What are the boundary conditions of differentiation in episodic memory?

What are the boundary conditions of differentiation in episodic memory? Adam F. Osth (adamosth@gmail.com) The Ohio State University 1835 Lazenby Hall Columbus, OH 43210, USA Simon Dennis (simon.dennis@gmail.com) The Ohio State University 1835 Lazenby Hall Columbus, OH 43210, USA Abstract One of the critical findings in recognition memory is the null list-strength effect (LSE), which states that repeating study items does not hurt the performance of other studied items. Episodic memory models were able to predict the null LSE by using the principle of differentiation, in which repetitions of an item accumulate into a single strong memory trace. A hypothesized boundary of differentiation is that repetitions of an item in different contexts will create new traces. Two experiments tested this hypothesis by repeating words across different study-test cycles rather than within a single list followed by a test on all of the studied lists. Results indicated that as the proportion of strong items increased, there was both a null LSE and a non-significant decrease in the FAR, which is contrary to the predicted strength-based mirror effect. These two results in tandem provide a challenge for differentiation models. Keywords: Recognition memory; Memory models; List-strength effect Episodic memory; Introduction Episodic recognition memory is typically tested using the yes/no recognition task, in which participants are presented with a list of words to study and during a subsequent test phase are instructed to say ―yes‖ to the previously studied words and ―no‖ otherwise. In the 1980’s, a class of simulation models referred to as global memory models were developed to account for data from this paradigm, as well as other paradigms such as free recall, cued recall, and associative recognition. While these models posited different storage operations, such as composite storage, in which all memory traces compile into a single memory representation (TODAM: Murdock, 1982; the Matrix model: Pike, 1984), and multiple trace storage, in which individual memory traces are separable and accessible (SAM: Gillund & Shiffrin, 1984; Minerva 2: Hintzman, 1988), all of these models posit a retrieval process in which the probe item is matched against all of the memory traces from the study list in parallel, producing a ―familiarity‖ value for the test item that is compared to a criterion to make a decision. A success of the global memory models was the ability to account for the list length effect, in which increases in the size of the study list produce decreases in discriminability, an effect which was believed to be a very robust finding in recognition memory at the time (although recent examinations of the list length paradigm have uncovered several confounds, see Dennis & Humphreys, 2001; Dennis, Lee, & Kinnell, 2008). These models were able to make this prediction because each memory trace contributes additional variance to the match strengths for both target and distracter items, increasing the overlap between these distributions and decreasing discriminability as a consequence (Clark & Gronlund, 1996). A majority of these models were also able to predict how strengthening items by additional repetitions in the study list produce increases in the hit rates for strengthened items. This is because of the additional trace assumption: repetitions of an item produce additional memory traces that add match strength to target items during retrieval. A consequence of this assumption is that repetitions of items have the same effect on the variance of the match strengths as increasing the length of a study list. This is the basis of the list-strength prediction, in which increasing the strength of the studied items reduces performance for non- strengthened items due to the interference from the additional traces produced by the repetitions. The list-strength prediction of the global memory models was tested by Ratcliff, Clark, and Shiffrin (1990) in a series of experiments with all of them resulting in null list-strength effect. Specifically, weak items were not harmed by the strengthening of other items on the study list and strong items did not benefit from the presence of weak items. This was a challenge for virtually all of the global memory models at the time as well as several other memory models, as it was not possible for them to predict a list-length effect while simultaneously predicting a null list-strength effect (see Shiffrin, Ratcliff, & Clark, 1990 for a technical description of how each memory model fails in these predictions). A potential solution to this problem posited by Shiffrin et al. (1990) was the differentiation hypothesis. This proposes a revision to the learning process in which repetitions instead accumulate into a single strong memory trace that is more responsive to its own cue and less responsive to other cues, thus keeping the number of traces constant as strength is increased and preventing interference from increasing. The differentiation mechanism not only predicts a null list- strength effect, but also predicts a lower false alarm rate