Now that we are confident in our understanding of the mechanisms and interconnections driving the evolution of Tadro3s, we can be confident in doing what we meant to do all along: not just create an evolutionary simulation but test a hypothesis about the biological system that the simulation represents. We proposed the hypothesis that natural selection for enhanced feeding behavior drove the evolution of vertebrae in early vertebrates. From this hypothesis we came up with a primary prediction that we tested: selection for enhanced feeding behavior will cause the population of Tadro3s to evolve stiffer tails.
Our data refute this prediction (see Figure 4.1). Hence, the hypothesis from which it came is also refuted. We found just the opposite of our expectation in some cases—selection for enhanced feeding behavior caused the population of Tadro3s to evolve more flexible tails (see Figure 4.1, generational change in stiffness following selection). If we buy our own argument that Tadro3s and their water world represent important aspects of the earliest vertebrates, we have to conclude that selection on feeding behavior was unlikely to have been the primary driver of the evolution of vertebrae. In science this kind of failure is called progress.
But if not feeding, then what drove the evolution of vertebrae? The positive relationship that we’ve shown between tail stiffness and swimming speed + turning maneuvers offer one alternative hypothesis: selection for speed and maneuverability alone drove the evolution of vertebrae. The problem is that in order for selection to work on locomotor abilities alone, it can’t be simultaneously working on other things like feeding behavior. If it does, we get these oscillating patterns of change that we saw in this experiment, when stiffness is sometimes correlated with feeding behavior and sometimes not.
This kind of complex response to selection, by the way, is very realistic in living fishes, as David Reznik of the University of California and his colleagues have shown.[46] Based on extensive review of the literature on responses of fish populations to selection, they contend that a behavior like acceleration performance is influenced by a network of traits, all of which, because of their genetic properties and functional connections to other behaviors, can be under countervailing selection pressures at the same time. Dominant selection pressures on a population can also vary in the wild, Reznik and his colleagues have shown, as predators move in and out of small pools containing mating subpopulations of Trinidadian guppies.
Predators appear to create strong selection pressures in other species of fish as well. Dohlf Schluter and his colleagues at the University of British Columbia have shown that moving threespine sticklebacks from ocean habitats to freshwater lakes—migrations that occur naturally—appears to release the immigrant population from predation pressures found in marine environments.[47] Without predation from other vertebrates, the sticklebacks respond genetically and, over generational time, grow faster and produce less body armor. Moreover, Richard Blob, of Clemson University, and his colleagues suggest that predation may have provided the selection pressure for stream gobies to evolve a remarkable ability to scale waterfalls in Hawaii.[48]
A different kind of interpretation of our results is that we didn’t really test a hypothesis about vertebrae because we had used the stiffness of the notochord as a proxy for the number of vertebrae. Even though this relationship is mechanically based, with stiffness and vertebrae being positively related, what if something else about vertebrae mattered? Perhaps the presence of vertebrae increases stiffness and changes the curvature of the tail, its shape, and the way it acts as a propeller?
Another valid criticism of our work with Tadro3 is that its brain was just too simple to adequately model even a simple system like the tunicate tadpole larva. But, we counter, the point is to create the simplest system possible because even the simplest autonomous agents produce complex behaviors and complex evolutionary patterns. You test your hypothesis using the simplified model. Then, based on the results of the test, you interpret what happened; you work to understand the mechanisms operating at multiple levels in your system. Interpretation, as we’ve seen, is darned tricky, even when you operate under the KISS principle.
What we’ve learned from Tadro3 is that neither the selection pressure, the design of Tadro3 itself, nor an interaction of the selection environment and the Tadro3 agent explain why vertebrae are likely to have evolved. We haven’t solved the puzzle!
So our next step is to think about how to make Tadro and the selection pressure a bit more complex. Thinking about our design principles from Chapter 3, we have to make sure that we understand the biology well enough so that we can understand what it is that we want in our Tadro4 and its world. We’ve just set up predation as a great candidate for an additional selection pressure. What we need to know much more about, though, is this tricky business of brains and behavioral complexity. At the very least Tadro4 will have to be a smart prey that is able to eat and, at the same time, avoid being eaten.
Chapter 5
THE LIFE OF THE EMBODIED MIND
SOMETHING STRANGE JUST HAPPENED IN THE LAST chapter. Did you notice? When we applied selection pressure on our Tadro3s they responded by evolving next-generations of smarter Tadro3s with better feeding behavior than their parents had—in a real sense, they got smarter. But when our population of Tadro3s became smarter, they did so by evolving their bodies, not their brains.
How in the artificial-water-world can Tadros, or any robot for that matter, become smarter? And even if they can gain intelligence, how can intelligence be “in the body” rather than “in the brain”? Isn’t the brain the seat of intelligence? And by the way, as long we’re inquiring, where is the brain of a Tadro3 anyway, and what is it doing?
We need to tackle these questions because they help put Tadro3, which you met in the last chapter, and Tadro4, which you’ll meet later in this chapter, into the broader context of intelligent machines. Although I’m not claiming that either Evolvabot is going to win a merit scholarship to attend Vassar, I will claim that Tadros—by virtue of being goal directed, autonomous, and physically embodied—have intelligence. Hang on: the ride is going to be bumpy!
What we’ve got here, thanks to Tadro3 (there, I’ll blame the robot), is not a failure to communicate but rather an opportunity to lay bare some of our conceptual problems. Most humans would argue, for example, that Tadro3s are not intelligent. Yet clearly, the autonomous, self-propelled Tadro3 has something. Let’s call it skilclass="underline" the ability to detect light, move toward it, then hang around it. Yes, say the humans, but moths possess the same skill, and we know that moths aren’t intelligent. We do? What do you mean by “intelligent”? Intelligence is not just some simple skill, they say, like detecting and finding light; that’s more like a reflex. Instead, the argument goes, intelligence involves the skill of thinking, using our minds in the special, linguistic way that only humans do. Thus, many humans speak of “human-like intelligence” as the sine qua non of intelligence.
46
Cameron K. Ghalambor, Jeffrey A. Walker, and David N. Reznick, “Selection, Adaptation and Constraints on the Evolution of Burst Swimming Performance,”
47
Rowan D. H. Barrett, Sean M. Rogers, and Dolph Schluter, “Natural Selection on a Major Armor Gene in Threespine Stickleback,”
48
Richard W. Blob, Sandy M. Kawino, Kristine N. Moody, William C. Bridges, Takashi Maie, Margaret B. Ptacek, Matthew L. Julius, and Heiko L. Schoenfuss, “Morphological Selection and the Evaluation of Potential Tradeoffs Between Escape from Predators and the Climbing of Waterfalls in the Hawaiian Stream Goby