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Further Reading

FEATURE: Science

Software for Sex

Successful Mating Habits Require Intelligent Programming

by Richard W. Stevens

Software. Sex. There wouldn't seem to be much correlation between the two, as we computer geeks sporting programmable calculators and pocket protectors in college observed with disheartening regularity. But the natural world proves otherwise: the most interesting parts of sex are software.

Software consists of the collection of information and the series of steps that together direct the operation of hardware. Common examples of hardware—personal computers, gaming systems, and smart phones—do next to nothing until they're loaded with software, such as operating systems, game programs, and applications.

Similarly, a biological hardware element in an animal—such as a leg, paw, heart, wing, tail, or jaw—won't do anything unless something sends a signal to it. A mere electrical signal sent to the element will make it twitch or contract, but that isn't enough to allow the animal to run, eat, or mate. To run, for example, a four-footed animal needs a number of electrical signals delivered in a precisely coordinated way via the nerves to all four legs.

We could build a robotic dog with four legs, but it would not run until we also built a controlling computer (more hardware) and programmed that computer (with software) to send those coordinated signals to the legs. The same must be true for biological dogs. For a bio dog to run, there must be a control system that is programmed to send the coordinated nerve signals to the legs. So it is for all animal hardware elements: for the hardware to work, there must be software that controls and directs it.

Sexual Software

Does biological software for running seem too pedestrian? People, including scientists from Charles Darwin's day to the present, have simply assumed that an animal with legs can naturally use them to walk, run, kick, and so forth. To paraphrase the memorable line from Field of Dreams, they assumed about biological features that "if nature builds it, it will work."

My Winter 2011 Salvo article, "Biological Software" (Salvo 19), explodes that assumption. In this era, we can all clearly distinguish between hardware (e.g., a game system) and software (e.g., an interstellar warfare simulation game). Hardware is inert without software. Legs don't work without software. And software doesn't appear by a series of lucky accidents—software must be designed.

Sex takes the problem to the next level: behavior. Among the animals, there are many species that engage in complex sexual and reproductive behaviors. For example, they do things to: (1) attract a mate; (2) persuade a potential mate to select them over a rival; (3) engage the biological equipment to transmit (or receive) seed cells to (or from) their mate; (4) prevent or deter competitors from approaching their mate; and (5) protect eggs and offspring. What's more, the males and females of the same species have markedly different software to accomplish some or all of these functions.

In neo-Darwinist theory, evolution proceeds when an animal species accidentally develops (through mutation) new or changed features that confer a "reproductive advantage" to its members. That advantage is transmitted to the offspring, which in turn benefit from it and pass it on to their own offspring, and so it goes down the line. But a lot of software resides within the category "reproductive behavior." If dinner, a movie, and a glass of wine can hardly describe or explain it all, Darwinian mechanisms can account for even less.

Chirp, Rock & Roll

Consider a cricket's various behaviors. The male crickets chirp a distinctive song to attract female crickets. Females do not chirp. A male cricket's chirps also ward off other males, and, in some species, the chirps establish a sequence of dominance among several crickets in one locale, like a "pecking order."

That's a whale of a lot of software. A program must exist for all of these male functions: (1) determining that chirping is appropriate and should start now (usually at night); (2) making the legs produce the chirping sound; (3) hearing and discerning the chirps of other males; and (4) deciding to move away or get into the "pecking ­order."

For her part, the female cricket listens for chirps. If she is ready for mating, she moves toward the source of the chirps. In some species, the female moves toward the source of the loudest or most distinctive chirps.

The female cricket therefore must possess software to: (1) decide she is ready to mate and thus to listen for chirps; (2) listen for and discern chirps from the correct species; (3) discriminate (potentially) between several chirps to decide which ones are desired; (4) determine the direction from which the winning chirps came; and (5) move toward the source of those chirps, continually recalculating and correcting her travel path to ensure that she reaches the chirps' source.

The Big If

All of these cricket behaviors require that the crickets be programmed in advance of the mating season. That fact alone proves that the behaviors were envisioned before they were needed and that the behaviors were encoded and stored inside the crickets' brains or bodies for later use. Lying beyond that fascinating territory, however, is the magic word of both sex and software: if.

Whenever a computer program or a behavior involves decision-making of any kind, there must be a conditional branching event. In many computer languages, conditional branching occurs via an "if–then" statement. Conditional branching if–then statements work as simply as this:

If such-and-such condition exists,
then do operation #1;
otherwise, do operation #2.

In the sex life of crickets and other living things, the if–then statement appears so constantly that we rarely think about it. Consider these examples:

These examples show only the large-scale decisions involved in mating; within each of them are many smaller-scale decisions. Detecting a smell, identifying a sound frequency or pattern, and observing a feather display or mating dance all require many comparisons of current input with remembered or expected conditions. To recognize a call from the same bird species, for example, a bird must have stored the frequency and pattern of the target call, and then, upon hearing sounds, must be able to compare the current sound with the stored sound-descriptive information. If there is a match, the bird recognizes the call; otherwise, the bird does something else.

Pre-Programming Requires Intelligent Design

Here is the key point: whenever we observe an if–then operation that alters a creature's behavior based upon some new or changed situation, we have discovered evidence of intelligent design. What makes us sure? The if–then operation incorporates previously acquired knowledge to decide whether to take a future pre-programmed action. Consider this example:

If a female cricket hears a male cricket's chirp,
then the female detects the direction of the sound and moves towards it;
otherwise, the female keeps listening.

In the if portion, the female cricket can only know she heard a male cricket's chirp if she had both (1) the chirp's sound-pattern information stored, and (2) the software to decode the incoming sound signals and compare them with the stored chirp information. The if portion thus depends upon information and software that must already exist in order to work correctly.

In the then portion, after the if condition is detected, the female takes a pre-programmed action, meaning her responsive action had to be determined in advance. Whatever entity made the cricket had to know what programmed response should occur.

Could the cricket's maker, then, be a non-intelligent, random, or unguided entity or force—a Thoughtless Thing? Such a Thing would not know about or be able to anticipate future events. That kind of maker could not program a female cricket to take a future action based upon a future event.

Moreover, a Thoughtless Thing could not and would not program a female cricket to recognize male cricket chirps or select a suitable male target based upon the chirp. A Thoughtless Thing cannot plan ahead. So even the if portion of the mate-detection process could not be devised by a Thoughtless Thing.

The neo-Darwinian evolution model depends upon mutations that make one animal specimen more fit than another to survive and reproduce. Reproduction—it's all about sexual success. Yet evolutionary theory says, in effect, that a Thoughtless Thing makes changes to species by unguided mutation and non-intelligent natural selection. But for the female cricket to successfully reproduce, she must have pre-programmed data and software that enable her to accomplish both the if portion and the then portion of the mate-detection process. Both portions require foreknowledge and pre-programming; thus, a Thoughtless Thing could never make a successfully sexy cricket.

The Birds & the Bling

Recognizing the software aspects of sex and reproductive activity doubles the fun of nature study. In his popular book, Nasty, Brutish and Short, Canadian science broadcaster Pat Senson reports on the quirky mating and related behaviors of dozens of species of bug, bird, and beast. For example, each male zebra finch has a unique song that he repeats over and over. The females try to choose the males whose songs are most complicated.

Imagine the software involved! The female zebra finch must be able to: (1) pick out and recognize a male zebra finch's song from all other environmental noise; (2) analyze the song for it's "complicatedness"; (3) rank potential mates by their songs; and (4) move toward the winning singer.

Consider next how Mr. Bowerbird might woo the future Mrs. Bowerbird. Senson describes several different romantic modes found among bowerbird species. The male satin bowerbird, for example, tries to attract a mate using different and selective tactics. First, he builds an avenue-style structure called a bower. It looks like a hallway built with a combination of long sticks and shorter twigs. He decorates his bower with bling, i.e., bright and shiny items, to attract a female.

Next, when a female decides the bowerbird's pad is impressive enough to visit, the male puts on his dancing show. He starts fluffing his feathers, flipping up his wings, making buzzing noises, and running back and forth. Researchers have learned that female bowerbirds of different ages judge the male's suitability differently. If they are not scared away by a too-aggressive dance, younger females seem to choose the male with the best bling in his bower. Older females size up the male by his complicated dance, and are less impressed by bling.

Some amazing software underlies the satin bowerbirds' behavior. The male birds come into adulthood with the programming to build a bower, to detect, select, and arrange bright and shiny objects, and to perform extravagant dances when females visit. The female birds have software to appraise the bower and its bling and to judge the masculine power displayed in the dance. The females thus come programmed with two evaluation strategies, and, depending on their age, will select one or the other strategy.

So far, the story of male display and female evaluation seems fascinating enough. But the male bowerbird has yet another software application in his feathery repertoire. Given that different-aged females seek different qualifications in the males, the males have the ability to figure out which attraction strategy will work better with the current lady bowerbird visitor. The smarter males attain success by playing down their dance when they want to impress but not scare away a younger female. To impress the more mature females, they turn up the dance excitement.

Seeing a Pattern Yet?

These brief descriptions of the mating procedures of crickets, finches, and bowerbirds only hint at their complexity. Underlying many of these procedures, however, is a ­function we daily perform millions of times but never notice: pattern recognition.

This seemingly automatic function is built into insects, birds, and many other animals—including humans. Pattern recognition is the combined process of receiving physical data, classifying it, describing it, analyzing its features, and making a decision based on the results.

It's everywhere. The female cricket detects, identifies, and moves toward the male's chirps. The female zebra finch hears and distinguishes one male's song from another. The female bowerbird can tell a shiny, decorated bower from a dull pile of twigs, and can distinguish one sultry dance from another, all in support of her choice of mate. At the most basic level, the male human can tell whether another human is female or male based first upon visual physical configurations.

Such pattern recognition has little to do with biological hardware—it is mostly the working of software. Researchers have worked hard to program computers to recognize patterns in stationary things, like handwriting, fingerprints, and photographs, as well as to track moving targets and decipher words in ongoing speech. Modern visual pattern recognition software consists of the basic elements shown in Figure 1.

Figure 1: Basic Elements of Visual Pattern Recognition

Figuring out how to replicate all of these sub-tasks within pattern recognition has occupied researchers for decades. Four main models of pattern recognition have emerged: template-matching, statistical, syntactical/structural, and neural network. These models quickly become math-intensive; the computer programs for them are complex and highly sophisticated. Some models outshine others in one task but not another. Teams of top-flight intellects are needed to develop the math and implement the software. Undirected physical forces play no role; none of this software happens by lucky accident.

The more you know about the challenges of pattern recognition, the more you marvel at the cricket, finch, and bowerbird. These creatures and myriad others come pre-programmed for visual and audio pattern recognition. Their software comes prepared to deal with expected future events. When their software works, they have sex! Spring is the season . . . to enjoy intelligent design. •


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Articles from the current issue (Salvo 28: Spring 2014)

Highly Creative

Three More Things Only a PhD Can Believe

by Louis Markos

Repro-Technology

Artificial Conception & Its Discontents

by Mark Oshinskie

Data Basic III

Is Information, Not Matter, the Foundation of Life?

by Denyse O'Leary

 

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