The conjoint-recognition model (CRM) implements fuzzy-trace theory’s opponent process conception of false memory. Within the family of measurement models that separate the memory effects of recollection and familiarity, CRM is the only one that accomplishes this for false as well as true memory. We assembled a corpus of 537 sets of conjoint-recognition data, with estimates of CRM’s parameters plus goodness-of-fit statistics being available for all the data sets. This corpus was used to conduct a meta-analysis of CRM’s underlying process assumptions by pitting two theoretical interpretations of the model against each other: (a) the original interpretation, which assumes that its retrieval parameters tap a single recollection process (verbatim retrieval) and a single familiarity process (gist retrieval), and (b) the dual-recollection interpretation, which assumes that its parameters also tap a second recollection process (context retrieval). The two interpretations generate a series of differential predictions that fall into three groups—namely, predictions about invariant relations among parameters, about the structure of CRM’s parameter space, and about the location of individual parameters within the space. When these predictions were evaluated with the corpus, the results converged on the dual-recollection interpretation. The results also resolved a long-standing uncertainty about whether the familiarity process for true memory is semantically or perceptually driven.

We removed a key uncertainty in the Deese/Roediger/McDermott (DRM) illusion. The mean backward associative strength (MBAS) of DRM lists is the best-known predictor of this illusion, but it is confounded with semantic relations between lists and critical distractors. Thus, it is unclear whether associative relations, semantic relations, or both foment the illusion. In Experiment 1, we developed a tool for investigating this question—a normed pool of materials in which subjects rated the gist strength of 120 DRM lists that varied widely in MBAS. This produced a mean gist strength (MGS) statistic for each list, which allowed MGS and MBAS to be manipulated factorially. In Experiment 2, we conducted the first MGS (high vs. low) × MBAS (high vs. low) factorial study of the DRM illusion. To measure how MGS and MBAS affect underlying retrieval processes, we implemented a conjoint recognition design. For raw memory performance, MGS affected both true and false recognition of critical distractors, and it affected both true and false recognition of list words. MBAS did not affect true or false recognition of list words or true recognition of critical distractors. With false recognition of critical distractors, it had a reliable effect in one condition when MGS was low, but it had no effect in another condition. At the level of retrieval processes, increasing MGS increased the familiarity of critical distractors’ semantic content, and it also increased the familiarity of list words’ semantic content.

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Recollection rejection is traditionally defined as using verbatim traces of old items’ presentations to reject new similar test cues, in old/new recognition (e.g., rejecting that couch is old by retrieving verbatim traces of sofa’s presentation). We broaden this conceptualization to include (a) old as well as new similar test cues, (b) using verbatim traces for acceptance as well as rejection, and (c) using illusory verbatim traces of unpresented items (phantom recollection) as well as actual verbatim traces (true recollection). The expanded model describes how true recollection and phantom recollection generate memory decisions by creating matches and mismatches between comparisons of test cues to the content of retrieved verbatim traces versus comparisons of test cues to the content of test questions. This model generates a series of predictions about verbatim editing. Some are intuitive, such as the prection that performance will be more accurate for old cues than for new similar ones. Others are counterintuitive and conflict with an alternative model, such as correct rejections are easier than hits and that correct rejection rates will be more stable over time than hit rates. Meta-analyses of a corpus of conjoint recognition data sets provided support for the model’s predictions.

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