (β = 9.07, p = 0.004). These results suggest that despite lower levels of T than US males, Tsimane males exhibit acute increases in T at the same relative magnitude reported by studies in industrialized settings, with larger increases in T for those who report better individual performance.”  I covered this partly in my introduction to testosterone earlier in the week.

Individual plastic responses by males to rivals reveal mismatches between behavior and fitness outcomes

“Behaviour (mating duration) was remarkably sensitive to the level of competition and fully reversible, suggesting that substantial costs arise from the incorrect expression of even highly flexible behaviour. However, changes in mating duration matched fitness outcomes (offspring number) only in scenarios in which males experienced zero then high competition. Following the removal of competition, mating duration, but not offspring production, decreased to below control levels. This indicates that the benefit of increasing reproductive investment when encountering rivals may exceed that of decreasing investment when rivals disappear.”

The dynamics of coordinated group hunting and collective information transfer among schooling prey

“Predators were found to frequently form coordinated hunting groups, with up to five individuals attacking in line formation. Attacks were associated with increased fragmentation and irregularities in the spatial structure of prey groups, features that inhibit collective information transfer among prey. Prey group fragmentation, likely facilitated by predator line formation, increased (estimated) per capita risk of prey, provided prey schools were maintained below a threshold size of approximately 2 m2.”

Aging-related increases in behavioral variability: relations to losses of dopamine D1 receptors

“Increasing ISDs [intraindividual standard deviation] were associated with increasing age and diminished D1 binding in several brain regions (anterior cingulate gyrus, dorsolateral prefrontal cortex, and parietal cortex) for the interference, but not control, condition. Analyses of partial associations indicate that the association between age and IIV in the interference condition was linked to D1 receptor losses in task-relevant brain regions. These findings suggest that dysfunctional DA modulation may contribute to increased variability in cognitive performance among older adults.”

Testosterone: an introduction

Today I want to talk about testosterone.  I had intended for this post to be a short one, but then I kept digging and digging and, well, it turns out that testosterone is a pretty interesting subject.  What I’m going to do today is give a bit of a review on it, and talk about the effect that it has on personal decision-making.  In the next post, I’ll relate testosterone to social decision-making.

Testosterone does a lot of things, and most of them seem to revolve around social status effects – although that simplification may end up making things more confusing.  What testosterone does do is, over time, enhance muscle performance and redistribute immune resources to prepare for injury (remember my post on social status and healing?).  Several things cause increased levels of testosterone, with competition and sex being foremost among them.  This isn’t just physically aggressive competition, either; chess will give you bursts of testosterone.  Historically, calorically stressed populations will see seasonal variations in testosterone levels when men need to suppress aggressive behaviors during child rearing, or get ready for fighting and healing from fighting for status and mates.  But don’t think that testosterone directly will make an individual wildly aggressive.  As Robert Sapolsky notes in The Trouble With Testosterone

Round up some male monkeys…number 3, for example, can pass his day throwing around his weight with numbers 4 and 5, ripping off their monkey chow, forcing them to relinquish the best spots to sit in, but, at the same time, remembering to deal with numbers 1 and 2 with shit-eating obsequiousness…Take that third monkey and inject him with testosterone.  Inject a ton of it in him…And no surprise, when you check the behavioral data, it turns out that he will probably be participating in more aggressive actions than before…Is he now raining aggressive terror on any and all in the group, frothing in an androgenic glaze of indiscriminate violence?  Not at all.  He’s still judiciously kowtowing to numbers 1 and 2 but has become a total bastard to numbers 4 and 5.

Males in industrialized societies, however, don’t have any caloric needs causing them to suppress testosterone.  In order to examine testosterone in a more ‘natural’ setting, Trumble et al. turned to ‘pathogenically stressed forager-horticulturalists of the Bolivia Amazon’ (ie, poor and hungry people of the Amazon) who do indeed have lower testosterone levels.  These tribal people were brought together and organized into teams for a soccer tournament.  They found that testosterone was higher after the game in all participants, whether they won or lost.  But pay attention to this: the individuals who thought they performed better had larger increases in testosterone immediately following the game.  An hour later?  The difference disappeared.

It’s this type of effect of confidence that caused (Wright et al. 2012) to examine the effect of testosterone on group collaboration.  They put pairs of female subjects in a room and gave them a visual task where they had to decide which of two sets of bars were brighter.  When the subjects disagreed, they were allowed to discuss it and then one of the pair had to make a decision based on the joint beliefs.  But some of those subjects were given testosterone injections!  And the ones who were given testosterone were more likely to trust their own injections.  Although this seems like a nice result, we don’t really know what is happening during the verbal discussion.  Does the testosteroned subject just verbally browbeat the other subject?  What’s going on there?

So testosterone may act to reinforce egocentric behavior.  How about risk-taking?  That’s a little more complicated.  In a gambling game – with the subject only competing against themselves – (Stanton et al. 2011) showed that high risk taking in a gambling task is associated with high testosterone, but the same group later showed that (Stanton et al. 2011 [2]) there is actually a U-shaped curve.  Subjects with intermediate levels of testosterone are actually risk-averse, while low and high levels are risk-neutral.  This is an important point, and something to keep in mind; often we are not sampling the whole distribution of testosterone levels, and the simple ‘high’ versus ‘low’ dichotomy may be misleading.

So we have seen that testosterone probably goes up and down based on caloric resources and in the presence of competition and mates (or mating).  We’ll conclude next time with a discussion of how testosterone affects sociality, and how things are even more complicated than high/low or U-shaped.

References

Trumble, B., Cummings, D., von Rueden, C., O’Connor, K., Smith, E., Gurven, M., & Kaplan, H. (2012). Physical competition increases testosterone among Amazonian forager-horticulturalists: a test of the ‘challenge hypothesis’ Proceedings of the Royal Society B: Biological Sciences, 279 (1739), 2907-2912 DOI: 10.1098/rspb.2012.0455
Wright, N., Bahrami, B., Johnson, E., Di Malta, G., Rees, G., Frith, C., & Dolan, R. (2012). Testosterone disrupts human collaboration by increasing egocentric choices Proceedings of the Royal Society B: Biological Sciences, 279 (1736), 2275-2280 DOI: 10.1098/rspb.2011.2523

Stanton, S., Liening, S., & Schultheiss, O. (2011). Testosterone is positively associated with risk taking in the Iowa Gambling Task Hormones and Behavior, 59 (2), 252-256 DOI: 10.1016/j.yhbeh.2010.12.003

Stanton, S., Mullette-Gillman, O., McLaurin, R., Kuhn, C., LaBar, K., Platt, M., & Huettel, S. (2011). Low- and High-Testosterone Individuals Exhibit Decreased Aversion to Economic Risk Psychological Science, 22 (4), 447-453 DOI: 10.1177/0956797611401752
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