The Flow Engine Framework: A cognitive model of optimal human experience

Abstract

Flow is a well-known concept in the fields of positive and applied psychology. Examination of a large body of flow literature suggests there is a need for a conceptual model rooted in a cognitive approach to explain how this psychological phenomenon works. In this paper, we propose the Flow Engine Framework, a theoretical model explaining dynamic interactions between rearranged flow components and fundamental cognitive processes. Using an IPO framework (Inputs – Processes – Outputs) including a feedback process, we organize flow characteristics into three logically related categories: inputs (requirements for flow), mediating and moderating cognitive processes (attentional and motivational mechanisms) and outputs (subjective and objective outcomes), describing the process of the flow. Comparing flow with an engine, inputs are depicted as flow-fuel, core processes cylinder strokes and outputs as power created to provide motion.

Advancing our understanding of psychological flow: A scoping review of conceptualizations, measurements, and applications.

“…The construct clear task goals subsumes the example terms (utilized by review sources) including “specified objectives,” “clarity of purpose,” “confidence”; “immediate and unambiguous feedback” subsumes the example constructs “coherent, noncontradictory demands” and “direct feedback”; and “self-efficacy” subsumes the example constructs “self-confidence” and “self-trust.” See Figure 2 and for further details. The premise behind goal- and feedback-related concepts being included in this theme appears to be that if goal (that is, clear goals) and diagnostic information are not clear and immediate (that is, immediate and unambiguous feedback), then the experience may be confusing or overdemanding (and no longer optimally challenging; Cseh, 2016; Drengner et al, 2018; Guertin, 2016; Keller & Landhausser, 2012; Moneta, 2017; Simlesa et al, 2018; Valenzuela et al, 2018). When examining goal type, sources reported that goal congruency (for example, Keller & Landhausser, 2012), personal growth and exploration goals (for example, “seeing how well I can do”; Swann et al, 2016), “open” (that is, nonoutcome orientated) as opposed to “fixed” (that is, outcome orientated) goals (Swann et al, 2017), and task-orientated goals (Csikszentmihalyi, 2014c) result in increased flow.…”

Flow Experiences During Visuomotor Skill Acquisition Reflect Deviation From a Power-Law Learning Curve, but Not Overall Level of Skill

“…Overall, the lack of mechanistic hypotheses about the processes underlying the proposed dependencies in the Flow models make it difficult to make definite predictions in novel tasks, especially ones with changing task demands and skill, such as here. Prior work has provided some (neuro)cognitive, information-processing views on Flow (Marr, 2001; Cowley et al, 2008; Šimleša et al, 2018). Such work could provide an approach to make cognitive hypotheses about Flow, but these models have not been empirically tested, so it is unclear which (if any) to follow.…”

An 8P Theoretical Framework for Understanding Creativity and Theories of Creativity

“…In this state, people become fully immersed in a challenging activity, not to achieve a goal, but rather to enjoy that activity. Flow is often associated with creativity (Šimleša, Guegan, Blanchard, Tarpin‐Bernard, & Buisine, 2018).…”