Mehta et al. focused on conversation-centric systems to develop qualitative metrics for the user's successful engagements, instead of quantitative measures of "inappropriate utterances" (in which the AI misunderstands player input and responds nonsensically) and other technical failures. After a "breakdown" in conversation, how effectively the user incorporates it into the overall understanding of the characters and story depends on design features. In their study of "Façade", some AI actor breakdowns include shallow semantic understanding, inverted meanings, and the timing of responses. Artificially intelligent agents have trouble translating ambiguous user input into the limited narrative meaning system, as when "sad" or "hurt" is interpreted only as a reference to clinical depression. Similarly, negative and positive user sentiments are often confused with one another, especially if a positive statement is preceded by disagreement with a negative one. This is exacerbated if a user cannot send a reply fast enough – their utterance is understood as referring to the most recent speaker, leading to unintended interactions. However, background information is referenced often enough for a player to invent narrative justifications for apparently strange behaviors, which most participants did.