Question

Hodos & Campbell (1969) wrote “Once upon a time, biologists tended to think that evolution was guided by a single law of progression that caused simple organisms to become more complex, and ‘lower’ species to ascend ‘the scale,’ but that view is no longer tenable . Do you agree? If so, what is the explanation for the increasing `complexity’ of organisms that developed over the 4.5 billion years of life on earth?

Answer 1

Brain evolution is a complex weave of species similarities and differences, bound by diverse rules and principles.

Once upon a time, biologists tended to think that evolution was guided by a single law of progression that caused simple organisms to become complex, and “lower” species to ascend “the scale”, but that view is no longer tenable (Hodos & Campbell 1969). Nor is it sensible to argue that all of biology can be explained solely in terms of Darwin’s law of natural selection (Rosenberg 2001), for natural selection has to work with raw materials that are subject to a variety of other principles or laws, including what we generally call developmental and/or physical constraints (Gould & Lewontin 1979, Alberch 1982). Although this view of biology as being governed by a multitude of laws bothers some philosophers of science (Beatty 1997), it is not really troublesome, for most complex systems, including those studied by physicists, tend to be governed by a variety of laws, forces and factors that interact. Therefore, biologists ought not to whittle down their set of laws, but seek a unitary theory that accommodates and unifies a lot of different laws.

Since that unifying theory is incomplete, most evolutionary explanations are just partial explanatory “sketches” rather than full-fledged theories. Still, they are a good first step. Within that general framework, the target book’s most general purpose is to specify some likely rules, principles, or laws of brain evolution – and to indicate how they might interact. Regrettably, many of the mentioned principles are vague and the synthesis is incomplete. Hopefully, those imperfections will prompt readers to seek more supporting evidence or to construct alternative hypotheses. Most importantly, I hope that further work will clarify the mechanistic bases of the various brain evolution principles. Once we know those mechanisms, we will know not only why the principles exist, but also why there sometimes are “exceptions to the rule”. Although this goal remains distant, recent advances in developmental, behavioral, and computational neuroscience make it possible to envision such a mechanistic grounding of brain evolution principles.

Hopefully, that dream will stimulate more neuroscientists to engage in evolutionary research. As Hans Spemann (1927), a famous embryologist, once wrote: “We still stand in the presence of riddles, but not without hope of solving them. And riddles with the hope of solution – what more can a man {or woman} of science desire?”

Evolutionary neuroscience has pre-evolutionary roots but flourished only after Darwin’s “Origin of Species” forced increasing numbers of intellectuals to contemplate “Man’s Place in Nature” by Huxley. Remarkably, Darwin understood that speciation by natural selection produces family trees, bushes, or corals , rather than linear scales. Unfortunately, many of Darwin’s contemporaries continued to see evolution as unilinear and unfailing progressive, with lower species gradually ascending some sort of phylogenetic scale (Bowler). This scalar view of evolution has now become extinct among practicing evolutionary neuroscientists (Hodos & Campbell), but it lives on in many other minds. Similarly, many “classic” notions about how vertebrate brains evolved (e.g., by adding neocortex to an ancestral “smellbrain”) continue to hold sway among many non-specialists (MacLean 1990), even though they have long been disproved (Northcutt).

An intriguing aspect of the history of evolutionary neuroscience is that it has been marked by a protracted tug-of-war between those who emphasize species differences in brain organization and those who dwell on similarities. One major reason for that tension is that the human mind, when confronted with input as complex and multifarious as the data on vertebrate brain organization, generally seeks order (similarities) amidst confusion; once order has been detected, it can admit that species differences exist and then embark on a new round of seeking similarities. If the human mind indeed works this way, then it is only natural that some scientists, at some points in time, emphasize species similarities while others home in on differences. According to the quantum physicist David Bohm (1957), it is precisely this tension between similarities and differences, between order and disorder, that leads to the discovery of scientific laws and principles - which is why the target book deals with both species similarities and differences. The latter are emphasized mainly because they have thus far received less attention.

Adult brains can be studied at several levels of analysis, which form three logically distinct (but interacting) hierarchies of brain structure: regions, cell types, and molecules (Striedter & Northcutt 1991, Striedter 1999). Molecules are generally more conserved than brain regions, but within each hierarchy, conservation generally wanes as one descends levels. For instance, individual brain nuclei are less conserved than major brain divisions, and specific neuropeptides are less conserved than the major neuropeptide families. Therefore, all adult brain archetypes (common plans of construction) lack detail. In order to obtain more detailed archetypes, we must limit our analysis to smaller taxonomic groups, but even those more limited archetypes remain abstractions, not real brains. Although this is well known, neurobiologists routinely write about “the rodent brain,” “the mammalian brain,” or even “the vertebrate brain” as if they were talking about a specific brain rather than a highly generalized abstraction. Such linguistic sleight of hand cannot hide species differences for long.

Increases of absolute brain size during evolution reinforced stronger structuring of brain connectivity. One consequence is the hierarchical cluster structure of neural systems that combines predominantly short, but not strictly minimal, wiring with short processing pathways. Principles of "large equals well-connected" and "minimal wiring" do not completely account for observed patterns of brain connectivity. A structural model promises better predictions.

Relative brain size – that is, brain size relative to what one would expect in an “average” animal of the same type and body size – has increased more often than it has decreased among the vertebrates. Crucially, the increases in relative brain size occurred not in a linear sequence “from fish to man” but independently in a whole slew of different lineages (Northcutt 1984). Although relative brain size is difficult to quantify (Deacon 1990a) and tough to correlate against behavior (van Dongen 1998), it does appear that increases in relative brain size were generally accompanied by increases in social and/or foraging complexity (Humphrey 1976, Parker & Gibson 1977, Byrne & Whiten 1988). In contrast, those lineages that have reduced their relative brain size (e.g., the basking sharks) lead relatively simple lives.

Absolute brain size increased repeatedly in several different lineages, and those increases were associated with several law-like changes in brain organization. Specifically, the increases in absolute brain size were linked to increases in brain complexity, as measured by the number of distinct brain regions, and to changes in brain region proportions, with late-born regions generally becoming disproportionately large. The size-related changes in regional proportions probably caused major changes in neuronal connectivity, with proportionately enlarged regions becoming “better connected.” This, in turn, most likely led to major changes in brain function, with the more widely connected regions becoming disproportionately influential. In addition, evolutionary increases in absolute brain size were generally accompanied by decreases in average connection density, causing larger brains to become structurally and functionally more modular. Thus, the single variable of absolute brain size ties together many different attributes of brains. Since those links are causal in nature, they have explanatory force.

It is important to stress, however, that the variable of absolute brain size does not capture or explain all of the variation that we see in brains. The evolutionary origin of the mammalian neocortex, for instance, is not explicable in terms of any change in absolute brain size. Clearly, some evolutionary changes in brain structure were causally independent of changes in absolute brain size. The origin of most major vertebrate lineages involved some key neuronal innovations (such as the neocortex) that were causally unrelated to changes in absolute brain size but crucial to that lineage’s overall success. Nonetheless, those key innovations were relatively rare. Occasionally they pushed brain evolution onto novel “tracks” but, within those tracks, brains varied mainly in absolute brain size (and its diverse correlates). Using this metaphor of “tracks,” we can say, for instance, that human brains are firmly on the primate track but became so large that they evolved a plethora of size-related specializations.

Most evolutionary increases in relative brain size were accompanied by increases in absolute body size. This coincidence is probably related to the fact that proportional brain size generally decreases with increasing body size (Haller 1762), which means that larger animals tend to have “more room” within their heads for enlarged brains; moreformally. This evolution of brain answers the increasing `complexity’ of organisms that developed over the 4.5 billion years of life on earth.