Complex, Dynamic, & Internal: As Simple As Possible, But No Simpler Than That

In response to the Attention to Thoughts (A2T) model, scholars reflected on and raised critical questions about the proposed complex dynamic systems theory, its computational formalization, and its implications for theory and study of internal attention and internally-directed cognition (IDC). We identify and reflect on three major themes that cut across these response papers related to, complexity, temporal dynamics, and internal states as a focus of scientific inquiry. (1) As Simple As Possible, But No Simpler: Necessary Complexity. We delineate the importance of developing formalized and dynamic systems theory to model behavioral complexity in IDC. Specifically, behavioral variations or processes which exhibit a range of trajectories and states of variable levels of temporal stability, that emerge from reciprocal and (often) non-linear interactions between attentional, mnemonic and affective processes, that unfold over time and context. (2) Complex Dynamical Systems Emerge in Time. We reflect on the observation that temporal trajectories, that self-organize into relatively stable patterns, across time-scales, emerge from moment-to-moment interactions within the system over micro time-scales; and that through circular causality that facilitates systemic self-regulation, emergent higher-level structures or macro time-scale trajectories function to constrain these moment-to-moment interactions within the system. In turn, we relate to future developments of A2T to model developmental, learning and plasticity processes in IDC that emerge over macro time-scales. (3) The Elephant in the Lab: Is Robust Scientific Theory and Study of Internal States Possible? We reflect on the conditions wherein external attention is, and is not, likely a meaningful proxy for internal attention, and the implications therein for the study of attention in mental health and related phenomena sub-served by IDC. Finally, we relate to future developments of A2T that could reflect theorized computational heterogeneity in objects competing for internal attentional selection.