This paper describes the theoretical background of an Act-R model of discovery learning in a simulated, conceptual domain: optics. It is assumed that learning in a simulation context consists of both implicit and explicit learning. The Act-R model under development tries to capture both learning types.

Adaptive fact learning systems have been developed to makeoptimal use of testing and spacing effects by taking intoaccount individual differences in learning efficiency.Measures derived from these systems, capturing theindividual differences, predict later performance in similarand different fact learning tasks. Additionally, there is a richbody of literature showing that individual differences ingeneral cognitive ability or working memory capacity canpredict scores on achievement tests. If these measures alsoinfluence fact learning, incorporating them might furtherenhance adaptive systems. However, here we provideevidence that performance during fact learning is neitherrelated to working memory capacity nor general cognitiveability. This means that the individual differences captured byour adaptive learning system encapsulate characteristics oflearners that are independent of their general cognitive ability.Consequently, adaptive learning methods should focusprimarily on memory-related processes.

Temporal preparation is influenced by factors across a range of time scales, from effects of the previous trial to learningeffects throughout entire experiments. Theories on temporal preparation thus far have failed to offer a complete account ofthese effects. We present the formal multiple trace theory of temporal preparation (fMTP), a computational framework thatintegrates theories on time perception, motor planning, and associative learning. At fMTP’s core lies Hebbian, associativelearning between a layer of time cells and a motor layer. Its preparatory state is governed by the automatically retrieval oftraces formed in the past. We show that fMTP, with only this single implicit learning mechanism, accounts for behavioralphenomena across a range of time scales that previously have been considered to be the result of distinct processes.Furthermore, for experimental setups where the predictions of existing accounts and fMTP differ, the data aligns with ourmodel.

There is conflicting evidence regarding the effect of acute physical exercise on peoples ability to memorise declarativeinformation. Some studies have found that exercising before learning improves memorisation, while others have foundan adverse effect. We measured memorisation in 70 recruits for the Special Forces unit of the Dutch army during theirfirst week of training. Recruits used a computer-guided learning system to study the names of locations on a map directlybefore and directly after a high-intensity speed march. In the learning session following the speed march, responses werefaster but less accurate than before, particularly at the start of the session. We fitted a computational cognitive model ofhuman memory to the responses made in each learning session to obtain a continuous index of memorisation. This indexshowed a small improvement after the speed march, suggesting that memory representations formed after high-intensityphysical exercise were slightly more stable.