Dynamics of Internal Attention and Internally-Directed Cognition: The Attention-to-Thoughts (A2T) Model

Abstract

The propensity to focus attention inwards is fundamental to human mental life and internally-directed cognition (IDC) [e.g., mindwandering, (mal)adaptive self-reflection]. Yet, understanding of the mechanisms through which internal attention shapes IDC is limited. We argue that understanding the systemic complexity and dynamics of how internal attention interacts with other cognitive processes can significantly facilitate our capacity to predict and model (mal)adaptive IDC. We, therefore, introduce the Attention-to-Thoughts model—a dynamic systems theory and computational model of internal attention in IDC. Through the model we aim to, first, conceptually and computationally define momentary states of this dynamic system; second, simulate and predict differential temporal trajectories of this dynamic system through which IDC emerges. Through experimental simulations, we explore how Attention-to-Thoughts may be used to better understand how internal attention selection is expressed from moment-to-moment; how internal attention unfolds by documenting how, as a function of contextual demands for focused attention, internal attentional selection iteratively transacts with working-memory and emotion; and, in turn, how maladaptive IDC (e.g., repetitive negative thinking, cognitive dyscontrol) emerges from temporal trajectories of the dynamic system of internal attention. Finally, we highlight key conceptual, computational, and methodological directions for the study of internal attention, IDC, and related phenomena (e.g., mindfulness).

Keywords:

• Internal attention
• internally directed cognition
• mindwandering
• dynamic systems
• rumination

This article is referred to by:

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Disclosure Statement

No potential conflict of interest was reported by the author(s).

Notes

1 Computational approaches have helped to advance theories of (external) attention, and in cases where different theories converge to similar predictions, to then clarify theoretical differences in underlying processes and computations (e.g., percept vs. exemplar similarity estimation or signal-over-noise detection) (see Logan, 2004 for a review). Such theories are based on the rich history of psychophysics research and accordingly focus on external attention (Chun et al., 2011). A2T focuses on internal attention in IDC, and integrates other components into a dynamic system subserving IDC, rather than focus solely on attentional processes (Smith & Sewell, 2013). Previous theories nevertheless influenced conceptualizations in A2T—most notably executive control driven attentional weights for biasing and optimizing selection, and the inclusion of noise/stochastic processes (Logan, 2004).

2 However, the features/dimension on which selection is based can be changed when applying the model to specific topics of interest.

3 For computational/simulation purposes we view this component as a repeatedly updating first-in-first-out storage device containing the last five selected representations/objects.

4 As described in Model components section, the Representations in WM component is computationally implemented a short-term storage of five representations. Where neutral representations have a value of 0 and negative representations a value of 1.

Additional information

Funding

This research received no specific grant from any funding agency, commercial or not-for-profit sectors.