The cultural origins of human cognition

Fitting a line to a bivariate dataset can be a deceptively complex problem, and there has been much debate on this issue in the literature. In this review, we describe for the practitioner the essential features of line-fitting methods for estimating the relationship between two variables: what methods are commonly used, which method should be used when, and how to make inferences from these lines to answer common research questions. A particularly important point for line-fitting in allometry is that usually, two sources of error are present (which we call measurement and equation error), and these have quite different implications for choice of line-fitting method. As a consequence, the approach in this review and the methods presented have subtle but important differences from previous reviews in the biology literature. Linear regression, major axis and standardised major axis are alternative methods that can be appropriate when there is no measurement error. When there is measurement error, this often needs to be estimated and used to adjust the variance terms in formulae for line-fitting. We also review line-fitting methods for phylogenetic analyses. Methods of inference are described for the line-fitting techniques discussed in this paper. The types of inference considered here are testing if the slope or elevation equals a given value, constructing confidence intervals for the slope or elevation, comparing several slopes or elevations, and testing for shift along the axis amongst several groups. In some cases several methods have been proposed in the literature. These are discussed and compared. In other cases there is little or no previous guidance available in the literature. Simulations were conducted to check whether the methods of inference proposed have the intended coverage probability or Type I error. We identified the methods of inference that perform well and recommend the techniques that should be adopted in future work.

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Bivariate line-fitting methods for allometry.

The human brain is often considered to be the most cognitively capable among mammalian brains and to be much larger than expected for a mammal of our body size. Although the number of neurons is generally assumed to be a determinant of computational power, and despite the widespread quotes that the human brain contains 100 billion neurons and ten times more glial cells, the absolute number of neurons and glial cells in the human brain remains unknown. Here we determine these numbers by using the isotropic fractionator and compare them with the expected values for a human-sized primate. We find that the adult male human brain contains on average 86.1 +/- 8.1 billion NeuN-positive cells ("neurons") and 84.6 +/- 9.8 billion NeuN-negative ("nonneuronal") cells. With only 19% of all neurons located in the cerebral cortex, greater cortical size (representing 82% of total brain mass) in humans compared with other primates does not reflect an increased relative number of cortical neurons. The ratios between glial cells and neurons in the human brain structures are similar to those found in other primates, and their numbers of cells match those expected for a primate of human proportions. These findings challenge the common view that humans stand out from other primates in their brain composition and indicate that, with regard to numbers of neuronal and nonneuronal cells, the human brain is an isometrically scaled-up primate brain.

/pdf/equal-numbers-of-neuronal-and-nonneuronal-cells-make-the-1qeo5k9sgo.pdf

Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled‐up primate brain

https://www.cell.com/cell/pdf/S0092-8674(13)00533-3.pdf

Dynamics of Hippocampal Neurogenesis in Adult Humans

Social cognition in humans is distinguished by psychological processes that allow us to make inferences about what is going on inside other people—their intentions, feelings, and thoughts. Some of these processes likely account for aspects of human social behavior that are unique, such as our culture and civilization. Most schemes divide social information processing into those processes that are relatively automatic and driven by the stimuli, versus those that are more deliberative and controlled, and sensitive to context and strategy. These distinctions are reflected in the neural structures that underlie social cognition, where there is a recent wealth of data primarily from functional neuroimaging. Here I provide a broad survey of the key abilities, processes, and ways in which to relate these to data from cognitive neuroscience.

/pdf/the-social-brain-neural-basis-of-social-knowledge-31lnyrce96.pdf

The Social Brain: Neural Basis of Social Knowledge

For over a century, various neuroanatomical measures have been employed as assays of cognitive ability in comparative studies. Nevertheless, it is still unclear whether these measures actually correspond to cognitive ability. A recent meta-analysis of cognitive performance of a broad set of primate species has made it possible to provide a quantitative estimate of general cognitive ability across primates. We find that this estimate is not strongly correlated with neuroanatomical measures that statistically control for a possible effect of body size, such as encephalization quotient or brain size residuals. Instead, absolute brain size measures were the best predictors of primate cognitive ability. Moreover, there was no indication that neocortex-based measures were superior to measures based on the whole brain. The results of previous comparative studies on the evolution of intelligence must be reviewed with this conclusion in mind.

Overall Brain Size, and Not Encephalization Quotient, Best Predicts Cognitive Ability across Non-Human Primates

The human brain stands out among mammals by being unusually large. The expensive-tissue hypothesis explains its evolution by proposing a trade-off between the size of the brain and that of the digestive tract, which is smaller than expected for a primate of our body size. Although this hypothesis is widely accepted, empirical support so far has been equivocal. Here we test it in a sample of 100 mammalian species, including 23 primates, by analysing brain size and organ mass data. We found that, controlling for fat-free body mass, brain size is not negatively correlated with the mass of the digestive tract or any other expensive organ, thus refuting the expensive-tissue hypothesis. Nonetheless, consistent with the existence of energy trade-offs with brain size, we find that the size of brains and adipose depots are negatively correlated in mammals, indicating that encephalization and fat storage are compensatory strategies to buffer against starvation. However, these two strategies can be combined if fat storage does not unduly hamper locomotor efficiency. We propose that human encephalization was made possible by a combination of stabilization of energy inputs and a redirection of energy from locomotion, growth and reproduction.

Energetics and the evolution of human brain size

The Expensive Brain: a framework for explaining evolutionary changes in brain size.

In the ongoing discussion about brain evolution in vertebrates, the main interest has shifted from theories focusing on energy balance to theories proposing social or ecological benefits of enhanced intellect. With the availability of a wealth of new data on basal metabolic rate (BMR) and brain size and with the aid of reliable techniques of comparative analysis, we are able to show that in fact energetics is an issue in the maintenance of a relatively large brain, and that brain size is positively correlated with the BMR in mammals, controlling for body size effects. We conclude that attempts to explain brain size variation in different taxa must consider the ability to sustain the energy costs alongside cognitive benefits.

/pdf/metabolic-costs-of-brain-size-evolution-p4luuyezfm.pdf

Metabolic costs of brain size evolution

https://www.zora.uzh.ch/id/eprint/6063/1/Isleretal2008forZORA.pdf

Karin Isler

Papers

Overall Brain Size, and Not Encephalization Quotient, Best Predicts Cognitive Ability across Non-Human Primates

Energetics and the evolution of human brain size

The Expensive Brain: a framework for explaining evolutionary changes in brain size.

Metabolic costs of brain size evolution

Endocranial volumes of primate species: scaling analyses using a comprehensive and reliable data set