There have been many proposals of how time-to-collision is computed (see Sun & Frost for a review). But the results of different tasks were not conclusive for any of these models. According to new evidence of development and tuning of tasks, we propose a simple recurrent neural network to account for these phenomena. Specifically we simulated ontogenic development and tuning to speed ranges through training. Results were similar to human performance: less-trained-networks responses consistently anticipate to slow objects or large objects, and this behaviour diminishes with training.
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