We have perceptual biases to see man-made objects; maybe extended exposure to manufactured environments in our cities has changed the way we see the world

A perceptual bias for man-made objects in humans. Ahamed Miflah Hussain Ismail, Joshua A. Solomon, Miles Hansard and Isabelle Mareschal. Proceedings of the Royal Society B, Volume 286, Issue 1914, November 6 2019. https://doi.org/10.1098/rspb.2019.1492

Abstract: Ambiguous images are widely recognized as a valuable tool for probing human perception. Perceptual biases that arise when people make judgements about ambiguous images reveal their expectations about the environment. While perceptual biases in early visual processing have been well established, their existence in higher-level vision has been explored only for faces, which may be processed differently from other objects. Here we developed a new, highly versatile method of creating ambiguous hybrid images comprising two component objects belonging to distinct categories. We used these hybrids to measure perceptual biases in object classification and found that images of man-made (manufactured) objects dominated those of naturally occurring (non-man-made) ones in hybrids. This dominance generalized to a broad range of object categories, persisted when the horizontal and vertical elements that dominate man-made objects were removed and increased with the real-world size of the manufactured object. Our findings show for the first time that people have perceptual biases to see man-made objects and suggest that extended exposure to manufactured environments in our urban-living participants has changed the way that they see the world.

3. Discussion

We examined biases in people's classification of different types of natural images. In experiment 1, we found that when an ambiguous hybrid image was formed of structures from two different image categories, classification was biased towards the man-made categories (houses and vehicles) rather than towards the non-man-made categories (animals and flowers). This ‘man-made bias’ is not a bias towards any specific spatial frequency content. Additional experiments (see electronic supplementary material, §S5) revealed that the bias is (1) common across urban-living participants in different countries, and (2) not simply a response bias. The results of experiment 2 replicated and extended the results of experiment 1 to demonstrate that the bias was affected by the real-world size of man-made objects (but not animal size), with a stronger bias for larger man-made objects. Reduced biases for small man-made objects may be explained by shared feature statistics (e.g. curvature) between small (but not large) man-made objects and both small and large animals [22]. However, we highlight that the bias is not only for larger man-made objects, because we still obtained man-made biases even when small man-made objects were paired with animals. We propose that this man-made bias is the result of expectations about the world that favour the rapid interpretation of complex images as man-made. Given that the visual diet of our urban participants is rich in man-made objects, our results are consistent with a Bayesian formulation of perceptual biases whereby ambiguous stimuli result in biases towards frequently occurring attributes [5].

We stress that the man-made bias is not merely a manifestation of the relative insensitivity to tilted (i.e. neither vertical nor horizontal) contours, commonly known as the ‘oblique effect’ [23,24]. Our participants exhibited biases in favour of man-made objects even when cardinal orientations had been filtered out of them. This occurred despite the fact that the power spectra of houses and vehicles were largely dominated by cardinal orientations, whereas those of animals and flowers were largely isotropic (electronic supplementary material, §S6 and figure S6). Whereas the oblique effect was established using narrow-band luminance gratings on otherwise uniform backgrounds, it cannot be expected to influence the perception of broad-band, natural images, such as those used in our experiments. Indeed, if anything, detection thresholds for cardinally oriented structure tend to be higher than those for tilted structure, when those structures are superimposed against broad-band masking stimuli [25].

We note however that we do not claim that intercardinal filtering removes all easily detectable structures from the images in man-made categories. Indeed, houses and vehicles almost certainly contain longer, straighter and/or more rectilinear contours than flowers and animals. Therefore, we also performed a detection experiment to examine if increased sensitivity to structural features that might dominate man-made categories could account for the man-made biases by measuring detection thresholds (see electronic supplementary material, §S7). It revealed that houses and vehicles did not have lower detection thresholds (i.e. the minimum root mean square contrast required to reliably detect images from each category) than images from the non-man-made categories. This finding provides strong ammunition against any sensitivity-based model of the man-made bias. Whatever structure is contained in the unfiltered images of houses and vehicles, that structure proved to be, on average, no easier to detect than the structure contained in unfiltered images of animals and flowers.

The lack of a bias for animals and a difference in sensitivity between image categories appears to contradict past findings from Crouzet et al. [15], who report that the detection of animals precedes that of vehicles using a saccadic choice task. However, comparing contrast sensitivity (detection) to saccadic reaction (decision) is problematic, especially with high contrast stimuli [26]. Secondly, the difference could be attributed to the background of images that must be classified. While Crouzet et al. [15] controlled contextual masking effects on image category by presenting images occurring in both man-made and natural contexts, our images in the detection experiment were embedded in white noise with the same amplitude spectrum as the image (electronic supplementary material, figure S7). As Hansen & Loschky [27] report, the type of mask used (e.g. using a mask sharing only the amplitude spectrum with the image versus one sharing both amplitude and phase information with the image) affects masking strength. It is still unclear which type of masks work best across different image categories [27].

Although we carefully controlled the spatial frequency content of our stimuli in experiments 1 and 2, it is conceivable that the bias towards man-made objects arises at a level intermediate between the visual system's extraction of these low-level features and its classification of stimuli into semantic categories. To investigate whether any known ‘mid-level’ features might be responsible for the bias towards man-made objects, we repeated experiments 1 and 2 with HMAX, a computer-based image classifier developed on the basis of the neural computations mediating object recognition in the ventral stream of the visual cortex [28,29], allowing it to exploit mid-level visual features in its decision processes (see electronic supplementary material, §§S4 and S10). We also classified hybrids from experiment 2 with the AlexNet Deep Convolutional Neural Network (DNN), which could potentially capture more mid-level features [30] (see electronic supplementary material, §S9). Results indicate that human observers' bias for man-made images seems not to be a simple function of the lower and mid-level features exploited by conventional image-classification techniques.

However, we must concede that HMAX and AlexNet do not account for all possible intermediate feature differences between object categories, for instance 3D viewpoint [31]. If we are frequently exposed to different viewpoints of man-made but not non-man-made objects, this might lead to a man-made bias too. Therefore, more experiments where categorical biases can be measured after equating object categories for intermediate features are needed to pinpoint the level at which the man-made bias occurs. Indeed, the bias for man-made objects might have nothing to do with visual features at all. It may stem from (non-visual) expectations that exploit regularities of the visual environment [6]. To be clear: we are speculating that the preponderance of man-made objects in the environment of urban participants could bias their perception such that it becomes efficient at processing these types of stimuli.

When might such a bias develop? Categorical concepts and dedicated neural mechanisms for specific object categories seem to develop after birth, with exposure [32–34]. This suggests that expectations for object categories are likely to develop with exposure too. However, if expectations occur at the level of higher-level features associated with object categories, we cannot discount the possibility that expectations may be innate. For instance, prior expectations for low-level orientation has been attributed to a hardwired non-uniformity in orientation preference of V1 neurons [6]. Similarly, we may have inhomogeneous neural mechanisms for higher-level features too. Recently identified neural mechanisms selectively encoding higher-level features of objects (e.g. uprightness [35]) add to this speculation. It remains to be determined when and how man-made biases arise and whether they are adaptable to changes in the environment. Further, the perceptual bias that we demonstrate may be altered by testing conditions, which limit its generalizability. For instance, low spatial frequency precedence in image classification is altered by the type of classification that must be performed (e.g. classifying face hybrids for its gender versus expression) [36].

The Chinese Economic Miracle—Half of China’s river water and 90% of its groundwater is unfit to drink; Beijing has roughly the same amount of water per person (145 cubic meters) as Saudi Arabia

Chapter 6. The Chinese Economic Miracle: How Much Is Real… How Much Is a Mirage? Michael Beckley. Dec 2019, adapted from his book Unrivaled: Why American Will Remain the World’s Sole Superpower (Cornell University Press, 2018). https://www.aei.org/wp-content/uploads/2019/12/Chapter-6-The-Chinese-Economic-Miracle-How-Much-Is-Real%E2%80%A6-How-Much-Is-a-Mirage.pdf

Abstract: China’s economic growth over the past three decades has been spectacular, but the veneer of doubledigit growth rates has masked gaping liabilities that constrain China’s ability to close the wealth gap with the United States. China has achieved high growth at high costs, and now the costs are rising while growth is slowing. New data that accounts for these costs reveals that the United States is several times wealthier than China, and the gap may be growing by trillions of dollars every year.

Introduction
This conclusion may surprise many people, given that China has a bigger GDP, a higher investment rate, larger trade flows, and a faster economic growth rate than the United States. How can China outproduce, outinvest, and outtrade the United States—and own nearly $1.2 trillion in US debt—yet still have substantially less wealth?
The reason is that China’s economy is big but inefficient. It produces vast output but at an enormous expense. Chinese businesses suffer from chronically high production costs, and China’s 1.4 billion people impose substantial welfare and security burdens. The United States, by contrast, is big and efficient. American businesses are among the most productive in the world, and with four times fewer people than China, the United States has much lower welfare and security costs.
GDP and other standard measures of economic heft ignore these costs and create the false impression that China is overtaking the United States economically. In reality, China’s economy is barely keeping pace as the burden of propping up loss-making companies and feeding, policing, protecting, and cleaning up after one-fifth of humanity erodes China’s stocks of wealth.

The Real Wealth of Nations
For decades, analysts have measured national wealth in gross rather than net terms, relying primarily on GDP and its components, such as trade and financial flows and investment spending. These gross indicators, however, overstate the wealth of populous countries because they count the benefits of having a large workforce but not the costs of having many people to feed, police, protect, and serve. These costs add up. In fact, they consume most of the resources in every nation. Analysts, therefore, must deduct them to accurately assess the wealth of nations.

...

Natural Capital
The main elements of natural capital are water, energy resources, and arable land, all of which are
necessary to sustain life and power agriculture and industry. The United States has 10% more
renewable freshwater than China, and the actual gap is much larger, because half of China’s river
water and 90% of its groundwater is unfit to drink, and 25% of China’s river water and 60% of its
groundwater is so polluted that the Chinese government has deemed it “unfit for human contact” and
unusable even for agriculture or industry.
China’s per capita availability of water is less than one-quarter of the United States’ and less than
one-third the world’s average, and roughly one-third of China’s provinces and two-thirds of its major
cities suffer from extreme water scarcity. Beijing, for example, has roughly the same amount of water per person (145 cubic meters) as Saudi Arabia. Dealing with water scarcity costs China roughly $140 billion per year in government expenditures and reduced productivity versus $12 billion for the United States.
The United States has three times as much oil and natural gas as China and twice as much coal. China heavily subsidizes its renewable energy and nuclear power industries, but both combined still
account for less than 5% of China’s energy use compared to 12% of the United States’.
China has large reserves of shale oil and natural gas, but it has not been able to tap them and may
never do so. One reason is that China’s shale deposits were left behind by prehistoric lakes and,
consequently, have rock layers that are more ductile and less amenable to hydraulic fracturing than
the brittle marine shales in North America. Another reason is that China lacks the water necessary
for fracking. Each shale-gas well requires fifteen thousand tons of water a year to run, and China
would need to drill thousands of wells a year to launch a successful industry. China has nowhere near that amount of water located close to its major shale basins, which are concentrated in Jilin and Liaoning, two of China’s driest provinces.
China currently depletes $400 billion of its energy resources per year and pays foreign countries
another $500 billion in energy imports, whereas US annual depletion and net import costs are
currently $140 billion and $120 billion respectively. This divergence in energy fortunes is likely to
expand in the decades ahead, because the United States will become a net energy exporter around
2025, whereas China, already the world’s largest net energy importer, will import 80% of its oil and
45% of its natural gas.
Finally, the United States has 45% more arable land than China, and again the true size of the gap is probably much larger because large chunks of China’s farmland are too polluted, desiccated, or both to support agriculture. According to a recent Chinese government study, water pollution has destroyed nearly 20% of China’s arable land, an area the size of Belgium. An additional 1 million square miles of China’s farmland has become desert, forcing the resettlement of 24,000 villages and pushing the edge of the Gobi Desert to within 150 miles of Beijing. In 2008, China became a net importer of grain, breaking its traditional policy of self-sufficiency, and in 2011 China became the world’s largest importer of agricultural products. The United States, by contrast, is the world’s top food exporter and China’s top supplier.

Cerebral blood flow rates in recent great apes are greater than in Australopithecus species that had equal or larger brains

Cerebral blood flow rates in recent great apes are greater than in Australopithecus species that had equal or larger brains. Roger S. Seymour, Vanya Bosiocic, Edward P. Snelling, Prince C. Chikezie, Qiaohui Hu, Thomas J. Nelson, Bernhard Zipfel and Case V. Miller. Volume 286, Issue 1915, November 13 2019. https://doi.org/10.1098/rspb.2019.2208

Abstract: Brain metabolic rate (MR) is linked mainly to the cost of synaptic activity, so may be a better correlate of cognitive ability than brain size alone. Among primates, the sizes of arterial foramina in recent and fossil skulls can be used to evaluate brain blood flow rate, which is proportional to brain MR. We use this approach to calculate flow rate in the internal carotid arteries (Q˙ICA), which supply most of the primate cerebrum. Q˙ICA is up to two times higher in recent gorillas, chimpanzees and orangutans compared with 3-million-year-old australopithecine human relatives, which had equal or larger brains. The scaling relationships between Q˙ICA and brain volume (Vbr) show exponents of 1.03 across 44 species of living haplorhine primates and 1.41 across 12 species of fossil hominins. Thus, the evolutionary trajectory for brain perfusion is much steeper among ancestral hominins than would be predicted from living primates. Between 4.4-million-year-old Ardipithecus and Homo sapiens, Vbr increased 4.7-fold, but Q˙ICA increased 9.3-fold, indicating an approximate doubling of metabolic intensity of brain tissue. By contrast, Q˙ICA is proportional to Vbr among haplorhine primates, suggesting a constant volume-specific brain MR.

[Q with a dot is first derivative of Q (rate of change with time, in this case)]

1. Introduction

Brain size is the usual measure in discussions of the evolution of cognitive ability among primates, despite recognized shortcomings [1]. Although absolute brain size appears to correlate better with cognitive ability than encephalization quotient, progression index or neocortex ratio [2,3], an even better correlate might be brain metabolic rate (MR), because it represents the energy cost of neurological function. However, brain MR is difficult to measure directly in living primates and impossible in extinct ones.

One solution to the problem has been to measure oxygen consumption rates and glucose uptake rates on living mammals in relation to brain size and then apply the results to brain sizes of living and extinct primates. Because physiological rates rarely relate linearly to volumes or masses of tissues, any comparison requires allometric analysis. For example, brain MR can be analysed in relation to endocranial volume (≈ brain volume, Vbr) with an allometric equation of the form, MR = aVbrb, where a is the elevation (or scaling factor, indicating the height of the curve) and b is the scaling exponent (indicating the shape of the curve on arithmetic axes). If b = 1.0, then MR is directly proportional to brain size. If b is less than 1, then MR increases with brain size, but the metabolic intensity per unit volume of neural tissue decreases. If b is greater than 1, the metabolic intensity of neural tissue increases. The exponent for brain MR measured as oxygen consumption and glucose use across several mammalian species is approximately 0.86, and the exponent for cortical brain blood flow rate in mammals is between 0.81 and 0.87 [4,5]. The similarity of the exponents indicates that blood flow rate is a good proxy for brain MR in mammals in general. The exponents are less than 1.0, which shows that brain MR and blood flow rate increase with brain size but with decreasing metabolic and perfusion intensities of the neural tissue.

Recent studies show that blood flow rate in the internal carotid artery (Q˙ICA)$({\dot{Q}}_{\mathrm{ICA}})$ can be calculated from the size of the carotid foramen through which it passes to the brain [6]. The artery occupies the foramen lumen almost entirely [7–9], therefore defining the outer radius of the artery (ro), from which inner lumen radius (ri) can be estimated, assuming that arterial wall thickness (ro – ri) is a constant ratio (w) with lumen radius (w = (ro – ri)/ri), according to the law of Laplace. The haemodynamic equation used to calculate Q˙ICA${\dot{Q}}_{\mathrm{ICA}}$ is referred to as the ‘shear stress equation’, and attributed to Poiseuille: Q˙=(τπr3i)/(4η)$\dot{Q}=(\tau \pi r_i^3)/(4\eta )$, where Q˙$\dot{Q}$ is the blood flow rate (cm3 s−1), τ is the wall shear stress (dyn cm−2), ri is the arterial lumen radius (cm) and η is the blood viscosity (dyn s cm−2) [10]. The technique was validated in mice, rats and humans, but was initially criticized [11], defended [12] and subsequently accepted [13]. However, the calculations involved three questionable assumptions: flow in the cephalic arteries conforms to Poiseuille flow theory, arterial wall shear stress can be calculated accurately from body mass (although there is no clear functional relationship between them) and the arterial wall thickness-to-lumen radius ratio (w) was a certain constant derived from only two values in the literature.

We have now made significant advancements to the initial methodology by replacing the shear stress equation, and its assumptions, with a new equation derived empirically from a meta-analysis of Q˙$\dot{Q}$ versus ri in 30 studies of seven cephalic arteries of six mammalian genera, arriving at an allometric, so-called ‘empirical equation’, Q˙$\dot{Q}$ = 155 ri2.49 (R2 = 0.94) [14]. The equation is based on stable cephalic flow rates, which vary little between rest, intense physical activity, mental exercise or sleep [14]. The equation also eliminates reliance on the somewhat tenuous estimation of arterial wall shear stress from body mass. We have also improved the calculation with a more extensive re-evaluation of carotid arterial wall thickness ratio (w = 0.30) from 14 imaging studies on humans (electronic supplementary material, text and table S1 for data and references). The present investigation implements these recent methodological advancements and re-evaluates the scaling of Q˙ICA${\dot{Q}}_{\mathrm{ICA}}$ as a function of Vbr in extant haplorhine primates and in fossil hominins. The point of our study is to clarify these relationships between Homo sapiens, Australopithecus and modern great apes (Pongo, Pan, Gorilla) to resolve an apparent allometric conundrum within our previous studies: one analysis based on 34 species of extant Haplorhini, including H. sapiens, resulted in the equation Q˙ICA =8.82×10−3${\dot{Q}}_{\mathrm{ICA}}\hspace{0.17em}=8.82\times {10}^{-3}$Vbr0.95 [6], while another analysis of 11 species of fossil hominin, also including H. sapiens, produced the equation Q˙ICA=1.70×10−4${\dot{Q}}_{\mathrm{ICA}}=1.70\times {10}^{-4}$Vbr1.45 [15]. Humans are on both analyses with the largest brains, but the exponents of these equations are markedly different, and the lines converge. The present study confirms that hominin ancestors had lower Q˙ICA${\dot{Q}}_{\mathrm{ICA}}$ than predicted from Vbr with the haplorhine equation. Q˙ICA${\dot{Q}}_{\mathrm{ICA}}$ in modern great apes is about twice that in Australopithecus species, despite similar or smaller Vbr.