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I'm looking for experiments measuring how people manage growing task complexity.

For example, consider a task, which on the surface resembles a Go No-go task, with the first instruction:

  1. Given a sequence of circles of either red or green, when you see a green circle followed by a red circle, press the red button.

After some practice, a second instruction is added:

2a. Given a sequence of circles of either red, blue or green, when you see a blue circle followed by a red circle, press the blue button.

This instruction doesn't add much to the task complexity, but consider a more complex instruction such as:

2b. Given a sequence of circles of either red, blue or green, when you see a blue circle followed by a red circle, say the word "blue".

In this instruction, an output modality is added. However, one could imagine another instruction where an interfering pattern is added.

2c. Given a sequence of circles of either red, blue or green, when you see a blue circle followed by a red circle, press the yellow button.

Or an instruction where a cognitive component is added:

2d. Given a sequence of circle of either red or green, as well as the occasional digit, when you see a red circle followed by a digit, say the value of digit+1

Are the experiments/models that predict the performance on various tasks given increasing complexity? The best reviews I could find were:

However, these didn't seem to include any tasks with gradually increasing complexity. Are there experiments or models that address this? I feel like this is related to "cognitive load", but I couldn't find any resources describing adaptation to "cognitive load" with practice.

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  • $\begingroup$ Reminded me of a not not game (and many others). Don't think that if someone would give hardest level from the start you'd manage to complete it as soon as if play normally from the start. $\endgroup$ – rus9384 Oct 28 '17 at 18:37
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    $\begingroup$ Some of the (low level) 'task switching' stuff and cognitive models they use I have read into somewhat could be relevant. E.g. 'memory-for-goals' by Altmann, and Mosell. ACT-R might be able to model and predict the performance on such tasks $\endgroup$ – Steven Jeuris Oct 30 '17 at 8:14

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