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Inference of ecological and social drivers of human brain-size evolution

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Mauricio González-Forero at University of St Andrews
Mauricio González-Forero
Andy Gardner
Andy Gardner
Andy Gardner
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Abstract and Figures

The human brain is unusually large. It has tripled in size from Australopithecines to modern humans¹ and has become almost six times larger than expected for a placental mammal of human size². Brains incur high metabolic costs³ and accordingly a long-standing question is why the large human brain has evolved⁴. The leading hypotheses propose benefits of improved cognition for overcoming ecological⁵⁻⁷, social⁸⁻¹⁰ or cultural¹¹⁻¹⁴ challenges. However, these hypotheses are typically assessed using correlative analyses, and establishing causes for brain-size evolution remains difficult15,16. Here we introduce a metabolic approach that enables causal assessment of social hypotheses for brain-size evolution. Our approach yields quantitative predictions for brain and body size from formalized social hypotheses given empirical estimates of the metabolic costs of the brain. Our model predicts the evolution of adult Homo sapiens-sized brains and bodies when individuals face a combination of 60% ecological, 30% cooperative and 10% between-group competitive challenges, and suggests that between-individual competition has been unimportant for driving human brain-size evolution. Moreover, our model indicates that brain expansion in Homo was driven by ecological rather than social challenges, and was perhaps strongly promoted by culture. Our metabolic approach thus enables causal assessments that refine, refute and unify hypotheses of brain-size evolution.
| Best-fitting scenarios across adult Homo, and resulting predicted life history for H. sapiens. a, Best adult fitting scenarios across Homo (figure modified with permission from figure 8.1 of ref. 1 ). Pie charts and plots respectively show the challenge combination and shape of EEE versus skill that yielded the best adult fit using the same Q and R parameters (Extended Data Figs. 6, 7). b, Life history with the challenge combination yielding the best adult fit for H. sapiens. Resulting life periods are indicated above the body mass plot. Vertical lines are ages at which the growth strategy changes suddenly; within childhood, they occur when brain growth begins and terminates.
| Best-fitting scenarios across adult Homo, and resulting predicted life history for H. sapiens. a, Best adult fitting scenarios across Homo (figure modified with permission from figure 8.1 of ref. 1 ). Pie charts and plots respectively show the challenge combination and shape of EEE versus skill that yielded the best adult fit using the same Q and R parameters (Extended Data Figs. 6, 7). b, Life history with the challenge combination yielding the best adult fit for H. sapiens. Resulting life periods are indicated above the body mass plot. Vertical lines are ages at which the growth strategy changes suddenly; within childhood, they occur when brain growth begins and terminates.
… 
Shape of EEE versus skill a–c, Plots of EEE (e), its speed and acceleration with respect to skill level under power competence and exponential competence with only ecological challenges (that is, P1 = 1) for the parameter values used and without maternal provisioning (that is, φ = 0, so that e = S1). For comparison, the curves for power competence are displaced to the left by 1 unit because skill level at birth is 1 for power competence but 0 for exponential competence. a, b, e and its speed at birth and during young ages are smaller for exponential competence than for power competence. c, However, the acceleration in e at birth and at young ages is larger for exponential competence than for power competence.
Shape of EEE versus skill a–c, Plots of EEE (e), its speed and acceleration with respect to skill level under power competence and exponential competence with only ecological challenges (that is, P1 = 1) for the parameter values used and without maternal provisioning (that is, φ = 0, so that e = S1). For comparison, the curves for power competence are displaced to the left by 1 unit because skill level at birth is 1 for power competence but 0 for exponential competence. a, b, e and its speed at birth and during young ages are smaller for exponential competence than for power competence. c, However, the acceleration in e at birth and at young ages is larger for exponential competence than for power competence.
… 
Detailed life history resulting from the best-fitting scenario for H. sapiens Plots correspond to Fig. 4b. a, The growth strategy generating the life history. b, The resulting growth metabolic rate. c, d, The mass of all tissues. e, The skill level. For comparison with the model’s predicted skill level, xk∗\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{{\rm{k}}}^{\ast }$$\end{document}, the black dots in e are the observed cumulative distribution of self-reported acquisition ages of food production skills in female Tsimane horticulturalists³⁶.
Detailed life history resulting from the best-fitting scenario for H. sapiens Plots correspond to Fig. 4b. a, The growth strategy generating the life history. b, The resulting growth metabolic rate. c, d, The mass of all tissues. e, The skill level. For comparison with the model’s predicted skill level, xk∗\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{{\rm{k}}}^{\ast }$$\end{document}, the black dots in e are the observed cumulative distribution of self-reported acquisition ages of food production skills in female Tsimane horticulturalists³⁶.
… 
Method implementation a, Typical result with convergence to an uninvadable growth strategy. For the ith best-response iteration, the growth strategy shown is the resident (v) whose best response (u~∗)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$({\mathop{u}\limits^{ \sim }}^{\ast })$$\end{document} is shown next, which is the resident of the i + 1th iteration. Convergence to a best response to itself (u∗)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$({u}^{\ast })$$\end{document} was declared visually, in this case, at iteration 21. b–f, Reporting variables across the best-response iterations in a. b–e, Resulting adult body mass, brain mass, skill level and encephalization quotient across iterations. These values tend to converge more quickly than the growth strategy (a). f, Rather than visually declaring convergence, convergence should ideally be declared when the difference between mutant and resident is below a chosen threshold. However, numerical jittering prevented the use of this criterion. For example, f shows the maximum of ũ∗(t)-v(t)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left|{\tilde{\rm{u}}}^{\ast }(t)-v(t)\right|$$\end{document} across t for each iteration in a. Without numerical jittering, this maximum should decrease as the growth strategy approaches a best response to itself. However, numerical jittering causes this maximum to be at least equal to the maximum mutation size δ = 0.1. The maximum of ũ∗(t)-v(t)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left|{\tilde{\rm{u}}}^{\ast }(t)-v(t)\right|$$\end{document} is occasionally greater than δ because ũ∗\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\tilde{\rm{u}}}^{\ast }$$\end{document} and v have different partitions over t and we use the following approximation: for each t in the t partitioning of ũ∗\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\tilde{\rm{u}}}^{\ast }$$\end{document}, we find the closest t in the t partitioning of v and calculate the difference ũ∗(t)-v(t)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left|{\tilde{\rm{u}}}^{\ast }(t)-v(t)\right|$$\end{document} at these relatively close times; this may occasionally cause the difference to be the larger than δ when strategies change suddenly with t. Alternative measures of convergence were similarly inadequate (for example, Σtũ∗(t)-v(t)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\rm{\Sigma }}}_{t}\left|{\tilde{\rm{u}}}^{\ast }(t)-v(t)\right|$$\end{document}). g, We implement maternal provisioning differently than before²⁴ to incorporate it when there are social challenges. The difference yields no detectable difference in predicted brain and body mass with only ecological challenges after slightly adjusting the EEE from maternal provisioning of a newborn (φ0): before²⁴, φ0 = 0.6 for power and φ0 = 0.8 for exponential competence were used; here, φ0 = 0.4 for power and φ0 = 0.6 for exponential competence were used. h, Three ways to measure adult fit: (1) at the predicted age of adulthood xB∗ta-XBta\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left({x}_{{\rm{B}}}^{\ast }\left({t}_{{\rm{a}}}\right)-{X}_{{\rm{B}}}\left({t}_{{\rm{a}}}\right)\right)$$\end{document}; (2) at the observed age of adulthood xB∗(τa)-XB(τa)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left({x}_{{\rm{B}}}^{\ast }({\tau }_{{\rm{a}}})-{X}_{{\rm{B}}}({\tau }_{{\rm{a}}})\right)$$\end{document}; and (3) at the predicted age of adulthood for the prediction and at the observed age of adulthood for the observation xB∗ta-XB(τa)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left({x}_{{\rm{B}}}^{\ast }\left({t}_{{\rm{a}}}\right)-{X}_{{\rm{B}}}({\tau }_{{\rm{a}}})\right)$$\end{document}. We use option 2.
Method implementation a, Typical result with convergence to an uninvadable growth strategy. For the ith best-response iteration, the growth strategy shown is the resident (v) whose best response (u~∗)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$({\mathop{u}\limits^{ \sim }}^{\ast })$$\end{document} is shown next, which is the resident of the i + 1th iteration. Convergence to a best response to itself (u∗)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$({u}^{\ast })$$\end{document} was declared visually, in this case, at iteration 21. b–f, Reporting variables across the best-response iterations in a. b–e, Resulting adult body mass, brain mass, skill level and encephalization quotient across iterations. These values tend to converge more quickly than the growth strategy (a). f, Rather than visually declaring convergence, convergence should ideally be declared when the difference between mutant and resident is below a chosen threshold. However, numerical jittering prevented the use of this criterion. For example, f shows the maximum of ũ∗(t)-v(t)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left|{\tilde{\rm{u}}}^{\ast }(t)-v(t)\right|$$\end{document} across t for each iteration in a. Without numerical jittering, this maximum should decrease as the growth strategy approaches a best response to itself. However, numerical jittering causes this maximum to be at least equal to the maximum mutation size δ = 0.1. The maximum of ũ∗(t)-v(t)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left|{\tilde{\rm{u}}}^{\ast }(t)-v(t)\right|$$\end{document} is occasionally greater than δ because ũ∗\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\tilde{\rm{u}}}^{\ast }$$\end{document} and v have different partitions over t and we use the following approximation: for each t in the t partitioning of ũ∗\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\tilde{\rm{u}}}^{\ast }$$\end{document}, we find the closest t in the t partitioning of v and calculate the difference ũ∗(t)-v(t)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left|{\tilde{\rm{u}}}^{\ast }(t)-v(t)\right|$$\end{document} at these relatively close times; this may occasionally cause the difference to be the larger than δ when strategies change suddenly with t. Alternative measures of convergence were similarly inadequate (for example, Σtũ∗(t)-v(t)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\rm{\Sigma }}}_{t}\left|{\tilde{\rm{u}}}^{\ast }(t)-v(t)\right|$$\end{document}). g, We implement maternal provisioning differently than before²⁴ to incorporate it when there are social challenges. The difference yields no detectable difference in predicted brain and body mass with only ecological challenges after slightly adjusting the EEE from maternal provisioning of a newborn (φ0): before²⁴, φ0 = 0.6 for power and φ0 = 0.8 for exponential competence were used; here, φ0 = 0.4 for power and φ0 = 0.6 for exponential competence were used. h, Three ways to measure adult fit: (1) at the predicted age of adulthood xB∗ta-XBta\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left({x}_{{\rm{B}}}^{\ast }\left({t}_{{\rm{a}}}\right)-{X}_{{\rm{B}}}\left({t}_{{\rm{a}}}\right)\right)$$\end{document}; (2) at the observed age of adulthood xB∗(τa)-XB(τa)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left({x}_{{\rm{B}}}^{\ast }({\tau }_{{\rm{a}}})-{X}_{{\rm{B}}}({\tau }_{{\rm{a}}})\right)$$\end{document}; and (3) at the predicted age of adulthood for the prediction and at the observed age of adulthood for the observation xB∗ta-XB(τa)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left({x}_{{\rm{B}}}^{\ast }\left({t}_{{\rm{a}}}\right)-{X}_{{\rm{B}}}({\tau }_{{\rm{a}}})\right)$$\end{document}. We use option 2.
… 
Effects of Q and R parameters a, b, Effects of maintenance costs (Bi) on the corresponding tissue mass or skill level. Each Bi tends to decrease the value xi∗(τa)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{i}^{\ast }({\tau }_{{\rm{a}}})$$\end{document} for the corresponding i, but not necessarily for the other i (see c, d). c, d, Effect of Bi on adult brain mass, body mass and encephalization quotient. With power competence (c), when Bb = 310 and 340 MJ kg⁻¹ per year (y), the predicted adult brain mass is xb∗(τa)=1.0298\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{{\rm{b}}}^{\ast }({\tau }_{{\rm{a}}})=1.0298$$\end{document} and 0.9133 kg, respectively. With exponential competence (d), when Bb = 310, 340 and 370 MJ kg⁻¹ y⁻¹, the predicted adult brain mass is xb∗(τa)=1.542\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{{\rm{b}}}^{\ast }({\tau }_{{\rm{a}}})=1.542$$\end{document}, 1.3973 and 1.2767 kg, respectively. e, f, Effects of Br when Br is small. When Br varies between 70 and 2,700 MJ kg⁻¹ y⁻¹, Br has no detectable effect on adult brain mass and encephalization quotient. g, h, Ontogenetic fit with H. sapiens around the used values for each of the R parameters (except δ). The ontogenetic fit is approximately maximized around the benchmark values chosen previously²⁴, which are also used here (except for φ0 given our improved implementation of φ). i, Effect of Br on the predicted life history with exponential competence. In the left column, from top to bottom, as Br decreases, the allocation to the growth of reproductive tissue during adolescence increases (ur∗\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${u}_{{\rm{r}}}^{\ast }$$\end{document} between tm and ta) and adolescence shortens. In the central column, the increased allocation to the growth of reproductive tissue increases the mass of reproductive tissue, but brain mass does not change with Br for Br ≥ 70 MJ kg⁻¹ y⁻¹. In the right column, as the mass of reproductive tissue increases, body mass increases slightly, which is more noticeable for Br ≤ 100 MJ kg⁻¹ y⁻¹. An exceedingly small Br (<70 MJ kg⁻¹ y⁻¹) disrupts the predicted life history, which with Br = 60 MJ kg⁻¹ y⁻¹ is severely different from that of H. sapiens (for example, there is brain growth late in life and reproductive growth from birth). Similar results arise for even smaller Br. In a–i there are only ecological challenges and we use the previous²⁴ definition of φ.
+9
Effects of Q and R parameters a, b, Effects of maintenance costs (Bi) on the corresponding tissue mass or skill level. Each Bi tends to decrease the value xi∗(τa)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{i}^{\ast }({\tau }_{{\rm{a}}})$$\end{document} for the corresponding i, but not necessarily for the other i (see c, d). c, d, Effect of Bi on adult brain mass, body mass and encephalization quotient. With power competence (c), when Bb = 310 and 340 MJ kg⁻¹ per year (y), the predicted adult brain mass is xb∗(τa)=1.0298\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{{\rm{b}}}^{\ast }({\tau }_{{\rm{a}}})=1.0298$$\end{document} and 0.9133 kg, respectively. With exponential competence (d), when Bb = 310, 340 and 370 MJ kg⁻¹ y⁻¹, the predicted adult brain mass is xb∗(τa)=1.542\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{{\rm{b}}}^{\ast }({\tau }_{{\rm{a}}})=1.542$$\end{document}, 1.3973 and 1.2767 kg, respectively. e, f, Effects of Br when Br is small. When Br varies between 70 and 2,700 MJ kg⁻¹ y⁻¹, Br has no detectable effect on adult brain mass and encephalization quotient. g, h, Ontogenetic fit with H. sapiens around the used values for each of the R parameters (except δ). The ontogenetic fit is approximately maximized around the benchmark values chosen previously²⁴, which are also used here (except for φ0 given our improved implementation of φ). i, Effect of Br on the predicted life history with exponential competence. In the left column, from top to bottom, as Br decreases, the allocation to the growth of reproductive tissue during adolescence increases (ur∗\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${u}_{{\rm{r}}}^{\ast }$$\end{document} between tm and ta) and adolescence shortens. In the central column, the increased allocation to the growth of reproductive tissue increases the mass of reproductive tissue, but brain mass does not change with Br for Br ≥ 70 MJ kg⁻¹ y⁻¹. In the right column, as the mass of reproductive tissue increases, body mass increases slightly, which is more noticeable for Br ≤ 100 MJ kg⁻¹ y⁻¹. An exceedingly small Br (<70 MJ kg⁻¹ y⁻¹) disrupts the predicted life history, which with Br = 60 MJ kg⁻¹ y⁻¹ is severely different from that of H. sapiens (for example, there is brain growth late in life and reproductive growth from birth). Similar results arise for even smaller Br. In a–i there are only ecological challenges and we use the previous²⁴ definition of φ.
… 
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LETTER https://doi.org/10.1038/s41586-018-0127-x
Inference of ecological and social drivers of human
brain-size evolution
Mauricio González-Forero1* & Andy Gardner1
The human brain is unusually large. It has tripled in size from
Australopithecines to modern humans1 and has become almost
six times larger than expected for a placental mammal of human
size2. Brains incur high metabolic costs3 and accordingly a long-
standing question is why the large human brain has evolved
4
. The
leading hypotheses propose benefits of improved cognition for
overcoming ecological5–7, social8–10 or cultural11–14 challenges.
However, these hypotheses are typically assessed using correlative
analyses, and establishing causes for brain-size evolution remains
difficult15,16. Here we introduce a metabolic approach that enables
causal assessment of social hypotheses for brain-size evolution. Our
approach yields quantitative predictions for brain and body size
from formalized social hypotheses given empirical estimates of the
metabolic costs of the brain. Our model predicts the evolution of
adult Homo sapiens-sized brains and bodies when individuals face a
combination of 60% ecological, 30% cooperative and 10% between-
group competitive challenges, and suggests that between-individual
competition has been unimportant for driving human brain-size
evolution. Moreover, our model indicates that brain expansion in
Homo was driven by ecologic al rather than social challenges, and was
perhaps strongly promoted by culture. Our metabolic approach thus
enables causal assessments that refine, refute and unify hypotheses
of brain-size evolution.
The leading hypotheses for the evolution of brain size make differ-
ent suggestions as to which cognitive challenges have been the most
important in driving brain expansion. ‘Ecological-intelligence’ hypoth-
eses emphasize challenges posed by the non-social environment, for
example, finding, caching or processing food
5–7
(Fig.1). By contrast,
‘social-intelligence’ hypotheses emphasize challenges posed by the social
environment, for example, cooperating for resource extraction10,15,
manipulating others, avoiding manipulation or forming coalitions
and alliances to outcompete others8,9 (Fig.1). Social challenges have
been suggested to constitute particularly powerful drivers of brain
expansion, because they may have triggered evolutionary arms races
in cognition
8–10
. Finally, ‘cultural-intelligence’ hypotheses emphasize
challenges of learning from others, teaching and doing so when there is
accumulated cultural knowledge
11–14
. Empirical tests of these hypoth-
eses customarily investigate phylogenetically controlled correlations
between brain size (or the size of brain components) and candidate
selective factors (for example, diet type
5,17
, tactical-deception rate
18
,
group size
10,19
and social-learning proclivity
20
). However, establishing
causality has proven to be difficult. For example, given a positive cor-
relation, it is unclear whether large group sizes favour bigger brains or
big brains enable larger group sizes
16
. Moreover, there is the quantitative
problem of explaining not only why bigger brains are favoured, but also
why they are favoured to the particularly large size observed in humans
(around 1.3 kg for a body size ofapproximately 50 kg in females21,22).
To address these problems, we merge elements of metabolic theory23,
life-history theory and differential games to obtain quantitative predic-
tions for the evolution of brain and body size when individuals face
ecological and social challenges given metabolic costs of the brain. Our
approach incorporates social interactions into a previous non-social
model
24
(Supplementary Information1–3). As a first approximation,
we consider a female population and partition the body mass of each
individual into three tissues: ‘brain’, ‘reproductive’ and other ‘somatic’
tissue(Fig. 2a). Part of the energy consumption of reproductive tissue
is for the production and maintenance of offspring, whereas part of
energy consumption of the brain is for production (learning) and main-
tenance (memory) of energy-extraction skills. Accordingly, at each age
the individual has a certain skill level measured in information units
(that is, bytes). She extracts energy by using her skill level to overcome
ecological or social energy-extraction challenges. Success in an ecolog-
ical challenge depends on her own skill level, whereas success in a social
challenge depends on her skill level and that of her social partners. We
1School of Biology, University of St Andrews, St Andrews, UK. *e-mail: mgf3@st-andrews.ac.uk
Challenges
Ecological Social
Cooperative Competitive
Between individuals Between groups
‘Me versus nature’ ‘Us versus nature’ ‘Me versus you’ ‘Us versus them’
Fig. 1 | Ecological and social hypotheses for brain expansion. Ecological
hypotheses emphasize challenges ‘against nature’, whereas social
hypotheses emphasize challenges involving social partners. Here we
partition these hypotheses into four types of challenges that are expected
to trigger different evolutionary processes.
Corrected: Publisher Correction
Corrected: Author Correction
554 | NATURE | VOL 557 | 24 MAY 2018
© 2018 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
... This study also relates more broadly to the scientific literature on human brain size evolution; see Heldstab et al. (2022) for a survey. A recent study by Gonzalez-Forero and Gardner (2018) provides a quantitative analysis on the evolution of human brain and finds that ecological challenges for "finding, caching or processing food" are the main reason for human brain evolution. Robson and Kaplan (2003) provide an economic analysis on the development of human brain as health capital that is accumulated by bodily investment to reduce mortality. ...
... 4 See van Valen (1974) and Lynn (1990) for estimates of the cognitive advantage of a larger human brain size. See Gonzalez-Forero and Gardner (2018) for estimates of the metabolic costs of the human brain. 5 See Hansson and Stuart (1990) and Rogers (1994) for early economic models of natural selection of agents with different time preferences but not in a Malthusian environment. ...
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Why did the human brain evolve? This study develops a Malthusian growth model with heterogeneous agents and natural selection to explore the evolution of human brain size. We find that if the cognitive advantage of a larger brain dominates its higher metabolic costs, then the average brain size increases over time, which is consistent with the rising trend in human brain size that started over 2 million years ago. Furthermore, an improvement in hunting-gathering productivity (e.g., the discovery of using stone tools and fire in hunting animals and cooking food) helps to trigger this human brain size evolution. As the average brain size increases, the average level of hunting-gathering productivity also rises over time. Quantitatively, our model is able to replicate the trend in hominin brain evolution over the last 10 million years.
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... Moreover, despite being a foundational assumption of the SIH [10], the link between social bonding and cognition remains unclear. Indeed, in principle, interacting repeatedly with the same partner(s) could reduce uncertainty and allow partners to pool their skills, thus reducing cognitive demands [53][54][55]. Conversely, information processing abilities that enable individuals to detect and respond to a partner's state could facilitate the maintenance of successful cooperative relationships [26,56]. To evaluate these possibilities, an important step is to examine whether individual socio-cognitive performance is positively associated with the maintenance of strong social bonds. ...
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Article
Full-text available
The need to maintain strong social bonds is widely thought to be a key driver of cognitive evolution. Cognitive abilities to track and respond to information about social partners may be favoured by selection if they vary within populations and confer fitness benefits. Here we evaluate four key assumptions of this argument in wild jackdaws (Corvus monedula), corvids whose long-term pair bonds exemplify one of the putative social drivers of cognitive evolution in birds. Combining observational and experimental behavioural data with long-term breeding records, we found support for three assumptions: (i) pair-bond strength varies across the population, (ii) is consistent within pairs over time and (iii) is positively associated with partner responsiveness, a measure of socio-cognitive performance. However, (iv) we did not find clear evidence that stronger pair bonds lead to better fitness outcomes. Strongly bonded pairs were better able to adjust hatching synchrony to environmental conditions but they did not fledge more or higher quality offspring. Together, these findings suggest that maintaining strong pair bonds is linked to socio-cognitive performance and may facilitate effective coordination between partners. However, they also imply that these benefits are insufficient to explain how selection acts on social cognition. We argue that evaluating how animals navigate trade-offs between investing in long-term relationships versus optimizing interactions in their wider social networks will be a crucial avenue for future research.
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... Estudios recientes relacionan las fluctuaciones de la temperatura media anual con el tamaño corporal de los neandertales y Homo sapiens. Basándose en la hipótesis del estrés ambiental, 4 el que los homínidos más corpulentos viviesen en las regiones más frías estaba en línea con la regla biogeográfica de Bergmann 5 y estudios previos sobre homínidos y otros animales (Ruff, 1994;González-Forero y Gardner, 2018). Según esta hipótesis, el estrés térmico derivado de las temperaturas frías fue mitigado por la adaptación fenotípica: el cuerpo cada vez era más grande a causa de la selección natural, la plasticidad o ambas. ...
La relación esencial entre la ecología integral y la antropología evolutiva como factor fundamental en el proceso evolutivo del género Homo
Article
Full-text available
  • Dec 2024
El análisis de nuestra filogenia, basado esencialmente en los registros fósiles y arqueológicos, nos ofrece información crucial sobre la trayectoria evolutiva del género Homo. Sin embargo, el problema radica es la escasez de muestras para un análisis concluyente, ya que la diversidad de especies en muchos casos se infiere a partir de unos pocos individuos o fragmentos óseos distribuidos en amplias zonas geográficas. No obstante, el notable éxito evolutivo de nuestro género no se atribuye únicamente al incremento en la capacidad cerebral -desde Australopithecus, con un volumen cerebral aproximado de 500 cm3 a Homo sapiens entre 1500-1700 cm3- o a las variaciones genómicas. También deben considerarse otros factores, como los culturales, sociales y ecológicos. La interacción entre las distintas especies y su entorno en un ecosistema es fundamental en cualquier proceso evolutivo. Todo ajuste, regulación o interacción en el mismo, como indica Margalef, determinará el equilibrio necesario hacia la supervivencia o el declive del sistema. Para establecer un diálogo interdisciplinar entre la ecología integral y la antropología evolutiva, hemos considerado incluir las teorías cognitivas modernas como la Teoría del Compromiso Material (MET), la ciencia cognitiva (CS) y la biología cognitiva (BC). Estas teorías resaltan la importancia de considerar la cognición no solo como un proceso interno, sino como algo que está profundamente arraigado en el contexto material y social. Este artículo examina cómo el ambiente pudo influir en la evolución del género Homo y viceversa, proponiendo un análisis interdisciplinario que integra ecología ambiental, económica, social y cultural, o lo que el papa Francisco denominó ecología integral, con la antropología evolutiva.
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... Evolutionary anthropologists, linguists and cognitive scientists have suggested that changes in socio-spatial behaviour accompanied the evolution of key attributes of Homo sapiens, including our large brains, advanced cognitive capabilities and language [1][2][3]. These evolutionary models motivate research into functional relationships between spatial behaviour, social structure, cognition and communication, aligned with the socio-spatial interface framework advocated by Webber et al. [4]. ...
Beyond the here and now: hunter–gatherer socio-spatial complexity and the evolution of language
Article
Full-text available
Human evolutionary ecology stands to benefit by integrating theory and methods developed in movement ecology, and in turn, to make contributions to the broader field of movement ecology by leveraging our species’ distinct attributes. In this paper, we review data and evolutionary models suggesting that major changes in socio-spatial behaviour accompanied the evolution of language. To illustrate and explore these issues, we present a comparison of GPS measures of the socio-spatial behaviour of Hadza hunter–gatherers of northern Tanzania to those of olive baboons (Papio anubis), a comparatively small-brained primate that is also savanna-adapted. While standard spatial metrics show modest differences, measures of spatial diversity, landscape exploration and spatiotemporal displacement between individuals differ markedly. Groups of Hadza foragers rapidly accumulate a vast, diverse knowledge pool about places and things over the horizon, contrasting with the baboon’s narrower and more homogeneous pool of ecological information. The larger and more complex socio-spatial world illustrated by the Hadza is one where heightened cognitive abilities for spatial and episodic memory, navigation, perspective taking and communication about things beyond the here and now all have clear value. This article is part of the theme issue ‘The spatial–social interface: a theoretical and empirical integration’.
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The Meanings and Dividends of Man the Hunter
Preprint
  • Apr 2025
The phrase Man the Hunter is associated with sexist theories of human evolution, but wildly disparate use of the phrase has led to unnecessary scientific disagreement and popular misunderstanding. In this paper, we ask: what does Man the Hunter mean? We distinguish three historical meanings of Man the Hunter. First, in the early-to-mid 20th century, popular writers propagated ideas of human evolution that focused on hunting, men, aggression, rigid sexual divisions of labor, and immutable sex differences. These ideas gained widespread acceptance in popular circles and continue to be influential, but were rejected by anthropologists as early as the 1950s. Second, the Man the Hunter conference (1966) and resulting volume (1968) brought together hunter-gatherer scholars to synthesize how contemporary hunter-gatherers inform human prehistory. Third, Man the Hunter has referred to the field of hunter-gatherer behavioral ecology, which studies how human behavior is adapted to local environmental context, and has offered a critical foundation for present-day knowledge about hunting and gathering lifeways, and human origins, which we summarize. In the final section of the paper, we trace the historical development of these three meanings of Man the Hunter, situating their origins in evolutionary biology, ethnology, feminist studies, ethology, genetics, and other disciplines. While the latter two meanings were strongly intertwined, there was little intellectual exchange, and mostly antagonism, between these two and the earlier popularized meaning. Based on these disparate histories, conflating the three meanings of Man the Hunter should be avoided. Finally, we offer suggestions for improving scientific and popular discourse regarding Man the Hunter.
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Hominin brain size increase has emerged from within-species encephalization
Article
Full-text available
  • Nov 2024
  • P NATL ACAD SCI USA
The fact that rapid brain size increase was clearly a key aspect of human evolution has prompted many studies focusing on this phenomenon, and many suggestions as to the underlying evolutionary patterns and processes. No study to date has however separated out the contributions of change through time within vs. between hominin species while simultaneously incorporating effects of body size. Using a phylogenetic approach never applied before to paleoanthropological data, we show that relative brain size increase across ~7 My of hominin evolution arose from increases within individual species which account for an observed overall increase in relative brain size. Variation among species in brain size after accounting for this effect is associated with body mass differences but not time. In addition, our analysis also reveals that the within-species trend escalated in more recent lineages, implying an overall pattern of accelerating relative brain size increase through time.
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Re-evaluating the link between brain size and behavioural ecology in primates
Article
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Comparative studies have identified a wide range of behavioural and ecological correlates of relative brain size, with results differing between taxonomic groups, and even within them. In primates for example, recent studies contradict one another over whether social or ecological factors are critical. A basic assumption of such studies is that with sufficiently large samples and appropriate analysis, robust correlations indicative of selection pressures on cognition will emerge. We carried out a comprehensive re-examination of correlates of primate brain size using two large comparative datasets and phylogenetic comparative methods. We found evidence in both datasets for associations between brain size and ecological variables (home range size, diet and activity period), but little evidence for an effect of social group size, a correlation which has previously formed the empirical basis of the Social Brain Hypothesis. However, reflecting divergent results in the literature, our results exhibited instability across datasets, even when they were matched for species composition and predictor variables. We identify several potential empirical and theoretical difficulties underlying this instability and suggest that these issues raise doubts about inferring cognitive selection pressures from behavioural correlates of brain size. © 2017 The Author(s) Published by the Royal Society. All rights reserved.
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The social and cultural roots of whale and dolphin brains
Article
Full-text available
  • Nov 2017
  • Nat. Ecol. Evol.
Encephalization, or brain expansion, underpins humans' sophisticated social cognition, including language, joint attention, shared goals, teaching, consensus decision-making and empathy. These abilities promote and stabilize cooperative social interactions, and have allowed us to create a 'cognitive' or 'cultural' niche and colonize almost every terrestrial ecosystem. Cetaceans (whales and dolphins) also have exceptionally large and anatomically sophisticated brains. Here, by evaluating a comprehensive database of brain size, social structures and cultural behaviours across cetacean species, we ask whether cetacean brains are similarly associated with a marine cultural niche. We show that cetacean encephalization is predicted by both social structure and by a quadratic relationship with group size. Moreover, brain size predicts the breadth of social and cultural behaviours, as well as ecological factors (diversity of prey types and to a lesser extent latitudinal range). The apparent coevolution of brains, social structure and behavioural richness of marine mammals provides a unique and striking parallel to the large brains and hyper-sociality of humans and other primates. Our results suggest that cetacean social cognition might similarly have arisen to provide the capacity to learn and use a diverse set of behavioural strategies in response to the challenges of social living.Cetaceans show a similar increase in brain size as is seen in human evolution. Here, this increase is shown to be linked to an expansion in the social and ecological niche.
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Why are there so many explanations for primate brain evolution?
Article
Full-text available
The question as to why primates have evolved unusually large brains has received much attention, with many alternative proposals all supported by evidence. We review the main hypotheses, the assumptions they make and the evidence for and against them. Taking as our starting point the fact that every hypothesis has sound empirical evidence to support it, we argue that the hypotheses are best interpreted in terms of a framework of evolutionary causes (selection factors), consequences (evolutionary windows of opportunity) and constraints (usually physiological limitations requiring resolution if large brains are to evolve). Explanations for brain evolution in birds and mammals generally, and primates in particular, have to be seen against the backdrop of the challenges involved with the evolution of coordinated, cohesive, bonded social groups that require novel social behaviours for their resolution, together with the specialized cognition and neural substrates that underpin this. A crucial, but frequently overlooked, issue is that fact that the evolution of large brains required energetic, physiological and time budget constraints to be overcome. In some cases, this was reflected in the evolution of ‘smart foraging’ and technical intelligence, but in many cases required the evolution of behavioural competences (such as coalition formation) that required novel cognitive skills. These may all have been supported by a domain-general form of cognition that can be used in many different contexts. This article is part of the themed issue ‘Physiological determinants of social behaviour in animals’.
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The Descent of Man, and Selection in Relation to Sex
Chapter
  • May 2010
Introduction The nature of the following work will be best understood by a brief account of how it came to be written. During many years I collected notes on the origin or descent of man, without any intention of publishing on the subject, but...
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Darwin's Unfinished Symphony
Book
  • Sep 2018
Humans possess an extraordinary capacity for cultural production, from the arts and language to science and technology. How did the human mind—and the uniquely human ability to devise and transmit culture—evolve from its roots in animal behavior? This book presents a new theory of human cognitive evolution. It reveals how culture is not just the magnificent end product of an evolutionary process that produced a species unlike all others—it is also the key driving force behind that process. The book shows how the learned and socially transmitted activities of our ancestors shaped our intellects through accelerating cycles of evolutionary feedback. The truly unique characteristics of our species—such as our intelligence, language, teaching, and cooperation—are not adaptive responses to predators, disease, or other external conditions. Rather, humans are creatures of their own making. The book explains how animals imitate, innovate, and have remarkable traditions of their own. It traces our rise from scavenger apes in prehistory to modern humans able to design iPhones, dance the tango, and send astronauts into space. This book tells the story of the painstaking fieldwork, the key experiments, the false leads, and the stunning scientific breakthroughs that led to this new understanding of how culture transformed human evolution. It is the story of how Darwin's intellectual descendants picked up where he left off and took up the challenge of providing a scientific account of the evolution of the human mind.
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Theoretical Approaches in Evolutionary Ecology: Environmental Feedback as a Unifying Perspective
Article
  • Nov 2017
Evolutionary biology and ecology have a strong theoretical underpinning, and this has fostered a variety of modeling approaches. A major challenge of this theoretical work has been to unravel the tangled feedback loop between ecology and evolution. This has prompted the development of two main classes of models. While quantitative genetics models jointly consider the ecological and evolutionary dynamics of a focal population, a separation of timescales between ecology and evolution is assumed by evolutionary game theory, adaptive dynamics, and inclusive fitness theory. As a result, theoretical evolutionary ecology tends to be divided among different schools of thought, with different toolboxes and motivations. My aim in this synthesis is to highlight the connections between these different approaches and clarify the current state of theory in evolutionary ecology. Central to this approach is to make explicit the dependence on environmental dynamics of the population and evolutionary dynamics, thereby materializing the eco-evolutionary feedback loop. This perspective sheds light on the interplay between environmental feedback and the timescales of ecological and evolutionary processes. I conclude by discussing some potential extensions and challenges to our current theoretical understanding of eco-evolutionary dynamics.
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Coevolution of cultural intelligence, extended life history, sociality, and brain size in primates
Article
  • Jul 2017
Explanations for primate brain expansion and the evolution of human cognition and culture remain contentious despite extensive research. While multiple comparative analyses have investigated variation in brain size across primate species, very few have addressed why primates vary in how much they use social learning. Here, we evaluate the hypothesis that the enhanced reliance on socially transmitted behavior observed in some primates has coevolved with enlarged brains, complex sociality, and extended lifespans. Using recently developed phylogenetic comparative methods we show that, across primate species, a measure of social learning proclivity increases with absolute and relative brain volume, longevity (specifically reproductive lifespan), and social group size, correcting for research effort. We also confirm relationships of absolute and relative brain volume with longevity (both juvenile period and reproductive lifespan) and social group size, although longevity is generally the stronger predictor. Relationships between social learning, brain volume, and longevity remain when controlling for maternal investment and are therefore not simply explained as a by-product of the generally slower life history expected for larger brained species. Our findings suggest that both brain expansion and high reliance on culturally transmitted behavior coevolved with sociality and extended lifespan in primates. This coevolution is consistent with the hypothesis that the evolution of large brains, sociality, and long lifespans has promoted reliance on culture, with reliance on culture in turn driving further increases in brain volume, cognitive abilities, and lifespans in some primate lineages.
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