Browser willing, you will find colored dots moving about below. These dots show the origin of autism as described in The Sparseness Adaptation Syndrome.     ( Return to Introduction )

Each colored dot below stands for a person, family or tribe (more accurately, their genes). All the dots are basically the same, making similar random movements. The time scale is arbitrary. The colors show histories -- the average number of neighbors near each dot, figured over the dot's lifetime. The blue dots (•) show people who have had few neighbors on average. Autistic traits collect in these loners because greater distance from neighbors means smaller demand for social ability. This frees genes and brain parts used in social contact to shift to other uses -- to object-related uses, for example.

The simulator buttons should be fairly obvious. Click away! And think.

Average #Neighbors:   • 0-1.5     • 1.5-3     • 3+

Things to Notice
Click NEW to start a fresh history.
Notice the dots changing from red to green to blue: Even where individuals are the same their histories come to differ radically, and in a way that fosters autism.
Do you see that the green and blue dots usually appear first near the population surface? Which is likely to need social ability more often, a red "dot" or a blue one?
Notice that a dot's color does not change instantly when it enters a new level of sparseness or crowding: A dot's color depends not just on where it is, but on its entire evolutionary history.
Do the blue dots always stay in sparse areas, or can they exist in crowds? What might this have to do with autism today?
Do dots often or ever change back, for example from green to red?
Notice that blue dots, unrealistically, prevail in the long run. What realistic changes in the model might alter this? (For a clue see the note below on simplifications.)

On Adaptation

All the dots here are like clones, with identical behaviors on average. Their color shows only the history of their distance from neighbors. However, organisms that actually exist are adapted to their circumstances, as the paper explores in some depth. If organisms are adapted to their circumstances, and if long-term adaptations show in genes, then the circumstance of prolonged distance from neighbors will show in genes. For this reason, when the model is taken to represent events on the scale of thousands and millions of years the dot colors do represent expected genetic differences between gene pools. The spectrum of red, green and blue dot colors thus also shows an expected autism spectrum. Perhaps the most important point illustrated by the model is that even in a population of identical individuals symmetry is broken: The sameness is unlikely to last, and an autism spectrum is the expected result.

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Occasionally-Asked Questions

Surely many autistic people could not survive at rugged frontiers. Profound autism appears to be, if anything, maladaptive. Doesn't this contradict the Sparseness-Adaptation Theory of Autism?   Ask

It is possible to make up thousands of Just So stories about adaptation. What makes the Sparseness-Adaptation Theory of Autism different from any of these?   Ask

According to this theory shouldn't there be a high incidence of autism among Inuit people in the Arctic?   Ask

The Sparseness-Adaptation Theory of Autism predicts gender-related behavioral differences on average. Doesn't this make it a sexist theory?   Ask

Isn't the Sparseness-Adaptation Theory of Autism just more Godless Darwinism?   Ask

Simplifications

The model is kept simple in order to underscore that autism's evolution need not depend on complexities.
Some of the realistic complications one might add to the model, for example giving dots different mobilities and giving areas varied capacities to sustain life can actually make autism more pronounced, as later simulations may show.
Later simulations may also allow dot histories to affect their behavior, and provide more explicitly for birth, death and gene flow between pools. Some of these changes can increase population stirring and thus moderate effects of the population surface, as discussed in the paper.
Is any realistic change in the model likely to give all gene pools identical neighbor-distance histories?

Technical Notes

The starting distributions and all the individual movements are Gaussian-distributed. All 100 dots have the same chances of moving any particular distance or direction.
The distance and time scales are not fixed: The movements can stand for movements of a few feet or thousands of miles, occurring in moments or over eons.
Each dot neighborhood is about 5 times the diameter of a dot. Arithmetic averaging of neighbor number is computed over each dot's lifetime. Fixed square bins are used in the computation. Click bins to view them along with color-coding showing the local population density. (The tiny nameless button exposes and hides other buttons: hist displays a distance-from-center histogram, and loop starts new simulations every two minutes or so at the middle speed.)

Using fixed bins introduces errors that are most pronounced at the start of a simulation, before the dots have had a chance to move between bins. To minimize these artifacts, all the dots are given identical starting histories roughly equivalent to exposure to the mean initial population density for the average amount of time it takes a dot to wander to a neighboring bin -- about a second at the middle speed.

A fast nearest-neighbor algorithm that capitalized on the similarity of successive dot positions would help. (Please contact author if you know of one!)

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