Effective connectivity in cortical regions during bottom-up perception of biological motion under attentional load: an FMRI-DCM study

The ability to detect biological motion holds an evolutionarily important role in vital and social functions. However, in our daily lives, we perceive biological motion while we are at a task most of the time. In other words, it is perceived when our attention is directed at another thing. In this aspect, understanding the dynamics of its bottom-up perception is of high importance. Meanwhile, the attentional mechanisms and where their effects occur are a matter of debate in the literature, sparking off various theories, such as early selection, late selection, and attentional load theory. Dynamic causal modeling (DCM) is a suitable tool for investigating the dynamics of attentional effects on the network, enabling the bottom-up perception of biological motion, and comparing the existing theories in the literature with Bayesian graph models. To this end, we utilized the DCM approach with fMRI data collected using an attentional load paradigm and biological motion peripheral distractors [1]. In our model space, we modeled the theories of selective attention along with two complementary models. The Bayesian Model Selection (BMS) showed that the model that explained the data the best was the model where both attentional load conditions modulated all top-down connections rather than the models of existing theories. This showed that attentional effects take part in the bottom-up perception, not in a focused location, such as early or late, but in a more distributed manner throughout the processing pipeline. Further statistical tests on the model parameters yielded no difference between load conditions and between biological motion and scrambled motion in their modulation strengths. Yet, the strengths of biological motion on different connections were different from each other. A similar observation is also made for the low load condition but not for the scrambled motion and high load conditions. The former can be accepted as evidence for the differential processing of biological and scrambled motion. The latter may be explained by a spillover of perceptual resources on biological motion and causing competition in low-load conditions.