Part Of: Neuroeconomics sequence
Followup To: Basal Ganglia as Action Selector, Intro to Behaviorism
Content Summary: 2600 words, 13 min read

Towards a theory of habit

Life brims with habitual behavior.

All our life is but a mass of habits—practical, emotional, and intellectual—systematically organized for our weal or woe, and bearing us irresistibly toward our destiny, whatever the latter may be.

Ninety-nine hundredths or, possibly, nine hundred and ninety-nine thousandths of our activity is purely automatic and habitual, from our rising in the morning to our lying down each night. Our dressing and undressing, our eating and drinking, our greetings and partings, our hat-raisings and giving way for ladies to precede, nay, even most of the forms of our common speech, are things of a type so fixed by repetition as almost to be classed as reflex actions.

William James

Why do we find ourselves on autopilot so frequently? What happens in our brain when we switch from reflexive to reflective thought? Is there a way to objectively tell which mode your brain is in, right now?

Our brain betray the program they employ by the errors we express.

When you flip on a light switch, your behavior could be a result of the desire for illumination coupled with the belief that a certain movement will lead to it. Sometimes, however, you just turn on the light habitually without anticipating the consequences – the very context of having arrived home in a dark room automatically triggers your reaching for the light switch. While these two cases may appear similar, they differ in the extent to which they are controlled by outcome expectancy. When the light switch is known to be broken, the habit might still persist whereas the goal-directed action might not.

Yin & Knowlton (2006)

At a conceptual level, we can differentiate three cognitive phenomena: stimulus, response, and outcome. Habitual behavior uses the environment to guide its responses (an S-R map); goal-directed behavior directly optimizes the R-O relation. Goal-directed behavior occurs immediately. Habit emerges with overtraining.

In both behavioral modes, reward is used for day-by-day learning. But only goal-directed behavior is sensitive to rapid changes in the anticipated outcome. We can operationalize this with two metrics (Balleine & Dezfouli 2019):

1. Outcome expectancy: is behavior sensitive to changes in the environment?
2. Reward devaluation: is it sensitive to changes in intrinsic value?

Habitual behavior exhibits both.

• Sometimes, we flip the light switch despite knowing the causal path from the light switch to the bulb is severed.
• Sometimes, we open the refrigerator despite being full.

When a rat becomes sated, a moderately-trained rat will immediately reduce its reward-seeking behavior (e.g., press the lever fewer times). An extensively trained rat, however, will not respond to such devaluation events – a sign it is acting out of habit. Interestingly, habit only occurs in predictable environments. In a more complicated task, habit (and its index, devaluation sensitivity) does not occur.

Adjudicated Competition Theory: Model-Based vs Model-Free

The basal ganglia is an action selector, giving exclusive motor access to the behavioral program with the strongest bid. We have already seen data connecting this structure with reinforcement learning (RL). But in the RL literature, there are two different ways to implement a learner: a tree system which builds an explicit world-model, and a cache system which ignores all that complexity, and just remembers stimulus-response pairings.

These two modeling approaches have different costs and benefits:

• Tree Systems are very costly to compute, but learn quickly & are more responsive to changes in the environment.
• Cache Systems are easier to maintain, but learn slowly & are less responsive.

Besides driving behavior, both models also report their own uncertainty (i.e., error bars around the reward prediction). The adjudicated competition theory of habit (Daw et al 2005) suggests that the brain implements both models, and an adjudicator gives the reins to whichever model expresses the least uncertainty.

Because cache systems are more uncertain in novel environments (stemming from their low data efficiency), tree systems tend to predominate early. But as both systems learn, cache systems eventually become more relatively confident and take over behavioral control. This shift in relative uncertainty is thought to be the reason why our brains build habits if exposed to the same environment for a couple weeks.

Overtraining manufactures habits. But only sometimes! There are several quirks with our habit-generating machinery:

• Ratio intervals (which rewards behaviors as often as they are performed) tend to preclude habit formation. Interval training (which only provides a reward every so often) is much more habitogenic.
• Even interval training only generates habits in relatively simple circumstances: for certain tasks involves two actions, behavior can remain goal-directed indefinitely.

Amazingly, not only could Daw et al (2005) reproduce the basic phenomena of overtraining, but their model also reproduces these quirks as well!

Which two brain systems underlie goal-oriented and habitual behaviors, respectively? For that, we turn to the basal ganglia.

Three Loops: Sensorimotor, Associative, Limbic

The striatum receives input from the entire cortex. As such, the fibers which comprise the basal ganglia are rather thick. As our tracing technologies matured, anatomists were able to inspect these tracts at higher resolutions. In the 1990s, it was discovered that this “bundle of fibers” actually comprised (at least) three parallel circuits.

These are called the Sensorimotor, Associative, and Limbic loops, based on their respective cortical afferents:

It’s important to note important differences between the rodent and human striatum.

The mesolimbic and nigrostriatal dopaminergic pathways, discussed above, directly map onto the Limbic and Sensorimotor/Associative loops, respectively:

All three circuits (direct, indirect, and hyperdirect) exist in all three loops (Nougaret et al, 2013); however, I omitted hyperdirect from the above for simplicity.

Given its participation in the Limbic Loop, the mesolimbic pathway is also sometimes referred to as the reward pathway. Its component structures, the ventral tegmental area (VTA) and nucleus accumbens (NAc), are particularly important.

There have been attempts to refine these loops into more specific circuits. Pauli et al (2016), using contrastive methods, found a different 5-network parcellation, which doesn’t overlap much with the former paper. Using resting state methods, Choi et al (2012) found five ICNs embedded within the striatum. More recently, Greene et al (2020) localized individual-specific ICNs within the cortical-basal ganglia-thalamic circuit.

For now, we will mostly confine ourselves to a discussion of three loops.

Localizing The Controllers

The associative loop appears to be the basis of the goal-directed action (GD-A) system. If you lesion any component of the system, behavior becomes exclusively habitual. For example, lesions to the posterior dorsomedial striatum (pDMS), behavior becomes insensitive to changes in both reward contingency and reward value. The same effects occurs with lesions to the SNR, and mediodorsal thalamus (MD). Finally, lesions to the basolateral amygdala (BLA) also disrupt goal-directed behavior, plausibly by altering the reward signal provided by the substantia nigra (SNr).

The sensorimotor loop appears to be the basis of the habit system. If you lesion any component of the system, behavior becomes exclusively goal-directed. For example, after lesions to the dorsolateral striatum (DLS), behavior begins to track changes in both reward contingency and reward value. The same effects occurs with lesions to the GPi, and mediodorsal thalamus (MD). Finally, lesions to the posterior central nucleus of the amygdala (pCeN) also disrupt habitual behavior, plausibly by altering the reinforcement signal provided by the substantia nigra (SNc).

These conclusions are derived from both human and rodent behavioral studies (Balleine & O’Doherty 2010). In normal circumstances, these systems interoperate seamlessly. Damage to the either system, however, causes exclusive reliance on the other system.

The infralimbic cortex (IL) plays an important role in habitual behavior. Lesions to this site prevent the formation of habit (Killcross & Coutureau 2003), and even blocks expression of already formed habits (Smith et al 2012). The IL also appears critical for the formation & retention of both Pavlovian and instrumental extinction (Barker et al 2014) But habit-related activity seems to develop first in the dorsomedial striatum and only with overtraining in the IL (Smith & Graybiel 2013). In a similar manner, the prelimbic cortex (PL) appears to play an important role in goal-directed behavior.

Action Chunks and Sequence Learning

We have defined habits with respect to outcome contingency and value. But there is a third component: the sequence learning of motor skills.

Behavior is not produced continuously. Rather, it is emitted in ~200ms atomic chunks, or behavioral syllables. Some 300 syllables have been discovered in mice (Wiltschko et al 2015).

Syllables are not emitted in random order. It often pays to use representations of multi-syllable action chunks. These chunks are, in turn, concatenated into larger sequences.

How do we know this? Chunks can be detected with response time measures: within-chunk actions occur more quickly than actions at chunk boundaries. Statistical methods also exist to detect sequence boundaries (Acuna et al 2014).

Concatenation and execution response times also respond to dissociable events. Execution latencies are preferentially impacted by changing the location of the hand relative to the body; concatenation latencies preferentially respond to transcranial magnetic stimulation (TMS) of the pre-SMA area (Abrahamse et al 2013).

Action chunks tend to emerge organically every three or four keypresses. There exists an interesting analogy here to memory chunks, for example, we remember phone numbers with three or four digit chunks. The similarity between action and memory chunks may derive from a common neurological substrate.

Neural activity in the dorsolateral striatum (DLS, part of the sensorimotor loop) exhibits an interesting task bracketing pattern: firing peaks at the beginning and end of tasks. Martiros et al (2018) find that striatal projection neurons (FPNs) generate this bracketing pattern, and are complemented by fast-spiking striatal interneurons (FSIs) which fire continuously within the bracketing window.

This bracketing saves rewarding behaviors as a package for reuse. D2 antagonists don’t interfere with well-learned sequences, but does disrupt the formation of new chunks (Levesque et al 2007). Parkinson’s disease does too (Tremblay et al 2010).

Graybiel & Grafton (2015) argue that the dorsolateral striatum is specifically involved in developing skills: learning action sequences of particular value to the organism. This explains why both habitual and non-habitual skills are learned in the DLS. Indeed, innate fixed action patterns (e.g., grooming) are mediated here too (Aldridge et al 2004).

The supplementary motor area (SMA) plays a central role in implementing sequences. Rats organize their behavior with sequence learning, and lesions to the SMA disrupt these behaviors (Ostlund et al 2009). Similarly, magnetically interrupting the human SMA during a task blocks expression of the subsequent chunk (Kennerly et al 2004).

Within the SMA, the rostral pre-SMA seems to represent cognitive sequences; the caudal SMA-proper exercises motor sequences (Cona & Semenza 2016). Working memory tasks reliably activates pre-SMA, whereas language production reliably activates both pre-SMA and SMA-proper.

Hierarchy as Loop Integration

We have so far examined theories of loop competition. But consider the impact of dopamine shortages and surpluses in the various loops, per Krack et al (2010):

This data aligns with the organizational principle of hierarchy of the central nervous system. The limbic loop selects a desire, the associative loop explores its beliefs to identify a plan, and the sensorimotor loop translates those plans into motor commands. Here’s one possible interpretation, based on Guyenet (2018).

This hierarchical interpretation nicely complements results from sequence learning.

Hierarchical Collaboration Theory

We have seen computational and neurological evidence in favor of the adjudicated competition theory of habit. But the theory also has three important limitations. First, competitive models can explain devaluation behaviors, but struggles to replicate contingency responses. Second, it doesn’t explain sequence learning: why should habits coincide with the development of motor skill? Third, it doesn’t accord with hierarchy: why should habitual behaviors be concrete responses, rather than abstract actions.

This leads us to the hierarchical collaboration theory of habit. On Balleine & Dezfouli (2019)‘s model, the associative system passes command serially to the sensorimotor system. Changes in the reward environment are noticed immediately. However, as the sensorimotor system learns increasingly complex action sequences, the associative system only notices changes to the reward environment at sequence boundaries. In other words, only after a sequence is being executed will the associative system resume control. This would explain why sequence learning so strongly coincides with habit formation and reward insensitivity.

In order to model this alternative account, one must first extend RL to accommodate chunks. These chunks replace their component parts if the benefits of using that sequence exceeds its costs. This formalism is provided by Dezfouli & Balleine (2012). Dezfouli & Balleine (2013) found that their hierarchical model replicated, and in some cases outperformed, the competition model of Daw et al (2011).

The Balleine lab is not the only group to produce computational models of hierarchical collaboration. Baladron & Hamker (2020) produces an interesting model, which assigns the infralimbic (IL) cortex the role of loop shortcut between associative/goal-directed and sensorimotor/habitual systems. Their model is also interesting in that they localize the reward prediction error (RPE) to the limbic loop, while ascribing action prediction error (APE) and movement prediction error (MPE) to the associative and sensorimotor loops, respectively.

These are early days. I look forward to more granular models of habituation, with more attention to the limbic circuit. As our mechanistic models of habit formation improve, so too does our therapeutic reach. If Graybiel & Grafton (2015) is right, and addictions are simply over-strong habits, such models may someday prove useful in clinical settings.

Until next time.

References

1. Abrahamse et al (2013). Control of automated behavior: insights from the discrete sequence production task
2. Acuna et al (2014). Multi-faceted aspects of chunking enable robust algorithms.
3. Aldridge et al (2004). Basal ganglia neural mechanisms of natural movement sequences
4. Baladron & Hamker (2020). Habit learning in hierarchical cortex-basal ganglia loops
5. Balleine & Dezfouli (2019). Hierarchical action control: adaptive collaboration between actions and habits
6. Balleine & Dickinson (1998). Goal-directed instrumental action: contingency and incentive learning and their cortical substrates.
7. Balleine & O’Doherty (2010). Human and Rodent Homologies in Action Control: Corticostriatal Determinants of Goal-Directed and Habitual Action
8. Barker et al (2014). A unifying model of the role of the infralimbic cortex in extinction and habits
9. Choi et al (2012). The organization of the human striatum estimated by intrinsic functional connectivity
10. Cona & Semenza (2016). Supplementary motor area as key structure for domain-general sequence processing: a unified account
11. Daw et al (2011). Model-based influences on humans’ choices and striatal prediction errors
12. Dezfouli & Balleine (2012) Habits, action sequences, and reinforcement learning
13. Dezfouli & Balleine (2013). Actions, Action Sequences and Habits: Evidence That Goal-Directed and Habitual Action Control Are Hierarchically Organized
14. Graybiel & Grafton (2015). The striatum: where skills and habits meet
15. Greene et al (2020). Integrative and Network Specific Connectivity of the Basal Ganglia and Thalamus Defined in Individuals.
16. Guyenet (2018). The Hungry Brain
17. Holland (2004). Relations between Pavlovian-i9nstrumental transfer and reinforcer devaluation.
18. Kesby et al (2018). Dopamine, psychosis and schizophrenia: the widening gap between basic and clinical neuroscience
19. Krack et al (2010). Deep brain stimulation: from neurology to psychiatry?
20. Levesque et al (2007). Raclopride-induced motor consolidation impairment in primates: role of the dopamine type-2 receptor in movement chunking into integrated sequences.
21. Martiros et al (2018). Inversely Active Striatal Projection Neurons and Interneurons Selectively Delimit Useful Behavioral Sequences
22. Pauli et al (2016). Regional specialization within the human striatum for diverse psychological functions
23. Killcross & Coutureau (2003). Coordination of actions and habits in the medial prefrontal cortex of rats.
24. Smith et al (2012). Reversible online control of habitual behavior by optogenetic perturbation of medial prefrontal cortex
25. Smith & Graybiel (2013). A dual operator view of habitual behavior reflecting cortical and striatal dynamics
26. Tremblay et al (2010). Movement chunking during sequence learning is a dopamine-dependent processs: a study conducted in Parkinson’s disease.
27. Wiltschko et al (2015). Mapping Sub-Second Structure in Mouse Behavior
28. Yin et al (2005) The role of the dorsomedial striatum in instrumental conditioning 

Part Of: Sociality sequence
Followup To: Intro to Confabulation
Content Summary: 2000 words, 20 min read

We do not have direct access to our mental lives. Rather, self-perception is performed by other-directed faculties (i.e., mindreading) being “turned inwards”. We guess our intentions, in exactly the same way we guess at the intentions of others.

Self-Knowledge vs Other-Knowledge

The brain is organized into perception-action cycles, with decisions mediating these streams.  We can represent this thesis as a simple cartoon, which also captures the abstraction hierarchy (concrete vs abstract decisions) and the two loop hypothesis (world vs body).

Agent files are the mental records we maintain about our relationships with people. Mindreading denotes the coalition of processes that attempt to reverse engineer the mental state of other people: their goals, their idiosyncratic mental states, and even their personality. Folk psychology contrasts this interpretive method of understanding other people with our ability to understand ourselves. 

We have powerful intuitions that self-understanding is fundamentally different than other-understanding. The Cartesian doctrine of introspection holds that our mental states and mechanisms are transparent; that is, directly accessible to us. It doesn’t matter which mental system generates the attitude, or why it does so – we can directly perceive all of this. 

Our Unconscious Selves

Cartesian thinking has fallen out of favor. Why? Because we discovered that most mental activity happens outside of conscious awareness.

A simple example should illustrate. When we speak, the musculature in our vocal tracts contort in highly specific ways. Do you have any idea which muscles move, and in which direction, to speak? No – you are merely conscious of the high-level desire. The way that those instructions are cached out at the more detailed motor commands is opaque to you. 

The first movement against transparency was Freud, who championed that a repression hypothesis: that unconscious beliefs are too depraved to be admitted to consciousness. But, after a brief detour through radical behaviorism, modern cognitive psychology tends to avow a plumbing hypothesis: that unconscious states are too complex (or not sufficiently useful) to merit admission to consciousness.

The distinction between unconscious and conscious processes can feel abstract, until you grapple with the limited capacity of consciousness. Why is it possible to read one, but not two books simultaneously? Why is it possible for most of us to remember a new phone number, but not the first twenty digits of pi, after the first 15 minutes of exposure? 

The ISA Theory

The Interpretive Sensory-Access (ISA) theory holds that our conscious selves are completely ignorant of our own mental lives save for the mindreading faculty. That is, the very same faculty used in our social interactions also constructs models of ourselves. 

It is important to realize that the range of perceptual data available for self-interpretation is larger than that available for people outside of ourselves. For both types of mindreading, we have perceptual data on various behaviors. In the case of self-mindreading, we also have access to our subvocalizations (inner speech) and the low-capacity contents of the global broadcast, more generally. 

Perhaps our mindreading faculties are more accurate, given they have more data on which to construct a self-narrative. 

The ISA theory explains the behavior-identity bootstrap; i.e., why the “fake it until you make it” proverb is apt. By acting in accordance with a novel role (e.g., helping the homeless more often), we gradually begin to become that person (e.g., resonating to the needs of others more powerfully in general). 

Theses, Predictions, Evidence

The ISA theory can be distilled into four theses:

1. There is a single mental faculty underlying our attributions if propositional attitudes, whether to ourselves or to others
2. That faculty has only sensory access to its domain
3. Its access to our attitudes is interpretive rather than transparent
4. The mental faculty in question evolved to sustain and facilitate other-directed forms of social cognition. 

The ISA theory is testable. It generates the following predictions:

1. No non-sensory awareness of our inner lives
2. There should be no substantive differences in the development of a child’s capacities for first-person and third-person understanding. 
3. There should be no dissociation between a person’s ability to attribute mental states to themselves and to others. 
4. Humans should lack any form of deep and sophisticated metacognitive competence. 
5. People should confabulate promiscuously. 
6. Any non-human animal capable of mindreading should be capable of turning its mindreading abilities on itself. 

These predictions are largely borne out in experimental data:

1. Introspection-sampling studies suggest that some people believe themselves to experience non-sensory attitudes. These data is hard for ISA theory to accommodate. But it is also hard for introspection-based theories to reconcile with – if we had transparent access to our attitudes, why do some people only experience them with a sensory overlay?
2. Wellman et al (2001) conducted a meta-analysis of well over 100 pairs of experiments in which children had been asked, both to ascribe a false belief to another persons and to attribute a previous false belief to themselves. They were able to find no significant difference in performance, even at the youngest ages tested. 
3. Other theorists (e.g., Nichols & Stich 2003) claim that autism exemplifies deficits in other-k but not in self-k, and schizophrenia is an impairment of self-k but not other-k. But on inspection, these claims have weak if nonexistent empirical support. These syndromes injure both forms of knowledge.
4. Transparent self-knowledge should entail robust metacognitive competencies. But we do not.  For example, the correlation between people’s judgments of learning and later recall are not very strong (Dunlosky & Metcalfe (2009)). 
5. The philosophical doctrine of first-person authority holds that we cannot hold false beliefs about our mental lives. The robust phenomena of confabulation discredits this hypothesis (Nisbett & Wilson (1977)). We are allergic to admitting “I don’t know why I did that”; rather, we invent stories about ourselves without realizing their contrived nature. I discuss this form of “sincere dishonesty” at length here.
6. Primates are capable of desire mindreading, and their behavior is consistent with their possessing some rudimentary forms of self-knowledge.

The ISA theory thus receives ample empirical confirmation.

Competitors to ISA Theory

There are many competitors to the ISA account. For the below, we will use attitude to denote non-perceptual mental representations such as desires, goals, reasons and decisions. 

1. Source tagging theories (e.g., Rey 2013) hold that, whenever the brain generates a new attitude, the generating system(s) add a tag indicating their source. Whenever that representation is globally broadcast, our conscious selves can inspect the tag to view its origin. 
2. Attitudinal working memory theories (e.g., Fodor 1983, Evans 1982) hold that, in addition to a perception-based working memory system, there is a separate faculty to broadcast conscious attitudes and decisions. 
3. Constitutive authority theories (e.g., Wilson 2002, Wegner 2002, Frankish 2009) admit that conscious events (e.g., suppose we say I want to go to the store) do not directly cause action. However, we do attribute these utterances to ourselves, and the subconscious metanorm I DESIRE TO REALIZE MY COMMITMENTS works to translate these conscious self-attributions to unconscious action programs. 
4. Inner sense theories hold that, as animal brains increased in complexity, there was increasing need for cognitive monitoring and control. To perform that adaptive function, the faculty of inner sense evolved to generate metarepresentations: representations of object-level computational state. There are three important flavors of this theory:

But there are data speaking against these theories

1. Contra source tagging, the source monitoring literature shows that people simply don’t have transparent access to the sources of their memory images. For example, Henkel et al (2000) required subjects to either see, hear, imagine as seen, or imagine as heard, a number of familiar events, such as a basketball bouncing. But people frequently misremembered which of these four mediums had produced their memory, when asked later. 
2. The capacity limits of sensory-based working memory explains nearly the entire phenomena of fluid g, also known as IQ (Colom et al 2004). If attitudinal working memory evolved alongside this system, it is hard to explain why it doesn’t contribute to fluid intelligence scores. 

More tellingly, however, each of the above theories fails to explain confabulation data. Most inner sense theories today (e.g., Goldman 2006) adopt a dual-method stance: when confabulating, people are using mindreading; else people are using transparent inner sense. But as an auxiliary hypothesis, dual-method theories fail to explain the patterning of when a person will make correct versus incorrect self-attributions. 

Biased ISA Theory

The ISA theory holds self-knowledge to be grounded in sparse but unbiased perceptual knowledge. But this does not seem to be the whole story. For we know that we are prone to overestimate the good qualities of the Self and Us, but underestimate the bad qualities of the Other and Them. 

For example, the fundamental attribution error describes the tendency to explain our own failings as contingent on the situation, but the failings of others to immutable character flaws. More generally, the argumentative theory of reasoning posits a justification faculty which subconsciously makes our reasons rosier, and our folk sociology faculty demonizes members of the outgroup. 

In social psychology, there is a distinction between dispositional beliefs (avowals that are generated live) and standing beliefs (those actively represented in long-term memory). The relationship between the content of what one says and the content of the underlying attitude may be quite complex. It is unclear whether these parochial biases act upon standing or dispositional beliefs. 

Explaining Transparency

The following section is borrowed from Carruthers (2020). 

In general, our judgments of others’ opinions come in two phases:

1. First pass representation of the attitude expressed, relying on syntax, prosody, and the salient feature of conversational context.
2. Lie Detection. Whenever the degree of support for the initial interpretation is lower than normal, or there is a competing interpretation in play that has at least some degree of support, or the potential costs of a misunderstanding are higher than normal, a signal would be sent to executive systems to slow down and issue inquiries more widely before a conclusion is reached. 

Why do our self-attributions feel transparent? Plausibly, because, the attribution of self-attitudes only undergo the first stage (not subject to disambiguation and lie detection systems). This architecture would likely generate the following inference rules:

1. One believes one is in mental state M → one is in mental state M.
2. One believes one isn’t in mental state M → one isn’t in mental state M.

The first will issue in intuitions of infallible knowledge, and the second in the intuition that mental states are always self-presenting to their possessors.

For example, consider the following two sentences

1. John thinks he has just decided to go to the party, but really he hasn’t. 
2. John thinks he doesn’t intend to go to the party, but really he does.

These sentences are hard to parse, precisely because the mindreading inference rules render them strikingly counterintuitive.

These intuitions may be merely tacit initially, but will rapidly transition into explicit transparency beliefs in cultures that articulate them. Such beliefs might be expected to exert a deep “attractor effect” on cultural evolution, being sustained and transmitted both because of their apparent naturalness. And indeed, transparency doctrines have been found in traditions from Aristotle, to the Mayans, to pre-Buddhist China.

Until next time. 

Inspiring Materials

These views are more completely articulated in Carruthers (2011). For a lecture on this topic, please see:

Works Cited

• Carruthers (2011). The Opacity of Mind
• Carruthers (2020). How mindreading might mislead cognitive science
• Colom et al (2004). Working memory is (almost) perfectly predicted by g
• Evans (1982). The Varieties of Reference. 
• Henkel et al (2000). Cross-modal source monitoring confusions between perceived and imagined events. 
• Fodor (1983). The Modularity of Mind. 
• Goldman (2006). Simulating Minds. 
• Frankish (2009). How we know our conscious minds. 
• Nichols & Stitch (2003). Mindreading: An Integrated Account of Pretence, Self-Awareness, and Understanding Other Minds
• Nisbett & Wilson (1977). Telling more than we can know: verbal reports on mental processes.
• Rey (2013). We aren’t all self-blind: A defense of modest introspectionism. 
• Wilson (2002). Strangers to ourselves
• Wegner (2002). The illusion of conscious will. 

Part Of: Neuroanatomy sequence
Content Summary: 2200 words, 22 min read

Four Cortical Networks

Cognitive neuroscience typically employs fMRI scans under a carefully crafted task structure. Such research localized various task functions to different neural structures (cortical areas). For example, these studies produced evidence suggesting that the hippocampus is the seat of autobiographical memory. 

In the early 2000s that researchers stumbled upon a different question, what brain regions are active when the brain is at rest? Here is Raichle (2015) describing his discovery of the default mode network. 

One of the guiding principles of cognitive psychology at that time was that a control state must explicitly contain all the elements of the associated task other than the one element of interest (e.g., seeing a word versus reading the same word). Using a control state of rest would clearly seem to violate that principle. Despite our commitment to the strategies of cognitive psychology in our experiments, we routinely obtained resting-state scans in all our experiments, a habit largely carried over from experiments involving simple sensory stimuli, in which the control state was simple the absence of the stimulus. At some point in our work, and I do not recall the motivation, I began to look at the resting-state scans minus the task scans. What immediately caught my attention was the fact that regardless of the task under investigation, the activity decreases almost always included the posterior cingulate and the adjacent precuneus. 

Well before the discovery of the default mode network, Peterson and Posner (1980) had put forward three networks underlying attention. The dorsal attention network generated salience maps across the perceptual field, and used these maps to orient to interesting stimuli. The ventral attention network is involved in attention switching to novel stimuli. The executive network produces top-down control of attention, for example translating the instruction “pay attention to the green triangle” to sustained attention on an otherwise-uninteresting object. 

Fox et al (2005) brought these two worlds together in their seminal paper, which identified a brain-wide task-positive network which anti-correlated with their task-negative network. Their use of resting-state functional connectivity MRI (rs-fcMRI) provided independent evidence of the existence of these networks.  By examining the cross-correlations of “noise” in the BOLD signal, one can identify regions that “fire together”, and may be functionally integrated.

Fox et al’s task-negative network was the default mode network. And the task-positive network seemed to contain two networks previously identified: the executive network, and the dorsal top-down attention network. The ventral attention network, however, was not identified in their analysis.

And that was the state of the world in 2006. Neuroscientists had identified four networks, which we will henceforth call intrinsic connectivity networks (ICNs). They are:

1. Executive Control
2. Dorsal Attention
3. Ventral Attention
4. Default Mode Network

Towards Eight Networks

While the data supporting the legitimacy of these networks was strong, these anatomical structures pose a fairly routine challenge in neuroscience: they correlate with “too many functions”. Take the default mode network. It is associated with mind-wandering, social cognition, self-reference, semantic concepts, and autobiographical memory. How could one structure produce these widely divergent behaviors?

In the case when you have too many functions, you have two options: look for more specific mechanisms (Q3), and group similar concepts (Q4). In many neuroscience applications, the former is more productive: reality has a surprising amount of detail.

Researchers began to find subnetworks within the executive control. 

Dosenbach et al (2007) found two networks within the “executive network”. They found a fronto-parietal control network (FPCN), involved in error correction, and control over task execution. They also found a cingulo-opercular control network (COCN), involved in task set maintenance. The FPCN was most active at task onset and errors, the COCN expressed activity consistently throughout the task.

These subgraphs usefully pick out useful psychological concepts. We have long known that rehearsal increases working memory capacity from 3 to 7 chunks. It seems the COCN produces this miracle (but recall that the contents of working memory, the stuff it rehearses, lives in perceptual cortex, Postle 2006). Likewise, psychologists have long studied the phenomenon of willpower or volition. The FPCN might be the neural substrate of this ability. 

Seeley et al (2007) also found substructures within the original executive network. But they didn’t see a rehearsal system in the cingulo-opercular regions. Instead, they found a salience network, which bound affective and emotional information into perceptual objects, and links to the basal ganglia reward system. 

Since publication, each of these networks have been replicated dozens of times, using a widely diverging set of paradigms (ROI vs voxel granularity, fMRI vs rs-fcMRI) and statistical techniques (graph theory, dynamical causal modeling, hierarchical clustering, and independent component analysis). 

Unfortunately, these subnetworks looked and behaved radically differently. For years, neuroscientists collected data using these diverging theories. Peterson & Posner (2012) updated theory of attention rely on Dosenbach’s rehearsal network, whereas many other articles took inspiration from Seeley’s salience network. 

And then, a miracle. Power et al (2011), using graph theoretic tools and more granular data, identified both salience and rehearsal networks hidden within the cingulo-opercular graph. Despite the close proximity of these two networks, they perform dramatically diverging functions (left image).

They also discussed the spatial distribution of these networks across cortex. Essentially, the attention networks are sandwiched between sensorimotor networks and prefrontal control networks. This configuration might play an important role in reducing wiring cost for between-network communication. 

This work was largely replicated in Yeo et al (2011), which in contrast to the biological tack of Powers et al (2011), used a more statistically-oriented approach.

ICNs are not exclusive to cortex. Habas et al (2009) found strong links between cerebellar substructures and various ICNs. Ji et al (2009) find correspondences between other midbrain structures (e.g., various nuclei in the amygdala).

Default Mode Network and Interoception

Power et al (2011) also compared network properties of their ICNs and discovered two categories of ICN:

• processing networks that are directly involved in perceptual-action loops. These networks tend to be very modular in their organization.
• control networks that modulate cybernetic loops. These networks tend to have more extra-subgraph relationships.

The above illustrates an intriguing finding: the default mode network is a processing network, rather than a control network. But what sense modality does underlie? 

The answer is straightforward to an affective neuroscientist. The default mode network and the salience network comprise the seat of the hot loop; it performs:

• interoception (viscerosensory body perception); and
• allostasis (visceromotor body regulation)

It is a cornerstone of dual cybernetic loops. Indeed, comparative studies with macaque monkeys put empirical meat on this assertion:

• { anterior cingulate cortex, dorsal amygdala, ventral anterior insula } perform visceromotor functions (allostasis)
• { dorsal anterior insula } perform viscerosensory functions (interoception). 

As Kleckner et al (2017) show, these assertions are born out by myriad human rs-fcMRI studies, and further bolstered by tract-tracing studies in non-human animals.

I’ll note in passing that most experts now detect three subgraphs within the default mode network (cf Andrews-Hanna et al 2014). But the functional signature of these subgraphs has not yet been worked out, so let me simply note this development in passing.

Network Neuroscience

We have so far discussed results from function-derived structures, with techniques such as rs-fcMRI computing ICNs from the dynamics of neural activity. A complementary research tradition can be described as anatomy-derived structures, which is a more anatomical emphasis on connectome studies. These two network types have important differences, including time scales (anatomy-derived structures tend to persist longer than task-dependent structures) and levels of detail (neuron versus region-of-interest). Nevertheless, these data can be made to usefully constrain one another (functional networks are beginning to look more like structural networks, and vice versa). 

These approaches have recently coalesced (Basset & Sporns 2017) into the new discipline of network neuroscience. Very similar techniques are used in network science and social network analysis in the analysis of social networks. 

If a neuron is a node in a graph, and a synapse is an edge, what properties does the graph of a human brain enjoy? There are several kinds of networks possible. Regular networks enjoy rich local connections, but few cross-graph connections. Random networks enjoy more long-term connections, but are less structured. Small-world networks represent a kind of middle ground, with lots of local structure but also afford the ability to make long-term connections.

With graph theoretic measures, we can quantitatively partition networks into sets of modules.  A hub is a node with high degrees of centrality (e.g. node degree: how many edges that node supports). A connector hub facilitates between module communication; a provincial hub promotes communication within modules. 

Connectome studies (anatomy-derived structural networks) have shown that brain hub regions are more densely interconnected than predicted on the basis of their degree alone. This set of unusually central connector hubs is called the rich club. The rich club is the most metabolically expensive areas of cortex: they are “high cost, high value”.  They are loosely analogous to DNS servers (the thirteen servers are the global basis of the internet)

Human neural architecture is thus a specific kind of small-world network, one equipped with a “rich club”. These topologies have been shown to exist in other species, such as macaque monkeys and cats. Interestingly, some hubs (posterior cingulate, precuneus, and medial frontal cortex) act as sinks (more afferent than efferent connections) whereas and hubs within attentional networks (incl. dorsal prefrontal, posterior parietal, visual, and insular cortex) act as sources (more efferent than afferent connections). 

What does this have to do with ICNs? As shown by von den Heuval & Sporns (2013b), the rich club seems to be the substrate of inter-ICN communication. 

Networks vs Consciousness

According to global workspace theory, consciousness contents are generated via a publicity organ which selects perceptual information worthy of further processing by downstream modules. There is, however, much disagreement about the mechanism of conscious contents. Theories include:

1. Dehaene and Changeux have focused on frontal cortex 
2. Edelman and Tononi on complexity in re-entrant thalamocortical dynamics 
3. Singer and colleagues on gamma synchrony
4. Flohr on NMDA synapses
5. Llinas on a thalamic hub
6. Newman and Baars on thalamocortical distribution from sensory cortex

Shanahan (2012) offered a new hypothesis, that the rich club has recently hypothesized as the basis of consciousness. Its central location and role synchronizing large-scale brain networks makes it a plausible suspect. However, it is unclear whether the rich club is primarily facilitated by corticocortical white matter, or corticothalamic reentrant loops. If the latter, the hypothesis would converge with existing theories that emphasize the role of the thalamus.

Traits and Individual Differences

Researchers are becoming increasingly aware of individual differences across brain regions. Here, for example, is the group-average ICNs contrasted with a single individual:

Seitzman et al (2019) describe clusters within these individual differences.

• Most network variance is at the individual level (less so between tasks, or over time).
• Most individual ICNs fall into two distinct trait-like variants: one with large DMN, the other with large FPN.
• It was the network assignment of variants, rather than their anatomical location, that differentiate the variants.
• ICN variants occur most often in associative networks, and most often in the right hemisphere.

At this time, it is hard to say what behavioral or personality differences are driven by these variants.

Networks vs Modules

ICNs comprise a central organizing principle of the nervous system. But they are not the only such principle; we have identified some fifteen others!

It is difficult to reconcile intrinsic connectivity networks (ICNs) with massive modularity, so that will be the topic of this section.

ICNs have been seized upon by some theorists in the Bayesian predictive coding traditions (e.g. Barrett & Simmons 2015) as evidence of the illegitimacy of modules. But most ICN theorists still admit the centrality of modules (e.g., Sporns & Betzel 2015). Here, for example, is von den Heuval & Sporns (2013a):

Since the beginning of modern neuroscience, the brain has generally been viewed as an anatomically differentiated organ whose many parts and regions are associated with the expression of specific mental faculties, behavioral traits, or cognitive operations. The idea that individual brain regions are functionally specialized and make specific contributions to mind is supported by a wealth of evidence from both anatomical and physiological studies. These studies have documented highly specific cellular and circuit properties, finely tuned neural responses, and highly differentiated regional activation profiles across the human brain. Functional specialization has become one of the enduring theoretical foundations of cognitive neuroscience. 

Most researchers now admit the interaction of both principles (specialization and integration). It is unclear how it could be otherwise. I have personally read far too many papers that have described activity in the dorsolateral prefrontal cortex as task-specific, without considering it is a simple expression of the volitional control or working memory rehearsal networks. Similarly, I have read dozens of reviews of the anterior insula that would have profited from the realization that it participates in at least three different ICNs. 

The three streams hypothesis integrates notions of massive modularity, cortical streams, the abstraction hierarchy, and the cybernetic loop hypothesis. It is less clear how ICNs might integrate with these organizing principles. 

Does the ventral temporal parietal junction (vTPJ) only perform integrative functions in service of the ventral attention network (VAN)? Or does the real estate claimed by these ICNs also used to perform specialized computations such as mindreading? The latter proposition strikes me as more likely. But I’d like to see more data on this. To be continued…

Wrapping Up

The human cortex has intrinsic connectivity networks (ICNs) that coordinate to provide integrative services on behalf of our central nervous system. Researchers have so far identified the following networks:

1. Default mode network (DMN) and its three subnetworks described in Andrews-Hanna et al 2014
2. Ventral Attention Network (VAN), reinterpreted by Ji et al 2019 as the language network.
3. Dorsal Attention Network (DAN)
4. Fronto-Parietal Control Network (FPCN) implicated in volitional control, and per Duncan et al 2020, possibly fluid intelligence.
5. Cingulo-Opercular Control Network (COCN)
6. Salience Network, reinterpreted by Ji et al 2019 as a subnetwork within COCN.

A recent reanalysis by Ji et al (2019) adds three more networks into the mix:

• Posterior Multimodal (PMM), possibly involved in spatiotemporal and narrative cognition.
• Ventral Multimodal (VMM), possibly involved in semantic categorization.
• Orbito-Affective (ORA), definitively linked to reward processing.

Until next time.

Works Cited

I’ve put the papers I found especially helpful in bold.

1. Andrews-Hanna et al (2014). The default network and self-generated thought: component processes, dynamic control, and clinical relevance. 
2. Bassett & Sporns (2017). Network Neuroscience
3. Barrett & Simmons (2015). Interoceptive predictions in the brain. 
4. Christoff et al (2016). Mind-wandering as spontaneous thought: a dynamic framework
5. Dosenbach et al (2007). Distinct brain networks for adaptive and stable task control in humans
6. Duncan et al (2020). Integrated Intelligence from Distributed Brain Activity.
7. Fox et al (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks
8. Gordon et al (2018). Individual-specific features of brain systems identified with resting state functional correlations
9. Kleckner et al (2017). Evidence for a large-scale brain system supporting allostasis and interoception in humans. 
10. Laird et al (2011). Behavioral Interpretations of Intrinsic Connectivity Networks. 
11. Habas et al (2009). Distinct Cerebellar Contributions to Intrinsic Connectivity Networks
12. Ji et al (2019). Mapping the human brain’s cortical-subcortical functional network organization
13. Peterson & Posner (1990). The attention system of the human brain
14. Peterson & Posner (2012). The Attention System of the Human Brain: 20 Years After
15. Postle (2006). Working memory as an emergent property of the mind and brain.
16. Power et al (2011). Functional Network Organization of the Human Brain
17. Raichle (2015). The Brain’s Default Mode Network
18. Seeley et al (2007). Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control
19. Seitzman et al (2019). Trait-like variants in human functional brain networks
20. Shanahan (2012). The Brain’s Connective Core and its Role in Animal Cognition
21. Sporns & Betzel (2015). Modular Brain Networks
22. Von den Heuval & Sporns (2013a). Network hubs in the human brain 
23. Von den Heuval & Sporns (2013b) An Anatomical Substrate for Integration among Functional Networks in Human Cortex
24. Yeo et al (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity

Part Of: Demystifying Culture sequence
Content Summary: 1100 words, 11 min read

Ecological Dominance

Compared to the erects, sapiens are uniquely ecologically dominant. The emergence of hunter-gatherers out of Africa 70,000 years ago caused:

• The extermination of hundreds of megafauna species (more than 90%)
• Dwarfing of the surviving species.
• A huge increase in the frequency and impact of fire (we used fire to reshape ecosystems to our liking)

12,000 years ago, we began domesticating animals and plants. The subsequent agricultural revolution unlocked powerful new ways to acquire energy, which in turn increased our species’ population density.

• 9000 BCE:   5 million people
•          1 CE:   300 million people
•   2100 CE:   11,000 million people

200 years ago, the Industrial Revolution was heralded by the discovery of energy transduction: that electricity can be used to run a vacuum, or freeze meat products.

These population explosion correlates with a hefty ecological footprint:

• We have altered more than one-third of the earth’s land surface.
• We have changed the flow of two-thirds of the earth’s rivers.
• We use 100 times more biomass than any large species that has ever lived.
• If you include our vast herds of domesticated animals, we account for more than 98% of terrestrial vertebrate biomass.

Elhacham et al (2020) show that human-generated materials now outweigh the planet’s biomass:

Three Kinds of Theories

As with any other species, the scientist must explain how ours has affected the ecosystem. We can do this by examining how our anatomies and psychologies differ from other animals, and then consider which of these human universals explain our ecological dominance.

Pound for pound, other primates are approximately twice as strong. We also lack the anatomical weaponry of our cousins; for example, our canines are much less dangerous.

So, strength cannot explain our dominance. Three other candidate theories tend to recur:

1. We are more intelligent and creative. Theories of this sort focus on e.g., the invention of Mode 3 stone tools.
2. We are more cooperative and prosocial. Theories of this sort focus on e.g., massively cooperative hunting expeditions.
3. We accumulate powerful cultural adaptations. Theories of this sort focus on e.g., how Inuit technology became uniquely adaptive for their environment.

Let’s take a closer look!

Intelligence-Based Theories

Is intellect the secret for our success? Consider the following theories:

First, generative linguists like Noam Chomsky argue that language is not about communication: recursion is an entirely different means of cognition; the root of our species’ creativity. To him, the language instinct (as a genetic package) appeared abruptly at 70 kya, and transformed the mind from a kluge of instincts to a mathematical, general-purpose processor. Language evolution is said to coincide with the explosion of technology called behavioral modernity.

Second, evolutionary psychologists like Leda Cosmides & John Tooby advocate the massive modularity hypothesis: the mind isn’t general purpose processor; it is instead more like a swiss army knife. We are not more intelligent because we have fewer instincts, but more. Specifically, we accrued hundreds of hunter-gatherer instincts in the intervening millenia and these instincts give us our characteristically human flexibility.

Third, social anthropologists like David Lewis-Williams argues that a change in consciousness made us more intelligent. We are the only species that has animistic spirituality, these are caused by numinous experiences. These altered states of consciousness were the byproducts of our consciousness machinery rearranging itself. Specifically, he invokes Dehaene’s theory that while all mammals experience primary consciousness, only sapiens have second-order consciousness (awareness of their own awareness). This was allegedly the event that caused fully modern language.

Sociality-Based Theories

Is sociality the secret for our success? Consider the following theories:

First, sociobiologists like Edward O Wilson thinks that the secret of our success is because of group selection: that vigorous between-group warfare created selective pressure for within-group cooperation. As our ethnic psychology (and specifically, ethnocentrism) became more pronounced, sapien tribes began behaving much like superorganisms. A useful analogy is eusocial insects like ants, who became are arguably even more ecologically dominant than humans.

Second, historians like Yuval Harari thinks that mythology (fictional orders) is the key ingredient enabling humans to act cooperatively. Political and economic phenomena don’t happen in a vacuum: they are caused by certain ideological commitments e.g., nationalism and the value of a currency. To change our myths is to refactor the social structure of our society.

Culture-Based Theories

Is culture the secret for our success? Consider the following theory:

Anthropologists like Richerson, Boyd and Henrich argue that cumulative cultural knowledge comprises a dual-inheritance system, and propose a theory of gene-culture coevolution. They are that an expanding collective mind gave individuals access to unparalleled know-how. This is turn emboldened our niche stealing proclivities: “like the spiders, hominins could trap, snare, or jet their prey; but the latter could also ambush, excavate, expose, entice, corral, hook, spear, preserve, or contain a steadily enlarging range of food types.” Socially-learned norms induce our cooperation, and socially-learned thinking tools explain our intelligence.

My Take

Contra Chomsky,

• Protolanguage evolved roughly 2 mya. Even grammatically modern language evolved much earlier than 70 kya.
• Language is not an intellectual superweapon. Its primary purpose is externalization & transmission of mental content.
• I agree that something qualitatively changed 70 kya. But “behaviorally modern” technologies were compiled gradually in Africa, beginning 200 kya.

Contra Cosmides & Tooby:

• I agree wholeheartedly with the massive modularity hypothesis. It accords well with modern cognitive neuroscience.
• While selection endowed us with hunter-gatherer instincts (e.g., folk biology), I don’t think such instincts provide sufficient explanatory power.

Contra David Lewis-WIlliams:

• I need hard evidence showing that animals never hallucinate, before appropriating numinous experiences as a human universal.
• Global Workspace Theory (GWT) enjoys better empirical support than integrated information theory.
• I don’t understand the selective pressure or mechanistic implications for changes to our conscious machinery.

Contra sociality-first theories

• Group selection is still immersed in controversy, especially the free-rider problem.
• Why must myths be the causal first movers? Surely other factors matter more..

My own thinking most closely aligns with culture-based explanations of our ecological dominance. This sequence will try to explicate this culture-first view.

But at present, culture-first theories leaves several questions unanswered:

• What, specifically, is the behavioral and biological signature of a social norm? For now, appeals to norm psychology risk explaining too much.
• How did our species (and our species alone) become psychologically equipped to generate cumulative culture?
• If erectus was a cultural creature, why did the rate of technological innovation so dramatically change between erectus and sapiens?

Someday I hope to explore these questions too. Until then.

References

1. Elhacham et al (2020). Global human-made mass exceeds all living biomass
2. Tim Flannery. The Future Eaters
3. David Lewis-Williams. The Mind in The Cave.
4. Yuval Harari. Sapiens.
5. Henrich, The Secret of Our Success

Part Of: Demystifying Culture sequence
Content Summary: 1200 words, 12 min read

Context

Recall that human beings have two different vehicles for learning:

• Individual Learning: using personal experiences to refine behavioral techniques, and build causal models of how the world works.
• Social Learning: using social interactions to learn what other people have learned.

Today, we will try to explain the following observations:

• Most cultural traditions have adaptive value.
• This value typically cannot be articulated by practitioners.

Why should this be the case?

Example 1: Manioc Detoxification

Consider an example of food preparation, provided by Joseph Henrich:

In the Colombian Amazon, a starchy tuber called manioc has lots of nutritional value, but also releases hydrogen cyanide when consumed. If eaten unprocessed, manioc can cause chronic cyanide poisoning. Because it emerges only gradually after years of consuming manioc that tastes fine, chronic poisoning is particularly insidious, and has been linked to neurological problems, paralysis of the legs, thyroid problems, and immune suppression.

Indigenous Tukanoans use a multistep, multiday processing technique that involves scraping, grating, and finally washing the roots in order to separate the fiber, starch, and liquid. Once separated, the liquid is boiled into a beverage, but the fiber and starch must then sit for two more days, when they can then be baked and eaten. Chemical analyses confirm that each major step in the processing is necessary to remove cyanogenic content from the root. [5]

Yet consider the point of view of a woman learning such techniques. She may never have seen anyone get cyanide poisoning, because the techniques work. And she would be required to spend about four hours per day detoxifying manioc. [4]

Consider what might result if a self-reliant Tukanoan mother decided to drop seemingly unnecessary steps from the processing of her bitter manioc. She might critically examine the procedure handed down to her from earlier generations and conclude that the goal of the procedure is to remove the bitter taste. She would quickly find that with the much less labor-intensive process of boiling, she could remove the bitter taste. Only decades later her family would begin to develop the symptoms of chronic cyanide poisoning.

Here, the willingness of the mother to take on faith received cultural practices is the only thing preventing the early death of her family. Individual learning does not pay here; after all, it can take decades for the effects of the poison to manifest. Manioc processing is causally opaque.

The detoxification of dozens of other food products (corn, nardoo, etc) are similarly inscrutable. In fact, history is littered with examples of European explorers imperfectly copying indigenous food processing techniques, and meeting gruesome ends.

Example 2: Pregnancy Taboos

Another example, again from Henrich:

During pregnancy and breastfeeding, women on Fiji adhere to a series of food taboos that selectively excise the most toxic marine species from their diet. These large marine species, which include moray eels, barracuda, sharks, rock cod, and several large species of grouper, contribute substantially to the diet in these communities; but all are known in the medical literature to be associated with ciguatera poisoning.

This set of taboos represents a cultural adaptation that selectively targets the most toxic species in women’s usual diets, just when mothers and their offspring are most susceptible. [2] To explore how this cultural adaptation emerged, we studied both how women acquire these taboos and what kind of causal understandings they possess. Fijian women use cues of age, knowledge, and prestige to figure out from whom to learn their taboos. [3] Such selectivity alone is capable of generating an adaptive repertoire over generations, without anyone understanding anything.

We also looked for a shared underlying mental model of why one would not eat these marine species during pregnancy or breastfeeding: a causal model or set of reasoned principles. Unlike the highly consistent answers on what not to eat and when, women’s responses to our why questions were all over the map. Many women simply said they did not know and clearly thought it was an odd question. Others said it was “custom.” Some did suggest that the consumption of some of the species might result in harmful effects to the fetus, but what precisely would happen to the fetus varied greatly: many women explained that babies would be born with rough skin if sharks were eaten and smelly joints if morrays were eaten.  

These answers are blatant rationalizations: “since I’m being asked for a reason, let me try to think one up now”.  The rationale for a taboo is not perceived by its adherents. This is yet another example of competence without comprehension.

A Theory of Overimitation

Human beings exhibit overimitation: a willingness to adopt complex practices even if many individual steps are inscrutable. Overimitation requires faith, defined here as a willingness to accept information in the absence of (or even contrasting with) your personal causal model.

We have replicated this phenomenon in the laboratory. First, present a puzzle box to a child, equipped with several switches, levers, and pulleys. Then have an adult teach the child how to open the box and get the treat inside. If the “solution” involves several useless procedures e.g., tapping the box with a stick three times, humans will imitate the entire procedure. In contrast, chimpanzees ignore the noise, and zoom in on the causally efficacious steps.

Why should chimpanzees outperform humans in this experiment? Chimpanzees don’t share our penchant for mimicry. Chimpanzees are not gullible by default. They must try to parse the relevant factors using the gray matter between their ears.

Humans fare poorly in such tests, because these opaque practices are in fact useless. But more often in our prehistory, inscrutable practices are nevertheless valuable. We are born to go with the flow.

In a species with cumulative culture, and only in such a species, faith in one’s cultural inheritance often yields greater survival and reproduction.

Is Culture Adaptive? Mostly.

We humans do not spend much time inspecting the content of our cultural inheritance. We blindly copy it. How then can cultural practices be adaptive?

For the same reason that natural selection produces increasingly sophisticated body plans. Communities with effective cultural practices outcompete their neighbors.

Overimitation serves to bind cultural practices together into holistic traditions. This makes another analogy to natural selection apt:

• Genes don’t die, genomes die. Natural selection transmits an error signal for an entire genetic package.
• Memes don’t die, traditions die. Cultural selection transmits an error signal for an entire cultural package.

Just as genomes can host individual parasitic elements (e.g., transposons), so too cultural traditions can contain maladaptive practices (e.g., dangerous bodily modifications). As long as the entire cultural tradition is adaptive, dangerous ideas can persist undetected in a particular culture.

Does Reason Matter? Yes.

So far, this post has been descriptive. It tries to explain why sapiens are prone to overimitation, and why faith is an adaptation.

Yet individual learning matters. Without it, culture would replicate but not improve. Reason is the fuel of innovation. We pay attention to intelligent, innovative people because of another cultural adaptation: prestige.

Perhaps the powers of the lone intellect are less stupendous than you were brought up to believe.

But we need not be slaves to neither our cultural nor our genetic inheritance. We can do better.

Related Resources

1. Henrich (2016). The Secret Of Our Success.
2. Henrich & Henrich (2010). The evolution of cultural adaptations: Fijian food taboos protect against dangerous marine toxins
3. Henrich & Broesch (2011). On the nature of cultural transmission networks: evidence from Fijian villages for adaptive learning biases
4. Dufour (1984). The time and energy expenditure of indigenous women horticulturalists in the northwest Amazon. 
5. Dufour (1994). Cassave in Amazonia: Lessons in utilization and safety from native peoples. 

Part Of: Anthropogeny sequence
Followup To: The Walking Ape
Content Summary: 2100 words, 21 min read

A Brief Review of Human Evolution

The most recent common ancestor of humans and chimpanzees lived 7 mya (million years ago). The very first unique hominin feature to evolve was bipedality, which was an adaptation for squat-feeding. The australopiths were bipedal apes. They could walk comfortably, but retained their adaptations for tree living as well. Dental morphology and microwear together suggest that australopiths acquired food from a new source: tubers (the underground storage organs of plants).

Climate change is responsible for the demise of the australopiths. Africa began drying out about 3 million years ago, making the woodlands a harsher and less productive place to live. Desertification would have reduced the wetlands where australopiths found fruits, seeds, and underwater roots. The descendents of Australopithecus had to adapt their diet.

The paranthropes adapted by promoting tubers from backup to primary food. These impressive creatures comprise a blend of human and cow-like features. In contrast, the habilines (e.g., Homo Habilis) took a different strategy: meat eating. These creatures had the same small bodies, but larger brains. Their hands show adaptations for flexibility, and their shoulders and elbows for throwing missiles. They began making stone tools (Mode 1 tools, the Oldowan industry). They presumably used these anatomical and cultural gifts to compete with other scavengers on the savannah (projectiles to repulse competitors, stone flakes to speedily butcher a carcass).

The habilines in turn gave rise to

• [1.9 mya] The erects (H erectus)  with near-modern anatomies.
• [0.9 mya] The archaics (H heidelbergensis) appear, who eventually give rise to the Neanderthals, Denisovans, and us.
• [0.3 mya] The moderns (H sapiens) emerge out of Africa, and completely conquer the globe.

A Closer Look

Yes, humans are apes. But why do we look so different from our closest living relative, the chimpanzee?

I have previously explained why we are bipedal (flexible waist, straight backs, walking on two feet).

But why do we have scent glands in our armpits? Fat in our asses? Such weird hair? Hairless skin with massive subcutaneous fat deposits?

Most of these changes were introduced with Homo Erectus:

Natural selection explains why bodies change. Anatomical innovations are selected when they enable more efficient exploitation of some particular niche.

So what ecological niche forged the modern human body?

Where Homo Erectus Evolved

The australopiths never made it beyond the southern margins of the Sahara. Because the adaptation of equatorial species inhibits their colonization of temperate regions, the successful emigration of the erects out of Africa strongly suggests that this was a northern, not a tropical species.

To evolve adaptations to dry, open country, the erects would have had to suffer a period of isolation from other hominins, in an appropriately discrete habitat. There were few, perhaps no, places in tropical or Southern Africa that could have provided such a combination. Comparing these constraints with the distribution of Homo Erectus fossils, comparative zoologist Jonathan Kingdon submits there the two most plausible contenders where the erects could have evolved are the Atlas Mountains, or Arabia.

Nasal evidence corroborates the hypothesis that they evolved in a desert environment. The entry to the primate nasal passage is flat, with straightforward air intake. Erect skulls show the first evidence of a protruding nose. A protruding nose forces the air at a “right angle” before entering the nasal cavity.

One of the responsibility of the nasal passage is to humidify the air before it is passed to the lungs. The increase in room and turbulence serves to amplify the humidification of inhaled air. Our noses are adaptations for desert living (Pontzer et al 2021).

A New Thermoregulation System

There are two things unique to human skin:

• Functional hairlessness. We modern humans have hair, but it is so thin compared to chimpanzees that we are effectively hairless.
• Eccrine sweat glands. Our skin also contains a novel approach to sweat glands.

These two features are linked: we now know in exquisite molecular detail how incipient hair follicles are converted into eccrine glands (Lu et al 2016).

Other primates rely on oil-based apocrine sweat glands. The emergence of water-based eccrine glands in humans led to the “retirement” of apocrine glands in our lineage. The distribution of odor-producing apocrine glands was ultimately confined to our underarms and pubic regions.

Losing our hair had two important side-effects:

• Skin pigmentation. Fur protects against ultraviolet radiation. Without it, melanin was used as an alternate form of natural sunscreen.
  • Why do otherwise-bald humans have hair at the tops of their heads? This is the location of maximal radiation.
  • Why didn’t all humans remain dark-skinned? Melanin also inhibits the skin’s production of Vitamin D, and different locales have different radiation levels, requiring new tradeoffs to be struck.
• Subcutaneous fat. Ever seen a hairless chimpanzee? Human skin is much less wrinkled than other skin. Why? Even in non-obese people, humans store more of their body fat below the skin (versus in the abdomen, or between the muscles). This change has three complementary causes:
  1. carnivores tend to store fat in this way,
  2. mitigate the hernia risk associated with bipedality
  3. replace the insulation services of fur, without interfering with sweat system.

We have reviewed four changes in human skin. Rather than a discrete event, these changes presumably evolved gradually, and in tandem.

Yes, but why are we hairless? There are many competing theories.

Jonathan Kingdon claims these skin adaptations arose late, as a parasite avoidance mechanism induced by increased population densities. Two rationales are provided: hair is a potent vector of infection, and the eccrine sweat system also has antibiotic properties.

This interpretation is challenged by genetic evidence that shows hominins were naked at least 1.2 mya, if not earlier (Rogers et al, 2004).

However, given the evidence suggesting Homo Erectus evolved in a desert climate, the most parsimonious theory seems to involve thermoregulation. We were exposed to less direct radiation given our upright posture; fur no longer served as critical of a role. But the overall climate was warm and dry,  

Humans as Cursorial Species

A cursorial animal is one that is adapted for long-distance running, rather than animals with high acceleration over short distances; thus, a leopard is considered cursorial, while a cheetah is not. Other examples include wolves, horses, and ostriches.

Fit human amateurs can regularly run 10 kilometers, and longer distances such as marathons (42 kilometers) are achieved by tens of thousands of people each year. Such distances are unknown if not impossible for any other primate, but are comparable to those observed in specialized mammalian cursors on open habitats. African hunting dogs, for example, travel an average 10km per day.

Racing horses can gallop 10 kilometers at 9 meters per second. However, the sustainable galloping speeds in horses decline considerably for runs longer than 10-15 minutes. Well-conditioned human runners exceed the predicted preferred galloping speed for a 65-kg quadruped, and can even occasionally outrun horses over extremely long distances.

Thus, despite our embarrassingly slow sprinting speed, human beings can outcompete even cursorial animals at endurance running over large distances. How come? The answer has to do with our unique cooling system.

When other mammals trot, they cool themselves by panting. However, above certain speeds a quadruped transitions to a full gallop, which precludes panting. A horse can trot all day, but it cannot gallop continuously without overheating.

Human adaptations for running, and our unique eccrine sweat-based cooling system, meant that humans have a larger trot/gallop (jog/sprint) transition threshold. Our superior cooling technology is accentuated in high heat. We are literally the only mammal that can run a marathon in high heat.

Why are we Born to Run?

Our bodies are designed for endurance running. We are cursorial animals. But why?

To achieve this, hominids exploited a new form of predation called persistence hunting. The most successful persistence hunts will involve:

• Time: middle of the day (during peak heat)
• Target: big prey (overheats faster)

If you chase a big animal above its trot/gallop transition speed, the animal will easily distance itself and begin panting. But you can track the animal, and chase it again before it has the opportunity to fully recover. Repeat this process, and after 10-25 km you will successfully drive the prey into hyperthermia. This style of hunting has a remarkable 75% success rate. Modern hunters typically prefer to use the bow and arrow, but persistence hunting is still in their repertoire. Before the invention of projectile weapons some 71 kya, persistence hunting surely played a larger role.

We know that habilines ate meat (many bones show signs of their butchery). But they likely acquired meat by scavenging, as they were not particularly effective carnivores. Their adaptations for projectiles were presumably used to repulse competitors, and stone tools certainly helped speedily butcher a carcass.

Of the dozens of running adaptations in our Homo Erectus, a substantial fraction already exist in habilines. Presumably the re-invention of our skin had begun too. These processes presumably began for simple reasons (it pays to move quickly, and have less fur, in the savannahs that emerged 3 mya).

Persistence hunting completely changed the game. Adaptations for running brought steep rewards. In a typical persistence hunt, the hunter averages an energy expenditure of 850 Kcal; they energy gains from big game is multiple times larger. Compare the calorie budget for a modern-day hunter-gatherer with that of chimps: in our prime, we produce twice as many calories as we consume!

Life is fundamentally about getting energy to make more life.

Persistence hunting was the turning-point in human evolution. Our species began winning, in terms of our reliably acquiring surplus energy. This surplus was the reason why our lineage could “afford” bigger brains, taller bodies, more frequent births, and longer childhood. All of these characteristics have improved gradually & continuously since the erects emerged.

Our Cursorial Adaptations

We have looked at the reasons behind our running. What does anatomy tell us?

First, let’s compare the physics of walking vs running:

• Walking is an inverted pendulum mechanism.  Our feet and our hips alternate as the center of rotation.
• Running is a mass-spring mechanism. Ligaments transfer foot-strike kinetic energy into tendons, which is released as we bounce onward.

Walking doesn’t require springs – but running does. And the bodies of erects have two new ligaments that serve precisely this purpose:

• The Achilles’ tendon stores and releases 35% of energy expended while running (but not walking). In chimps, this tendon is 1cm long. In erects, it is 10cm and much thicker.
• The dome-shaped arch of the foot is another spring, which lowers the cost of running by 17%.

During bipedal running the risk of falling and sprained ankles is high, which in the ancestral environment had adaptive consequences. Thus, the human body also developed many stabilization techniques:

• Gluteus maximus. Barely active during walking, this muscle contracts forcefully during running to prevent the trunk from toppling forward. 
• Various head stabilization devices. Promotes vision continuity and protects the brain (watch a runner with a ponytail sometime).
• Enlarged semicircular canals (balance organs) in inner ear, which can be seen by measuring certain dimensions of fossilized skulls.

I have listed five features of our anatomy that relate to endurance running. Lieberman et al (2006) list twenty:

As you can see, not all of these running adaptations emerged with Homo Erectus. Homo Habilis already shows adaptations for running. It would not surprise me in the slightest if that species also saw the beginnings of our skin trajectory.

Adaptations for running came at a price. We have lost our ability to climb trees. We are the first primate to lose this ability.

Takeaways

Why do humans look so different from our closest living relative, the chimpanzee?

Why do we have scent glands in our armpits? Fat in our asses? Such weird hair? Hairless skin with massive subcutaneous fat deposits?

Animal body plans are designed to excel in a particular niche. Our bodies are designed for persistence hunting. Compared to other primates, our anatomies optimize for thermoregulation, efficient energy transfer, and stabilization during running.

Chimpanzees don’t need to exercise to stay fit. We do. Our health sees dramatic benefits from aerobic exercise, especially running.

References

• Bramble & Lieberman (2004). Endurance running and the evolution of Homo
• Lieberman et al (2006). The human gluteus maximus and its role in running
• Lu et al (2016). Spatiotemporal antagonism in mesenchymal-epithelial signaling in sweat versus hair fate decision.
• Pontzer et al (2021). Evolution of water conservation in humans
• Rogers et al (2004). Genetic Variation at the MCiR Locus and the Time since Loss of Human Body Hair

Part Of: Anthropogeny sequence
See Also: Born to Run: a theory of human anatomy
Content Summary: 2100 words, 21 min read

The Universality of Cooking

Cooking is a human universal. It has been practiced in every known human society. Rumors to the contrary have never been substantiated. Not only is the existence of cooked foods universal, but most cuisines feature cooked foods as the dominant source of nutrition.

Raw foodists comprise a community dedicated to consuming uncooked food. Of course, compared to historical hunter-gatherers, modern raw foodists enjoy a wide variety of advantages. These include:

1. Elaborate food preparation (pounding, purees, gently warming),
2. Elimination of seasonal shortages (supermarkets)
3. Genetically enhanced vegetables with more sugar content and fewer toxins.

Despite these advantages, raw foodists report significant weight loss (much more than vegetarians!). Further, raw foodists suffer from increasingly severe reproductive impairments, which have been linked to not getting enough energy.  

Low BMI and impaired reproduction are perhaps manageable in modern times, but are simply unacceptable to hunter-gatherers living at subsistence levels.

The implication is clear: there is something odd about us. We are not like other animals. In most circumstances, we need cooked food.

The Energetics of Cooking

Life exists to find energy in order to make more copies of itself. Feeding and reproduction are the twin genetic imperatives.

Preferences are subject to natural selection. The fact that we enjoy cooked food suggests that cooking provides an energy boost to its recipients. The raw-foodist evidence hints towards this conclusion as well. But there is also direct evidence in rats that cooking increases energy gains.

In the following experiments, rat food was either processed/pounded, cooked, neither, or both. After giving this diet over the course of four days, rats in each condition were weighed.

For starches (left) and meat (right), cooking is by far more effective at preventing weight loss and promoting weight gain. Tenderizing food can sometimes help, but that technique pales in comparison to cooking.  

The above results were taken from rats. But similar results have replicated in calves, lambs, piglets, cows, and even salmon. It seems to be universally true that cooking improves the energy derived from digestion, sometimes up to 30%.

How does cooking unlock more energy for digestion?

First, denaturation occurs when the internal bonds of a protein weaken, causing the molecule to open up. Heat predictably denatures (“unfolds”) proteins, and denatured proteins are more digestible because their open structure exposes them to the action of digestive enzymes.

Besides heat, three other techniques promote denaturation: acidity, sodium chloride, and drying. Cooking experts constantly harp on these exact techniques, because it aligns with eating preferences.

Second, tender foods is another boon to digestion, because they offer less resistance to the work of stomach acid.  If you take rat food, and inject air into the pellets, that does not augment denaturation. Nevertheless, softening food in this way improves the energy intake of the rat.

Cooking does have negative effects. It can cause a loss of vitamins, and give rise to long-term toxic molecules called Maillard compounds, which are linked to cancer. But from an evolutionary perspective, these downsides are overshadowed by the impact of more calories. In subsistence cultures, better fed mothers have more, happier, and healthier children. When our ancestors first obtained extra calories by cooking their food, they and their descendants past on more genes than others of their species who ate raw.

A Brief Review of Human Evolution

The most recent common ancestor of humans and chimpanzees lived 6 mya (million years ago). But the first three million years of our heritage are not particularly innovative, anatomically. The australopiths were essentially bipedal apes. They could walk comfortably, but retained their adaptations for tree living as well. There is some evidence that australopiths acquired food from a new source: tubers (the underground energy storage system of plants).

Climate change is responsible for the demise of the australopiths. Africa began getting dryer about 3 million years ago, making the woodlands a harsher and less productive place to live. Desertification would have reduced the wetlands where Australopiths found fruits, seeds, and underwater roots. The descendents of Australopithecus had to adapt their diet.

The paranthropes adapted by promoting tubers (underground storage organs of plants) from backup to primary food. In contrast, the habilines (e.g., Homo Habilis) took a different strategy: meat eating. These creatures inherited tool making from the late australopiths (Mode 1 tools, the Oldawan industry- was discovered in Ethiopia 2.6 mya), and used these tools to scrape meat off of bones). The habilines are more ecologically successful, and lead to:

• 1.9 mya: The erects (e.g., Homo erectus/ergastor) with significantly larger brains and near-modern anatomies.
• 0.7 mya: The archaics (e.g., Homo Heidelbergensis) appear, who eventually give rise to the Neanderthals, Denisovans, and us.
• 0.3 mya The moderns (e.g., Homo Sapiens) emerge out of Africa, and completely conquer the globe.

Here is a sketch of how our body plans have changed across evolutionary time:

Explaining Hominization

The transition from habiline to erects deserves a closer look. We know erects evolved to be persistence hunters. But a number of paradoxes shroud their emergence:

1. Digestive Apparati. The erect diet appears to be mainly meat and tubers. Both require substantial jaw strength and digestive apparati. Yet the Homo genus features a dramatically reduced digestive apparatus. How was smaller mouths, weaker jaws, smaller teeth, small stomachs, and shorter colons an adaptive response to eating meat and starches?
2. Expensive Tissue. Australopiths brain size stayed relatively constant at 400 ccs (10% of resting metabolism). Erect brains began to grow. This transition ultimately yielded a 1400 cc brain (20% of resting metabolism) in archaic humans. How did the erects find the calories to finance this expansion?
3. Time Budget. The above anatomical features of erects are geared towards endurance running, which suggests that their lifestyle involved persistence hunting. Chimps have about 20 minute intervals in between searching for & chewing food. Thus, chimps can only afford to spend 20 minutes hunting before giving up. How did erects perform the risky behavior of persistence hunting, which consumes 3-8 hours of time?
4. Thermal Vulnerability. As part of their new hunting capabilities, erects became the naked ape (with a new eccrine sweat gland system to prevent overheating). But Homo Erectus also managed to migrate to non-African climates such as Europe. How did these creatures stay warm?
5. Predator Safety. Erects lost their anatomical features for arboreal living, which suggests they slept on the ground. Terrestrial sleeping is quite dangerous on the African savannah. How did erects avoid predation & extinction?

All of these confusing phenomena can be explained if we posit H. erectus discovered the use of fire, and its application in cooking:

1. Digestive Apparati. As we have seen, the primary role of cooking is to “externalize digestion”, and to increase the efficiency of our digestive tract. Cooked meat and starches are incredibly less demanding to process than their raw alternatives. This explains our reduced guts. By some estimates, the decrease in digestive tissue corresponds with a 10% energy savings by our erect ancestors.
2. Expensive Tissue. Cooking increases the metabolic yield of most foodstuffs by ~30%. For reference, a 5% increase in ripe fruit for chimpanzees reduces interbreeding interval (time between children) by four months. 30% is an absurdly large energy gain, enough to “change the game” for the erects..
3. Time Budget. Cooking freed up massive amounts of time otherwise spent chewing. Chimpanzees can take 4-7 hours per day chewing; humans only need one hour per day. This frees up massive amounts of time, which can be used for e.g., hunting.
4. Thermal Vulnerability. It is very difficult to explain a hairless Homo Erectus thriving on the colder Asian continent without control of fire.
5. Predator Safety. It is very difficult to explain how erects were not preyed upon to extinction without fire to identify & deter predators. Hadza hunter-gatherers comfortably sleep through the night, typically by taking turns “on watch” while the others rest.

The Archaeological Record

We are positing that erects learned to create and controlling fire 2 mya. Is that a feasible hypothesis?

Habilines had learned how to create stone tools 2.6 million years ago. By the time of the erects, techniques to create these tools had persisted for 600,000 years. So it is safe to say that our ancestors were able to retain useful cultural innovations.

Independent environmental reasons link fire-making with H Erectus. The Atlas mountain range is the most likely birthplace of this species, and this dry area fires triggered by lightning are an annual hazard. Hominins living in such environments would be more intimately familiar with fire than those with less combustible vegetation zones.

Erects would have seen sparks when they hit stones together to make tools. But the sparks produced by many kinds of rock are too cool to catch fire. However, when pyrites (a fairly common ore) are hit against flint, the results are used by hunter-gatherers to reliably produce fire. The Atlas mountain range is renowned for being exceptionally rich in minerals:

Why is Morocco one of the world’s great countries for minerals? No glaciers! Many of the world’s most colorful minerals are found in deposits at the surface, formed over time by the interaction of water, air and rock. Glaciers remove all of that good stuff (as happened in Canada recently, geologically speaking) –  and with no recent glaciation, Morocco hosts many fantastic occurrences of minerals unlike any in parts of the world stripped bare during the last Ice Age.

Since this mountain range contains pyrites, early erects could have found themselves inadvertently making fire rather often.

Once it is created, fire is relatively easy to keep going. And it does not take much creativity to stick food a fire. Moreover, modern-day chimps prefer cooked food over raw; it is hard to imagine H Erectus finding cooked food distasteful. All of these considerations suggest an early control of fire is at least plausible.

We can consult the archaeological record to see record of man-made fire (i.e., hearths). This is bad news for the cooking hypothesis! There is strong evidence for hearths dating back to 800 mya and the advent of archaic humans. Before then, there are six sites that seem to be hearths; but these are not universally acknowledged as such.

But absence of evidence isn’t evidence of absence, right?

No! That idiom is wrong. Silence is evidence of absence. It’s just that the strength of the evidence depends on the nature of the hypothesized entity.

• If you think an unidentified planet orbits the Sun, a lack of evidence would weigh heavily against the hypothesis.
• If you think an unidentified pebble orbits the Sun, a lack of evidence doesn’t say much one way of the other.

Wrangham argues that evidence of hearths are more fragile than e.g. fossils, and points to facts like there are zero hearths recorded for modern humans during European “ice ages” – but we know these must have existed. It is possible that the contested hearth sites will ultimately be vindicated, and that we just can’t see much evidence.

Despite these claims about evidential likelihood, the silence of the archaeological record is undeniably a significant objection to the theory.

Weighing The Evidence

Is the cooking hypothesis true? Let us weigh the evidence, and contrast it with alternative hypotheses.

The most plausible alternative hypothesis is that archaic humans H. Heidelbergensis discovered cooking. But the emergence of that species involved an increase in brain size, and more sophisticated culture & hunting technology.  Neither adaptation seems strongly connected to cooking. In contrast, the H. Erectus adaptations would have all been strongly affected by cooking. 

Moreover, alternative hypotheses must still answer the five paradoxes of hominization:

1. Digestive Apparati. Why did erects evolve smaller mouths, weaker jaws, smaller teeth, small stomachs, and shorter colons?
2. Expensive Tissue. How did the erects find the calories to finance more brain tissue?
3. Time Budget. How could erects afford spending 3-8 hours per day engaged in the risky strategy hunting?
4. Thermal Vulnerability. Erects also managed to migrate to non-African climates such as Europe. How did these creatures stay warm?
5. Predator Safety. Erects slept on the ground. How did they avoid predation?

The habilines ate meat. It is unclear how they did so (hunting or scavenging), but we have strong evidence that they did. Meat is a much higher quality food than tubers (cf. paranthropes) or fruit (cf. chimpanzees). The meat-eating hypothesis argues that meat eating was the primary driver of hominization.

Meat-eating resolves the Expensive Tissue paradox (meat allows for brain growth) and Digestive Apparati (carnivores are known to have smaller guts). But it doesn’t address why a meat-eater would develop smaller canines. And it struggles to explain how the reduction in gut size is compatible with the tuber component of the erect diet. And what about time budget, thermal vulnerability, and predator safety? The meat eating hypothesis fails to address these paradoxes entirely.

Which is more likely to occur in the next twenty years: undisputed evidence for early control of fire, or an alternate theory that resolves all five hominization paradoxes?

My money is on the former.

References

• Wrangham (). Catching Fire: How Cooking Made Us Human
• Aiello & Wheeler(1995). The expensive tissue hypothesis: the brain and the digestive system in primate and human evolution.

Part Of: Demystifying Ethics sequence
Content Summary: 1700 words, 17 min read

The contents of our social intuitions is not arbitrary. They are not entirely plastic to changes in environment. Rather, the brain are built with innate social intuition generators, which bias the content of social judgments.

Generator 1: Care/Harm

Parents care for their children. This imperative of natural selection is directly expressed in caregiving mechanisms in the brain. While the proper domain of caregiving is one’s kin, other modules (such as the mammalian attachment module) can elicit caregiving behaviors towards non-kin.

For primates living in close proximity, male violence is an increasingly noxious threat. Accordingly, Cushman et al (2012) show evidence for a violence aversion device, which triggers a strong autonomic reaction to actions of violence committed by oneself (but not others). Here is an example of their experimental apparatus: underneath the X is a fake leg. Even though they knew the action was harmless, delivering the blow caused significant visceral distress, compared to watching it being done by someone else.

The violence aversion device is sensitive to calculations of personal force which is used to generate feelings of agency in the brain. The alarm only triggers when our body directly delivers force onto another person. This explains why the alarm triggers in the footbridge dilemma (“push the fat man to save five lives”) but not the trolley problem (“flip a switch to kill one and save five”).

Generator 2: Proportional Fairness

Main Article: Evolutionary Game Theory

When interacting with other organisms, one can act purely selfishly or cooperatively. The Prisoner’s Dilemma illustrates that acting in one’s self-interest can lead to situations where everyone loses. There is strong evolutionary pressure to discover cooperative emotions: devices that avert the tragedy of the commons.

The Iterated Prisoner’s Dilemma (IPD) makes game theory more social, where many players compete for resources multiple times. While one-off PD games favor selfish behavior, IPD can favor strategies that feature reciprocal altruism, such as Tit-for-Tat. More generally, IPD strategies do best if they are nice, retaliating, and forgiving.

Social equality is a special case of proportionality: when contributions are equal, so too should rewards. But when contributions are unequal, most adults affirm reward inequality. We have a deep intuitive sense of karma: what people deserve depends on how much effort they expend.

Generator 3: Dominance

Main Article: An Introduction to Primate Societies

When animals’ territory overlaps, they often compete for access to resources (e.g., food and reproductive access).

Fighting is accompanied with risk: the stronger animal could be unlucky, the weaker animal could lose their life. Similar to human warfare, both sides suffer less when the weaker side preemptively surrenders. The ability to objectively predict the outcome of a fight is therefore advantageous.

Suppose the need for fight-predictions is frequent, and do not often change (physical strength changes only slowly over an animal’s life). Instead of constantly assessing physical characteristics of your opponent, it is simpler to just remember who you thought was stronger last time.

This is the origin of the dominance hierarchy. The bread and butter of dominance hierarchies is status signaling. Dominant behaviors (e.g., snarling) evokes submissive behaviors (e.g., looking away).

Generator 4: Autonomy

Consider the following facts.

1. The earliest groups of humans seem to have been governed by an egalitarian ethic, much as surviving communities of nomadic hunters and gatherers still are.
2. That ethic is unique among other species of great apes that are our closest cousins. Most notably, chimps and gorillas live in bands led by despotic alpha males.
3. As human societies developed settled agriculture and then civilization, despotism and hierarchy reemerge.

How can we explain these things? Perhaps a new emotional system evolved: autonomy. It motivated groups of non-dominant humans to form coalitions against any potential alpha despot. This trend is born out in the data: about half of all murders cross-culturally have an anti-bullying motive. But murder is not the only sanctioning device, followers also use techniques such as criticism, ridicule, disobedience, deposition, and desertion (Boehm, 2012).

Our species never lost its capacity for despotism. But in the human inverted hierarchy, our species discovered a newfound will to tear down authority figures, which created within us a capacity for egalitarianism. These two systems (Autonomy and Dominance) live in tension with one another, and one can “gain the upper hand” by changes in the broader cultural milieu (cf., agriculture and the collapse of egalitarian societies).

Generator 5: Purity / Disgust

Main Article: The Evolution of Disgust

The human brain comes equipped with two systems:

1. Poison monitoring is a faculty of the digestive system. It evolved to regulate food intake and protect the gut against harmful substances.
2. Infection avoidance is a faculty of the immune system. It evolved to protect against infection from pathogens and parasites, by avoiding them.

In humans, these two systems were entangled in the emotion of disgust. This explains the otherwise baffling diversity of disgust elicitors & behaviors.

Disgust motivated the creation of food taboos (e.g., don’t eat pork) and purity laws (e.g., don’t put your feet on the table).

Generator 6: Group Loyalty

Two people can put Us ahead of Me by belonging to a cooperative group, provided that group members can reliably identify one another. Specifically, we possess a group membership device which uses symbols to delineate different factions. Members of the ingroup are treated warmly (ethnocentrism); members of the outgroup are treated poorly (xenophobia). We even pay more attention to members of the ingroup, leading to such phenomena as outgroup homogeneity (c.f., evangelical Christians describing non-evangelicals as “the world”).

Ethnic psychology describes modules in our brain responsible for constructing groups. We are particularly interested in constructing stereotypes of other groups. Our brains already come equipped with folk biology modules that delineate different species of flowers, for example. Gilwhite et al (2001) adduce evidence that ethnic groups are treated as biological “species” in the human brain.

The Right Kind of List

We’ve discussed six intuition generators: care/harm, proportional fairness, dominance, autonomy, purity/disgust, and group loyalty.  

Is our list too long? So many mechanisms to explain human social behavior would seem to violate parsimony. Are we adorning our theory with epicycles? Are we overfitting our model?

In fact, I affirm the massive modularity hypothesis: that the human brain contains dozens of mental modules, each of which have distinctive phylogeny, ontogeny, anatomy, behavioral profile, and ecological motivation. I have not conjured these entities to explain morality. Rather, I am drawing a small subset from my overarching project to describe the architecture of mind.

Implications for the Norm System

Recall the the moral/conventional distinction:

• Conventional judgments (should / should not) are intuitions of socially appropriate behavior, and associated with embarrassment.
• Moral judgments (good / evil) are also judgments about behavior, but more associated with anger, inflexibility, condemnation, and guilt.

Jonathan Haidt claims that these generators are responsible for moral intuitions. But the above generators also underlie the structure of our conventional norms. After all, there are plenty of mildly disrespectful behaviors that even the most conservative people would not describe as evil.

We have identified dozens of other specialized modules in the human brain. Why is e.g.,  feeling of knowing (recognition memory) not on our list? Because there were no biocultural pressures to integrate it with the norm acquisition and norm evaluation systems. We call our six modules social intuition generators because they have become intertwined with our normative machinery.

An Explanation of American Politics

People are genetically and environmentally disposed to respond to certain generators more strongly than others. Social matrices encode how many stimuli activate a given social intuition, and how strongly. 

People with similar matrices tend to gravitate towards similar political parties. When you measure the social matrices of American citizens, you can see large differences between the social intuitions of Democrats and Republicans (Graham et al, 2009).

These differences in social matrices explain much of American politics.

• Why do Democrats praise entitlements, but Republicans denounce them? Because Democrats heavily emphasize Care for the poor, whereas Republicans more strongly reverberate to questions of Proportional Fairness (moral hazard).
• Why are Democrats more skeptical of patriotism than their Republican counterparts? Perhaps because they respond to Loyalty to country less.
• How can both groups claim to value Proportional Fairness? There are two competing explanations for poor outcomes: environmental (bad luck) or personal (poor character). Liberals tend to focus on the former, conservatives on the latter.
• How can both groups claim to value Autonomy? For liberals, Autonomy responds ethnic oppression: perceived injustices done in the name of one’s tribe. The foundation is expressed as group symmetry. For conservatives, Autonomy responds to government oppression: perceived injustices in the form of taxes, nanny state, and regulations. The foundation is expressed as political liberty.

Looking Forward

Moral Foundations Theory is the invention of Jonathan Haidt, who introduces the concept in his excellent 2012 book The Righteous Mind: Why Good People are Divided by Politics and Religion. You can explore your moral matrix at yourmorals.org.

This post is 90% exposition, and 10% innovation. I innovate in the preceding two sections, by a) linking the six “taste buds” to mental modules that modulate inputs to the normative system, and b) broadening its reach to conventional (non-moral) norms.

In his book, Haidt makes the case the conservatives are more ethically sophisticated, because their moral judgments respond to a larger number of taste buds. But besides appealing to the ethos of Durkheim and Burke, Haidt doesn’t investigate the normative status of the social intuition generators in sufficient detail.

Here are three questions I would like to explore, at some point:

• What is the normative status of e.g., disgust? If we could dampen or amplify disgust reactions in human beings, what would be the end result?
• Social matrices encode different modes of existence that are hard to comprehend unless they are lived. What sort of social matrices are underexplored? Does there exist entirely novel modes of existence that we simply have not yet tried out?
• What does the moral matrix of a successful metamorality look like? How do we promote positive outcomes when moral communities must live with one another?

Related Resources

• Boehm (2012). Hierarchy in the Forest: The Evolution of Egalitarian Behavior
• Haidt (2012). The Righteous Mind: Why Good People are Divided by Politics and Religion.
• Graham et al (2009). Liberals and conservatives rely on different sets of moral foundations.
• Cushman et al (2012). Simulating murder: the aversion to harmful action
• GilWhite et al (2001). Are ethnic groups biological “species” to the human brain? Essentialism in our cognition of some social categories