Multimodal large language models (MLLMs) are frequently exposed to auxiliary textual context, the impact of which on vision-language tasks remains underexplored. In this paper, we investigate the influence of task-irrelevant context by formulating it as a controlled intervention within a binary visual judgment framework. Through controlled paired comparisons with and without auxiliary textual context, we observe that irrelevant text consistently biases model predictions across diverse benchmarks. To move beyond performance metrics, we characterize this sensitivity through "decision margin", which is defined by the log-probability difference between candidates. Our analysis reveals an empirical pattern: context-conditioned margins are often well approximated by an affine transformation of their context-free counterparts. We further interpret the fitted affine parameters as metrics for visual commitment preservation and directional answer bias. These findings provide a margin-level diagnostic view of irrelevant-context effects in MLLMs, indicating that irrelevant context does not manifest as stochastic noise, but as an estimable distortion of model preference.