What’s in an Association? The Relationship Between Similarity and Episodic Memory for Associations Gregory E. Cox ([email protected]) Amy H. Criss 430 Huntington Hall, Department of Psychology, Syracuse University, Syracuse, NY 13244 USA Abstract Table 1: Examples of study and test pairs used by Dosher (1984) and Dosher and Rosedale (1991). When two events occur closely in time, an “association” exists Partial study list Test pair between memories for those events. When a pair of associ- + + ated events is semantically similar, it is easier to recognize the PRESENT—GIFT PRESENT—GIFT (S E ) CENTER—SUM complete pair and easier to tell the complete pair apart from CENTER—SUM (S−E+) pairs of events that did not co-occur; there is also, however, a TOTAL—MIDDLE DINNER—VOW bias to report that similar events had co-occurred, even when DINNER—SUPPER (S+E−) they had not. A new experiment shows that these phenomena PROMISE—SUPPER occur whenever two events share features, whether those fea- SUMMIT—PERSON − − tures are perceptual or conceptual in nature and whether the CURTAIN—PATTERN SUMMIT—PATTERN (S Eu ) events themselves are verbal or non-verbal. We present a dy- MOVIE—FILM − − namic model for storage and recognition of associations that MOTIVE—REASON MOVIE—REASON (S Er ) shows how all these results can be explained by the princi- ple that shared features lead to correlated processing of similar events, which in turn increases capacity to process associative study and test phases of this task, it is possible to assess what information. kinds of item information lead to better associative memory Keywords: Memory; associative recognition; similarity. and, therefore, to learn about how the mnemonic content of individual events (items) is related to the content of associa- Introduction tions formed between them. When two events occur closely in time, it is often the case Using this task, Dosher (1984) and Dosher and Rosedale that an “association” is formed between the memories for (1991) investigated the relationship between semantic simi- those events. That is, not only is information about the events larity and episodic memory for associations by using different themselves stored in memory (often called “item” informa- kinds of study and test pairs (Table 1). S+E+ pairs are those tion), so is information about the fact that they co-occurred that are both semantically related (S+) and studied together (often called “associative” information). The ability to store (E+); S−E+ pairs are those that are semantically unrelated both kinds of information underlies numerous cognitive func- (S−) and studied together (E+); S+E− pairs were not stud- tions, such as the ability to associate a word with its referent, ied together (E−) but are semantically related (S+). There are to discover analogies between similar scenarios, and to learn two kinds of S−E− pairs, i.e., pairs that are neither semanti- − − causal relationships between events. However, it remains un- cally related nor had been studied together: S Eu pairs are clear what the relationship is between memory for individual formed by rearranging pairs of items that had originally been − − events (items) and for combinations of events (associations). studied with unrelated items; S Er pairs are formed by rear- In particular, it is not clear what the content of an associa- ranging pairs of items that had been studied with semantically tion is—does it depend on properties of the associated items related items. They found three critical results: or is it independent? Based on a set of results regarding the 1. Correct recognition of an episodic association is improved relationship between similarity and associative memory, we when pairs are semantically related (S+E+ > S−E+). present a model in which associative information is based 2. False recognition of a rearranged pair is reduced when its upon alignment of item representations. Results from a new members were originally studied as part of semantically- − − − − experiment lend support to this model. related pairs (S Er < S Eu ). Memory for associations can be studied using the associa- 3. Semantically related rearranged pairs (S+E−) tend to be tive recognition paradigm. In this task, participants study a falsely recognized as having been studied, but primarily set of pairs of items such as words or images. In a subsequent when responding is rapid (S+E− ≈ S+E+ early, S+E− ≈ − − test phase, participants are asked to distinguish between pairs S Eu late). of items that were studied together (“intact” pairs) from those It is difficult for any single account to explain all these re- that were studied separately (“rearranged” pairs). Because sults: The first two results indicate that the presence of a se- the items in each test pair were always studied, this task selec- mantic relationship between a pair of items leads to stronger tively measures memory for the associations formed between encoding of their episodic relationship, since it not only im- items that were studied at the same time. Good associative proves correct recognition, but aids correct rejection as well. memory is indicated by the ability to correctly recognize in- Results 1 and 3 might lead one to conclude that semantically tact pairs (high hit rate and/or fast correct recognition) and to related pairs are more familiar by virtue of co-occurring more reject rearranged pairs (low false alarm rate and/or fast correct often in general, but in fact such words do not tend to co-occur rejection). By manipulating the kinds of item pairs used in the (synonyms or antonyms are used in place of one another, not 250 next to one another), nor would this explain result 2. Result 2 4. If shared features are used to encode an item at study but might be attributed to an encoding-specificity effect (Tulving they are no longer available at test, the similarity between & Thompson, 1973), but this would not explain the other re- the test item and memory for the studied item is reduced. sults nor why the effect is larger for pairs that were originally It is apparent that many of these consequences map onto the studied with a related word. Result 3 could indicate that asso- partial explanations offered in the Introduction—the aim of ciative recognition depends initially on an overall assessment our model is to show how they all flow from the single notion of “relatedness”, and a second source of purely episodic infor- that shared features lead to correlated processing. mation “suppresses” this initial bias (e.g., a “recall-to-reject” Representation and storage The event of encountering a mechanism). However, the suppression account does not ex- pair of items at either study or test is represented in work- plain why S+E+ > S−E+ even for slower responses (if se- ing memory as a set of binary (0 or 1) features. There are mantic relatedness were suppressed, this should mitigate the three types of feature, as depicted in the top row of Figure 1: advantage for S+E+ pairs) nor why it is easier to reject S−E− r context features, which represent the time and location of the pairs than S−E− (unless studying a semantically related pair u study event; item-specific features, which represent the per- also made that pair easier to recall, which could allow S+E+ ceptual and conceptual aspects of each item; and associative pairs to retain their advantage even for slow responses). features which represent the co-occurrence of the two items. Recent work from our laboratory suggests these results There is a limited capacity to hold features in working mem- may be a function of interactions between item memory and ory. This capacity is determined by the number of unique memory for associations. Cox and Shiffrin (2017) proposed a features across the event, such that when items are similar dynamic model for item and associative recognition in which less capacity is needed to represent them and more capacity is associative recognition decisions were based on a set of fea- available to represent associative features. Each item feature tures that emerged from the interrelation and/or elaboration of has probability s of being shared between two semantically the features of the component items. Because associative fea- related words. The proportion of features devoted to encod- tures can only emerge after enough item features have been ing associative information is pA for unrelated items and is processed, this model implies a strong interaction between 1−(1− pA)(1− pAs) for related items (in other words, either item and associative retrieval. Clear evidence for such an in- an associative feature is encoded normally with probability teraction was found by Cox and Criss (2017), however they pA or there is a shared feature with probability s and that ca- also found that item and associative information were also pacity is used for an associative feature with probability pA). separable, in that some decisions could be made on the basis If the pair is presented for study, its working memory rep- of just one kind of information (cf. Buchler, Light, & Reder, resentation is stored as a trace in long-term memory1. Stor- 2008). This prior work focused on the mechanisms involved age tends to be incomplete and error-prone. Because con- at retrieval, rather than what happens during encoding, leav- text features are persistent in the environment, we assume that ing unspecified the precise nature of the interactions involved. all available context features are stored in the memory trace. However, due to limited time and attentional resources, not A Dynamic Model of Associative Encoding all item or associative features may be stored—we let u de- While it would be possible to explain the set of results just note the probability that a non-context feature gets stored in reviewed in terms of multiple processes, we present a model the trace.