Category Archives: EvoPsych

Pay No Attention To The Calories Behind The Curtain

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Obesity is a touchy issue for many, as a recent twitter debacle demonstrated. However, there is little denying that the average body composition in the US has been changing in the past few decades: this helpful data and interactive map from the CDC shows the average BMI increasing substantially from year to year. In 1985, there was no state in which the percentage of residents with a BMI over 30 exceeded 14%; by 2010, there was no state for which that percentage was below 20%, and several for which it was over 30%. One can, of course, have debates over whether BMI is a good measure of obesity or health; at 6’1″ and 190 pounds, my BMI is approximately 25, nudging me ever so-slightly into the “overweight” category, though I am by no stretch of the imagination fat or unhealty. Nevertheless, these increases in BMI are indicative of something; unless that something is people putting on substantially more muscle relative to their height in recent decades – a doubtful proposition – the clear explanation is that people have been getting fatter.

Poorly-Marketed: The Self-Esteem-Destroying Scale

This steep rise in body mass in the recent years requires an explanation, and some explanations are more plausible than others. Trying to nominate genetic factors isn’t terribly helpful for a few reasons: first, we’re talking about drastic changes over the span of about a generation, which typically isn’t enough time for much appreciable genetic change, barring very extreme selection pressures. Second, saying that some trait or behavior has a “genetic component” is all but meaningless, since all traits are products of genetic and environmental interactions. Saying a trait has a genetic component is like saying that the area of a rectangle is related to its width; true, but unhelpful. Even if genetics were helpful as an explanation, however, referencing genetic factors would only help explain the increased weight in younger individuals, as the genetics of already-existing people haven’t been changing substantially over the period of BMI growth. You would need to reference some existing genetic susceptibility to some new environmental change.

Other voices have suggested that the causes of obesity are complex, unable to be expressed by a simple “calories-in/calories-out” formula.  This idea is a bit more pernicious, as the former half of that sentence is true, but the latter half does not follow from it. Like the point about genetic components, this explanation also suffers from the idea that it’s particularly unlikely the formula for determining weight gain or loss has become substantially more complicated in the span of a single generation. There is little doubt that the calories-in/calories-out formula is a complicated one, with many psychological and biological factors playing various roles, but its logic is undeniable: you cannot put on weight without an excess of incoming energy (or a backpack); that’s basic physics. No matter how many factors affect this caloric formula, they must ultimately have their effect through a modification of how many calories come in and go out. Thus, if you are capable of monitoring and restricting the number of calories you take in, you ought to have a fail-proof method of weight management (albeit a less-than-ideal one in terms of the pleasure people derive from eating).

For some people, however, this method seems flawed: they will report restricted-calorie diets, but they don’t lose weight. In fact, some might even end up gaining. The fail-proof methods fails. This means either something is wrong with physics, or there’s something wrong with the reports. A natural starting point for examining why people have difficulty managing their weight, even when they report calorically-restrictive diets, then, might be to examine whether people are accurately monitoring and reporting their intakes and outputs. After all, people do, occasionally, make incorrect self-reports. Towards this end, Lichtman et al (1992) recruited a sample of 10 diet-resistant individuals (those who reported eating under 1200 calories a day for some time and did not lose weight) and 80 control participants (all had BMIs of 27 of higher). The 10 subjects in the first group and 6 from the second were evaluated for reported intake, physical activity, body composition, and energy expenditure over two weeks. Metabolic rate was also measured for all the subjects in the diet-resistant group and for 75 of the controls.

Predicting the winner between physics and human estimation shouldn’t be hard.

First, we could consider the data from the metabolic rate: the daily estimated metabolic rate relative to fat-free body mass did not differ between the groups, and deviations of more than 10% from the group’s mean metabolic rate were rare. While there was clearly variation there, it wasn’t systematically favoring either group. Further, the total energy expenditure by fat-free body mass did not differ between the two groups either. When it came to losing weight, the diet-resistant individuals did not seem to be experiencing problems because they used more or less energy. So what about intake? Well, the diet-resistant individuals reported taking in an average of 1028 calories a day. This is somewhat odd, on account of them actually taking in around 2081 calories a day. The control group weren’t exactly accurate either, reporting 1694 calories in a day when they actually took in 2386. In terms of percentages, however, these differences are stark: the diet-resistant sample’s underestimates were about 150% as large as the controls.

In terms of estimates of energy expenditure, the picture was no brighter: diet-resistant individuals reported expending 1022 calories through physical activity each day, on average, when they actually exerted 771; the control group though the expended 1006, when they actually exerted 877. This means the diet-resistant sample were overestimating by almost twice as much as the controls. Despite this, those in the diet-resistant group also held more strongly to the belief that their obesity was caused by genetic and metabolic factors, and not their overeating, relative to controls. Now it’s likely that these subjects aren’t lying; they’re just not accurate in their estimates, though they earnestly believe them. Indeed, Lichtman et al (1992) reported that many of the subjects were distressed when they were presented with these results. I can only imagine what it must feel like to report having tried dieting 20 times or more only to be confronted with the knowledge that you likely weren’t doing so effectively. It sounds upsetting.

Now while that’s all well and good, one might object to these results on the basis of sample size: a sample size of about 10 per group clearly leaves a lot to be desired. Accordingly, a brief consideration of a new report examining people’s reported intakes is in order. Archer, Hand, and Blair (2013) examined people’s self-reports of intake relative to their estimated output across 40 years of  U.S. nutritional data. The authors were examining what percentage of people were reporting biologically-implausible caloric intakes. As they put it:

“it is highly unlikely that any normal, healthy free-living person could habitually exist at a PAL [i.e., TEE/BMR] of less than 1.35’”

Despite that minor complication of not being able to perpetually exist past a certain intake/output ratio, people of all BMIs appeared to be offering unrealistic estimates of their caloric intake; in fact, the majority of subjects reported values that were biologically-implausible, but the problem got worse as BMI increased. Normal-weight BMI women, for instance, offered up biologically-plausible values around 32-50% of the time; obese women reported plausible values around 12 to 31% of the time. In terms of calories, it was estimated that obese men and women tended to underreport by about 700 to 850 calories, on average (which is comparable to the estimates obtained from the previous study), whereas the overall sample underestimated around 280-360. People just seemed fairly inaccurate as estimating their intake all around.

“I’d estimate there are about 30 jellybeans in the picture…”

Now it’s not particularly odd that people underestimate how many calories they eat in general; I’d imagine there was never much selective pressure for great accuracy in calorie-counting over human evolutionary history. What might need more of an explanation is why obese individuals, especially those who reported resistance to dieting, tended to underreport substantially more than non-obese ones. Were I to offer my speculation on the matter, it would have something to do with (likely non-conscious) attempts to avoid the negative social consequences associated with obesity (obese people probably aren’t lying; just not perceiving their world accurately in this respect). Regardless of whether one feels those social consequences associated with obesity are deserved or not, they do exist, and one method of reducing consequences of that nature is to nominate alternative casual agents for the situation, especially ones – like genetics – that many people feel you can’t do much about, even if you tried. As one becomes more obese, then, they might face increased negative social pressures of that nature, resulting in their being more liable to learn, and subsequently reference, the socially-acceptable responses and behaviors (i.e. “it’s due to my genetics”, or, “I only ate 1000 calories today”; a speculation echoed by Archer, Hand, and Blair (2013)). Such an explanation is at least biologically-plausible, unlike most people’s estimates of their diets.

References: Archer, E., Hand, G., & Blair, S. (2013). Validity of U.S. national surveillance: National health and nutrition examination survey caloric energy intake data, 1971-2010. PLoS ONE, 8, e76632. doi:10.1371/journal.pone.0076632.

Lichtman et al. (1992). Discrepancy between self-reported and actual caloric intake and exercise in obese subjects. The New England Journal of Medicine, 327, 1893-1898.

 

Might Doesn’t Make Right, But It Helps

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There’s no denying the importance and ubiquitousness of violence and aggression. Despite the suggestion of the owner of the swamp castle in Monty Python’s Quest for the Holy Grail, people continue to “bicker and argue about who killed who“. Given that anger is often a key motivator of aggression, developing a satisfying account of anger can go a long way towards understanding and predicting when people will be likely to aggress against others. While there has been a great deal of focus placed on reducing violence, there tends to somewhat less mind paid to understanding the functions and uses of anger. The American Psychology Association, for instance, notes that anger can be a good thing because, “it can give you a way to express negative feelings…or motivate you to find solutions to problems”. They also warn that anger can “get out of hand”. While such suggestions sound plausible (minus the idea that “expressing” an emotion is good, in and of itself), they tend to lack the ability to deliver suitably textured predictions about the correlates or shape of anger, much less qualify what counts as “getting out of hand”.

Seems like he had that situation completely under control to me.

Of course, that’s not to suggest that is anger is always going to be useful in precisely the same measure as it gets delivered; just that we ought to be interested in attempting to understand the emotion before trying to diagnose the problems with it (in much the same fashion, one might wish to understand the function of, say, a fever, before figuring out whether we should try to reduce them). Towards that end, I would like to turn to a paper by Sell, Tooby, & Cosmides (2009) who posit an altogether more specific and biologically-plausible function for anger: the regulation and modification of welfare-tradeoff ratios (WTRs). These ratios essentially represent how much of your own welfare you’re willing to give up to improve the welfare of another. To use a simple economics example, imagine choosing between two options: $6 for yourself and $1 for someone else, or $5 for yourself and $5 for someone else. One’s WTR towards that someone else could be approximated, at least in some sense, by their choice in that and other dilemmas. This propensity to suffer losses to benefit others varies considerably across individuals.

This basic concept can be readily expanded to the wider social world: everything we do tends to have an effect on others and ourselves, and we would be better off, on the whole, if other people were relatively more willing to take our welfare into account when they acted. Sometimes that works out favorably for both parties, as is often the case in kin relationships (shared genetic interests tend to increase the willingness to trade off your own welfare for another’s); other times, it won’t work out so nicely. Since everyone would be better off if they could increase their WTRs with others and not everyone can possibly achieve that goal at once, WTRs tend to be aligned in non-optimal ways from at least someone’s perspective, if not most people’s. So let’s say someone isn’t taking my welfare into account in a way I deem acceptable when they act; what’s a guy to do? One available option is to attempt and “renegotiate” their WTR towards me through the threat of inflicting costs or withdrawing benefits; the  kinds of behaviors that anger helps motivate. Anger, then, might serve the function of attempting to regulate other people’s WTRs towards you (or your allies, and you by extension) by signalling the intention to inflict costs after behavior indicative of an unacceptably-low WTR.

This function immediately suggests some design features we ought to expect to find in the cognitive systems regulating anger, because not everyone is equally capable of inflicting costs on others. Accordingly, someone in a better position to inflict costs on others ought to be more readily roused to anger. One obvious indicator of that capacity to inflict costs would be one’s physical formidability: physically stronger males should be more capable of inflicting costs on others, and thus more willing to do so in order to modify the WTRs held by said others. This prediction was born out well in the data Sell et al (2009) collected: across various measures of men’s strength, the correlation between physical formidability and proneness to anger, history of fighting, sense of entitlement, and the perceived usefulness of violence were all high, ranging from approximately r = 0.3 to 0.5; for women, the same correlations were around 0.05 to 0.1. It was only physical formidability in men that proved to be a good predictor of aggression and anger, which makes a good deal of sense in light of the fact that women tend to be substantially less physically formidable in general.

A relationship that holds even when measured in Hulks.

Women are not without power, though, even if typically falling behind men in physical strength. Perhaps owing to their ability to recruit the physical strength of others, or leverage some other social capital, attractive women might also be especially prone to anger. This set of predictions was also confirmed: women who perceived themselves to be attractive – like strong men – were more prone to anger, felt greater entitlement, were more successful in conflicts, and found violence to be more useful after controlling for physical strength. Attractiveness, however, did not predict history of fighting in women, as was expected. While attractive men also tended to feel a greater sense of entitlement and reported more success in conflicts, the variables relating to fighting ability did not reliably correlate with attractiveness once the effect of physical formidability was partial outed. In other words, in relation to anger, what physical strength was for men, attractiveness was for women.

It should also be noted that neither attractiveness or physical strength correlated well with how long people tended to ruminate when angry. It wasn’t simply the case that strong men/attractive women were angrier for longer periods of time. We ought to expect anger to be roused strategically and contextually in order to solve specific problems; not just generally, as that is liable to cause more problems than it solves. These results also cut against some popular misconceptions, like people being angry to compensate for a lack of physical strength or attractiveness, as the people who lacked those qualities tended to be less prone to anger. These data would also cut against the suggestions from the APA that I initially mentioned: unless there’s some compelling reason to predict that physically strong males/attractive females are particularly likely to be prone to anger in order to “express their emotions” or “solve problems” more generally, we can see that those ostensible functions for anger are clearly lacking in some regards. They fail to deliver good predictions or satisfyingly account for the existing data.

These findings do raise some questions bearing deeper examination. The first of these concerns the often ambiguous nature of casual arrows: do men become more prone to anger and aggression as they become physically stronger, or might there be some developmental window at which point aggressive tendencies tend to become relatively canalized (i.e. does current physical strength matter, or does one’s strength at, say, age 16 matter more)? What role does social influence – in the form of larger groups of allies – bring? Are well-liked, but physically-weak men less or more likely to become angry easily? Does it matter whether one’s friends are physically imposing? How about if the target of one’s anger is more formidable than the one experiencing the anger? Admittedly, these are tricky questions to answer, owing largely to potential logistical issues in conducting the research in an ecologically-valid context, but they’re certainly worth considering.

“Experimental Recruitment: Please bring a dozen close friends”

Returning to the initial point about when anger gets “out of control”, we can see the question becomes a significantly more nuanced one. For starters, “out of control” will clearly depend on who you ask: while the angry individual might feel that they are not being treated appropriately by others in their social world, the targets of that anger might insist that the angry individual is being unreasonable in their requests for some particular treatment. Further, “out of control” for one individual does not necessarily equal the same amount of aggression for any other, at least in terms of the adaptive value of the behavior. One might also consider, at least at times, a lack of aggression and anger to be unsuitable behavior, such as when meek children are told to stand up to their bullies. The key point here is that we ought to expect all these considerations to vary strategically, rather than as a function of someone needing to “express their emotions” by “venting” them. If Sell et al (2009) are correct, anger can likely be reduced by altering these WTRs in non-aggressive fashions. Once the expected WTR for one party has been reached, the anger systems ought to be deactivated. Whether such methods are likely to be practically feasible is another matter entirely.

References: Sell, A., Tooby, J., & Cosmides, L. (2009). Formidability and the logic of human anger.  Proceedings of the National Academy of Sciences, 106, 15073-78.

Classic Research In Evolutionary Psychology: Learning

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Let’s say I were to give you a problem to solve: I want you to design a tool that is good at cutting. Despite the apparent generality of the function, this is actually a pretty vague request. For instance, one might want to know more about the material to be cut: a sword might work if your job is cutting some kind human flesh, but it might also be unwieldy to keep around the kitchen for preparing dinner (I’m also not entirely sure they’re dishwasher-safe, provided you managed to fit a katana into your machine in the first place). So let’s narrow the request down to some kind of kitchen utensil. Even that request, however, is a bit vague, as evidenced by Wikipedia naming about a dozen different kinds of utensil-style knives (and about 51 different kinds of knives overall). That list doesn’t even manage to capture other kinds of cutting-related kitchen utensils, like egg-slicers, mandolines, peelers, and graters. Why do we see so much variety, even in the kitchen, and why can’t one simple knife be good enough? Simple: when different tasks have non-overlapping sets of best design solutions, functional specificity tends to yield efficiency in one realm, but not in another.

“You have my bow! And my axe! And my sword-themed skillet!”.

The same basic logic has been applied to the design features of living organisms as well, including aspects of our cognition as I argued in the last post: the part of the mind that functions to logically reason about cheaters in the social environment does not appear to be able logically reason with similar ease about other, even closely-related topics. Today, we’re going to expand on that idea, but shift our focus towards the realm of learning. Generally speaking, learning can be conceived of as some change to an organism’s preexisting cognitive structure due to some experience (typically unrelated to physical trauma). As with most things related to biological changes, however, random alterations are unlikely to result in improvement; to modify a Richard Dawkins quote ever so slightly, “However many ways there may be of [learning something useful], it is certain that there are vastly more ways of [learning something that isn't". For this reason, along with some personal experience, no sane academic has ever suggested that our learning occurs randomly. Learning needs to be a highly-structured process in order to be of any use.

Precisely how structured "highly-structured" entails is a bit of a sticky issue, though. There are undoubtedly still some who would suggest that some general type of reinforcement-style learning might be good enough for learning all sorts of neat and useful things. It's a simple rule: if [action] is followed by [reward], then increase the probability of [action]; if [action] is followed by [punishment], then decrease the probability of [action]. There are a number of problems with such a simple rule, and they return to our knife example: the learning rule itself is under-specified for the demands of the various learning problems organisms face. Let’s begin with an analysis of what is known as conditioned taste aversion. Organisms, especially omnivorous ones, often need to learn about what things in their environment are safe to eat and which are toxic and to be avoided. One problem in learning about which are potential foods are toxic is that the action (eating) is often divorced from the outcome (sickness) by a span of minutes to hours, and plenty of intervening actions take place in the interim. On top of that, this is not the type of learning you want to need repeated exposures to in order to learn, as, and this should go without saying, eating poisonous foods is bad for you. In order to learn the connection between the food and the sickness, then, a learning mechanism would seem to need to “know” that the sickness is related to the food and not other, intervening variables, as well as being related in some specific temporal fashion. Events that conform more closely to this anticipated pattern should be more readily learnable.

The first study we’ll consider, then, is by Garcia & Koelling (1966) who were examining taste conditioning in rats. The experimenters created conditions in which rats were exposed to “bright, noisy” water and “tasty” water. The former condition was created by hooking a drinking apparatus up to a circuit that connected to a lamp and a clicking mechanism, so when the rats drank, they were provided with visual and auditory stimuli. The tasty condition was created by flavoring the water. Garcia & Koelling (1966) then attempted to pair the waters with either nausea or electric shocks, and subsequently measure how the rats responded in their preference for the beverage. After the conditioning phase, during the post-test period, a rather interesting sets of results emerged: while rats readily learned to pair nausea with taste, they did not draw the connection between nausea and audiovisual cues. When it came to the shocks, however, the reverse pattern emerged: rats could pair shocks with audiovisual cues well, but could not manage to pair taste and shock. This result makes a good deal of sense in light of a more domain-specific learning mechanism: things which produce certain kinds of audiovisual cues (like predators) might also have the habit of inflicting certain kinds of shock-like harms (such as with teeth or claws). On the other hand, predators don’t tend to cause nausea; toxins in food tend to do so, and these toxins also tend to come paired with distinct tastes. An all-purpose learning mechanism, by contrast, should be able to pair all these kinds of stimuli and outcomes equally well; it shouldn’t matter whether the conditioning comes in the form of nausea or shocks.

Turns out that shocks are useful for extracting information, as well as communicating it.

The second experiment to consider on the subject of learning, like the previous one, also involves rats, and actually pre-dates it. This paper, by Petrinovich & Bolles (1954), examined whether different deprivation states have qualitatively different effects on behavior. In this case, the two deprivation states under consideration were hunger and thirst. Two samples of rats were either deprived of food or water, then placed in a standard T-maze (which looks precisely how you might imagine it would). The relevant reward – food for the hungry rats and water for the thirsty ones – was placed in one arm of the T maze. The first trial was always rewarded, no matter which side the rat chose. Following that initial choice, the food was placed on the side of the maze the rat did not chose on the previous trial. For instance, if the rat went ‘right’ on the first trial, the reward was placed in the ‘left’ arm on the second trial. Whether the rat chose correctly or incorrectly didn’t matter; the reward was always placed on the opposite side as its previous choice. Did it matter whether the reward was food or water?

Yes; it mattered a great deal. The hungry rats averaged substantially fewer errors in reaching the reward than the thirsty ones (approximately 13 errors over 34 trials, relative to 28 errors, respectively). The rats were further tested until they managed to perform 10 out of 12 trials correctly. The hungry rats managed to meet the criterion value substantially sooner, requiring a median of 23 total trials before reaching that mark. By contrast, 7 of the 10 thirsty rats failed to reach the criterion at all, and, of the three that did, they required approximately 30 trials on average to manage that achievement. Petrinovich & Bolles (1954) suggested that these results can be understood in the following light: hunger makes the rat’s behavior more variable, while thirst makes its behavior more stereotyped. Why? The most likely candidate explanation is the nature of the stimuli themselves, as they tend to appear in the world. Food sources tend to be distributed semi-unpredictably throughout the environment, and where there is food today, there might not be food tomorrow. By contrast, the location of water tends to be substantially more fixed (where there was a river today, there is probably a river tomorrow), so returning to the last place you found water would be the more-secure bet. To continue to drive this point home: a domain general learning mechanism should do both tasks equally as well, and a more general account would seem to struggle to explain these findings.

Shifting gears away from rats, the final study for consideration is one I’ve touched on before, and it involves the fear responses of monkeys. As I’ve already discussed the experiment, (Cook & Mineka, 1989) I’ll offer only a brief recap of the paper. Lab-reared monkeys show no intrinsic fear responses to snakes or flowers. However, social creatures that they are, these lab-reared monkeys can readily develop fear responses to snakes after observing another conspecific reacting fearfully to them. This is, quite literally, a case of monkey see, monkey do. Does this same reaction hold in response to observations of conspecifics reacting fearfully to a flower? Not at all. Despite the lab-reared monkeys being exposed to stimuli they have never seen before in their life (snakes and flowers) paired with a fear reaction in both cases, it seems that the monkeys are prepared to learn to fear snakes, but not similarly prepared to learn a fear of flowers. Of note is that this isn’t just a fear reaction in response to living organisms in general: while monkeys can learn a fear of crocodiles, they do not learn to fear rabbits under the same conditions.

An effect noted by Python (1975)

When it comes to learning, it does not appear that we are dealing with some kind of domain-general learning mechanism, equally capable of learning all types of contingencies. This shouldn’t be entirely surprising, as organisms don’t face all kinds of contingencies with equivalent frequencies: predators that cause nausea are substantially less common than toxic compounds which do. Don’t misunderstand this argument: humans and nonhumans alike are certainly capable of learning many phylogenetically novel things. That said, this learning is constrained and directed in ways we are often wholly unaware of. The specific content area of the learning is of prime importance in determining how quickly somethings can learned, how lasting the learning is likely to be, and which things are learned (or learnable) at all. The take-home message of all this research, then, can be phrased as such: Learning is not the end point of an explanation; it’s a phenomenon which itself requires an explanation. We want to know why an organism learns what it does; not simply that it learns.

References: Cook M, & Mineka S (1989). Observational conditioning of fear to fear-relevant versus fear-irrelevant stimuli in rhesus monkeys. Journal of abnormal psychology, 98 (4), 448-59 PMID: 2592680

Garcia, J. & Koelling, R. (1966). Relation of cue to consequence in avoidance learning. Psychonomic Science, 4, 123-124.

Petrinovich, L. & Bolles, R. (1954). Deprivation states and behavioral attributes. Journal of Comparative Physiological Psychology, 47, 450-453.

Classic Research In Evolutionary Psychology: Reasoning

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I’ve consistently argued that evolutionary psychology, as a framework, is a substantial, and, in many ways, vital remedy to some wide-spread problems: it allows us to connect seemingly disparate findings under a common understanding, and, while the framework is by itself no guarantee of good research, it forces researchers to be more precise in their hypotheses, allowing for conceptual problems with hypotheses and theories to be more transparently observed and addressed. In some regards the framework is quite a bit like the practice of explaining something in writing: while you may intuitively feel as if you understand a subject, it is often not until you try to express your thoughts in actual words that you find your estimation of your understanding has been a bit overstated. Evolutionary psychology forces our intuitive assumptions about the world to be made explicit, often to our own embarrassment.

“Now that you mention it, I’m surprised I didn’t notice that sooner…”

As I’ve recently been discussing one of the criticisms of evolutionary psychology – that the field is overly focused on domain-specific cognitive mechanisms – I feel that now would be a good time to review some classic research that speaks directly to the topic. Though the research to be discussed itself is of recent vintage (Cosmides, Barrett, & Tooby, 2010), the topic has been examined for some time, which is whether our logical reasoning abilities are best convinced of as domain-general or domain-specific (whether they work equally well, regardless of content, or whether content area is important to their proper functioning). We ought to expect domain specificity in our cognitive functioning for two primary reasons (though these are not the only reasons): the first is that specialization yields efficiency. The demands of solving a specific task are often different from the demands of solving a different one, and to the extent that those demands do not overlap, it becomes difficult to design a tool that solves both problems readily. Imagining a tool that can both open wine bottles and cut tomatoes is hard enough; now imagine adding on the requirement that it also needs to function as a credit card and the problem becomes exceedingly clear. The second problem is outlined well by Cosmides, Barrett, & Tooby (2010) and, as usual, they express it more eloquently than I would:

The computational problems our ancestors faced were not drawn randomly from the universe of all possible problems; instead, they were densely clustered in particular recurrent families.

Putting the two together, we end up with the following: humans tend to face a non-random set of adaptive problems in which the solution to any particular one tends to differ from the solution to any other. As domain-specific mechanisms solve problems more efficiently than domain-general ones, we ought to expect the mind to contain a large number of cognitive mechanisms designed to solve these specific and consistently-faced problems, rather than only a few general-purpose mechanisms more capable of solving many problems we do not face, but poorly-suited to the specific problems we do. While such theorizing sounds entirely plausible and, indeed, quite reasonable, without empirical support for the notion of domain-specificity, it’s all so much bark and no bite.

Thankfully, empirical research abounds in the realm of logical reasoning. The classic tool used to assess people’s ability to reason logically is the Wason selection task. In this task, people are presented with a logical rule taking the form of “if P, then Q“, and a number of cards representing P, Q, ~P, and ~Q (i.e. “If a card has a vowel on one side, then it has an even number on the other”, with cards showing A, B, 1 & 2). They are asked to point out the minimum set of cards that would need to be checked to test the initial “if P, then Q” statement. People’s performance on the task is generally poor, with only around 5-30% of people getting it right on their first attempt. That said, performance on the task can become remarkably good – up to around 65-80% of subjects getting the correct answer – when the task is phrased as a social contract (“If someone [gets a benefit], then they need to [pay a cost]“, the most well known being “If someone is drinking, then they need to be at least 21″). Despite the underlying logical form not being altered, the content of the Wason task matters greatly in terms of performance. This is a difficult finding to account for if one holds to the idea of a domain-general logical reasoning mechanism that functions the same way in all tasks involving formal logic. Noting that content matters is one thing, though; figuring out how and why content matters becomes something of a more difficult task.

While some might suggest that content simply matters as a function of familiarity – as people clearly have more experience with age restrictions on drinking and other social situations than vaguer stimuli – familiarity doesn’t help: people will fail the task when it is framed in terms of familiar stimuli and people will succeed at the task for unfamiliar social contracts. Accordingly, criticisms of the domain-specific social contract (or cheater-detection) mechanism shifted to suggest that the mechanism at work is indeed content-specific, but perhaps not specific to social contracts. Instead, the contention was that people are good at reasoning about social contracts, but only because they’re good at reasoning about deontic categories – like permissions and obligations – more generally. Assuming such an account were accurate, it remains debatable as to whether that mechanism would be counted as a domain-general or domain-specific one. Such a debate need not be had yet, though, as the more general account turns out to be unsupported by the empirical evidence.

We’re just waiting for critics to look down and figure it out.

While all social contracts involve deontic logic, not all deontic logic involves social contracts. If the more general account of deontic reasoning were true, we ought to not expect performance difference between the former and latter types of problems. In order to test whether such differences exist, Cosmides, Barrett, & Tooby’s (2010) first experiment involved presenting subjects with a permission rule – “If you do P, you must do Q first” – varying whether P was a benefit (going out at night), neutral (staying in), or a chore (taking out the trash; Q, in this case, involved tying a rock around your ankle). When the rule was a social contract (the benefit), performance was high on the Wason task, with 80% of subjects answering correctly. However, when the rule involved staying in, only 52% of subjects got it right; that number was even lower in the garbage condition, with only 44% accuracy among subjects. Further, this same pattern of results was subsequently replicated in a new context involving filing/signing forms as well. This results is quite difficult to account for with a more-general permission schema, as all the conditions involve reasoning about permissions; they are, however, consistent with the predictions from social contract theory, as only the contexts involving some form of social contract ended up eliciting the highest levels of performance.

Permission schemas, in their general form, also appear unconcerned with whether one violates a rule intentionally or accidentally. By contrast, social contract theory is concerned with the intentionality of the violation, as accidental violations do not imply the presence of a cheater the way intentional violations do. To continue to test the distinction between the two models, subjects were presented with the Wason task in contexts where the violations of the rule were likely intentional (with or without a benefit for the actor) or accidental. When the violation was intentional and benefited the actor, subjects performed accurately 68% of the time; when it was intentional but did not benefit that actor, that percentage dropped to 45%; when the violation was likely unintentional, performance bottomed-out at 27%. These results make good sense if one is trying to find evidence of a cheater; they do not if one is trying to find evidence of a rule violation more generally.

In a final experiment, the Wason task was again presented to subjects, this time varying three factors: whether one was intending to violate a rule or not; whether it would benefit the actor or not; and whether the ability to violate was present or absent. The pattern of results mimicked those above: when benefit, intention, and ability were all present, 64% of subjects determined the correct answer to the task; when only 2 factors were present, 46% of subjects got the correct answer; and when only 1 factor was present, subjects did worse still, with only 26% getting the correct answer, which is approximately the same performance level as when there were no factors present. Taken together, these three experiments provide powerful evidence that people aren’t just good at reasoning about the behavior of other people in general, but rather that they are good at reasoning about social contracts in particular. In the now-immortal words of Bill O’Reilly, “[domain-general accounts] can’t explain that“.

“Now cut their mic and let’s call it a day!”

Now, of course, logical reasoning is just one possible example for demonstrating domain specificity, and these experiments certainly don’t prove that the entire structure of the mind is domain specific; there are other realms of life – such as, say, mate selection, or learning – where domain general mechanisms might work. The possibility of domain-general mechanisms remains just that – possible; perhaps not often well-reasoned on a theoretical level or well-demonstrated at an empirical one, but possible all the same. The problem in differentiating between these different accounts may not always be easy in practice, as they are often thought to generate some, or even many, of the same predictions, but in principle it remains simple: we need to place the two accounts in experimental contexts in which they generate opposing predictions. In the next post, we’ll examine some experiments in which we pit a more domain-general account of learning against some more domain-specific ones.

References: Cosmides L, Barrett HC, & Tooby J (2010). Adaptive specializations, social exchange, and the evolution of human intelligence. Proceedings of the National Academy of Sciences of the United States of America, 107 Suppl 2, 9007-14 PMID: 20445099

Evolutionary Psychology: Tying Psychology Together

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Every now and again – perhaps more frequently than many would prefer – someone who apparently fails to understand one or more aspects of the evolutionary perspective in psychology goes on to make rather public proclamations about what it is and what it can and cannot do for us. Notable instances are not particularly difficult to find. The most recent of these to cross my desk comes from Gregg Henriques, which takes a substantially less-nasty tone than I have come to expect. In it, he claims that evolutionary psychology does not provide us with a viable metatheory for understanding psychology, and he bases his argument on three main points: (1) evolutionary psychology is overly committed to the domain-specificity concept, (2) that the theory fails to have the correct map of complexity, and (3) it hasn’t done much for people in a clinical setting. In the course of making these arguments, I feel he stumbles badly on several points, so I’d like to take a little time to point out these errors. Thankfully, given the relative consistency of these errors, doing so is becoming more a routine than anything else.

So feel free to change the channel if you’ve seen this before.

Gregg begins with the natural starting point for many people in criticizing EP: while we have been focusing on how organisms solve specific adaptive problems, there might be more general adaptive problems out there. As Gregg put it:

The EP founders also overlooked the fact that there really is a domain general behavioral problem, which can be characterized as the problem of behavioral investment

There are a number of things to say about such a suggestion. Thankfully, I have said them before, so this is a relatively easy task. To start off, these ostensibly domain-general problems are, in fact, not all that general. To use a simple example, consider one raised by Gregg in his discussion of behavioral investment theory: organisms need to solve the problem of obtaining more energy than they spend to keep on doing things like being alive and mating. That seems like an awfully general problem, but, stated in such manner, the means by which that general problem is, or can be, solved are massively unspecified. How does an organism calculate its current caloric state? How does an organism decide which things to eat to obtain energy? How does an organism decide when to stop foraging for food in one area and pursue a new one? How is the return on energy calculated and compared against the expenditure? As one can quickly appreciate, the larger, domain-general problem (obtain more energy than one expends) is actually composed of very many smaller problems, and things can get complicated quickly. Pursuing mating rather than food, for instance, is unlikely to result in an organism obtaining more energy than it expends. This leaves the behavioral investment problem – broadly phrased – wanting in terms of any predictive power: why do organism pursue goals other than gaining and energy and under what conditions do they do so? The issue here, then, is not so much that domain-general problems aren’t being accounted for by evolutionary psychology, but rather that the problems themselves are being poorly formulated by the critics.

The next area in this criticism that Gregg stumbles on is the level of analysis that evolutionary psychology tends to work with. Gregg considers associative learning a domain general system but, again, it’s trivial to demonstrate it is not all that general. There are many things that associative learning systems do not do: regulate homeostatic processes, like breathing and heart rate, perceive anything, like light, sound, pleasure, or pain, generate emotions, store memory, and so on. In terms of their function, associative learning systems only really seem to do one thing: make behavior followed by reward more likely than behavior followed by discomfort, and that’s only after other systems have decided what is rewarding and what is not. That this system can apply the same function to many different inputs doesn’t make it a domain-general one. The distinction that Gregg appears to miss, then, is that functional specificity is not the same as input specificity. Calling learning a domain-general system is a bit like calling a knife a domain-general tool because it can be used to cut many different objects. Try to use a knife to weld metal, and you’ll quickly appreciate how domain-specific the function of a knife is.

On top of that, there is also the issue that some associations are learned far more readily than others. To quote Dawkins, “However many ways there may be of being alive, it is certain that there are vastly more ways of being dead”. A similar logic applies to learning: there are many more potentially incorrect and useless things to learn than there are useful ones. This is why learning ends up being a rather constrained process: rats can learn to associate light and sound with shocks, but do not tend to make the association between taste and shock, despite the unpleasantness of the shock itself. Conversely, associations between taste and nausea can be readily learned, but not between light and nausea. To continue beating this point to death, a domain-general account of associative learning has a rather difficult time explaining why some connections are readily learned and others are not. In order to generate more textured predictions, you need to start focusing on the more-specific sub-problems that make up the more general one.

And if doing so is not enough of a pain-in-the-ass, you’re probably doing it wrong.

On a topic somewhat-related to learning, the helpful link provided by Gregg concerning behavioral investment theory has several passages that, I think, are rather diagnostic of the perspective he has about evolutionary psychology:

Finally, because [behavioral investment/shutdown theory] is an evolutionary model, it also readily accounts for the fact that there is a substantial genetic component associated with depression (p.61)…there is much debate on the relative amount of genetic constraint versus experiential plasticity in various domains of mental functioning (p.70).

The problem here is that evolutionary psychology concerns itself with far more than genetic components. In the primer on evolutionary psychology, the focus on genetic components in particular is deemed to be nonsensical in the first place, as the dichotomy between genetic and environmental itself is a false one. Gregg appears to be conflating “evolutionary” with “genetic” for whatever reason, and possibly both with “fixed” when he writes:

In contrast to the static model suggested by evolutionary psychologists, The Origin of Minds describes a mind that is dynamic and ever-changing, redesigning itself with each life experience

As far as I know, no evolutionary psychologist has ever suggested a static model of the mind; not one. Given that evolutionary psychologists is pluralized in that sentence, I can only assume that the error is made by at least several of them, but to whom “them” refers is a mystery to me. Indeed, this passage by Gregg appears to play by the rules articulated in the pop anti-evolutionary psychology game nearly perfectly:

The second part of the game should be obvious. Once you’ve baldly asserted what evolutionary psychologists believe – and you lose points if, breaking tradition, you provide some evidence for what evolutionary psychologists have actually claimed in print and accurately portray their view – point out the blindingly obvious opposite of the view you’ve hung on evolutionary psychology. Here, anything vacuous but true works. Development matters. People learn. Behavior is flexible. Brains change over time. Not all traits are adaptations. The world has changed. People differ across cultures. Two plus two equals four. Whatever.

The example is so by-the-book that little more really needs to be said about it. Somewhat ironically, Gregg suggests that the evolutionary perspective creates a straw man of other perspectives, like learning and cultural ones. I’ll leave that suggestion without further comment.

The next point Gregg raises concerning complexity I have a difficult time understanding. If I’m parsing his meaning correctly, he’s saying that culture adds a level of complexity to analyses of human behavior. Indeed, local environmental conditions can certainly shape how adaptations develop and are activated, whether due to culture or not, but I’m not sure precisely how that is supposed to be a criticism of evolutionary psychology. As I mentioned before, I’m not sure a single contemporary evolutionary psychologist has ever been caught seriously suggesting something to the contrary. Gregg also makes some criticism of evolutionary psychology not defining psychology as he would prefer. Again, I’m not quite sure I catch his intended meaning here, but I fail to see how that it is a criticism of the perspective. Gregg suggests that we need psychology that can apply to non-humans as well, but I don’t to see how an evolutionary framework fails that test. No examples are given for further consideration, so there’s not much more to say on that front.

Gregg’s final criticism  amounts to a single line, suggesting that an evolutionary perspective has yet to unify every approach people take in psychotherapy. Not being the expert on psychotherapy myself, I’ll plead ignorance to the success that an evolutionary framework has had in that realm, and no evidence of any kind is provided for assessment. I fail to see why such a claim has any bearing on whether an evolutionary perspective could do so; I just wanted to make note that the criticism has been heard, but perhaps not formulated into a more appreciable fashion.

Final verdict: the prosecution seems confused.

Criticisms of an evolutionary perspective like these are unfortunately common and consistently misguided. Why they continue to abound despite their being answered time and again from the field’s origins is curious. Now in all fairness, Gregg doesn’t appear hostile to the field, and deems it “essential” for understanding psychology. Thankfully, the pop anti-evolutionary psychology game captures this sentiment as well, so I’ll leave it on that note:

The third part of the game is not always followed perfectly, and it is the hardest part. Now that you’ve shown how you are in full command of the way science is conducted or some truth about human behavior that evolutionary psychologists have missed, it’s important to assert that you absolutely acknowledge that of course humans are the product of evolution, and of course humans aren’t exempt from the principles of biology.

Look, you have to say, I’m not opposed to applying evolutionary ideas to humans in principle. This is key, as it gives you a kind of ecumenical gravitas. Yes, you continue, I’m all for the unity of science and cross-pollination and making the social sciences better, and so on. But, you have to add – and writing plaintively, if you can, helps here – I just want things to be done properly. If only evolutionary psychologists would (police themselves, consider development, acknowledge learning, study neuroscience, run experiments, etc…), then I would be just perfectly happy with the discipline.

Having Their Cake And Eating It Too

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Humans are a remarkably cooperative bunch of organisms. This is a remarkable fact because cooperation can open the door wide to all manner of costly exploitation. While it can be a profitable strategy for all involved parties, cooperation requires a certain degree of vigilance and, at times, the credible threat of punishment in order to maintain its existence. Figuring out how people manage to solve these cooperative problems has provided us with no shortage of research and theorizing, some of which is altogether more plausible than the rest. Though I haven’t quite figured out the appeal yet, there are many thoughtful people who favor the group selection accounts for explaining why people cooperate. They suggest that people will often cooperate in spite of its personal fitness costs because it serves to better the overall condition of the group to which they belong. While there haven’t been any useful predictions that appear to have fallen out of such a model, there are those who are fairly certain it can at least account for some known, but ostensibly strange findings.

That is a rather strange finding you got there. Thanks, Goodwill.

One human trait purported to require a group selection explanation is altruistic punishment and cooperation, especially in one-shot anonymous economic games. The basic logic goes as follows: in a prisoner’s dilemma game, so long as that game is a non-repeated event, there is really only one strategy, and that’s defection. This is because if you defect when your partner defects, you’re better off than if you cooperated; if you partner cooperated, on the other hand, you’re still better off if you defect. Economists might thus call the strategy of “always defect” to be a “rational” one. Further, punishing a defector in such conditions is similarly considered irrational behavior, as it only results in a lower payment for the punisher than they would have otherwise had. As we know from decades of research using these games, however, people don’t always behave “rationally”: sometimes they’ll cooperate with other people they’re playing with, and sometimes they’ll give up some of their own payment in order to punish someone who has either wronged them or, more importantly, wronged stranger. This pattern of behavior – paying to be nice to people who are nice, and paying to punish those who are not – has been dubbed “strong reciprocity”. (Fehr, Fischbacher, & Gachter, 2002)

The general raison d’etre of strong reciprocity seems to be that groups of people which had lots of individuals playing that strategy managed to out-compete other groups of people without them. Even though strong reciprocity is costly on the individual level, the society at large reaps larger overall benefits, as cooperation has the highest overall payoff, relative to any kind of defection. Strong reciprocity, then, helps to force cooperation by altering the costs and benefits to cooperation and defection on the individual level. There is a certain kind of unfairness inherent in this argument, though; a conceptual hypocrisy that can be summed up by the ever-popular phrase, “having one’s cake and eating it too”. To consider why, we need to understand the reason people engage in punishment in the first place. The likely, possibly-obvious candidate explanation just advanced is that punishment serves a deterrence function: by inflicting costs on those who engage in the punished behavior, those who engage in the behavior fail to benefit from it and thus stop behaving in that manner. This function, however, rests on a seemingly innocuous assumption: actors estimate the costs and benefits to acting, and only act when the expected benefits are sufficiently large, relative to the costs.

The conceptual hypocrisy is that this kind of cost-benefit estimation is something that strong reciprocators are thought to not to engage in. Specifically, they are punishing and cooperating regardless of the personal costs involved. We might say that a strong reciprocator’s behavior is inflexible with respect to their own payments. This example is a bit like playing the game of “chicken”, where two cars face each other from a distance and start driving at one another in a straight line. The first drive to turn away loses the match. However, if both cars continue on their path, the end result is a much greater cost to both drivers than is suffered if either one turns. If a player in this game was to adopt an inflexible strategy, then, by doing something like disabling their car’s ability to steer, they can force the other player to make a certain choice. Faced with a driver who cannot turn, you really only have one choice to make: continue going straight and suffer a huge cost, or turn and suffer a smaller one. If you’re a “rational” being, then, you can be beaten by an “irrational” strategy.

Flawless victory. Fatality.

So what would be the outcome if other individuals started playing the ever-present “always defect” strategy in a similarly inflexible fashion? We’ll call those people “strong defectors” for the sake of contrast. No matter what their partner does in these interactions, the strong defectors will always play defect, regardless of the personal costs and benefits. By doing so, these strong defectors might manage to place themselves beyond the reach of punishment from strong reciprocators. Why? Well, any amount of costly punishment directed towards a strong defector would be a net fitness loss from the group’s perspective, as costly punishment is a fitness-reducing behavior: it reduces the fitness of the person engaging in it (in the form of whatever cost they suffer to deliver the punishment) and it reduces the fitness of the target of the punishment. Further, the costs to punishing the defectors could have been directed towards benefiting other people instead – which are net fitness gains for the group – so there are opportunity costs to engaging in punishment as well. These fitness costs would need to be made up for elsewhere, from the group selection perspective.

The problem is that, because the strong defectors are playing an inflexible strategy, the costs cannot be made up for elsewhere; no behavioral change can be affected. Extending this game of chicken analogy to the group level, let’s say that turning away is the “cooperative” option, and dilemmas like these were at least fairly regular. They might not have involved cars, but they did involve a similar kind of payoff matrix: there’s only one benefit available, but there are potential costs in attempting to achieve it. Keeping in line with the metaphor, it would be in the interests of the larger population if no one crashed. It follows that between-group selective pressures favor turning every time, since the costs are guaranteed to be smaller for the wider population, but the sum of the benefits don’t change; only who achieves them does. In order to force the cooperative option, a strong reciprocator might disable their ability to turn so as it alters the cost and benefits to others.

The strong reciprocators shouldn’t be expected to be unaffected by costs and benefits, however; they ought to be affected by such considerations, just on the group level, rather than the individual one. Their strategy should be just as “rational” as any others, just with regard to a different variable. Accordingly, it can be beaten by other seemingly irrational strategies – like strong defection – that can’t be affected by the threats of costs. Strong defectors which refuse to turn will either force a behavioral change in the strong reciprocators or result in many serious crashes. In either case, the strong reciprocator strategy doesn’t seem to lead to benefits in that regard.

Now perhaps this example sounds a bit flawed. Specifically, one might wonder how appreciable portions of the population might come to develop an inflexible “always defect” strategy in the first place. This is because the strategy appears to be costly to maintain at times: there are benefits to cooperation and being able to alter one’s behavior in response to costs imposed through punishment, and people would be expected to be selected to achieve and avoid them, respectively. On top of that, there is also the distinct concern that repeated attempts at defection or exploitation can result in punishment severe enough to kill the defector. In other words, it seems that there are certain contexts in which strong defectors would be at a selective disadvantage, becoming less prevalent in the population over time. Indeed, such a criticism would be very reasonable, and that’s precisely the because the always defect population behaves without regard to their personal payoff. Of course, such a criticism applies in just as much force to the strong reciprocators, and that’s the entire point: using a limited budget to affect the lives of others regardless of its effects on you isn’t the best way to make the most money.

The interest on “making it rain” doesn’t compete with an IRA.

The idea of strong defectors seems perverse precisely because they act without regard to what we might consider their own rational interests. Were we to replace “rational” with “fitness”, the evolutionary disadvantage to a strategy that functions as if behaving in such a manner seems remarkably clear. The point is that the idea of a strong reciprocator type of strategy should be just as perverse. Those who attempt to put forth a strong reciprocator type of strategy as plausible account for cooperation and punishment attempt to create a context that allows them to have their irrational-agent cake and eat it as well: strong reciprocators need not behave within their fitness interests, but all the other agents are expected to. This assumption needs to be at least implicit within the models, or else they make no sense. They don’t seem to make very much sense in general, though, so perhaps that assumption is the least of their problems.

References: Fehr, E., Fischbacher, U., & Gachter, S. (2002). Strong reciprocity, human cooperation, and the enforcement of social norms. Human Nature, 13, 1-25 DOI: 10.1007/s12110-002-1012-7

Can Rube Goldberg Help Us Understand Moral Judgments?

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Though many people might be unfamiliar with Rube Goldberg, they are often not unfamiliar with Rube Goldberg machines: anyone who has ever seen the commercial for the game “Mouse Trap” is at least passingly familiar with them. Admittedly, that commercial is about two decades old at this point, so maybe a more timely reference is in order:OK Go’s music video for “This too shall pass” is a fine demonstration (or Mythbusters, if that’s more your cup of tea). The general principle behind a Rube Goldberg machine is that it completes an incredibly simple task in an overly-complicated manner. For instance, one might design one of these machines to turn on a light switch, but that end state will only be achieved after 200 intervening steps and hours of tedious setup. While these machines provide a great deal of novelty when they work (and that is a rather large “when”, since there is the possibility of error in each step), there might be a non-obvious lesson they can also teach us concerning our cognitive systems designed for moral condemnation.

  Or maybe they can’t; either way, it’ll be fun to watch and should kill some time.

In the literature on morality, there is this concept known as the doctrine of double effect. The principle states that actions with harmful consequences can be morally acceptable provided a number of conditions are met: (1) the act itself needs to be morally neutral or better, (2) the actor intends to achieve some positive end through acting; not the harmful consequence, (3) the bad effect is not a means to the good effect, and (4) the positive effects outweigh the negative ones sufficiently. While that might all seem rather abstract, two concrete and popular examples can demonstrate the principle easily: the trolley dilemma and the footbridge dilemma. Taking these in order, the trolley problem involves the following scenario:

There is a runaway trolley barreling down the railway tracks. Ahead, on the tracks, there are five people tied up and unable to move. The trolley is headed straight for them. You are standing some distance off in the train yard, next to a lever. If you pull this lever, the trolley will switch to a different set of tracks. Unfortunately, you notice that there is one person on the side track. You have two options: (1) Do nothing, and the trolley kills the five people on the main track. (2) Pull the lever, diverting the trolley onto the side track where it will kill one person.

In this dilemma, most people who have been surveyed (about 90% of them) suggest that it is morally acceptable to pull the lever, diverting the train onto the side track. It also fits the principle of double effect nicely: (1) the act (redirecting the train) is not itself immoral, (2) the actor intends a positive consequence (saving the 5) and not the negative one (1 dies), (3) the bad consequence (the death) is not a means of achieving the outcome, but rather a byproduct of the action (redirecting the train), and (4) the lives saved substantially outweigh the lives lost.

The footbridge dilemma is very similar in setup, but different in a key detail: in the footbridge dilemma, rather than redirecting the train to a sidetrack, a person is pushed in front of it. While the person dies, that causes the train to stop before hitting the 5 hikers, saving their lives. In this case, only about 10% of people say it’s morally acceptable to push the man. We can see how double effect fails in this case: (1) the act (pushing the man) is relatively on the immoral side of things, (2) the death of the person being pushed in intended, and (3) the bad consequence (the man dying) is the means by which the good consequence is achieved; the fact that the positive consequences outweigh the negative ones in terms of lives saved is not enough. But why should this be the case? Why do consequences alone not dictate our actions, and why can factors as simple as redirecting a train versus pushing a person make such tremendous differences in our moral judgments?

As I suggested recently, the answer to both of those questions can be understood through beginning our analysis of morality with an analysis of condemnation. These questions can be rephrased in that light to the following forms: “Why might people wish to morally condemn someone for achieving an outcome that is, on the whole, good?” and, “Why might people be less inclined to condemn certain outcomes, contingent on how they’re brought about?” The answer to the first question is fairly straightforward: I might wish to morally condemn someone because their actions (or failing to morally condemn them) might have some direct costs on me, even if they benefit others. For instance, I might wish to condemn someone for their behavior in the trolley or footbridge problem if it’s my friend dying, rather than a stranger. That some generally morally positive outcome was achieved is irrelevant to me if it was costly from my perspective. Natural selection doesn’t design adaptations for the good of the group, so that the group’s welfare is increased seems besides the point. Of course, a cost is a cost is a cost, so why should it matter to me at all if my friend was killed by being pushed or having the train sent towards him?

“DR. TEDDY! NOOOO!”

Part of that answer depends on what other people are willing to condemn. Trying to punish someone for their actions is not always cheap or easy: there’s always a chance of retaliation by the punished party or their allies. After all, a cost is a cost is a cost to both me and them. This social variable means that attempting to punish others without additional support might be completely ineffective (or at least substantially less effective) at times. Provided that other parties are less likely to punish negative byproducts, relative to negative intended outcomes, this puts pressure on you to attempt and persuade others that the person you want to punish acted with intent, whereas it puts the reverse pressure on the actor; to convince others they did not intend that bad outcome. This brings us back to Rube Goldberg, the footbridge dilemma, and a slight addition to doctrine of double effect.

There are some who argue that the doctrine of double effect isn’t quite complete. Specifically, there is an unappreciated third type of action: one in which a person acts because a negative outcome will obtain, but they do not intend that outcome (what is known as “triple effect”). This distinction is a bit trickier to grasp, so another example will help. Say that we’re again talking about the footbridge dilemma: there is a man standing on the bridge over the tracks with the oncoming train scheduled to hit the 5 hikers. However, we can pull a lever which will drop the man onto the track where he will be hit, thus stopping the train and saving the five. This is basically identical to the standard footbridge problem, and most people would deem it unacceptable to pull the lever. But now let’s consider another case: again, the man is standing on the bridge, but the mechanism that will drop him off the bridge is a light sensor. If light reflects off the train onto the sensor, the bridge will drop the man, he will die, and the 5 will be saved. Seeing the oncoming train, someone, Rube-Goldberg style, shines a spotlight on the train, illuminating it; the illumination hits the sensor, dropping the man onto the track, killing him and saving the five hikers.

There are some (Otsuka, 2008) that argue there is no meaningful difference between these two cases, but in order to make that claim, they need to infer something about the actor’s intentions in both cases, and precisely what one infers affects the subsequent shape of the analysis. Were one to infer that there is really only one problem to be solved – the train that going to kill 5 people – then the intentions of the person pulling the lever to illuminate the train and pulling the lever to drop the man are equivalent and equally condemnable. However, there is another inference one could make in the light case, as there are multiple facets to the problem: the train will both kill 5 and the train isn’t illuminated. If one intends to solve the latter problem (so now there will be an illuminated train about to kill 5 people) one also, as a byproduct of solving that problem, causes both the problem of 5 people getting killed to be solved and the death of man who got dropped onto the track. Now one could argue, as Otsuka (2008) does, that such an example fails because people could not be plausibly motivated to solve the non-illuminated part of the problem, but that seems like largely a matter of perspective. The addition of the light variable introduces, if even to some small degree, plausible deniability capable of shifting the perception of an outcome from intended to byproduct. Someone pulling the lever could have been doing so in order to illuminate the train or to drop the man onto the track, but it’s not entirely unambiguous which is the case.

“Well how was I supposed to know I was doing something dangerous?”

The light case is also a relatively simple one: there are only 3 steps (shine light on train, light opens door, door opening causes man to fall and stop train), and perfect knowledge is assumed (the person shining the light knew this would happen). Changing either or these variables would likely have the effect of altering the blame of the actor: if the actor didn’t know about the light sensor or the man on the footbridge, condemnation would likely decrease; if the action involved 10 steps, rather than 3, this could potentially introduce further plausible deniability, especially if any of those steps involved the actions of other people. It would be in the actor’s best interests to thus deny their knowledge of the outcome, or separate the outcome from their initial action as broadly as possible. Conversely, someone looking to condemn the actor would need to do the reverse.

Now maybe this all sounds terribly abstract, but there are real-life cases to which similar kinds of analysis can apply. Consider cases where a child is bullied at school and later commits suicide. Depending on one’s perspective in these kinds of cases, one might condemn or fail to condemn the bullies for the suicide (though one might still blame them for the bullying); one might also, however, condemn the parents for not being there for the child as they should have, or one might blame no one but the suicide victim themselves. As one thinks about ways in which the suicide could have been prevented, there are countless potential Rube-Goldberg kinds of variables in the casual chain to point to (violent media, the parents, the bullies, the friends, their diet, the suicide victim, the school, etc), the modification of any of which might have prevented the negative outcome. This gives condemners (who may wish to condemn people for initially-unrelated reasons) a wide-array of potential plausible targets. However, each of these potential sources also gives the other sources some way of mitigating and avoiding blame. While such strategic considerations tend to make a mess of normative moral theories, they do provide us the required tools to actually begin to understand morality itself.

References: Otsuka, M. (2008). Double Effect, Triple Effect and the Trolley Problem: Squaring the Circle in Looping Cases. Utilitas, 20, 92-110 DOI: 10.1017/S0953820807002932

Better Fathers Have Smaller Testicles, But…

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There is currently an article making the rounds in the popular media (or at least the range of media that I’m exposed to) suggesting that testicular volume is a predictor of paternal investment in children: the larger the testicles, the less nurturing, fatherly behavior we see. I get the nagging sense that stories about genitals tends to get a larger-than-average share of attention (I did end up tracking the article down, after all), and that might have motivated both the crafting and sharing of this study (at least in the media. I can’t speak directly to the author’s intentions, though I can note the two domains often fail to overlap). In any case, more attention does not necessarily mean that people end up with an accurate picture of the research. Indeed, the percentage of people who will – or even can – read the source paper itself is vastly outnumbered by those who will not. So, for whatever it’s worth, here’s a more in-depth look at the flavor of the week research finding.

Our next new flavor will come out at the end of the month…

The paper (Mascaro, Hackett, & Rilling, 2013) begins with a discussion of life history theory. With respect to sexual behavior, life history theory posits that there is a tradeoff between mating effort and parental effort: the energy an organism spends investing in any single offspring is energy not spent in making new ones. Since then name of the game in evolution is maximizing fitness, this tradeoff needs to be resolved, and can be in various ways. Humans, compared to many other species, tend to fall rather heavily on the “investing” side of the scale, pouring immense amounts of time and energy into each highly-dependent offspring. Other species, like Salmon, for instance, invest all their energy into a single bout of mating, producing many offspring, but investing relatively less in each (as dead parents often make poor candidates for sources of potential investment). Life history theory is not just useful for understanding between-species differences though; it is also useful for understanding individual differences within species (as it must be, since the variation in the respective traits between species needed to have come from some initial population without said variance).

Perhaps the most well-known examples are the between-sex differences in life history tradeoffs among mammals, but let’s just stick to humans to make it relatable. When a woman gets pregnant, provided the baby will carried to term, her minimum required investment is approximately 9 months of pregnancy and often several years of breastfeeding, much of which precludes additional reproduction. The metabolic and temporal costs of this endeavor are hard to overstate. By contrast, a male’s minimum obligate investment in the process is a single ejaculate and however long intercourse took. One can immediately see that men tend to have more to gain from investing in mating effort, relative to women, at least from the minimum-investment standpoint. However, not all men have as much potential to achieve those mating-effort gains; some men are more attractive sexual partners, and others will be relatively shut-out of the mating market. If one cannot compete in the mating domain, it might pay to make oneself more appealing in the investment domain where they can more effectively compete. Accordingly, if one tends to attempt the investment strategy (though this need not mean a consciously-chosen plan), it’s plausible their body might follow a similar investment strategy, placing fewer resources into the more mating-orientated aspects of our physiology: specifically, the testicles.

Unsurprisingly, testicular volume appears to be correlated with a number of factors, but most notably sperm production (this especially the case between species, as I’ve written about before). Those men who tend to preferentially pursue a mating strategy (relative to an investment one) have slightly-different adaptive hurdles to overcome, most notably in the insemination and sperm competition arenas. Accordingly, Mascaro, Hackett, & Rilling (2013) predicted that we ought to see a relationship between testes size (representing a form of mating effort) and nurturing offspring (representing a form of parental effort). Enter the current study, where 70 biological fathers who were living with the mother of their children had their testicular volume (n = 55) and testosterone levels (n = 66) assessed. Additionally, reports of their parental behavior were also collected, along with a few other measures. As the title of the paper suggests, there was indeed a negative correlation (-0.29) between reported care-giving and testicle volume. This is the point where the highlighted finding begins to need qualifications, however, due to another pesky little factor: testosterone. Testosterone levels were also found to negatively correlate with reports of care-giving (-0.27), as well as the father’s reported desire to provide care (-0.26). Given that these are correlations, it’s not readily apparent that testicular volume per se would be the metaphorical horse pulling the cart.

Pulling the cart, metaphorically, “all the way“, that is.

Perhaps also unsurprisingly, testicular volume showed what the authors called a “moderate positive correlation” with testosterone levels (0.26, p = 0.06). As an aside, I find it interesting that the authors had, only a few sentences prior, reported an almost identically-sized correlation (r = -0.25, p = 0.06) between testicular volume and desire to invest in children, but there they labeled the correlation as a “strong trend”, rather than a “moderate correlation”. The choice of wording seems peculiar.

In any case, if bigger balls tended to go together with more testosterone, it becomes more difficult to make the case for testicular volume itself to be driving the relationship with parenting behaviors. In order to attempt and solve this problem, Mascaro, Hackett, & Rilling (2013) created a regression model, using testicular volume, testosterone levels, father’s earning, and hours worked as predictors of childcare. In that model, the only significant predictor of childcare was testosterone level.

Removing the “father’s earning” and “number of hours worked” variables from the regression model resulted in a gain in predictive value for testicular volume (though it was still not significant) but, again, it was testosterone that appeared to be having the greater effect. Whether or not it would be defensible to modify the regression model in that particular way in the first place is debatable, as the modification seems to be done in the interest of making testicular volume appear relatively more predictive than it was previously (also, removing those two previous factors resulted in the model accounting for quite a bit less of the variance in fathers’ overall childcare behaviors). Just because the authors had some a priori prediction about testicular volume and not about hours worked or money earned seems like only a mediocre reason for justifying the exclusion of the latter two variables while retaining the former.

There was also some neuroscience included in the study concerning the men looking at pictures of children’s faces and correlating the neural responses with childcare, testicular volume, and testosterone. I’ll preface what I’m about to say with the standard warning: I’m not the world’s foremost expert on neuroscience, so there is a distinct possibility I’m misunderstanding something here. That said, the authors did find a relationship there between testicular volume and neural response to children – a relationship that was apparently not diminished when controlling for testosterone.  It should be noted that, again, unless I’m misunderstanding something, this connection didn’t appear to translate into significant increases in the childcare actually displayed by the males in the study once the effects of testosterone were considered (if it did, it should have shown up in the initial regression models). Then again, I have historically been overly-cautious about inferring much from brain scans, so take from that what you will.

I’ve got my eye on you, imaging technology…

To return to the title of this post, yes, testicular volume appears to have some predictive value in determining parental care, but this value tends to be reduced, often substantially so, once a few other variables are considered. Now I happen to think that the hypotheses derived from life history theory are well thought out in this paper. I imagine I might be inclined to have made such predictions myself. Testicular measures have already given us plenty of useful information about the mating habits of various species, and I would expect there is still value to be gained from considering them. That said, I would also advise some degree of caution in attempting to fit the data to these interesting hypotheses. Using selective phrasing to highlight some trends (the connection between testicular volume and desire to provide childcare) relative to others (the connection between testicular volume and testosterone) because they fit the hypothesis better makes me uneasy. Similarly, dropping variables from a regression model to improve the predictive power of the variable of interest is also troublesome. Perhaps the basic idea might prove more fruitful were it to be expanded to other kinds of men (single men, non-fathers, divorced, etc) but, in any case, I find the research idea quite an interesting step, and I look forward to hearing a lot more about our balls in the future.

References: Mascaro, J., Hackett, P., & Rilling, J. (2013). Testicular volume is inversely correlated with nurturing-related brain activity in human fathers. Proceedings of the National Academy of Sciences of the United States of America.

Conscience Does Not Explain Morality

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“We may now state the minimum conception: Morality is, at the very least, the effort to guide one’s conduct by reason…while giving equal weight to the interests of each individual affected by one’s decision” (emphasis mine).

The above quote comes to us from Rachaels & Rachaels (2010) introductory chapter entitled “What is morality?” It is readily apparent that their account of what morality is happens to be a conscience-centric one, focusing on self-regulatory behaviors (i.e. what you, personally, ought to do). These conscience-based accounts are exceedingly popular among many people, academics and non-academics alike, perhaps owing to its intuitive appeal: it certainly feels like we don’t do certain things because they feel morally wrong, so understanding morality through conscience seems like the natural starting point. With all due respect to the philosopher pair and the intuitions of people everywhere, they seem to have begun their analysis of morality on entirely the wrong foot.

So close to the record too…

Now, without a doubt, understanding conscience can help us more fully understand morality, and no account of morality would be complete without explaining conscience; it’s just not an ideal starting point for beginning our analysis (DeScioli & Kurzban, 2009; 2013). This is because moral conscience does not, in and of itself, explain our moral intuitions well. Specifically, it fails to highlight the difference between what we might consider ‘preferences’ and ‘moral rules’. To better understand this distinction, consider two following statements: (1) “I have no interest in having homosexual intercourse”, and (2) “Homosexual intercourse is immoral”. These two statements are distinct utterances, aimed at expressing different thoughts. The first expresses a preference, and that preference would appear sufficient for guiding one’s behavior, all else being equal; the latter statement, however, appears to express a different sentiment altogether. That second sentiment appears to imply that others ought to not have homosexual intercourse, regardless of whether you (or they) want to engage in the act.

This is the key distinction, then: moral conscience (regulating one’s own behavior) does not appear to straightforwardly explain moral condemnation (regulating the behavior of others). Despite this, almost every expressed moral rule or law involves punishing others for how they behave – at least implicitly. While the specifics of what gets punished and how much punishment is warranted vary to some degree from individual to individual, the general form of moral rules does not. Were I to say I do not wish to have homosexual intercourse, I’m only expressing a preference, a bit like stating whether or not I would like my sandwich on white or wheat bread. Were I to say homosexuality is immoral, I’m expressing the idea that those who engage in the act ought to be condemned for doing so. By contrast, I would not be interested in punishing people for making the ‘wrong’ choice about bread, even if I think they could have made a better choice.

While we cannot necessarily learn much about moral condemnation via moral conscience, the reverse is not true: we can understand moral conscience quite well through moral condemnation. Provided that there are groups of people who will tend to punish for you for doing something, this provides ample motivation to avoid engaging in that act, even if you otherwise highly desire to do so. Murder is a simple example here: there tend to be some benefits for removing specific conspecifics from one’s world. Whether because those others inflict costs on you or prevent the acquisition of benefits, there is little question that murder might occasionally be adaptive. If, however, the would-be target of your homicidal intentions happens to have friends and family members that would rather not see them dead, thank you very much, the potential costs those allies might inflict need to be taken into account. Provided those costs are appreciably great, and certain actions are punished with sufficient frequency over time, a system for representing those condemned behaviors and their potential costs – so as to avoid engaging in them – could easily evolve.

“Upon further consideration, maybe I was wrong about trying to kill your mom…”

That is likely what our moral conscience represents. To the extent that behaviors like stealing from or physically harming others tended to be condemned and punished, we ought to be expected to have a cognitive system to represent that fact. Now perhaps that all seems a bit perverse. After all, many of us simply experience the sensation that an act is morally wrong or not; we don’t necessarily think about our actions in terms of the likelihood and severity of punishment (we do think such things some of the time, but that’s typically not what appears to be responsible for our feeling of “that’s morally wrong”. People think things are morally wrong regardless of whether one is caught doing it). That all may be true enough, but remember, the point is to explain why we experience those feelings of moral wrongness; not to just note that we do experience them and that they seem to have some effect on our behavior. While our behavior might be proximately motivated by those feelings of moral wrongness, those feelings came to exist because they were useful in guiding out behavior in the face of punishment. That does raise a rather important question, though: why do we still feel certain acts are immoral even when the probability of detection or punishment are rather close to zero?

There are two ways of answering that question, neither of which is mutually exclusive with the other. The first is that the cognitive systems which compute things like the probability of being detected and estimate the likely punishment that will ensue are always working under conditions of uncertainty. Because of this uncertainty, it is inevitable that the system will, on occasion, make mistakes: sometimes one could get away without repercussions when behaving immorally, and one would be better off if they took those chances than if they did not. One also needs to consider the reverse error as well, though: if you assess that you will not be caught or punished when you actually will, you would have been better off not behaving immorally. Provided the costs of punishment are sufficiently high (the loss of social allies, abandonment by sexual partners, the potential loss of your life, etc), it might pay in some situations to still avoid behaving in morally unacceptable ways even when you’re almost positive you could get away with it (Delton et al, 2012). The point here is that it doesn’t just matter if you’re right or wrong about whether you’re likely to be punished: the costs to making each mistake need to be factored into the cognitive equation as well, and those costs are often asymmetric.

The second way of approaching that question is to suggest that the conscience system is just one cognitive system among many, and these systems don’t always need to agree with one another. That is, a conscience system might still represent an act as morally unacceptable while other systems (those designed to get certain benefits and assess costs) might output an incompatible behavioral choice (i.e. cheating on your committed partner despite knowing that it is morally condemned to do so, as the potential benefits are perceived as being greater than the costs). To the extent that these systems are independent, then, it is possible for each to hold opposing representations about what to do at the same time. Examples of this happening in other domains are not hard to find: the checkerboard illusion, for instance, allows us to hold both the representation that A and B are different colors and that A and B are the same color in our mind at once. We need not be of one mind about all such matters because our mind is not one thing.

“Well, shoot; I’ll get the glue gun…”

Now, to be sure, there are plenty of instances where people will behave in ways deemed to be immoral by others (or even by themselves, at different times) without feeling the slightest sensation of their conscience telling them “what you’re doing is wrong”. Understanding how the conscience develops, and the various input conditions likely to trigger it – or fail to do so – are interesting matters. In order to make better progress on researching them, however, it would benefit researchers to begin with an understanding of why moral conscience exists. Once the function of conscience – avoiding condemnation – has been determined, figuring out what questions to ask about conscience becomes an altogether easier task. We might expect, for instance, that moral conscience is less likely to be triggered when others (the target and their allies) are perceived to be incapable of effective retaliation. While such a prediction might appear eminently sensible when beginning with condemnation, it is not entirely clear how one could deliver such a prediction if they began their analysis with conscience instead.

References: Delton, A., Krasnow, M., Cosmides, L., & Tooby, J. (2012). Evolution of direct reciprocity under uncertainty can explain human generosity in one-shot encounters.  Proceedings of the National Academy of Sciences, 108, 13335-13340.

DeScioli P, & Kurzban R (2009). Mysteries of morality. Cognition, 112 (2), 281-99 PMID: 19505683

DeScioli P, & Kurzban R (2013). A solution to the mysteries of morality. Psychological bulletin, 139 (2), 477-96 PMID: 22747563

Rachaels, J. & Rachels S. (2010). The Elements of Moral Philosophy. New York, NY: McGraw Hill.

Simple Rules Do Useful Things, But Which Ones?

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Depending on who you ask – and their mood at moment – you might come away with the impression that humans are a uniquely intelligent species, good at all manner of tasks, or a profoundly irrational and, well, stupid one, prone to frequent and severe errors in judgment. The topic often penetrates into lay discussions of psychology, and has been the subject of many popular books, such as the Predictably Irrational series. Part of the reason that people might give these conflicting views of human intelligence – either in terms of behavior or reasoning – is the popularity of explaining human behavior through cognitive heuristics. Heuristics are essentially rules of thumb which focus only on limited sets of information when making decisions. A simple, perhaps hypothetical example of a heuristic might be something like a “beauty heuristic”. This heuristic might go something along the lines of when deciding who to get into a relationship with, pick the most physically attractive available option; other information – such as the wealth, personality traits, and intelligence of the perspective mates – would be ignored by the heuristic.

Which works well when you can’t notice someone’s personality at first glance.

While ignoring potential sources information might seem perverse at first glance, given that one’s goal is to make the best possible choice, it has the potential to be a useful strategy. One of these reasons is that the world is a rather large place, and gathering information is a costly process. The benefits of collecting additional bits of information are outweighed by the costs of doing so past a certain point, and there are many, many potential sources of information to choose from. To the extent that additional information helps one make a better choice, making the best objective choice is often a practical impossibility. In this view, heuristics trade off accuracy with effort, leading to ‘good-enough’ decisions. A related, but somewhat more nuanced benefit of heuristics comes from the sampling-error problem: whenever you draw samples from a population, there is generally some degree of error in your sample. In other words, your small sample is often not entirely representative of the population from which it’s drawn. For instance, if men are, on average, 5 inches taller than women the world over, if you select 20 random men and women from your block to measure, your estimate will likely not be precisely 5 inches; it might be lower or higher, and the degree of that error might be substantial or negligible.

Of note, however, is the fact that the fewer people from the population you sample, the greater your error is likely to be: if you’re only sampling 2 men and women, your estimate is likely to be further from 5 inches (in one direction or the other) relative to when you’re sampling 20, relative to 50, relative to a million. Importantly, the issue of sampling error crops up for each source of information you’re using. So unless you’re sampling large enough quantities of information capable of balancing that error out across all the information sources you’re using, heuristics that ignore certain sources of information can actually lead to better choices at times. This is because the bias introduced by the heuristics might well be less predictively-troublesome than the degree of error variance introduced by insufficient sampling (Gigerenzer, 2010). So while the use of heuristics might at times seem like a second-best option, there appear to be contexts where it is, in fact, the best option, relative to an optimization strategy (where all available information is used).

While that seems to be all well and good, the acute reader will have noticed the boundary conditions required for heuristics to be of value: they need to know how much of which sources of information to pay attention to. Consider a simple case where you have five potential sources of information to attend to in order to predict some outcome: one of these is sources strongly predictive, while the other four are only weakly predictive. If you play an optimization strategy and have sufficient amounts of information about each source, you’ll make the best possible prediction. In the face of limited information, a heuristic strategy can do better provided you know you don’t have enough information and you know which sources of information to ignore. If you picked which source of information to heuristically-attend to at random, though, you’d end up making a worse prediction than the optimizer 80% of the time. Further, if you used a heuristic because you mistakenly believed you didn’t have sufficient amounts of information when you actually did, you’ve also made a worse prediction than the optimizer 100% of the time.

“I like those odds; $10,000 on blue! (The favorite-color heuristic)”

So, while heuristics might lead to better decisions than attempts at optimization at times, the contexts in which they manage that feat are limited. In order for these fast and frugal decision rules to be useful, you need to be aware of how much information you have, as well as which heuristics are appropriate for which situations. If you’re trying to understand why people use any specific heuristic, then, one would need to make substantially more textured predictions about the functions responsible for the existence of the heuristic in the first place. Consider the following heuristic, suggested by Gigerenzer (2010): if there is a default, do nothing about it. That heuristic is used to explain, in this case, the radically different rates of being an organ donor between countries: while only 4.3% of Danish people are donors, nearly everyone in Sweden is (approximately 85%). Since the explicit attitudes about the willingness to be a donor don’t seem to differ substantially between the two countries, the variance might prove a mystery; that is, until one realizes that the Danes have an ‘opt in’ policy to be a donor, whereas the Swedes have an ‘opt out’ one. The default option appears to be responsible for driving most of variance in rates of organ donor status.

While such a heuristic explanation might seem, at least initially, to be a satisfying one (in that it accounts for a lot of the variance), it does leave one wanting in certain regards. If anything, the heuristic seems more like a description of a phenomenon (the default option matters sometimes) rather than an explanation of it (why does it matter, and under what circumstances might we expect it to not?). Though I have no data on this, I imagine if you brought subjects into the lab and presented them with an option to give the experimenter $5 or have the experimenter give them $5, but highlighted the first option as default, you would probably find very few people who did not ignore the default heuristic. Why, then, might the default heuristic be so persuasive at getting people to be or fail to be organ donors, but profoundly unpersuasive at getting people to give up money? Gigerenzer’s hypothesized function for the default heuristic – group coordination – doesn’t help us out here, since people could, in principle, coordinate around either giving or getting. Perhaps one might posit that another heuristic – say, when possible, benefit the self over others – is at work in the new decision, but without a clear, and suitably textured theory for predicting when one heuristic or another will be at play, we haven’t explained these results.

In this regard, then, heuristics (as explanatory variables) share the same theoretical shortcoming as other “one-word explanations” (like ‘culture’, ‘norms’, ‘learning’, ‘the situation’, or similar such things frequently invoked by psychologists). At best, they seem to describe some common cues picked up on by various cognitive mechanisms, such as authority relations (what Gigerenzer suggested formed the following heuristic: if a person is an authority, follow requests) or peer behavior (the imitate-your-peers heuristic: do as your peers do) without telling us anything more. Such descriptions, it seems, could even drop the word ‘heuristic’ altogether and be none the worse for it. In fact, given that Gigerenzer (2010) mentions the possibility of multiple heuristics influencing a single decision, it’s unclear to me that he is still be discussing heuristics at all. This is because heuristics are designed specifically to ignore certain sources of information, as mentioned initially. Multiple heuristics working together, each of which dabble in a different source of information that the others ignore seem to resemble an optimization strategy more closely than heuristic one.

And if you want to retain the term, you need to stay within the lines.

While the language of heuristics might prove to be a fast and frugal way of stating results, it ends up being a poor method of explaining them or yielding much in the way of predictive value. In determining whether some decision rule even is a heuristic in the first place, it would seem to behoove those advocating the heuristic model to demonstrate why some source(s) of information ought to be expected to be ignored prior to some threshold (or whether such a threshold even exists). What, I wonder, might heuristics have to say about the variance in responses to the trolley and footbridge dilemmas, or the variation in moral views towards topics like abortion or recreational drugs (where people are notably not in agreement)? As far as I can tell, focusing on heuristics per se in these cases is unlikely to do much to move us forward. Perhaps, however, there is some heuristic heuristic that might provide us with a good rule of thumb for when we ought to expect heuristics to be valuable…

References: Gigerenzer, G. (2010). Moral Satisficing: Rethinking Moral Behavior as Bounded Rationality Topics in Cognitive Science., 2, 528-554 DOI: 10.1111/j.1756-8765.2010.01094.x