Recent research from Queens College, City University of New York, reveals a fundamental limitation in the attention mechanisms of large language models. By applying the classic Stroop psychological test to leading multimodal AI systems, researchers demonstrated that while these models excel at long-context memory and pattern recognition, they lack the cognitive inhibitory control required to manage conflicting information under sustained cognitive load. The study evaluated state-of-the-art models, including GPT-4o, Claude 3.5 Sonnet, and next-generation iterations like GPT-5, Claude Opus 4.1, and Gemini 2.5 Pro. Participants were asked to identify the ink color of words that either matched their semantic meaning, conflicted with it, or were neutral. The experiment systematically varied word list lengths from five to forty entries to measure performance degradation under increasing interference. Initial tests with short sequences showed strong conflict resolution, with GPT-4o achieving 91 percent accuracy. However, accuracy plummeted as context expanded. At forty words, the same model accuracy dropped to 15 percent under conflicting conditions. Claude 3.5 Sonnet exhibited similar collapse, falling to 24 percent. Further analysis confirmed this was not a failure of visual encoding or context window capacity. Models correctly read words regardless of length and accurately identified colors in non-conflicting scenarios. The performance breakdown occurred exclusively during cross-modal interference, where semantic tokenization overwhelmed diluted color signals in the unified attention layer. Notably, newer models showed marginal overall improvements but retained the core architectural deficiency. Unlike human subjects, who demonstrate adaptive performance recovery after detecting conflict, the tested models exhibited no self-correcting feedback loops. Some models even recognized the test rules cognitively but failed to adjust their execution behavior. When prompted to use chain-of-thought reasoning, certain systems bypassed the native attention mechanism entirely by generating and running external code, effectively admitting the native architecture inability to resolve the conflict. The researchers attribute this limitation to the inherent nature of Transformer self-attention. As a statistically driven, feedforward weighting system, it dynamically allocates focus but lacks a biological execution control network capable of top-down regulation and real-time conflict suppression. As information density increases, attention resources become diluted, causing target goals to be overwritten by dominant semantic priors. The findings underscore the need for novel evaluation frameworks that extend beyond static benchmarking to include dynamic cognitive load assessments. To achieve robust general intelligence, future architectures may require integration of active gating mechanisms inspired by the human prefrontal cortex. Emerging approaches, such as selective self-attention that modulates focus temperature and differential Transformers that filter irrelevant context, represent early steps toward bridging this gap. Ultimately, advancing artificial intelligence will demand not only the ability to prioritize relevant information, but also the sustained cognitive discipline to maintain objectives amid persistent interference.