Testing the Cultural Brain Hypothesis
Testing the Cultural Brain Hypothesis Ryutaro Uchiyama & Michael Muthukrishna London School of Economics and Political Science Department of Psychological and Behavioural Science
Evolution of the social brain
Social Brain Hypothesis
nthropol. 2003.32:163-181. Downloaded from www.annualreviews.org rovided by Cornell University on 09/10/17. For personal use only.
THE SOCIAL BRAIN
165
Dunbar 2003
Figure 1 Mean social group size for individual primate taxa (principally, one species per genus) plotted against relative neocortex volume (indexed as neocortex volume divided by the volume of the rest of the brain). Simian (solid symbols) and hominoid (open symbols) taxa are shown separately. The datapoint for humans is that obtained by Dunbar (1992a). With a logged axis, neocortex ratio is mathematically identical to the
… a complex problem
[(Figure_1)TD$IG]
Review
in Cognitive Sciences … a complexTrends problem
November 2012, Vol. 16, No. 11
Kennedy & Adolphs, 2012 Figure 1. The social brain: from structures to networks. (a) Structures. There are a number of brain regions, only a subset of which are depicted here, that are now known to
… a complex problem
“[relative brain size] correlates with many indices of social complexity, including social group size, number of females in the group, grooming clique size, frequency of coalitions, male mating strategies, the prevalence of social play, the frequency of tactical deception, and the frequency of social learning.” Dunbar & Shultz, 2007
… a complex problem
“[relative brain size] correlates with many indices of social complexity, including social group size, number of females in the group, grooming clique size, frequency of coalitions, male mating strategies, the prevalence of social play, the frequency of tactical deception, and the frequency of social learning.” Dunbar & Shultz, 2007 Downloaded from http://rspb.royalsocietypublishing.org/ on September 10, 2017
Proc. R. Soc. B (2012) 279, 2157–2162 doi:10.1098/rspb.2011.2574 Published online 1 February 2012
Orbital prefrontal cortex volume predicts social network size: an imaging study of individual differences in humans Joanne Powell1,2, Penelope A. Lewis4, Neil Roberts1,3, Marta Garcı´a-Fin˜ana2 and R. I. M. Dunbar5,* 1
Magnetic Resonance and Image Analysis Research Centre (MARIARC ), and 2Department of Biostatistics, University of Liverpool, Liverpool L69 3BX, UK 3 Clinical Research Imaging Centre (CRIC ), College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK 4 School of Psychological Sciences, University of Manchester, Manchester M13 9PL, UK 5 British Academy Centenary Research Project, Institute of Cognitive and Evolutionary Anthropology, University of Oxford, Oxford OX2 6PN, UK
2012
2010
… a complex problem
• • • • • • • •
Brain size Brain architecture Brain development Body size Sociality Group size Life history Intelligence
}
Covariation
… a complex problem
• • • • • • • •
Brain size Brain architecture Brain development Body size Sociality Group size Life history Intelligence
}
Covariation
Trying to crack an inverse problem. Useful strategy: to hypothesize a generative process that explains the data.
Cultural Brain Hypothesis Muthukrishna, Doebeli, Chudek, & Henrich (under review)
Cultural Brain Hypothesis Muthukrishna, Doebeli, Chudek, & Henrich (under review)
Length of juvenile period Brain size Adaptive knowledge
Group size / sociality
Reliance on social learning
Efficiency of independent (asocial) learning Reproductive skew Social knowledge transmission fidelity Productivity of environment
Model parameters
Cultural Brain Hypothesis Muthukrishna, Doebeli, Chudek, & Henrich (under review)
Cultural Brain Hypothesis
Muthukrishna, D
Muthukrishna, Doebeli, Chudek, & Henrich (under review)
(A) Theoretical
Figure 10. Brain size and group size. (A) Our model’s empiri
size and group size (𝒓 = 𝟎. 𝟒𝟐 [Asocial], 𝒓 = 𝟎. 𝟕𝟐 [Social]). (B
comparison plot, but we have generated one from his data. Cultural Brain Hypothesis Muthukrishna, Doebeli, Chudek, & Henrich (under review)
(A) Theoretical
Figure 13. Group size and the juvenile period. (A) Our mo
Strengths of the model •
Makes specific causal predictions about the relationships among strongly covarying variables.
•
Predictions for the whole gamut of sociality, from solitary species to the most gregarious ones.
•
The human take-off (i.e., cumulative CE) can be treated as one trajectory that is encompassed in a more general evolutionary dynamic.
to cumulative cultural evolution. In Figure 14 below, we plot b Cultural Brain Hypothesis Muthukrishna, Doebeli, Chudek, & Henrich (under review)
acquiring that amount of information.
Analysis of primate data
Analysis of primate data •
Data gathered from Reader, Hager & Laland (2011) and life history databases •
(so similar to Street, Navarette, Reader, & Laland, 2017)
•
PGLS, then select best-fit models based on AIC
•
A variant of the method of phylogenetic path analysis proposed by von Hardenberg & Gonzalez-Voyer (2013). •
•
(Proper path analysis based on conditional independence can be computationally intense)
Display the total set of relationships among all selected models.
Fox, Muthukrishna, Shultz (in press)
Fox, Muthukrishna, Shultz (in press)
Group2
0.97 0.99
Body
1 8 0.
0.62
0.89
79 0.
8 8 . 0
Brain
1.0
56 0.
La-tude
Social repertoire
Diet
Next steps: Results inconclusive w.r.t. their degree of consistency with the CBH, so… Use a computing cluster to do full path analysis Look at other taxa, including relatively asocial ones
Evolution of the social brain
Social Brain Hypothesis
nthropol. 2003.32:163-181. Downloaded from www.annualreviews.org rovided by Cornell University on 09/10/17. For personal use only.
THE SOCIAL BRAIN
165
Dunbar 2003
Figure 1 Mean social group size for individual primate taxa (principally, one species per genus) plotted against relative neocortex volume (indexed as neocortex volume divided by the volume of the rest of the brain). Simian (solid symbols) and hominoid (open symbols) taxa are shown separately. The datapoint for humans is that obtained by Dunbar (1992a). With a logged axis, neocortex ratio is mathematically identical to the
… a complex problem
[(Figure_1)TD$IG]
Review
in Cognitive Sciences … a complexTrends problem
November 2012, Vol. 16, No. 11
Kennedy & Adolphs, 2012 Figure 1. The social brain: from structures to networks. (a) Structures. There are a number of brain regions, only a subset of which are depicted here, that are now known to
… a complex problem
“[relative brain size] correlates with many indices of social complexity, including social group size, number of females in the group, grooming clique size, frequency of coalitions, male mating strategies, the prevalence of social play, the frequency of tactical deception, and the frequency of social learning.” Dunbar & Shultz, 2007
… a complex problem
“[relative brain size] correlates with many indices of social complexity, including social group size, number of females in the group, grooming clique size, frequency of coalitions, male mating strategies, the prevalence of social play, the frequency of tactical deception, and the frequency of social learning.” Dunbar & Shultz, 2007 Downloaded from http://rspb.royalsocietypublishing.org/ on September 10, 2017
Proc. R. Soc. B (2012) 279, 2157–2162 doi:10.1098/rspb.2011.2574 Published online 1 February 2012
Orbital prefrontal cortex volume predicts social network size: an imaging study of individual differences in humans Joanne Powell1,2, Penelope A. Lewis4, Neil Roberts1,3, Marta Garcı´a-Fin˜ana2 and R. I. M. Dunbar5,* 1
Magnetic Resonance and Image Analysis Research Centre (MARIARC ), and 2Department of Biostatistics, University of Liverpool, Liverpool L69 3BX, UK 3 Clinical Research Imaging Centre (CRIC ), College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK 4 School of Psychological Sciences, University of Manchester, Manchester M13 9PL, UK 5 British Academy Centenary Research Project, Institute of Cognitive and Evolutionary Anthropology, University of Oxford, Oxford OX2 6PN, UK
2012
2010
… a complex problem
• • • • • • • •
Brain size Brain architecture Brain development Body size Sociality Group size Life history Intelligence
}
Covariation
… a complex problem
• • • • • • • •
Brain size Brain architecture Brain development Body size Sociality Group size Life history Intelligence
}
Covariation
Trying to crack an inverse problem. Useful strategy: to hypothesize a generative process that explains the data.
Cultural Brain Hypothesis Muthukrishna, Doebeli, Chudek, & Henrich (under review)
Cultural Brain Hypothesis Muthukrishna, Doebeli, Chudek, & Henrich (under review)
Length of juvenile period Brain size Adaptive knowledge
Group size / sociality
Reliance on social learning
Efficiency of independent (asocial) learning Reproductive skew Social knowledge transmission fidelity Productivity of environment
Model parameters
Cultural Brain Hypothesis Muthukrishna, Doebeli, Chudek, & Henrich (under review)
Cultural Brain Hypothesis
Muthukrishna, D
Muthukrishna, Doebeli, Chudek, & Henrich (under review)
(A) Theoretical
Figure 10. Brain size and group size. (A) Our model’s empiri
size and group size (𝒓 = 𝟎. 𝟒𝟐 [Asocial], 𝒓 = 𝟎. 𝟕𝟐 [Social]). (B
comparison plot, but we have generated one from his data. Cultural Brain Hypothesis Muthukrishna, Doebeli, Chudek, & Henrich (under review)
(A) Theoretical
Figure 13. Group size and the juvenile period. (A) Our mo
Strengths of the model •
Makes specific causal predictions about the relationships among strongly covarying variables.
•
Predictions for the whole gamut of sociality, from solitary species to the most gregarious ones.
•
The human take-off (i.e., cumulative CE) can be treated as one trajectory that is encompassed in a more general evolutionary dynamic.
to cumulative cultural evolution. In Figure 14 below, we plot b Cultural Brain Hypothesis Muthukrishna, Doebeli, Chudek, & Henrich (under review)
acquiring that amount of information.
Analysis of primate data
Analysis of primate data •
Data gathered from Reader, Hager & Laland (2011) and life history databases •
(so similar to Street, Navarette, Reader, & Laland, 2017)
•
PGLS, then select best-fit models based on AIC
•
A variant of the method of phylogenetic path analysis proposed by von Hardenberg & Gonzalez-Voyer (2013). •
•
(Proper path analysis based on conditional independence can be computationally intense)
Display the total set of relationships among all selected models.
Fox, Muthukrishna, Shultz (in press)
Fox, Muthukrishna, Shultz (in press)
Group2
0.97 0.99
Body
1 8 0.
0.62
0.89
79 0.
8 8 . 0
Brain
1.0
56 0.
La-tude
Social repertoire
Diet
Next steps: Results inconclusive w.r.t. their degree of consistency with the CBH, so… Use a computing cluster to do full path analysis Look at other taxa, including relatively asocial ones
