Abstract
People can often learn new tasks quickly. This is hard to explain with cognitive models because they either need extensive task‐specific knowledge or a long training session. In this article, we try to solve this by proposing that task knowledge can be decomposed into skills. A skill is a task‐independent set of knowledge that can be reused for different tasks. As a demonstration, we created an attentional blink model from the general skills that we extracted from models of visual attention and working memory. The results suggest that this is a feasible modeling method, which could lead to more generalizable models.
Highlights
Humans have the impressive ability to learn certain relatively simple tasks with minimal instruction and in a very short period of time
We compared the behavior of the models with human performance. This was done to verify the feasibility of the basic models and to check how well the final attentional blink (AB) model could model the AB phenomenon
The comparisons were made with existing data from the literature, except for the visual search model as we had found no suitable data to compare it with
Summary
Humans have the impressive ability to learn certain relatively simple tasks with minimal instruction and in a very short period of time. Participants have often never encountered these tasks before, yet are quickly able to work out what to do This quick learning suggests that people reuse previously learned skills and apply them to new contexts (Salvucci, 2013; Taatgen, Huss, Dickison, & Anderson, 2008). If a task requires a stimulus to be remembered for later recall, people do not have to work out how to remember the stimulus, but they can reuse the already learned “remembering skill.”. It would be unnecessary, in this case, to reinvent the wheel. Learning how to do a new task means selecting the appropriate skills, assuming all these skills have already been acquired
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