ORCID

0009-0001-9904-0111

Faculty Advisor

Dr. Daniel Stephens

Keywords

Keywords: algorithmic attention, visual salience, variable rewards, infinite scrolling, recommender systems, attention governance, digital wellbeing, ASM, CAS, attention safeguard models, cognitive attentional standards, Delante Clark, I.D.E.A.S

Abstract

Algorithmic recommendation systems and interface designs shape attention by combining visually salient cues with uncertain reward timing and low friction interaction. These conditions can sustain anticipatory checking, extend time on task, and reduce natural disengagement points through patterns such as infinite scrolling and autoplay. This paper synthesizes research across neuroscience, human computer interaction, behavioral economics, and artificial intelligence to argue that attention capture is a predictable outcome of incentive driven design rather than an individual failure of self regulation. It advances the Attention Safeguard Models and the Cognitive Attentional Standard as system level interventions that regulate exposure conditions, pacing, and amplification velocity without adjudicating content meaning. The contribution is a governance framework that supports auditable design requirements for cognitive sustainability and a research agenda for testing CAS thresholds with field measures of engagement and recovery.

College

College of Community Innovation and Education

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