A symbolic model of human attentional networks

An increasing body of evidence has shown that attention is a multi-type and multilevel cognitive faculty. The dominant computational modeling approaches to attention have often focused on one specific type of attention at one specific level. In particular, various connectionist modeling techniques at the subsymbolic level have been widely adopted. In this paper, we report a symbolic computational model of the Attentional Network Test, which simultaneously involves different types of attention (alerting, orienting, and executive control), each subserved by distinctive attentional networks in the brain. The model was developed in ACT-R, a rule-based cognitive architecture. The results show that the model, by sequentially firing rules at a rate of about one every 40 ms, was able to capture the effect of each attentional network. The model implies that while the attentional networks can be distinguished at both neuroanatomical and behavioral levels, different attentional networks may adopt similar computational operations at least at a symbolic rule level.