By Maurice Holt
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Showed signs of highlighting) vs. agents that learned the correct responses to all 8 training items in the context environment but did not exhibit signs of attention. Attentional mechanisms clearly confer a fitness benefit in the context environment. torsion of preference is not quite as strong as that seen in the hill-climbing simulations, but it does demonstrate clear highlighting effects. The high output-rate solutions showed no preference for E or L when presented with either of the critical test items.
This distinction between the attentional and non-attentional solutions allows us to look at whether or not an attentional mechanism is truly adaptive in the context environment. If attention is one of many equivalent ways to learn quickly and perform well in the environment, then we should not see any clear difference between the overall fitness of the attentional creatures when compared with those that do not exhibit signs of learned attention. 8 shows that this is not the case. If we plot the average fitness levels of the bestperforming 10% of the agents in both the attentional and non-attentional solutions, we see that the agents with learned attention are clearly outperforming the agents that have not evolved an attention-based solution.
Slow Learning Rate (Hid−Out) Avg. Slow Learning Rate (In−Hid) Avg. 9 Data from a single population showing the relationship between learning rates, fitness, and highlighting across generations. The top graph shows the average agent fitness and the percentage of the agents in the population that show highlighting effects. Fitness has been scaled so that a fitness level of 100 indicates perfect performance across all learning trials. The lower graph displays the average learning rates for the same population.
2nd Int'l Conference on Numerical Methods in Fluid Dynamics by Maurice Holt