Conversely, the genome can access direct sensory observables, because those observables involve a priori-fixed “neural addresses.” For example, the genome could hardwire a cute-face-detector which hooks up to retinal ganglion cells (which are at genome-predictable addresses), and then this circuit could produce physiological reactions (like the release of reward). This kind of circuit seems totally fine to me.
Related: evolutionary psychology used to have a theory according to which humans had a hardwired fear of some stimuli (e.g. spiders and snakes). But more recent research has moved towards a model where, rather than “the fear system” itself having innate biases towards picking up particular kinds of fears, our sensory system (which brings in data that the fear system can then learn from) is biased towards paying extra attention to the kinds of shapes that look like spiders and snakes. Because these stimuli then become more attended than others, it also becomes more probable that a fear response gets paired with them.
This, in turn, implies that human values/biases/high-level cognitive observables are produced by relatively simpler hardcoded circuitry, specifying e.g. the learning architecture, the broad reinforcement learning and self-supervised learning systems in the brain, and regional learning hyperparameters.
The original WEIRD paper is worth reading for anyone who hasn’t already done so; it surveyed various cross-cultural studies which showed that a variety of things that one might assume to be hardwired were actually significantly culturally influenced, including things such as optical illusions:
Many readers may suspect that tasks involving “low-level” or “basic” cognitive processes such as vision will not vary much across the human spectrum (Fodor 1983). However, in the 1960s an interdisciplinary team of anthropologists and psychologists systematically gathered data on the susceptibility of both children and adults from a wide range of human societies to five “standard illusions” (Segall et al. 1966). Here we highlight the comparative findings on the famed Müller-Lyer illusion, because of this illusion’s importance in textbooks, and its prominent role as Fodor’s indisputable example of “cognitive impenetrability” in debates about the modularity of cognition (McCauley & Henrich 2006). Note, however, that population-level variability in illusion susceptibility is not limited to the Müller-Lyer illusion; it was also found for the Sander-Parallelogram and both Horizontal-Vertical illusions.
Segall et al. (1966) manipulated the length of the two lines in the Müller-Lyer illusion (Fig. 1) and estimated the magnitude of the illusion by determining the approximate point at which the two lines were perceived as being of the same length. Figure 2 shows the results from 16 societies, including 14 small-scale societies. The vertical axis gives the “point of subjective equality” (PSE), which measures the extent to which segment “a” must be longer than segment “b” before the two segments are judged equal in length. PSE measures the strength of the illusion.
The results show substantial differences among populations, with American undergraduates anchoring the extreme end of the distribution, followed by the South African-European sample from Johannesburg. On average, the undergraduates required that line “a” be about a fifth longer than line “b” before the two segments were perceived as equal. At the other end, the San foragers of the Kalahari were unaffected by the so-called illusion (it is not an illusion for them). While the San’s PSE value cannot be distinguished from zero, the American undergraduates’ PSE value is significantly different from all the other societies studied.
As discussed by Segall et al., these findings suggest that visual exposure during ontogeny to factors such as the “carpentered corners” of modern environments may favor certain optical calibrations and visual habits that create and perpetuate this illusion. That is, the visual system ontogenetically adapts to the presence of recurrent features in the local visual environment. Because elements such as carpentered corners are products of particular cultural evolutionary trajectories, and were not part of most environments for most of human history, the Müller-Lyer illusion is a kind of culturally evolved by-product (Henrich 2008).
These findings highlight three important considerations. First, this work suggests that even a process as apparently basic as visual perception can show substantial variation across populations. If visual perception can vary, what kind of psychological processes can we be sure will not vary? It is not merely that the strength of the illusory effect varies across populations – the effect cannot be detected in two populations. Second, both American undergraduates and children are at the extreme end of the distribution, showing significant differences from all other populations studied; whereas, many of the other populations cannot be distinguished from one another. Since children already show large population-level differences, it is not obvious that developmental work can substitute for research across diverse human populations. Children likely have different developmental trajectories in different societies. Finally, this provides an example of how population-level variation can be useful for illuminating the nature of a psychological process, which would not be as evident in the absence of comparative work.
This, in turn, implies that human values/biases/high-level cognitive observables are produced by relatively simpler hardcoded circuitry, specifying e.g. the learning architecture, the broad reinforcement learning and self-supervised learning systems in the brain, and regional learning hyperparameters.
… the evolved modularity cluster posits that much of the machinery of human mental algorithms is largely innate. General learning—if it exists at all—exists only in specific modules; in most modules learning is relegated to the role of adapting existing algorithms and acquiring data; the impact of the information environment is de-emphasized. In this view the brain is a complex messy cludge of evolved mechanisms.
There is another viewpoint cluster, more popular in computational neuroscience (especially today), that is almost the exact opposite of the evolved modularity hypothesis. I will rebrand this viewpoint the “universal learner” hypothesis, aka the “one learning algorithm” hypothesis (the rebranding is justified mainly by the inclusion of some newer theories and evidence for the basal ganglia as a ‘CPU’ which learns to control the cortex). The roots of the universal learning hypothesis can be traced back to Mountcastle’s discovery of the simple uniform architecture of the cortex.[6]
The universal learning hypothesis proposes that all significant mental algorithms are learned; nothing is innate except for the learning and reward machinery itself (which is somewhat complicated, involving a number of systems and mechanisms), the initial rough architecture (equivalent to a prior over mindspace), and a small library of simple innate circuits (analogous to the operating system layer in a computer). In this view the mind (software) is distinct from the brain (hardware). The mind is a complex software system built out of a general learning mechanism.
Related: evolutionary psychology used to have a theory according to which humans had a hardwired fear of some stimuli (e.g. spiders and snakes). But more recent research has moved towards a model where, rather than “the fear system” itself having innate biases towards picking up particular kinds of fears, our sensory system (which brings in data that the fear system can then learn from) is biased towards paying extra attention to the kinds of shapes that look like spiders and snakes. Because these stimuli then become more attended than others, it also becomes more probable that a fear response gets paired with them.
The original WEIRD paper is worth reading for anyone who hasn’t already done so; it surveyed various cross-cultural studies which showed that a variety of things that one might assume to be hardwired were actually significantly culturally influenced, including things such as optical illusions:
See also the previous LW discussion of The Brain as a Universal Learning Machine.
Strictly speaking, the plot could be 100% noise without error bars, sample size, or similar info. So maybe worth including that.