Online social networks have reshaped public discourse by enabling large-scale, user-driven discussions on societal topics. However, such discussions often exhibit a narrowing of attention, where collective focus converges on a limited subset of topic aspects while neglecting alternative viewpoints. We term this phenomenon tunnel vision. Distinct from echo chambers, filter bubbles, or other ideological polarizations, tunnel vision emerges at the aspect level, reflecting reduced topical diversity rather than alignment of opinions. This paper presents a formal framework for defining and quantifying tunnel vision in online discourse. We propose two entropy-based metrics, Aspect-Sentiment Pairwise Entropy (ASPE) and Coverage-Adjusted Aspect-Sentiment Entropy (CASE), to measure both the distribution and completeness of aspect-level engagement. To investigate the emergence and dynamics of tunnel vision, we simulate discourse using LLM-simulated agents guided by Bayesian cognitive modeling within an artificial social environment. Our experiments show that tunnel vision naturally arises over time and is shaped by cognitive constraints, including users’ perception windows and expressive capacity. These results offer a new lens on attention dynamics in digital spaces and establish a quantitative basis for detecting and mitigating aspect-level discourse narrowing.