The 2-high-threshold (2HT) model of recognition memory assumes that test items result in distinct internal states: they are either detected or not, and the probability of responding at a particular confidence level that an item is "old" or "new" depends on the state-response mapping parameters. The mapping parameters are independent of the probability that an item yields a particular state (e.g., both strong and weak items that are detected as old have the same probability of producing a highest-confidence "old" response). We tested this conditional independence assumption by presenting nouns 1, 2, or 4 times. To maximize the strength of some items, "superstrong" items were repeated 4 times and encoded in conjunction with pleasantness, imageability, anagram, and survival processing tasks. The 2HT model failed to simultaneously capture the response rate data for all item classes, demonstrating that the data violated the conditional independence assumption. In contrast, a Gaussian signal detection model, which posits that the level of confidence that an item is "old" or "new" is a function of its continuous strength value, provided a good account of the data.