The skill-based approach:developing and applying a modelling method based on skill reuse

Skill reuse is a commonly accepted aspect of human cognition. However, it is hardly ever applied in the construction of cognitive models. By not taking skill reuse into account, a risk exists that the models created in such a way are too specific and therefore do not add much to our knowledge of human cognition. In this dissertation we have developed a modelling approach that is centred around considering skill reuse and therefore can change this situation. By following this method skill reuse is considered and the models that are created following this method will add more to our general understanding of human cognition. This dissertation discusses the modelling approach, the steps we have taken in creating this approach and the approach is applied to two experimental paradigms

Testing the skill-based approach:Consolidation strategy impacts attentional blink performance

Humans can learn simple new tasks very quickly. This ability suggests that people can reuse previously learned procedural knowledge when it applies to a new context. We have proposed a modeling approach based on this idea and used it to create a model of the attentional blink (AB). The main idea of the skill-based approach is that models are not created from scratch but, instead, built up from reusable pieces of procedural knowledge (skills). This approach not only provides an explanation for the fast learning of simple tasks but also shows much promise to improve certain aspects of cognitive modeling (e.g., robustness and generalizability). We performed two experiments, in order to collect empirical support for the model’s prediction that the AB will disappear when the two targets are consolidated as a single chunk. Firstly, we performed an unsuccessful replication of a study reporting that the AB disappears when participants are instructed to remember the targets as a syllable. However, a subsequent experiment using easily combinable stimuli supported the model’s prediction and showed a strongly reduced AB in a large group of participants. This result suggests that it is possible to avoid the AB with the right consolidation strategy. The skill-based approach allowed relating this finding to a general cognitive process, thereby demonstrating that incorporating this approach can be very helpful to generalize the findings of cognitive models, which otherwise tends to be rather difficult

A Skill-Based Approach to Modeling the Attentional Blink

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

The temporal dynamics of attention:Thinking about oneself comes at a cost in sub‐clinical depression but not in healthy participants

Self-relevant stimuli seem to automatically draw attention, but it is unclear whether this comes at a cost for processing subsequent stimuli, and whether the effect is depending on one’s mental state (i.e. depression). To address this question, we performed two experiments. In Experiment 1, 45 participants were to report two words (T1 and T2) in an attentional blink (AB) paradigm. T1 was a personality characteristic varying in self-rated self-relevance, whereas T2 was a neutral word. A generalized linear mixed model (GLMM) was applied to compare the T1 and T2 accuracies when T1 was high or low self-relevant. A positive effect of self-relevance was found on T1, without observable carry-over effects on T2 performance. However, in Experiment 2, a GLMM applied on 93 participants showed that T1 self-relevance can affect T2, showing opposite effects depending on sub-clinical depression score. Our findings imply that people with low depression scores process self-relevant stimuli more efficiently, which is reflected in a reduced AB. In contrast, individuals with higher scores in depression demonstrated a difficulty to withdraw attention from self-relevant information, reflected in an increased AB. Our findings thus reveal that a processing advantage for highly self-relevant stimuli comes at either a subsequent cost or benefit in temporal attention depending on one’s mental disposition

Data-driven cognitive skills with an application in personalized education

How can we explain that people are capable of performing new tasks with no or little instruction? Earlier work has proposed that new tasks can be acquired by a rapid composition of cognitive skills, and implemented this in the ACT-R and PRIMs cognitive architectures. Here, we discuss a possible application of rapid composition in building tutoring systems. The goal is to identify underlying skills through unsupervised machine learning from a dataset of arithmetic learning for students in a Dutch vocational program. The resulting skill graph is used as a basis for a tutoring system. The results show evidence for predictive power of the system and tentative evidence of a learning benefit compared to control groups

Testing the Skill-based Approach: Consolidation strategy impacts Attentional Blink performance

Data accompanying the journal article Testing the Skill-based Approach: Consolidation strategy impacts Attentional Blink performance published in PLOS ONE. It contains the datasets (without personal information) for both experiments and a small R file that can be used to read in the data