Interpreting the Influence of Implicitly Activated Memories on Recall and Recognition

Transcription

1 Psychological Review Copyright 1998 by the American Psychological Association; Inc. 1998, VoL 105, No. 2, X/98/$3.00 Interpreting the Influence of Implicitly Activated Memories on Recall and Recognition Douglas L. Nelson and Vanesa M. McKinney University of South Florida Nancy R. Gee State University of New York College at Fredonia Gerson A. Janczura University of Brasflia A model concerning the influence of implicitly activated information on cued recall and recognition is presented. The model assumes that studying a familiar word activates its associates and creates an implicit representation in long-term working memory. Test cues also activate their associates, with memory performance determined by a sampling process that operates on the intersection of information activated by the test cue with information previously activated by the studied word. Successful sampling is enhanced by preexisting connections among the associates of the studied word and by preexisting connections between it and the retrieval cue. However, the usefulness of the implicit representation is reduced by the activation of competing associates and by shifts of attention before testing. Experiments designed to test predictions of the model indicate that the associates of a familiar word can exert a powerful effect on its cued recall and recognition. In everyday life words become associated with one another as a result of hundreds of experiences with them in varying contexts. This information is represented in memory and is remarkably consistent across different people in the same culture (Cramer, 1968). Regardless of whether people live in Florida, Illinois, or Washington, we can be reasonably confident that the word TABLE makes them think of chair. The purpose of the present article is to come to a better understanding of how these associates influence memory performance. We claim that experiencing a familiar word activates its associates and that this activation produces an implicit representation in long-term working memory (LTWM) that can affect the recall and recognition of the item that actually occurred. This claim implies that remembering is not only a function of what people do during encoding and what kind of retrieval cues they receive, but also a function of what they knew beforehand. Memory for a recent experience reflects an interaction between the nature of the encoding, the nature of the retention test, and the hundreds of previous experiences that people have had with the materials. Researchers have been concerned with the influence of known Douglas L. Nelson and Vanesa M. McKinney, Department of Psychology, University of South Florida; Nancy R. Gee, Department of Psychology, State University of New York College at Fredonia; Gerson A. Janczura, Department of Psychology, University of Brasflia, Brasflia, Brazil. This research was supported by Mental Health Grant from the National Institute of Mental Health. Our special thanks go to Jie Xu for her help in collecting the data for Experiment 2 and especially to Mike Anderson, Cathy McEvoy, John Wixted, and an anonymous reviewer for their comments on a version of the article. Correspondence concerning this article should be addressed to Douglas L. Nelson, Department of Psychology, University of South Florida, Tampa, Florida Electronic mail may be sent to nelson@ luna.cas.usf.edu. information on memory for some time (e.g., Alba & Hasher, 1983; Ericsson, Krampe, & Tesch-Romer, 1993; Underwood, 1957). Most relevant to the present article is Underwood's (1965, 1983) suggestion that associatively related words are elicited implicitly in the course of perceiving a presented word and that this process is responsible for the false recognition that occurs when associatively related words are presented as distractors on recognition tests. However, with notable exceptions, most memory researchers seem to have separated into two groups during the 1970s and 1980s, with one emphasizing the study of episodic memory and the other emphasizing the study of semantic memory. Those with interests in studying memory for recent episodes have been preoccupied with attempts to understand how variations in encoding strategy (e.g., Bower, 1970; Paivio, 1971), level of processing (e.g., Craik & "Ihlving, 1975), materials (e.g., Nelson, 1979), and testing conditions (e.g., Gillund & Shiffrin, 1984) affect memory for recently experienced information. These researchers focused on variables affecting the accuracy of memory for a specific episode, and although this effort produced an array of outstanding models for characterizing memory for recent episodes, the role of preexisting knowledge has generally been ignored in most attempts at model building (e.g., Eich, 1982; Gillund & Shiffrin, 1984; Hintzman, 1986; Humphreys, Bain, & Pike, 1989; Murdock, 1982). Researchers with strong interests in studying semantic memory, again with notable exceptions (e.g., Bahrick, Bahrick, & Wittlinger, 1975), have emphasized its organization and have addressed the general problem of how information in semantic memory is retrieved and classified (see Chang, 1986; Neely, 1991, for reviews). These researchers relied on speeded tasks and produced influential models (e.g., J. R. Anderson, 1983; Collins & Loftus, 1975). Most important for present purposes, they advanced our understanding of how activation provides 299

2 300 NELSON, McKINNEY, GEE, AND JANCZURA immediate access to prior knowledge most likely to be useful in the current context (e.g., J. R. Anderson, 1983, 1990, 1996; Kintsch, 1988). This research has deepened our understanding of comprehension, problem solving, and other cognitive processes, but it is fair to say that this work has not been primarily concerned with episodic memory and particularly with how knowledge implicitly activated during an encoding episode might affect memory for the experience. The aim of the present research is to suggest how these two approaches can be synthesized to provide a means for understanding how information about a recent episode combines with preexisting memories to affect memory for the experience. We begin by recognizing that our interest is in understanding memory processes under conditions involving a relatively high degree of uncertainty. In everyday life, people are often faced with the task of recovering information from the recent past in the presence of retrieval cues that arise in the current context. In reading text, the retrieval of an idea experienced earlier in a passage can be prompted by a related idea that comes later in the passage. The recall of a grocery list left on the kitchen counter can be prompted by walking the aisles of the store. The name of a new colleague can be cued by reading about a theory in his or her field, and so on. Success in all of these tasks is by no means certain because people know many ideas, colleagues, and theories, and because most grocery stores contain many interesting items. Memory for recent episodes certainly can succeed or fail because of the encoding operations and because of the effectiveness of the retrieval cues, but it also can succeed or fail because of what people know about the event to be remembered as a result of a lifetime of learning. We attempt to simulate or model the processes involved under uncertain retrieval conditions by using recognition tasks and the extralist cuing procedure (e.g., Bahrick, 1970; Nelson, Schreiber, & McEvoy, 1992). In extralist cuing, a list of target words is briefly presented, one at a time, and then a set of cues is presented, also one at a time, with each cue being related to one of the studied targets. The test cues are absent during the study trial and are related to their targets only by virtue of some preexisting relationship. In our research we use the extralist procedure because it relies heavily on preexisting connections between the test cue and target for success. The procedure also allows us to vary the nature of the encoding conditions, test instructions, and test cues independently, but most important, such manipulations can be systematically combined with the characteristics of words that are represented in long-term memory. These characteristics include well-known attributes such as their concreteness and frequency, as well as characteristics linked to their associates such as their number and connectivity. Just as familiar words differ in their concreteness and frequency, they also differ in how many associates they have and in the connectivity of these associates. These characteristics also include attributes that connect words together, including forward, backward, and indirect links. In the next section of the article, we describe the procedures used for measuring these characteristics, and in the section after that, we briefly describe our findings, which show how these characteristics affect success and failure in the extralist cuing task. In the third section, we present the details of a model that incorporates all of these variables in a set of related equations. The model is called PIER 2 because it updates and formalizes an earlier version of our model called PIERJ In the fourth section of the article, we describe the results of five experiments designed to evaluate untested predictions of the model. In the final section, we summarize the findings, compare PIER 2 with PIER and SAM ([ search of associative memory model ] ; Gillurid & Shiffrin, 1984; Raaijmakers & Shiffrin, 1981), and describe implications of this research for theories of LTWM, skilled performance, implicit memory, and false memory. Measuring Word Characteristics Represented in Long-Term Memory The claim that preexisting memories play a role in remembering recent episodes presupposes the ability to describe them, and given that the present research evaluates this assertion by using familiar words, the nature of lexical representation must be described. We assume that words are connected to their associates in memory as a result of language experience in everyday life (J. R. Anderson, 1983; Collins & Loftus, 1975). These associates vary in number, strength, and connectivity, and we assume that free association norms provide the best way to index this information (e.g., Cramer, 1968; Deese, 1965). Such norms are obtained by presenting individual words in booklets to many participants ( ) who are asked to write down the first word that comes to mind that means the same thing or that is strongly related to the presented word. Only a single associate is required to avoid problems of associative chaining and retrieval inhibition (McEvoy & Nelson, 1982), and because single responses provide more accurate estimates of set size (Howe, 1972; Joelson & Herrmann, 1978). The number of different associates given by two or more participants in the norms is used as an index of the relative set size of the normed word. Responses given by single participants are excluded because, although most are weakly related, they tend to be unreliable (Nelson & Schreiber, 1992). Set size as defined through free association norms appears to be a normally distributed variable, and we define small sets as those with 8 or fewer associates and large sets as those with 17 or more associates. Following tradition, the probability that any given associate is produced is used as an index of its relative strength in relation to the cue word. In extralist cuing, the associate serves as the studied word or target, and the cue word serves as the test cue, with preexisting cue-to-target strength referring to the probability that the test cue produces the target in the absence of study. In this sense, this measure captures the probability of guessing the target. This norming procedure yields the set of the strongest, most reliable associates and their respective probabilities of occurrence. Some associates are more probable and hence more strongly related to the initiating cue than others, and some sets contain more associates than others. This information is useful for exploring questions about the effects of associative structure on memory performance, but it is insufficient. Knowing only what the associates are provides no information about connections from these associates to the initiating cue, nor does it l PIER stands for processing implicit and explicit representations.

3 IMPLICITLY ACTIVATED INFORMATION AND MEMORY 301 provide information about associate-to-associate connections. What is needed is an n n associative matrix in which the associates of normed words are also normed with separate samples of subjects. For example, as illustrated in Figure 1, the word DINNER has a set of five associates: supper, eat, lunch, food, and meal. After norming DINNER, each of its associates was normed with separate samples of subjects (e.g., SUPPER was presented to one group, EAT to another, and so on). As can be seen by reading along the first column of the matrix, some of these words produce DINNER as a response (supper, lunch, and meal). The DIN- NER-to-supper-to-DINNER connection is an example of a twostep link (.54.55), and for convenience of reference we refer to such links as resonant connections because they return to the target. Also note that there are associate-to-associate connections throughout the remaining columns. In our terms, such connections define the connectivity of the initiating item, and as will become clear, we assume that such connections boost the amount of activation that comes to the target word from its associates. For example, the amount of activation sent back to DINNER from supper is augmented by the fact that supper receives some activation from two other words in the set, lunch and meal. Beginning with a large sample of words in 1990, we started norming the associates of normed words, and as of this writing, over 4,500 words have been entered into our database, allowing us to compute n n associative matrices for over 3,400 words (Nelson, McEvoy, & Schreiber, 1994). The total number of matrices is smaller than the total number of words because any word having an unnormed associate stronger than.04 was eliminated. Of the words comprising the pool, an average of 92% (SD = 8%) of their associates have been normed. The absence of a value in the matrix is interpreted as an indication that there is either no connection or that it is too weak to be measured by free association and therefore represents a negligible value that can be ignored. DINNER 1,00.54 SUPPER EAT LUNCH FOOD MEAL No Associates Ctmneellons of Dinner (Resonance) Ass~ciatel; o{ Dinner O, ,08 1, ,(~ Conneotlon8 (Connectivity) Figure 1. The n n associative matrix for r>ir, rner showing its associates and the connection strengths among them and DINNER as determined by free association procedures. The main advantage of the associative database is that it provides a means for selecting words having particular characteristics while controlling others. The important variables related to preexisting lexical representations consist of forward, backward, and indirect links between words serving as cues and targets, connections from the associates back to the target (resonance), connections among the associates of the target (connectivity), and finally, both cue set size and target set size. Because each of these variables theoretically can influence cued recall, all must be either manipulated or controlled along with other variables, and the norms provide the necessary data. For example, target resonance and connectivity can be manipulated, while other characteristics of the target, the test cue, and their relationship are held constant. Findings Related to Implicitly Activated Associates Selected findings based primarily on the extralist cuing task were used to constrain the development of the model, including the effects listed below. Cue and Target Set Size Effects Test cues that define smaller sets of related associates produce more accurate and faster recall than cues that define larger sets of associates (Nelson & McEvoy, 1979; Schreiber & Nelson, in press). Similarly, targets having smaller sets of related associates are more accurately and more quickly recalled than those with larger associative sets (Nelson & Friedrich, 1980; Schreiber, in press). Finally, target set size effects are obtained regardless of word frequency (Nelson & Xu, 1995), concreteness (Nelson & Schreiber, 1992), and word ambiguity (Gee, 1997). Target set size effects do not appear to interact with variables related to explicit encoding processes such as levels of processing and rate of presentation, nor are they modified by studying the targets in the presence of rhymes (Nelson, Bajo, & Canas, 1987; Nelson, Schreibel; & McEvoy, 1992). Finally, target set size effects do not normally interact with test instructions. Telling participants to use test cues to recall the first word that comes to mind ordinarily does not affect the magnitude of these effects compared with cued recall instructions (Nelson, Schreiber, & Holley, 1992). Target set size effects do interact with retention test, context, and attentional disruptions. For example, such effects are ob-" tained in extralist cued recall but not in recognition (e.g., Nelson, Canas, & Bajo, 1987). Target set size effects are also substantially reduced in the intralist cuing task. When targets are presented within a second or so of key context words during the study trial, such effects are nearly eliminated. This result holds for lists and for sentences (Nelson, Gee & Schreiber, 1992) and is attributed to the context-induced inhibition of the associates activated by the cue and target (M. C. Anderson, Bjork, & Bjork, 1994; M. C. Anderson & Spellman, 1995; Nelson, Schreiber, & McEvoy, 1992). What is more important for the present article, switching attention to a different task for several minutes prior to test also substantially reduces the magnitude of target set size effects. Studying additional lists of words, regardless of whether they are associatively related to the initial list, does not influence the

4 302 NELSON, McKINNEY, GEE, AND JANCZURA magnitude of this effect, but multiplying numbers for the same period can eliminate it (e.g., Nelson, McEvoy, Janczura, & Xu, 1993). Target set size effects are sensitive to disruptions produced by switching attention to a conceptually different task. Cue-to-Target Strength Effects Test cues with stronger forward connections to the target according to the norms are more effective than those with weaker connections (Bahrick, 1970; Nelson & McEvoy, 1979), and test cues that share associates with their targets are also more effective than cues that do not (e.g., Nelson, Bennett, & Leibert, 1997). Connectivity Target words with densely connected sets of associates are more likely to be recalled than targets with sparsely connected sets of associates. However, like the target set size effect, connectivity effects are substantially reduced in the intralist cuing task, presumably because of context-induced inhibition effects. Contrary to what is found with set size manipulations, the connectivity effect is more apparent when participants are more semantically oriented toward the study words (e.g., Nelson, Bennett, Gee, Schreiber, & McKinney, 1993). The Model: PIER 2 We know much more about set size effects than any of the other effects that we have only started to investigate as a result of recent advances with the norms. PIER was designed to explain set size effects, but it also served as a guide for exploring effects related to shared associates, resonance, connectivity, and so on, and PIER 2 evolved out of our attempts to deal with the effects of these newly discovered variables. The major assumptions concerning encoding, retention, recall, and recognition are elaborated later, and as will become clear, the mathematical development relies to some extent on using SAM as a framework (Gillund & Shiffrin, 1984; Raaijmakers & Shiffrin, 1981). In contrast to SAM, we have made no attempt to obtain parameter estimates in order to make quantitative fits of our model to the data. The model is described in verbal terms, and the equations are presented in the expectation that they clarify our intent and serve as a step toward a more formal expression of the bases for our predictions. Hence, PIER 2 is focused on qualitative (ordinal) predictions and provides guidelines for situations in which more intensive quantitative analyses might be appropriate. General Assumptions We assume that the performance of complex tasks such as reading, problem solving, and learning requires people to maintain large amounts of related information in an activated state and that LTWM serves this function (Cowan, 1988, 1995; Ericsson & Kintsch, 1995; Shiffrin & Schneider, 1977). Experiencing familiar concepts in new contexts is a highly practiced skill and involves the activation of relevant past information and, depending on the goal of the task, some effort in generating new representations arising from the novel aspects of the information being processed. We assume that participants will be aware of many aspects of their processing, but that much information will be automatically processed and will never reach a state of conscious awareness. For example, although participants may be aware of what they are rehearsing at the moment, comprehension is a highly skilled activity during which past associations are implicitly and directly activated in LTWM without awareness (e.g., Kintsch, 1988). One function of memory is to serve as a holding system for maintaining the activation of this information (Ericsson & Kintsch, 1995). LTWM is such a system, and it is necessary because it provides more ready access to vast amounts of information from the past while allowing for the processing of information held in the focus of attention. We assume that both consciously focused information and implicitly activated information encoded during a recent experience can contribute to recall. In PIER 2 the encoding of a familiar word in a new context produces two independent representations, an implicit representation resulting from the automatic activation of preexisting information and an explicit representation resulting from intentional processing activities. This distinction is compatible with the distinction between semantic and episodic memory, but we prefer the more theoretically neutral term representation to avoid the implication that different biologically based memory systems underlie our effects. Although the independent systems view may be correct and although our findings are generally consistent with this theoretical position, there is nothing in our findings that links them to specific brain substrates. The implicit representation arises when a familiar word activates its lexical representation in LTWM, which in turn automatically activates meaningfully related associates connected to it as a result of previous experience (e.g., J. R. Anderson, 1990). Such activation represents a form of unconscious memory retrieval that provides rapid, parallel, and direct access to what is known about the stimulus and that produces an implicit representation or trace that includes the target and its associates. Four findings suggest that participants are unaware of this activation and its influence. First, target set size effects are found regardless of whether participants study the targets under intentional or incidental conditions and regardless of whether the test instructions refer to the study episode (e.g., Nelson, Schreiber, & Holley, 1992; Nelson, Schreiber, & McEvoy, 1992). Second, the magnitude of these effects is uninfluenced by whether the test instructions require, encourage, or forbid guessing (Nelson, Schreiber, & McEvoy, 1992). Third, when participants are asked to estimate associative set size on a 7-point scale, their estimates are poorly correlated with estimates of set size obtained by free association (r =.02, n = 35; Schreiber & Nelson, in press). Finally, although the associates of a word can be brought to consciousness, participants have never been observed free associating during an experimental session unless asked to do so. Effects related to the associates of a studied word appear to arise from the implicit activation of related associates, and they display hallmarks of a priming phenomenon that is linked to the nature of the extralist cuing procedure. Moreover, evidence described later in the article suggests this activation can persist long enough to be captured by the retrieval cue. In contrast to the implicit representation, the explicit representation is created as a result of processing operations consciously deployed by the participant to meet task demands. Such

5 IMPLICITLY ACTIVATED INFORMATION AND MEMORY 303 operations are often suggested by the experimenter and include suggestions to remember, rehearse, name vowels, rate concreteness, and so on, and people are aware of and can report on how the items were encoded. Such operations produce a representation of the material that incorporates contextual information, connections to other list words created as a result of rehearsal, and so on (Raaijamkers & Shiffrin, 1981). In short, the explicit representation constitutes a record of memories produced as a result of explicit processing activities occurring during study, that is, a list memory. Like other memory models, PIER 2 assumes that the strength of the explicit representation of the target is affected by the nature and extent of the explicit processing activities carried out during the study trial and that such activities affect memory performance (Craik & Tulving, 1975; Gillund & Shiffrin, 1984; Nelson, Schreiber, & McEvoy, 1992). PIER 2 assumes that explicit and implicit representations leave independent traces and that the presentation of a related word as a retrieval cue has a variable probability of recovering one trace or the other, or both (see Figure 2 of Nelson, Schreiber, & McEvoy, 1992). The presence of list length effects, effects of presentation rate, and so on are attributed to the influence of the explicit representation, and the presence of target set size, connectivity, and related effects are attributed to the influence of the implicit representation. Because levels of processing manipulations affect semantic processing, such manipulations presumably influence both representations. In PIER 2 the presence of effects related to the implicit representation are not solely attributed to the heightened activation of the target and its associates in LTWM. Such effects are attributed to the activation of such information intersecting with the information currently activated by the test cue. The contribution of the implicit representation of the target depends upon what is activated by the test cue acting conjointly with the residual activation levels of this representation in LTWM. In this article, we present a model for the role of the implicit representation in episodic recall and recognition. PIER 2, like its predecessor, assumes that SAM provides an effective means for describing the relative contribution of the explicit representation to performance in these tasks, and so we focus exclusively on the role of the implicit representation. The model depends substantially on spreading activation assumptions to characterize events taking place during encoding and on a sampling algorithm to characterize events taking place during retrieval. The Implicit Representation: Encoding Assumptions We assume that the activation of the target and its associates within the implicit representation is influenced by its lexical attributes. For example, low-frequency words presumably receive a greater boost in activation from a single study experience than high-frequency words because they are encountered less often. They are "older" in terms of recency of experience and therefore benefit more from recent study (Jost's law; Boring, 1957). More important for present purposes, we assume that activation can spread from the implicit representation of the target to and among its associates and back along preexisting connections, and that this spread increases the strength of the implicit representation/ This assumption is illustrated in the Figure 2. Preexisting links between a hypothetical target and two of its associates illustrating connectivity and resonance. upper portion of Figure 2, which shows a hypothetical target ti and two of its associates, a i and ak. PIER 2 assumes that the implicit representation of the target is strengthened as a result of resonant connections, for example, the loop from ti to aj to ti (J. R. Anderson & Pirolli, 1984). The stronger the resonant connections are, the stronger the implicit representation will be. More specifically, the amount of activation from a particular associate is a function of preexisting target-to-associate strength multiplied by associate-to-target strength. For example, the values in Figure 1 show that the associate supper sends more activation to DINNER than does lunch because both the targetto-associate and associate-to-target connections are stronger. In addition, we assume that target activation will vary directly with the number of resonant connections and that activation from various sources summates. We also assume that the activation arriving at a t from the ti to a, to a t loop adds to the activation sent from aj to t~ from the preexisting resonant link. The activation of ti by aj is a function of the preexisting strength of the a t to ti connection plus the activation impinging on aj from other associates in the set, such as ak. The greater the number and strength of the connections impinging on any associate that is back connected to the target, the stronger the implicit representation of the target. Hence, connectivity among the associates of the target can influence how much activation the target receives by magnifying the amount of activation returned to it through its resonant connections. More formally, the strength of the target as an implicit representation, S(T~ ), is written as S(Ti) = ai + ~ ajr~. (1) J 2 PIER 2 makes no assumptions about whether activation automatically spreads beyond the initial associates of the target. The spread of activation among the associates of the target may occur automatically, or it may occur because each of the associates has already been activated by the target so that they "attract" activation from other activated associates.

6 304 NELSON, McKINNEY, GEE, AND JANCZURA The ai term is the activation of the implicit representation of the target as a result of its presentation, and because the target is the source of the activation, a~ is set to 1.00, but it could be set higher or lower depending on its characteristics, for example, whether it is a low- or a high-frequency word. The term aj is the activation of associate j occurring as a result of presenting n P the target, and ri: = w~j + Ek akrik is the resonance coming back to a~ from associatej as modified by associate-to-associate connections within the set for all wv > 0, and forj ~ k, k * i. The term w,~ is the activation coming back to target i from associate, j, and P is a weight with a limit of 1.00 that varies with the nature of processing allocated to the target word, with higher values assigned for semantic processing. In other words, because a, in Figure 2 is connected to aj, aj sends additional activation back to the target through its preexisting connection with the target. This amount can be increased because of links to ak from other associates in the set (not shown in Figure 2). However, this process is not endlessly recursive because we assume that links that go beyond three or more steps have negligible effects. The links that matter most are those involving two steps where one of the links is from the target. An example of the calculation of net activation intensity for the target word DIt, e~ea using Equation 1 is provided in Table 1. Finally, a variation of Equation I is used to calculate the strength of each associate in the target's set, S(Aj) = aj + akr~k. Essentially, the strength of the implicit representation k of any associate in the set is a function of the strength of the preexisting link from the target to the associate plus activation converging on it from other associates in the set. Equation 1 indicates that stronger target-to-associate, associate-to-target, and associate-to-associate connections increase the encoding strength of the implicit representation of the target. In this formalism, the encoding strength afforded any particular target is a function of the strength and number of the preexisting connections to, from, and among its associates. For example, greater strength occurs when more of the associates of the target connect back to it and when connectivity among its associates is high (tested in Experiment 1 ). The benefits of connectivity, however, are expected to vary with the nature of the processing during learning. As noted earlier, effects of connectivity among the associates of the target are somewhat more apparent after semantic than after nonsemantic processing (Nelson, Bennett, et al., 1993). We assume that resonance adds more to the strength of the implicit representation when attention to meaning has been maintained during the study trial (P is assigned a higher value). In the next section, we show how information in the test cue intersects with the implicit representation of the target and its associates to determine the probability of sampling the target during recall. The Implicit Representation: Recall Assumptions Cue-Target Intersection In extralist cuing tasks, access to the implicit representation is provided by a test cue that was physically absent during study and that is related to the target only by virtue of preexisting connections. PIER 2 assumes that such cues define a retrieval structure by activating related information in LTWM. In other Table 1 Calculation of Net Activation Intensity for the Target Word D~NER Using Equation 1 Resonant loops ak rjk w~: rq aj aj r U Supper Dinner-lunch (.10) lunch-supper ( ).0043 Dinner-meal (.09) meal-supper ( ).0068 Total Lunch Dinner-supper (.54) supper-lunch ( ).0202 Dinner-meat (.09) X meal-lunch (.06+.,.).0068 Dinner-food (.09) food-lunch ( ).0189 Total Meal Dinner-supper (.54) supper-meal ( ).0094 Dinner-eat (.11) eat-meat ( ).0089 Dinner-lunch (.10) x lunch-meal ( ).0083 Dinner-food (.09) food-meal ( ).0198 Total Activation from interassociative links.3577 Net encoding strength of DINNER a Note. Sample calculation of S(T~) = al + ~ ajr~ for the implicit representation of DINNER using the Figure J 1 strengths (r~ = w U + ~ akr~ and P = 1.00). In this case, S(Ti) is affected by three resonant loops to the k target involving, respectively, supper, lunch, and meal. Calculations reflecting the influence of ak on other associates are not shown because of lack of space (e.g.. connections from other associates to lunch in the dinner-lunch-supper loop). ~ Net strength of DINNER'S implicit representation: S(TI) = as + ~ a:~, with ai = P- = 1. I

7 IMPLICITLY ACTIVATED INFORMATION AND MEMORY 305 words, the test cue provides a pool of relevant associates, one of which is the target. Theoretically, the test cue activates its lexical representation and its associates, and this information intersects with the residual strengths of the target and its associates in LTWM. Target recovery then relies on sampling lexical representations produced as a result of the intersection. The success of this sampling process is determined in the model by the residual strengths of the target and its associates and by the preexisting strengths of the connections that link the test cue with the target. As will be shown, such connections involve more than the direct forward connection and they can become complex. By way of illustration, the intersection of LUNCH with DINNER is shown in the directed graph presented in Figure 3 (cf. Kiss, 1968). All connections were determined by free association norms, and the forward connection between these two words is illustrated by the direct path between them appearing in the center of the figure. However, despite its complexity, the graph has been simplified. The distances between related associates were drawn arbitrarily, and instead of including separate links for forward and backward connections, a single link with a double arrow was used, even though independent connections are the rule. Figure 3 indicates that some of the associates of the target are also associates of the test cue (e.g., eat), and some of the associates of the cue mediate the cue and the target (e.g., breakfast). PIER 2 assumes that connections arising from both shared associates and mediating associates contribute to cued recall because they help to bind the cue and target together as a unit. The formal model assumes that indirect as well as forward and backward links play an important role in facilitating target retrieval because such links contribute to net cue-to-target strength. However, rather than expressing the relationship among these links in terms of an activation algorithm, PIER 2 relies on a variation of SAM's concept of familiarity that ignores the role of context cues (Gillund & Shiffrin, 1984; Ratcliff & McKoon, 1988), with net cue-to-target strength given by box (~ k sa" ' eat upper %t /I Figure 3. Directed graph of the intersection of the test cue LtrNot and its associates with the target word Dn~r,~R and its associates. S(Qj, Ti) = ~. SjkS~ + k S~Su, (2) k where Qj is the test cue, Tj is the target, and k are the associates that join the cue and target through convergent and mediated links (weights on these terms can be added to address the effects of special experimental conditions; for example, prior familiarization with one connection or the other). Equation 2 predicts that stronger preexisting forward, backward, shared, and mediated connections will contribute to higher levels of net cue-to-target strength. This rule suggests that one word can be bound to another in a multiplicity of preexisting connections. Furthermore, each type of connection contributes independently to net strength so that strength can be present even though one or more types of link may be missing (effects of forward and backward links are tested in Experiment 3, and effects of the two types of indirect links are tested in Experiment 4). Finally, Equation 2 contains no allowance for an effect of three-step connections (e.g., the LUNCH-SUpper-meaI-DtNNER connection). Such connections exist but are too weak to have more than a negligible effect on cued recall (Nelson, Bennett, & Xu, 1997). Sample Computation of Net Cue-to-Target Strength Table 2 provides an example of how Equation 2 can be applied to the computation of net cue-to-target strength produced by the intersection of the study word Dn~,~R with the test cue LUNCH. The top section shows hypothetical encoding strengths of DINNER and its associates immediately after study and at the moment of testing. Immediately after study, the encoding strengths of DI~rER and its associates are determined by Equation 1. Hence, the strengths of dinner, lunch, and so on, are set at 1.35,.14, and so forth. Losses in encoding strength are expected as other words are studied and tested, and for simplicity of illustration, the values for the moment of testing shown in the second row of the top section of Table 2 assume that the residual encoding strengths of D~ER and its associates have been reduced by 10%. The encoding strength of the implicit representation of DINNER is now at 1.22, and the strengths of each of its associates has also been cut by 10% relative to initial levels. Despite these losses, the target and its associates are above baseline as a result of study. The middle section in Table 2 illustrates the calculation of net cue-to-target strength. Mediated connections (i.e., E~ SjkSki) were omitted to simplify the example. The top row in that section re-presents the residual strengths of D~NER and its associates at the moment of testing. The bottom row shows the connection strengths for the test cue LUNCH and its associates based on the normative data. Net cue-to-target strength is determined by cross-multiplying and summing the residual strengths of the target and its associates with the connection strengths activated by the cue at the moment of testing (i.e., ). The self-strength of LUNC8 is set to 1.00 because it is presented as the cue. Although the strength of DrrCN~R in relation to LUNCH is.27 before DINNER is studied, net cue-to-target strength is.55 after it has been studied. k

8 306 NELSON, McKINNEY, GEE, AND JANCZURA Table 2 Example of Calculating Net Cue-to-Target Strength With DINNER as the Studied Word and With Either LUNCH (Cued Recall) or DINNER (Recognition) as the Test Cues Activation of DINNER and associates immediately after study and at test after a 10% loss DINNER (target) DINNER LUNCH SUPPER EAT FOOD MEAL After study At test Calculation of net cue-to-target strength at test with LUNCH as the test cue INTERSECTION DINNER LUNCH SUPPER EAT FOOD MEAL DINNER LUNCH n S(Qj, Ti) = ~ SjkSik = S(LUNCH, DINNER) = ( ) + ( ") + (.02.50) + (.08.18) + (.21.27) + (.06.13) =.55 Calculation of net target-to-target strength at test with DINNER as the test cue INTERSECTION DINNER LUNCH SUPPER EAT FOOD MEAL DINNER DINNER S(Qj, T,) = ~ SjkSik = S(DINNER, DINNER) = (1.00 x 1.22) + (.10.13) + (.54 x.50) + (.11.18) + (.09 x.27) + (.09.13) = 1.56 Interference From Competing Associates The second point illustrated in Figure 3 is that many of the associates of the test cue are not linked to the target; for example, break is an associate of the test cue and if it is connected to DINNER the connection is likely to involve three or more intervening steps. Most associates of most targets are not closely related to the test cue (DINNER is actually an exception). Test cues and targets are generally selected because they share direct, indirect, or both direct and indirect links, but the great majority of cues and targets also have connections, sometimes very strong connections, to associates that do not provide close connecting links between the two words. For practical purposes, such associates can be treated as associates that are unique to the cue or to the target. The activation of such associates in LTWM appears to be responsible for producing cue and target set size effects because they compete with the target. We incorporate such effects directly into a sampling rule, assuming that extralist cuing is a function of the contrast between preexisting connections that bind the cue and target relative to connections related to competing associates that lead away from the target (cf. Tversky, 1977, p. 333). The numerator of this rule constitutes a signal involving direct as well as indirect associative links between the test cue and the target. The denominator of this ratio includes signal plus noise and consists of the adjusted summed strengths across all of the associative links activated by the test cue and the target in LTWM. More formally, the signal in this ratio is computed as net cueto-target strength according to Equation 2. The effectiveness of this signal, however, is proportional to the total strength produced by the signal and the noise arising from competing associates of the test cue and the target. The stronger and the more numerous the connections between the test cue and its competing associates, Y~q S(Q,Aq), and the stronger and the more numerous the connections between the target and its competing associates, Y.~ S(T,A,), the lower the probability of sampling the target in the presence of the test cue. 3 We assume that the probability of sampling the implicit representation of the target in the presence of a meaningfully related extralist cue is a function of its relative strength to the test cue, as shown in Equation 3, 4 Ps (T,/Qs) n s(aj, T,) (3) S(Qj, Ti) + a ~, S(Q,Aq) + /3 ~ S(T,At) q t a, fl > 0, where Is( T~/Qj) is normalized so that it lies between 0 and 1. Furthermore, q = associates of the test cue excluding the target and other associates that link the cue and the target, and t = associates of the target excluding the cue and those that link the cue and target. Finally, PIER 2 adopts SAM's sampling rules (e.g., Raaijmakers & Shiffrin, 1981). Items are sampled with replacement, and the process recycles and terminates when a sampled item meets the evaluation criterion or comes up against the stop criterion (Nelson, Schreiber, & McEvoy, 1992). Predictions and Explanations Equation 1 folds into Equation 2, which in turn folds into Equation 3, so that the probability of sampling the target in the presence of a retrieval cue is affected by each of the variables that we have identified. For example, resonant associate-to-target connections and connections among the associates are manifested in the encoding strength of the implicit representation of the target, S(T, ). This value at the moment of testing is one of the components of net cue-to-target strength defined in Equation 2, for example, the residual strength of DINNER illustrated in the middle section of Table 2 is presumably affected by both resonance and connectivity, and this value directly affects its 3 Each of these components can be computed by constructing a retrieval matrix similar to the one used to demonstrate Equation 2 for the cue and the target. However, for the cue component, instead of entering the test cue with the target, the test cue is entered with each of its nontarget associates, and then the sum across the cue-nontarget contrasts is taken. A similar algorithm is used to compute the target component. 4 The general form of this equation is as follows: f(a fl B)/f(A A B) + ot f(a - B) + /3 f(b - A). Although the similarities may be more apparent than real, the usefulness of the general form of this equation in both memory research and in rating the similarity of objects (Tversky, 1977) connects these two areas in potentially informative ways.

9 IMPLICITLY ACTIVATED INFORMATION AND MEMORY 307 recovery by the test cue LUNCH. In addition, the model explains why cues with stronger forward connections with their targets are more effective than cues that have weaker connections. Furthermore, because target-to-cue connections are incorporated into Equation 2, the model predicts that cues that are more strongly activated by the target during learning should be more effective as retrieval cues. This prediction is compatible with the application of the encoding specificity (transfer-appropriate processing) principle to the extralist cuing task (Tulving & Thomson, 1973). Finally, connections involving shared associates increase the probability of sampling the target, and mediated connections are predicted to have a similar effect. These effects are linked to S(Qj, T~) as defined in Equation 2. One of the unusual aspects of the model is that shared associates are predicted to facilitate as well as to interfere with recall (Experiment 4). Such associates contribute to the strength of the signal because they serve to link the cue and target, but because they are not connected directly to the target they can also serve as competitors. In general, the balance of facilitating and competing forces is more likely to be positive than negative because the effects of net cue-to-target strength presumably dominate the influence of set size. Both cue and target set size effects also emerge in Equation 3. The greater the number of competing associates linked to the test cue and to the target, the larger the values for E~ S(Q,Aq) and ~7 S(T,At), respectively. Greater numbers of competing associates reduce the probability of sampling the target because they add to noise, and given a fixed signal strength, more noise translates into reduced chances of choosing the target. Set size effects related to both the test cue and the target arise because the activated presence of competing associates reduces the probability of sampling the target in the presence of the test cue. Moreover, the magnitude of each set size effect is governed by a and/~ weights assigned, respectively, to unique associates of the test cue and target. Such weights are necessary in order to explain interaction effects linked to set size manipulations. FOr example, a is assigned a heavier weight when forward cue-totarget strength is weaker than when it is stronger because cue set size effects are more apparent for weaker than for stronger cues (Nelson, Schreiber, & McEvoy, 1992). In contrast, ~ would be assigned a heavier weight for stronger cues because target set size effects tend to be more apparent for stronger cues (Nelson, Schreiber, & McEvoy, 1992). Given the adoption of SAM's sampling rules, both models attribute list length effects in cued recall tasks to the same process. FOr example, in PIER 2 terms, the probability of sampling the explicit representation of the target on a given search cycle should decrease as list length increases. In general, because PIER 2 relies on SAM to explain effects linked with the explicit representation, both models explain the effects of variables that influence the encoding of the explicit representation in the same way. However, note that set size effects are built directly into the sampling equation in PIER 2, so set size effects, unlike list length effects, are not attributed to the sampling rules. The effects of competing associates add to noise and directly reduce the probability of sampling the target. In PIER 2 the application of the sampling rules primarily affects the recycling process, and as a result, the model attributes the longer search latencies associated with larger sets to greater numbers of search cycles. Test Instructions For extralist cuing tasks, the model assumes that explicit and implicit representations of the target contribute independently to target recovery. Targets can be recovered from either or both representations, with the relative contributions of these representations determined by how the test cues are used. The experimenter can manipulate test instructions so that participants can be asked to use the cues to recall list words (often called direct or explicit test instructions), or they can be asked to use the cues to recover related words that are compatible with the test cues (indirect or implicit test instructions). We assume that these two types of test instruction are orthogonal to the two types of representation in terms of their manipulation as independent variables. However, these variables are nonorthogonal in terms of their effects on target recovery. When test cues are used intentionally to recall recently studied targets, both representations presumably contribute to target recovery. In contrast, when the cues are used to produce the first related information that comes to mind, recovery depends largely on the implicit representation (e.g., Nelson, Schreiber, & Holley, 1992). The explicit representation appears to contribute less to the probability of target recovery when participants rely on automatic uses of memory regardless of whether automatic uses are determined by using the process dissociation procedure (Jacoby, 1991; Nelson, Bennett, & Xu, 1997) or by using implicit test instructions (e.g., Roediger & McDermott, 1993). Under such conditions, variables such as presentation rate and level of processing have substantially reduced effects. The explicit representation also appears to contribute little to target recovery for participants with amnesia regardless of what instructions they are given (see Squire, Shimamura, & Graf, 1985). For such individuals, recovery is apparently based on an intact implicit representation. The important point here is that PIER 2 assumes that many of the research findings on implicit versus explicit memory can be attributed to the relative influence of the two representations engendered by different test instructions. In PIER 2, different test instructions effectively change the goal of the retrieval process by asking participants to implement different strategies for recovering related words (J. R. Anderson, 1990). Hence, the implicit representation plays a role in retrieving related information regardless of whether participants are oriented toward retrieving a recent experience or toward recovering any related experience that meets the demands required by the test cue (e.g., a meaningfully related word, a fragment, and so on). In either case, the recovery of the target from the implicit representation is determined by the intersection of information activated by the cue with information previously activated by the target. Effects of Attention Shifts on Long-Term Working Memory According to PIER 2, the influence of the implicit representation depends on the strength of the cue-to-target intersection process. The activation of the target and its associates during study presumably leaves traces of activation in LTWM, and the

10 308 NELSON, McKINNEY, GEE, AND JANCZURA presentation of a related test cue merges with these traces with varying strengths. The presence of target set size, resonance, and connectivity effects in cued recall depend on sampling representations produced by the intersection of the cue and its associates with the target and its associates. The test cue is paramount in determining the usefulness of the implicit representation and in fact helps to explain why target set size and related effects are found in the first place. Most theories assume that activation decays substantially within a few hundred milliseconds. The assumption that the residual activation of the implicit representation intersects with information in the test cues provides a simple explanation of why the preceding effects are found at all. The function of the test cue is so important that some might argue that the cue itself is sufficient to generate all of our effects and that there is no need to assume that the implicit representation persists over the retention interval. However, several findings undermine this interpretation. First, target set size effects will not be obtained in the absence of a study trial. The recovery of targets having smaller sets will be equivalent to those with larger sets with both values approximating forward cue-to-target strength as measured by association norms. Second, target set size effects vary substantially with the context of the study trial. Presenting the test cue during study as a semantic context for the target substantially reduces both target set size and connectivity effects (e.g., Nelson, Gee, & Schreiber, 1992). If set size effects were produced only at test by the retrieval cue, then encoding context should have had negligible effects. Third, and more important for present purposes, target set size effects are nearly eliminated after several minutes of multiplying numbers when recall is prompted by weak cues (Nelson, McEvoy et al., 1993). If target set size effects were produced solely during retrieval by the test cue, then such effects should have been equally apparent on both immediate and delayed tests because the same cues were used on both tests. The results associated with the math task are particularly interesting because they suggest that conceptually directed shifts in attention may be more important than mere delay in reducing set size and connectivity effects. Other participants in these experiments (Nelson, McEvoy et al., 1993) studied additional lists of words in place of the math task. Such study reduced recall without affecting the magnitude of the target set size effect. Studying other lists is presumably no less difficult than doing multiplication, but from the participant's viewpoint, such study represents a continuous task so attention is never switched away from the memory task. As a result, the implicit representation remains relatively strong. In contrast, multiplication involves different processing operations compared with studying list words and requires a shift of attention away from the memory task. As a consequence of this shift, the implicit representation of the target is too diminished to be recovered by weak cues, and target set size and connectivity effects are reduced. As a result of this finding, we assume that the usefulness of the implicit representation is partly controlled by the test cue and partly controlled by continued attention to the task at hand. As long as the focus of attention remains on performing what is perceived to be a continuous task, the implicit representation of the target remains relatively strong, becoming weaker as the focus of attention moves from item to item during study and test and substantially weaker once attention has been diverted to a conceptually different task. By analogy, emerging thoughts occurring during normal conversation are often lost before they can be expressed after the topic of conversation suddenly switches. We assume that the diversion of attention is functionally equivalent to self-generated instructions to forget about the current task and focus on the new task. The conceptual focus of attention has changed (M. C. Anderson & Spellman, 1995). Hence, the greater loss engendered by disruption is produced more by the inhibition of one cognitive activity by the beginning of the next than by the erasure of the contents of LTWM. We also assume that the total loss of the implicit representation only approaches zero because activation of the target and its associates during encoding presumably strengthens connections between presented and activated information by small amounts (e.g., McClelland & Rumelhardt, 1986). In our view of working memory, the activation of collections of lexical entries strengthens preexisting connections among them (also see Landauer & Dumais, 1997). Nevertheless, as the strength of the implicit representation diminishes, cues that are normally sufficient for recovering it begin to fail, and variables such as set size, resonance, and connectivity begin to lose effectiveness. At some point, extralist cued recall is determined solely by the explicit representation or by the probability of guessing the target from associates activated by the cue. In the latter case, recall relies on general knowledge as indexed by normative strength. In the model, the reduced effect of target set size that occurs as a result of switching attention to a different task is attributed to the failure of the cue-to-target intersection process. Equation 2 is computed on the basis of the residual strengths of the target and its associates in LTWM during test, and to the extent that this information diminishes as a result of attention shifts, the probability of sampling the implicit representation of the target will decline and target set size effects will diminish. Furthermore, effects of resonance and connectivity are also predicted to be less apparent after attention shifts to competing tasks because the effects of these variables are closely tied to the strength of the implicit representation. However, reductions in these effects are not inevitable consequences of shifts of attention. Such reductions are contingent on the intersection process, which depends on both the residual strength of the implicit representation and on the strength of the connections activated by the test cue. The reductions produced by attention shifts that are so apparent with weakly related test cues should be less apparent with more strongly related test cues because stronger cues add a larger multiplier to the strength of the signal (tested in Experiment 2). In other terms, the implicit representation is not irreparably lost as a result of task disruptions but it has simply become weaker, and what has been weakened may be made more apparent by a stronger cue (e.g., Fischer & Glanzer, 1986; Glanzer, Fischer, & Dorfman, 1984; Tulving & Psotka, 1971). Recognition PIER 2 assumes that recognition, like recall, can be based on the explicit representation. Hence, the likelihood of correct recognition will vary with list length, level of processing, manipulations of encoding context, and so on, and we assume that

11 IMPLICITLY ACTIVATED INFORMATION AND MEMORY 309 global matching models like SAM provide straightforward explanations for how the explicit representation affects recognition success (e.g., Gillund & Shiffrin, 1984). PIER 2 assumes that the implicit representation of the target contributes independently to recognition success and that recognition of its implicit representation is a function of its net cue-to-target strength. On recognition tests, the test cue consists of the target instead of a related word (e.g., Tulving, 1983). Instead of entering the strength values for a related test cue into Equation 2, values for the target as the test cue are entered with the target also serving as the study word. The target is entered into the equation as both target and test cue, and the connection strengths are crossmultiplied and summed as before. In Table 2, the bottom section provides an example of how net strength of the implicit representation is computed for recognition. As in the calculation for extralist cued recall, the top section shows strengths for the target DINNER and its associates immediately after study and at the moment of test. This section is identical to the section used for cued recall because the study conditions are presumed to be identical to simplify comparisons. Once again, the values entered at the moment of testing reflect an arbitrary 10% loss of strength over the retention interval. The bottom section describes the hypothetical intersection of the strength values for the target DINNER at the moment of test and the strength values for DINNER when presented as the test cue. We set the self-strength of the target as a presented item to 1.00 and assume that, as a test cue, it activates its associates at normative levels. Finally, net cue-to-target strength is determined by cross multiplying and then summing the intersecting strength values as shown in the example at the bottom of Table 2. The use of Equation 2 in this manner predicts that recognizing the target as an implicit representation will be affected by several different variables. First, recognition is predicted to be higher when net cue-to-target strength is higher, and according to Equation 1, it is predicted to be higher when resonance and connectivity are higher. Words whose associates are more connected to the target and to each other should be easier to recognize than those whose associates are sparsely connected (tested in Experiment 5). Second, Equation 2 predicts that recognition will be higher when the residual strengths of the target and its associates are higher, and therefore any variable that reduces this residual strength is predicted to reduce the level of recognition. When residual strength approaches zero, Equation 2 predicts that the implicit representation of the target in LTWM will no longer affect recognition. Third, we assume that recognition based on the implicit representation is based directly on the computation of net cue-to-target strength. Normally, there is no sampling process in recognition as there is in cued recall and Equation 3 is not applicable. Hence, the model can explain why set size has no effect on recognition under normal testing conditions (Nelson, Canas, & Bajo, 1987). Experiments In this section of the article, we describe the results of five experiments that test PIER 2's predictions. Experiment 1 evaluated predictions based on Equation 1, namely that recall will vary as a function of both resonant connections from the associ- ates to the target as well as connections among the associates. Targets higher in resonance and connectivity should be recalled with a greater likelihood than those at the opposite ends of these dimensions. Experiment 2 tested the prediction that, after switching attention to a different conceptual task, the magnitude of target set size and connectivity effects will depend on the strength of the test cue. Experiment 3 tested the prediction that backward as well as forward strength would affect recall, and Experiment 4 tested the prediction that both shared associate strength and mediated strength contribute to recall. Finally, Experiment 5 tested the prediction that, in contrast to target set size, connectivity among the associates of a target will facilitate recognition. Experiment 1 According to Equation 1, connections from the target's associates that lead back to the target (resonance) and connections among the associates (connectivity) contribute to the strength of the implicit representation of the target. The stronger these links are, the stronger the implicit representation of the target. This assumption predicts that target recall in the extralist cuing task will be a positive function of resonance and connectivity because targets with higher levels of residual strength at test will produce stronger signals and will be more likely to be sampled (Equations 3). The main purpose of the present experiment was to test the prediction that recall would vary with both resonance and connectivity (Nelson, Bennett, et al., 1993). Manipulations of these characteristics were implemented by selecting study words from the normative database so that all combinations of high and low levels of resonance and connectivity were represented in the list. For example, for the target word ALIKE, all seven of its associates showed resonant connections with it and each of these associates was linked to an average of 2.57 other associates in the set. Hence, this word was classified as high in both resonance and connectivity. We assume that the number of such links is positively correlated with the summed strength of such connections and hence with the strength of the implicit representation as predicted by Equation 1. Participants studied these words under intentional remembering instructions that encouraged semantic processing, and then they were tested under standard extralist cuing conditions in which the recall of each studied word was cued with a related word. Me~od Design and participants. Resonance (high vs. low) and connectivity (high vs. low) were varied in a 2 2 within-subject factorial. Twentyfour students served in the experiment, with 12 assigned to each list. Participants were drawn from courses in introductory psychology, received extra credit for their participation, and were randomly assigned to lists in order of appearance. Materials. Target words and test cues for two different lists were taken from the meaning-association norms (see Appendix A). For one half of the targets in each list, resonance was high, and for the other half, it was low, respectively averaging 7.67 (SD = 3.95) and 1.92 (SD = 1.18) resonant links. For high resonance targets, 73% of their associates were back connected, whereas for low resonance targets, only 23% were back connected. At each level of resonance, the associates of one half of the targets were connected to an average of 2.57 (SD = 0.68)

12 310 NELSON, McKINNEY, GEE, AND JANCZURA other associates in the set, whereas the remainder were connected to an average of only 0.66 (SD = 0.46) associates in the set. When connectivity was high, each associate was connected to nearly three other associates in the set, whereas when it was low, each associate was connected to an average of less than one associate. The targets in the four conditions formed by the factorial were equated as closely as possible on set size, concreteness, and printed word frequency, and these measures averaged, respectively, (SD = 5.45), 4.75 (SD = 1.36), and 93 words per million occurrences (SD = 200). For the most part, the target words had moderately sized sets of associates, they were slightly above average in concreteness, and although frequency varied widely, they tended to occur relatively frequently. The test cues for these targets were also taken from the norms permitting control of cue set size, forward cue-to-target strength, backward targetto-cue strength, and number of indirect connections. Cue set size averaged associates (SD = 5.45), cue-to-target strength averaged.14 (SD =.06), target-to-cue strength averaged. 17 ( SD =.23 ), and finally, the mean numbers of shared associates and mediators were, respectively, 2.75 (SD = 1.60) and 1.00 (SD = 1.32). In general, the targets and test cues each had Slightly more than a dozen associates, and although forward and backward strengths relating the cues and their targets were relatively weak, the items tended to share a fairly high number of associates. Procedure. Standard extralist cuing procedures were used (e.g., see Nelson, Schreiber, & McEvoy, 1992). Participants were shown the target words one at a time on a screen for 3 s each, they read the words aloud when shown, and they were told to remember as many words as possible without being told how they would be tested. Immediately following the last study word, the instructions for the cued-recall test were explained, and an example of how the test cues were related to the target words was provided. The test trial was self-paced, and the orders of appearance of both study and test words were independently randomized for each participant. Results and Discussion Probability of correct recall for the four conditions varied as a function of both resonance and connectivity. When connectivity among the associates was high, recall was more likely when resonance was high (.74) than when it was low (.65). Similarly, when connectivity among the associates was low, recall also was greater when resonance was high (.65) as opposed to when it was low (.45). The effects of both resonance, F( 1, 23) = 11.50, MSE =.04, and connectivity, F( 1, 23) = 8.69, MSE =.06, were significant. Although resonance effects were more apparent when connectivity was low, the interaction between these sources was unreliable, F( 1, 23) = The results of this experiment replicate previous findings showing that high levels of connectivity among the associates of a word increase the probability that it will be recalled (Nelson, Bennett, et al., 1993). What is more important, the results show that resonant connections also affect recall. Words with higher levels of resonance are more likely to be recalled in the presence of a related cue. According to PIER 2, resonance and connectivity increase the strength of the implicit representation of the target, which increases the probability that it will be sampled during retrieval. Even though no attention was ever drawn to the associates of the studied words at any point during the experiment, 29% more high-connectivity, high-resonance words were recalled than low-connectivity, low-resonance words. Information coming back to the target from what it activates in LTWM apparently has a substantial effect. Experiment 2 PIER 2 assumes that the activation of the target and its associates in LTWM produces an implicit representation whose influence depends on the presentation of a related word as a test cue. The presence of target set size and connectivity effects depend on the residual strength of the implicit representation given the information in the retrieval cue. As noted earlier, the usefulness of this representation can be reduced by diverting the participant's attention to a different conceptual task just prior to testing. Target set size effects are substantially reduced after participants engage in a few minutes of multiplication, presumably because the implicit representation is too weak relative to the information provided by the test cue. One purpose of Experiment 2 was to test the prediction that connectivity effects, like set size effects, will be lost after the disruption. Participants studied a list of words differing in set size and in connectivity, and then they were tested under extralist cuing conditions, with one half of them tested immediately and the remainder tested after several minutes of multiplication. The second purpose of Experiment 2 was to evaluate the intersection assumption built into Equation 2. If this assumption is correct, then the presentation of stronger test cues should produce set size and connectivity effects both when testing is immediate and when it follows a conceptually based attention switch. For example, cues that share more associates with the target should produce both set size and connectivity effects even after the multiplication task. To test this prediction, we varied the number of associates shared by the test cue and target so that cues shared either 3-6 associates or 0-2 associates with their targets. When the cues share few associates with their targets, set size and connectivity effects should be observed on the immediate test but not after the math test. In contrast, when the test cues share many associates with their targets, both effects should be apparent on both tests. Me~od Design and participants. The experimental design formed a 2 2 x 2 2 mixed-model factorial. Task disruption (no disruption vs. disruption) and number of shared associates (few vs. many ) were manipulated between subjects, whereas target set size (small vs. large) and connectivity (high vs. low) were manipulated within subject. Twentyfour participants were assigned to each between-subjects condition with one half of them assigned to one list and the remaining one half to the other. They came from the same classes as used for Experiment 1 and they were assigned to conditions in a similar manner. Materials. Two 24-word lists were constructed for each level of associate sharing (Appendix B). The target words in each list reflect factorial combinations of set size and connectivity. Set size averaged 6.96 associates (SD = 1.25) and associates (SD = 3.06), respectively, for small and large set targets. Mean connectivity per associate averaged 2.42 (SD = 0.40) and 0.80 (SD = 0.20) for high- and lowconnectivity targets. Hence, targets with small and highly connected sets of associates had about seven associates, each of which was connected to more than two other associates in the set according to the norms. The set size manipulation was equated at each level connectivity. Other characteristics of the targets were also held constant. For example, the summed strengths of the two-step resonant connections from the associates to the target were equated at each level of set size and connectivity, averaging.08 (SD =.09) across all conditions. In addition, the probabil-

13 IMPLICITLY ACTIVATED INFORMATION AND MEMORY 311 ity of a resonant connection, printed frequency, and concreteness averaged.42, (SD =.20), 43 per million (SD = 63), and 5.07 (SD = 1.24), respectively. Resonance tended to be at moderate levels, the words appeared with moderate levels of frequency in the language, and they tended to be fairy concrete. As in Experiment 1, the test cues were also taken from the normative database. For few shared associates, cues shared an average of 1.18 (SD = 0.84) associates and 0.83 (SD = 1.08) mediators with their targets, and for many shared associates, cues shared an average of 3.63 (SD = 0.87) associates and 2.60 (SD = 1.82) mediators. Cue set size was equated in all conditions and averaged associates (SD = 4.35). Cue-to-target strength was also equated and averaged.09 (SD =.04). Unfortunately, as a result of experimenter error, target-to-cue strength covaried with set size so that targets with small sets had stronger targetto-cue connections (.30, SD =.28) than targets with large sets (.06, SD =.07). This difference was quite variable across items but given that both set size and back strength affect recall in the same direction, set size effects were expected to be exaggerated. Nevertheless, our interest in this experiment was in the influence of task disruption on the relative magnitude of the set size effect, and as long as the usual pattern of disruption is obtained for low sharing cues, the confounding does not present a serious problem. Procedure. The extralist cuing procedures used in Experiment 1 were also used in this experiment. However, instead of being tested immediately, participants in the math condition were given a sheet of paper containing 18 multiplication problems (e.g., 278 x 393, , and so on). They were asked to solve as many problems as quickly and as accurately as they could. After 10 min on this task, the cuedrecall instructions were read to them. Except for the math task itself, all participants were given the study and test instructions used for Experiment 1. Results and Discussion Results showing the probabilities of correct recall are presented in Table 3. To simplify the presentation of the findings, we describe the results separately for each level of associate sharing. The findings for few shared associates are shown in the first two rows of Table 3, and as can be seen, effects of both set size and connectivity were apparent on the immediate test. On the immediate test, words with smaller sets of associates and words with more connected sets of associates were more likely to be recalled than those with larger and less connected sets. After the math task, however, the effects of both set size Table 3 Probability of Correct Recall as a Function of Task Disruption, Set Size, Number of Shared Associates, and Connectivity No disruption Disruption Small set Large set Small set Large set Target type size size size size Few shared associates High connectivity Low connectivity Many shared associates High connectivity Low connectivity and connectivity were reduced. The results of a three-factor analysis of variance (ANOVA) indicated that the effects of task disruption were not reliable, F(1, 46) = 2.22, but the effects of set size, F(1, 46) = 53.96, MSE =.03, and connectivity, F(1, 46) = 14.21, MSE =.04, were significant. In addition, the two expected interactions were apparent in that both effects were reduced after the disruption. Task disruption interacted with set size, F( 1, 46) = 8.56, and with connectivity, F( 1, 46) = The Fisher's least significant differences (LSDs) were.07 for each of these interactions. No other sources approached the criterion for significance. The findings for many shared associates are shown in the bottom two rows of Table 3, and as can be seen, these results contrast with those found when cues shared only a few associates with their targets. Although recall was higher on the immediate test, target set size effects and connectivity effects were just as apparent after the math task as when testing was immediate. When the test cues shared many associates with the target, set size and connectivity effects showed little sign of diminishing after the math task. This description was supported by the results of a three-factor ANOVA that indicated that task disruption, F(1, 46) = 4.19, MSE =.07, set size, F(1, 46) = 21.78, MSE =.04, and connectivity, F( 1, 46) = 25.32, MSE =.03, were significant sources of variance. Recall was higher on the immediate test (.53) than after the math test (.45), higher when targets had small (.56) compared with large (.42) sets, and higher when targets had more connected (.56) as opposed to less connected (.43) sets. The Set Size Connectivity interaction, F(1, 46) = 9.67, MSE =.02, LSD =.06, was the only remaining significant effect. Set size effects were somewhat more apparent when connectivity was higher. All of the remaining interactions produced Fs < 1, including the interactions of set size and connectivity with test disruption. The results of Experiment 2 indicate that, as predicted, the connectivity and number of associates activated by a studied word affect the probability of its recall on immediate tests. This pattern replicates previous findings (e.g., Nelson, Bennett et al., 1993). In addition, given test cues that share few associates with their targets, target set size effects are substantially reduced when a math task is interposed between study and test. This pattern also replicates previous findings, all of which were obtained with weak cues (e.g., Nelson, Bajo, & Casanueva, 1985; Nelson, McEvoy, et al., 1993). Although set size effects were reduced in the present experiment, they remained statistically significant. This result is unusual compared with past studies and is presumably related to the fact that we inadvertently confounded target-to-cue strength with set size. However, despite this problem, the general pattern is the same in that set size effects significantly diminished after the disruption generated by the math task. One of the main new findings in this experiment is that connectivity effects are reduced by the math task. Although such effects are readily apparent on the immediate test, they are no longer evident after the math task. As predicted by the model, both set size and connectivity effects are reduced after the math task when test cues share few associates with their targets. In contrast, the second main new finding is that both of these effects transcend the disrupting influence of the math task when the test cues share many associates with their targets. With more

14 312 NELSON, McKINNEY, GEE, AND JANCZURA shared associates, net cue-to-target strength is great enough to overcome the negative influence of an attention switch. The strength of the intersection of the implicit representation of the target and its associates with the cue and its associates appears to be the key element that determines whether set size and connectivity effects are found. Experiment 3 PIER 2 assumes that net cue-to-target strength varies as a function of the preexisting forward and backward strengths between test cue and target (Equation 2). The model assumes that these links are distinct and independent (e.g., Humphreys & Galbraith, 1975; Keppel & Underwood, 1962) and that both links should affect cued recall. Furthermore, because the effects of backward target-to-cue links rely on residual strength resulting from the target encoding, they should contribute less to net cue-to-target strength than forward cue-to-target links that rely on currently activated connections. The purpose of Experiment 3 was to manipulate these connections independently to test this prediction. When both preexisting connections were strong, both words produced one another as associates in free association (e.g., PAINT produces DRAW and the reverse). When only one connection was strong, only one word produced the other as a direct associate, and when neither connection was strong neither word produced the other as a direct associate. Method Design, participants, and procedure. The experimental design conformed to a 2 2 between-subjects factorial involving manipulations of forward strength (strong vs. weak) and backward strength (strong vs. weak). Eighteen participants were assigned to each of these four conditions from the same sources and in the same manner as in previous experiments. One half of these participants were assigned to one word list, and the remainder were assigned to another. The extralist cuing procedures were identical to those used in Experiment 1, including the use of the 3-s presentation rate, self-paced test, and so on. Materials. Two 24-word lists were constructed by selecting items from the association norms (Appendix C). Four cues were selected for each target. One was connected in both forward and backward directions to the target, one was connected in the forward but not in the backward direction, one was connected in the backward but not in the forward direction, and one was not connected at all according to the free association measure. Hence, the targets were held constant, and the manipulations of preexisting strength were implemented by varying the selection of the test cues. Regardless of direction, strength averaged.13 (SD =.06) for strong connections and.00 for weak connections (the words were not produced as associates in the norms). Indirect connections were held constant across all four conditions. Regardless of the strengths of the forward and backward connections, test cues shared an average of 1.42 associates (SD = 1.07) and 0.95 (SD = 0.95) mediated connections with their targets. Thus, although not all pairs were directly related according to the normative database, all were about equally and weakly related in terms of indirect connections. Cue set size was also held constant, with each cue having an average of associates (SD = 4.85). Finally, target set size, mean number of resonant connections, and mean connectivity averaged, respectively, (SD = 4.41), 6.1,5 (SD = 3.12), and 1.41 (SD = 0.64). Results and Discussion Probability of correct cued recall when backward connections were strong was.50 and.23 for strong and weak forward connections, respectively, and when backward strength was weak these values were.33 and.03. Hence, recall was more likely when forward strength was strong (.42) than when it was weak (.13) and when backward strength was strong (.37) than when it was weak (.18). Each of these sources of variance had significant effects, respectively, F( 1, 68) = , and F( 1, 68) = 45.48, MSE =.01, and their interaction was unreliable (F< 1). As predicted, these results indicate that probability of recall in the extralist cuing task is a function of the preexisting connection strengths between the test cue and target in both forward and backward directions. In this task, the target is studied by itself, and the effects of target-to-cue strength can be attributed to priming effects occurring during the study trial. Presumably, studying the target activates its related associates, one of which can be the word subsequently used as the test cue, and the stronger the preexisting connection from the target to the word used as the cue, the higher the net cue-to-target strength (Equation 2). The effects of forward strength are attributed to the same process operating in the reverse direction. The test cue activates its associates, one of which is the target, and the stronger the preexisting connection from the cue to the target, the higher its net strength. Interestingly, at fixed levels of preexisting strength, variations in forward strength appear to have a greater effect than comparable variation in backward strength. Although both effects were reliable, the magnitude of the effects of forward strength is larger. Connections currently activated by the test cue seem to have a greater effect than connections previously activated by the target: This difference is predicted by the model because backward connections depend on residual levels of strength whereas forward connections depend on current levels. Experiment 4 Equation 2 predicts that net cue-to-target strength for the intersection between the test cue and target will vary as a function of both shared associates and mediated connections and that their effects will be additive. Because both variables increase the strength of the cue-to-target relationship, increases in either type of indirect connection will facilitate recall. As far as shared associates are concerned, the model makes the counterintuitive prediction that a word that is produced frequently by the cue and by the target but that does not itself produce the target in free association will nevertheless aid recall. This prediction is counterintuitive because it seems more natural to predict that such words would be more likely to serve only as competitors and hence be recalled in place of the target. Experiment 4 was designed to test these predictions. Participants studied a list of words and were then given related words as cues under extralist cuing conditions. The magnitude of shared associate strength was crossed with magnitude of mediated strength with other variables held constant. Method Design, participants, and procedure. The experimental design conformed to a 2 2 within-subject factorial involving manipulations of

15 IMPLICITLY ACTWATED INFORMATION AND MEMORY 313 preexisting mediated strength (strong vs. weak) and shared associate strength (strong vs. weak). Thirty participants were assigned to the experiment with one half assigned to one word list and the remainder to the other. The extralist cuing procedures were identical to those used in Experiment 1. Materials. Two 24-word lists were constructed by selecting items from the association norms (Appendix D ). When mediated strength was strong, it averaged.21 (SD =.04), and, when weak, it averaged.003 (SD =.003). Average shared associate strength was.20 (SD =.07) when strong and.003 (SD =.003) when weak. Number of indirect connections covaried with strength for each measure, and each type of indirect strength was equated within the other type. Other variables were equated as closely as possible within each of the four strength conditions, including cue set size, (SD = 2.90); target set size, (SD = 4.17); cue-to-target strength,. 12 (SD =.08); target-to-cue strength,.02 (SD =.03); mean number of resonant connections per target, 6.60 (SD = 3.86); mean connectivity, 1.37 (SD = 0.67); frequency, 120 (SD = 78); and concreteness, 4.82 (SD = 1.23). Results and Discussion When shared associate strength was high, the probabilities of correct recall for strong and weak mediated connections were.67 and.50, respectively. When shared associate strength was low, these probabilities were.54 and.44. These values made it clear that both types of indirect connections affected performance. Probability of cued recall was higher when mediated strength was high (.60) than when it was low (.47), and similarly, it was higher when shared associate strength was high (.58) than when it was low (.49). A repeated measures ANOVA indicated that the effects of both mediated strength, F( 1, 29) = 22.01, MSE =.02, and shared associate strength were reliable, F( 1, 29) = 8.25, MSE =.03. The effects of mediated strength appear to be somewhat larger because such effects reflect current as opposed to residual strength effects. Finally, the interaction was not significant, F(1, 29) = 1.11, MSE =.03, suggesting that these two sources of indirect strength had additive effects. The results of this experiment and those of the previous experiment clearly indicate that cued recall in the extralist cuing task is influenced by preexisting forward and backward direct connection strengths and by indirect sources of strength emanating from mediated connections between the test cue and the target and from shared associates linking the two items. These results are consistent with PIER 2's prediction that net cueto-target strength varies with all four sources of preexisting connection. Moreover, shared associates tend to facilitate more than they interfere with recall. Experiment 5 Equation 1 predicts that targets with many connections among their associates will produce stronger implicit representations than those with fewer connections. Because residual levels of strength feed into the calculation of net cue-to-target strength in Equation 2, high-connectivity words should be more readily recognized than low-connectivity words. The purpose of Experiment 5 was to evaluate this prediction. Participants studied long lists of words in which associative connectivity was crossed with printed frequency of occurrence. Studied words were either high or low in connectivity and high or low in frequency. In addition, these variables were crossed with test expectations. All participants were asked to remember as many words as possible, but one group was not told how they would be tested, whereas another was told to expect a recognition test. As with word frequency (Gorman, 1961), test expectations affect recognition. Participants who expect a recognition test produce higher recognition levels than those who do not (e.g., Balota & Neely, 1980; Schmidt, 1986). Word frequency and test expectation were used as manipulation checks on our procedures because we wanted to make sure that the experiment was sensitive to well-known phenomena. The study trial was followed by a single item recognition test in which equal numbers of studied and nonstudied words were presented with participants responding "old" when they thought that the test item was in the study list and "new" when they thought that it was not. Method Design and participants. The experimental design formed a 2 2 x 2 factorial with test expectation manipulated between subjects (told about the recognition test vs. not told). Both connectivity (high vs. low) and printed frequency (high vs. low) were manipulated within subject. Sixteen participants were recruited for each between-subjects condition from the same source and with the same incentive as used in the previous experiments. Of these participants, 8 were assigned to one list and 8 were assigned to the other. Participants were tested in replication blocks and were assigned to study conditions in order of appearance. Materials. Two lists of 48 words were constructed (Appendix E) by selecting words from the database according to estimates of associative connectivity and printed frequency (Kucera & Francis, 1967). Within each list, 12 high-frequency words were high in connectivity, 12 highfrequency words were low in connectivity, and so on. High- and lowconnectivity words averaged 2.06 (SD = 0.68) and 0.70 (SD = 0.43) connections among their associates, respectively. High-frequency words occurred an average of 222 (SD = 250) times per million words and low-frequency words occurred an average of 5 (SD = 3) times per million. Each of these variables was controlled at each level of the other variable in both lists. In addition, mean number of resonant connections per target, 3.90 (SD = 3.05), target concreteness, 4.43 (SD = 1.39), and target set size, (SD = 6.67), were controlled. Finally, 16 words unrelated to each study list were used as primacy-recency buffers, with 8 presented at the beginning of each 48 item critical list and the other 8 appearing at the end of this list. Memory for the buffers was not tested. Procedure. Participants were tested in individual sessions in which each list word was presented for 3 s during the study trial and at a selfpaced rate during testing. The order of items presented at study and at test were independently and unsystematically randomized for each participant. As each word was presented, participants read it aloud, and one group of participants was asked to remember as many words as possible without being told how they would be tested, whereas the other group was told to expect a recognition test. All participants were given a practice task on a short list to illustrate the presentation rate and to correct misunderstandings. At test, the study items were randomly intermixed with new items consisting of words from the other study list. Words that were "old" for one list were "new" for the other list, and the reverse. With this procedure, the manipulation of connectivity and frequency was completely balanced with the nature of the response. Results and Discussion The results for each test expectation are presented in Table 4, which shows the effects of connectivity and frequency for three measures of performance--hits, false alarms, and A '. The

16 314 NELSON, McKINNEY, GEE, AND JANCZURA A' measure adjusts for sensitivity differences (Grier, 1971). All three measures agree in showing that recognition varied with test expectations, frequency and connectivity. For example, for the A' measure, recognition was better when participants expected a recognition test (.91) than when they did not (.87). Recognition was also better for low-frequency (.94) than for high-frequency words (.84). Both sources of variance were significant, F( 1, 30) = 5.19, MSE =.01, and F( 1, 30) = 68.29, MSE =.005, respectively. More important, recognition was better when there were more connections among the associates of the targets (.91) than when there were fewer connections (.87), F( 1, 30) = 7.73, MSE =.005. An interaction between frequency and connectivity indicated that the beneficial effects of connectivity were more apparent for high- than for low-frequency words, F( 1, 30) = 7.70, MSE =.004. When frequency was high, the means for low- and high-connectivity items were.80 and.87, respectively; when frequency was low these values were.94 and.94. No other sources were significant, including the interaction between connectivity and test expectation, with all Fs near or below The results of Experiment 5 replicated well-established findings. Low-frequency words were more likely to be recognized than high-frequency words, and participants who expected a recognition test outperformed those who did not. As predicted by Equation 2, the results indicate that words with more connections among their associates were more likely to be recognized than those with fewer connections. This effect was more apparent when the words were higher in frequency than when they were lower in frequency, but this interaction may have been the result of ceiling effects for low-frequency words. The more important point is that these findings represent the first demonstration that connections among the associates of a studied word affect its recognition even though the encoding task drew no attention to these associates at any point during the experiment. General Discussion The results of these experiments are consistent with predictions based on PIER 2. First, probability of recall in the extralist cuing task was higher when there were more resonant connections from the associates back to the target and when there were more connections among the associates themselves. According to the model, the presence of such connections increases the encoding strength of the implicit representation of the target and its associates, which increases the probability that the target will be sampled during retrieval. Second, the effects of both connectivity and set size obtained on an immediate test were substantially reduced after attention was diverted to a conceptually different task, but only when weakly related test cues were used to prompt recall. When the test cues shared more associates with their targets, both connectivity and set size effects were as apparent after the disruption as they were on the immediate test. As suggested in the model, the intersection of the implicit representation of the target and its associates with the test cue and its associates appears to be a key factor in determining whether such effects will be found. Third, recall was higher when forward and backward connections involving the test cues and their targets were stronger, and despite equivalent levels of preexisting strength, forward cueto-target links had a larger effect that backward target-to-cue links. This difference is consistent with the expectation that, for connections having equivalent normative strengths, currently activated connections should contribute more to correct recall than residual connection strengths produced by the target during the study trial. Fourth, and finally, connectivity among the associates of the target affected target recognition. Targets are more likely to be recognized when they have more connections among their associates. This finding is consistent with the prediction that such connectivity increases the strength of the implicit encoding of the target during the study trial. Also, this finding contrasts with other results showing that target set size has no effect on recognition because set size has no effect on the strength of the implicit representation during study. Set size effects appear to arise only when the target must be sampled from among a set of competing associates or, in other words, when cues induce search processes. Taken together, the results of these experiments provide consistent support for the changes introduced into the model. The next two sections directly compare PIER 2 to its progenitor and to SAM in order to highlight their similarities and differences. PIER and PIER 2: How Do They Differ? PIER 2 is closely related to PIER in several ways. Both models incorporate separate assumptions concerning encoding and retrieval, and both assume that independent explicit and implicit representations are encoded during the study trial. Both models assume that the contribution of the explicit representa- Table 4 Recognition as a Function of Test Expectation, Word Frequency, and Connectivity Recognition test expectation Low frequency High frequency High connectivity Low connectivity High connectivity Low connectivity Not told Hits False alarms A' Told Hits False alarms A'

17 IMPLICITLY ACTIVATED INFORMATION AND MEMORY 315 tion is described well by SAM, and like SAM, both models assume that sampled items are evaluated to determine whether they meet test criteria (e.g., Gillund & Shiffrin, 1984). There are other similarities as well that derive from the fact that each model was designed to explain many of the same cued recall findings. However, there are also differences. PIER 2 is more than a formalized expression of the ideas expressed in PIER partly because the formalization process itself changed our thinking, but also because we now had to explain the effects of other variables. The original model was designed to explain set size effects and cue-to-target strength effects, but after developing this model and partly because of it, we discovered other potential variables that needed to be explored, including resonance, connectivity, target-to-cue strength, and strength arising from indirect connections between the cue and target. Although we might have done a better job of anticipating the complexities than we did, it shortly became clear that the original framework fell considerably short of providing a complete explanation of the effects of variables related to the implicit representation. The role of LTWM in the retrieval of recent episodes appears to be more involved than we had originally envisioned. Aside from differences in formal characteristics, PIER 2 also differs from PIER in five important respects. 1. PIER assumed that the associates linked to the target had no effect on the strength of the implicit representation, either positive or negative. PIER 2 assumes that the activation of related associates can influence such strength and that the extent of this influence is partly determined by resonance from the target's associates and by connections among its associates, but not by the number of associates. 2. PIER assumed that extralist cued recall involved a parallel search through explicit and implicit representations and that targets were recovered from one representation or the other, but not both. PIER 2 assumes that search can be directed toward recovering information from one representation or the other by manipulating test instructions. When directed toward recovering the target from the recently studied list, the model assumes that explicit and implicit representations contribute independently to recall, whereas when directed to produce related information associated with the test cue, only the implicit representation contributes. 3. Other than the notion of lost activation, PIER proposed no mechanism for the effects of disruption produced by the math task. PIER 2 uses the concept of cue-driven intersection to explain why the associates of the target have an effect in the first place, why the effects of these associates are reduced by disruptive tasks, and why the effects of the disruption can be overcome by using stronger cues. 4. PIER assumed that the implicit representation of the target had no effect on recognition because set size does not affect recognition. In contrast, PIER 2 assumes that the strength of the implicit representation is affected by the target's resonance and connectivity, and consequently the model predicts that such effects should be apparent in both cued recall and recognition. Such effects should be manifested whenever the residual activation of the target in LTWM is important. 5. One of the most important differences between the models concerns the hypothesized nature of the search process. PIER assumed that the target could be sampled directly from the associates activated by the test cue or from the total pool of associates activated by the test cue and the target without proposing a specific algorithm for accomplishing this task. PIER 2 assumes that multiple sources of preexisting information combine interactively to determine the probability of sampling the target. This probability is determined by net cue-to-target strength in LTWM relative to the total amount of information activated in LTWM by both the cue and the target. Whereas PIER was limited to explaining the effects of preexisting cueto-target strength and set size, PIER 2 incorporates the effects of all seven sources of information into a single sampling rule (Equation 3). Although PIER served as a useful framework for formulating our ideas, it cannot explain the bulk of the findings obtained in the present experiments, whereas PIER 2 is capable of handling them in a straightforward way. Finally, although there are many differences between these models, both suggest that words are not processed by the memory system only as individual entities but as collections of related concepts that have been cobbled together as a result of prior experience. PIER 2 and SAM Most memory theories, including SAM, have focused on the role that the episodic or explicit representation plays in recall and recognition and have mostly ignored the contribution of preexisting information (Eich, 1982; Gillund & Shiffrin, 1984; Hintzman, 1986; Murdock, 1982; Raaijmakers & Shiffrin, 1981). These models focus almost exclusively on episodic events and how such events shape and determine recall and recognition, and although this focus has produced outstanding models of episodic performance, these models fall short in explaining the role that LTWM plays in many tasks. Our approach to explaining our findings has not been to ignore or reject global memory models but to expand on them in ways that are compatible with their more fundamental assumptions. PIER 2 borrows SAM to explain effects that can be linked to the explicit representation, and therefore both models explain the same memory phenomena, that is, effects of list length, encoding strategy, and so on. Both models rely on assumptions that separate traces are accessed separately, that the sampling process is probabilistic, and that multiple cues can be combined interactively (see Equation 2). In many ways PIER 2 can be treated as an extension of SAM, but it differs in important respects, particularly because it adds a second representation to the encoding event, the implicit representation, and a second retrieval structure produced by the cueto-target intersection process. Unlike SAM, it assumes that, during the study trial, the activation of the associates of the target can influence the target's encoding strength within the implicit representation. The current formulation of SAM assumes that memory images are episodic, not semantic, and it is difficult to see how SAM can explain the effects of resonant connections from the associates of the target as well as effects related to connections among these associates. Essentially, we argue that information activated in LTWM can affect the recall and recognition of an item presented in an episodic task. PIER 2 also differs from SAM because it builds set size effects obtained in cued recall directly into the model. One advantage of directly incorporating cue and target set size effects is that they arise

18 316 NELSON, McKINNEY, GEE, AND JANCZURA independently and can be weighted differentially depending on factors such as forward cue-to-target strength. Another advantage is that the reduction of target set size effects produced by distracting tasks is built directly into the equation because sampling is computed on the basis of the intersection of the cue and target and their respective associates. In contrast, SAM would most likely attribute set size effects to one of its parameters, K~x. SAM is a sampling-with-replacement model with a fixed stop criterion (Raaijmakers & Shiffrin, 1981). Set size effects, like list length effects, would not occur if sampling were allowed to continue indefinitely because the sampling process would simply run until the correct item was recovered. Because indefinite sampling is highly unlikely, a stopping rule implemented by the K~ parameter was used to terminate the process after a fixed number of retrieval attempts. The likelihood of sampling the target before the stopping criterion is reached would be greater when the set is relatively small, just as it is greater when the list is relatively short. Unfortunately, although the K~x parameter provides a straightforward explanation of set size effects, it fails to capture the interactive nature of these effects. In order to explain why target set size effects are larger for stronger cues, are reduced after a task disruption, and so on, K~x would have to take on different values for each exception. The stopping criterion would have to be altered for each variable or condition that affects the magnitude of the set size effect, but the rationale for a constantly changing stop criterion is not obvious. Any iterative model of recall needs some mechanism to terminate sampling, and although Km~x works well to explain list length effects because such effects are usually constant across variations in other conditions, this parameter does not appear to work very well in explaining set size effects because of their interactive nature. Students of SAM might be tempted to explain set size effects away by attributing them to some other correlated variable that can be captured in the interactive cue assumption (e.g., Equation 2 in the present model). We have been trying to explain set size effects away since we first discovered them but we have been unsuccessful. Target set size effects are not the result of confounding set size with concreteness (Nelson & Schreiber, 1992), frequency (Nelson & Xu, 1995), connectivity (Nelson, Bennett, et al., 1993), or preexisting cue-to-target strength. These effects do not appear to be the result of confounding set size with any of the other six variables related to the implicit representation manipulated in our research program. Set size effects also do not appear to be the result of the natural confounding between set size and strength of the primary associate, r = -.76 (n = 4,501 ). Words with smaller associative sets tend to have stronger primary associates than those with larger sets. As a result, the implicit representation of targets with smaller sets of associates could have higher levels of encoding strength computed according to Equation 1, and Equation 3 could be eliminated. However, such a confounding predicts that target set size will affect recognition, but it does not (e.g., Nelson, Canas, & Bajo, 1987). Alternatively, stronger primaries associated with smaller set size targets could increase net cue-totarget strength as computed in Equation 2. Net strength, however, would be greater only if the test cue also produces the primary as an associate. As indicated in the bottom section of Table 2, associates of the target contribute to net strength only when the associate is also a member of the set activated by the test cue. Unique associates drop out of the computation of net strength because of the multiplication rule, and they become competitors. However, if primary associates are more likely than other associates to remain in the retrieval matrix over changes in test cues, then target set size effects could be attributed to confounded net strength and Equation 3 could be eliminated. The elimination of this equation would have simplified our task, but three findings argue against this alternative. First, a correlational study using the normative database indicated that there is no likely relation between set size and net strength involving shared associates. A sample of 23,486 cue-target pairs whose associates had all been normed were taken from the database, and the correlation between target set size and shared associate strength computed with normative values was r =.04. We interpret this finding to mean that primary associates were no more likely to remain in the retrieval matrix than other associates, thus eliminating a natural strength advantage for words with smaller sets. Second, as the results of Experiment 2 and other studies show, target set size effects are apparent when test cues share many associates with their targets and when they share only a few associates with them (Nelson, Bennett, et al., 1993). Finally, and most important, when primary strength has been controlled, target set size effects are still apparent (Nelson & Bajo, 1985). Other findings might have been cited as well, but the important point is that set size effects in the extralist cuing task do not seem to be the result of a confounding with a correlated attribute. As far as understanding the nature of the implicit representation is concerned, the best strategy seems to be to build such effects directly into the main working equation that also incorporates the potential influence of the other characteristics. Implications for Related Research Finally, we note that PIER 2 and its related findings have implications for other research endeavors. Comprehension and LTWM At least one theory of text comprehension assumes that associatively related words are initially activated during a meaning construction phase and then inhibited by the semantic context during a contextually based integration phase (Kintsch, 1988). Findings obtained in related paradigms (e.g., M. C. Anderson & Spellman, 1995; Gemsbacher & Faust, 1991) and in the intralist variation of our paradigm (e.g., Nelson, Schreiber, & McEvoy, 1992) provide strong support for the role of semantic context in inhibiting related associates. The present findings reinforce the importance of this role. When such context is absent, as it is in the extralist cuing task, or presumably, when context is present but fails to produce inhibition, related associates presumably remain activated in LTWM. Such information can be reactivated as a result of the intersection with the associates activated by a related word appearing later, regardless of whether the word appears as a test cue for recalling a list word or as just another word in a text. Equations 1, 2, and 3 essentially propose a specific model whereby the lexical by-products of the

19 IMPLICITLY ACTIVATED INFORMATION AND MEMORY 317 comprehension process can be recaptured by a related cue later in processing. Expertise The present findings are consistent with many expectations based on theories of skilled performance. Although individual differences in associative structure are important, our associative database, the recall and recognition findings, and our model are capturing some aspect of expertise that is relatively common. This is possible because our participants are experts in lexical processing, that is, extracting, storing, and retrieving meaning using words as the medium. In this sense, our work can inform and can be informed by research on skilled performance (e.g., Ericsson & Kintsch, 1995; Logan, 1989; Logan, Taylor, & Etherton, 1996). For example, the present findings suggest that LTWM plays a significant role as a temporary storage system for maintaining amounts of related information too large to be in the direct focus of attention. They also suggest that the activation of such information is subject to attentional demands such that even expert performance will tend to deteriorate when attention must be switched from one demanding conceptual task to another. Implicit and Explicit Memory Research leading to the implicit-explicit memory distinction is largely based on manipulating test instructions, and current theories interpret this research as reflecting the contributions of different memory systems (e.g., Schacter, 1989; Tulving, 1985) or different processes (e.g., Jacoby, 1983; Roediger, 1990). PIER 2 assumes that test instructions modify retrieval strategies used by participants to produce the recovery of related words. Just as levels of processing instructions can be used to bias the encoding of the items presented during study, different test instructions can be used to bias the processing of the cues during test. Test instructions can be used to bias recovery operations toward specific recent experiences or toward any experience from the past that is compatible with the test cue. However, the model goes a bit further than most process models in assuming that such instructions differentially engage two different types of representation produced during the study trial, an explicit representation produced as a result of conscious processing activities and an implicit representation produced as a result of automatic activation. Theoretically, both representations contribute to memory performance when participants are explicitly asked to use the cues to recover recently studied words. The explicit representation is recovered when cues are used to search "list" memory. In contrast, when any past experience is allowed, target recovery is largely determined by the implicit representation. Regardless of the test instructions, the implicit representation is recovered because the test cue automatically reactivates the target and its associates in LTWM. In PIER 2, differences in test instructions engender different target recovery processes, which in turn rely to some extent on the recovery of different information about the target experience. False Memory PIER 2 has clear implications for some of the recent research on the creation of false memories (McDermott, 1996; Payne, Elie, Blackwell, & Neuschatz, 1996; Roediger & McDermott, 1995). In the paradigm used by these researchers, participants study the associates of a target word that is not presented during learning. They then free recall the studied items, and this cycle is repeated until many word lists have been studied and recalled. The most important finding to emerge from this research shows that memory for the nonpresented "target" words is quite high and sometimes exceeds memory for studied words. This finding is important for theory and perhaps for society, and it is also consistent with expectations based on PIER 2. This consistency arises because the list memory paradigm used to study false memories approximates the inverse of the paradigm used in the present experiments. In the extralist cuing task, participants study the target, and the effects of its nonpresented associates on its later recall are investigated. In the false memory paradigm, participants study the associates of the target, and the effects of such study on the recall of the nonpresented target are investigated. In the general framework provided by PIER 2, both paradigms are investigating the influence of automatically activated information on memory, in the one case on memory for something that has occurred and in the other on memory for something that has not. In the extralist case, the associates are activated by the study of the target, and in the false memory case, the target is activated by the study of its associates. Given this similarity, the framework provided by PIER 2 can be used to make predictions about the occurrence of false memories. For example, stronger preexisting associate-to-target connections, as well as the reverse, should produce higher levels of false memories than weaker connections. The model not only suggests that the source of false memories lies in their automatic activation by the studied words, but it also suggests specific variables to investigate, namely, those that influence the recall of the target in the extralist cuing paradigm. Finally, given that subjects usually study about 15 related words in the false memory paradigm, it seems likely that at least some false memory items will reach conscious awa?eness during learning. Such items may be inadvertently rehearsed with other list words so that they become part of the explicit representations of these words. To the extent that false memory items intrude during learning, variables such as levels of processing, study time, and so on will increase the number of false memories that are observed. PIER 2 attributes the origin of false memories to the activation of related information in LTWM, and within its general framework, it suggests that such activation may bring them directly into the experience of the episode itself. Conclusions What the present findings make most clear is that the task of understanding the recall and recognition of recent episodes demands a better understanding of how memory performance can succeed or fail because of what people know about the event to be remembered as a result of a lifetime of learning. Without rejecting or ignoring the importance of the quantitative and qualitative characteristics of the nature of the encoding experience, PIER 2 offers one approach for understanding how prior knowledge activated in LTWM interacts with information activated by a retrieval cue to affect the recall and recognition of

20 318 NELSON, McKINNEY, GEE, AND JANCZURA recently experienced events. Although PIER 2 represents only one piece of the puzzle of understanding memory for recently experienced events, we believe that a complete model of episodic memory cannot exist without addressing the influence of LTWM. References Alba, J. W., & Hasher, L. (1983). Is memory schematic? Psychological Bulletin, 93, Anderson, J. R. (1983). The architecture of cognition. Cambridge, MA: Harvard University Press. Anderson, J. R. (1990). The adaptive character of thought. Hillsdale, NJ: Erlbaum. Anderson, J. R. (1996). AC'I2. A simple theory of complex cognition. American Psychologist, 51, Anderson, J. R., & Pirolli, E L. (1984). Spread of activation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10, Anderson, M. C., Bjork, R. A., & Bjork, E. L. (1994). Remembering can cause forgetting: Retrieval dynamics in long-term memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 20, Anderson, M. C., & Spellman, B. A. (1995). On the status of inhibitory mechanisms in cognition: Memory retrieval as a model case. Psychological Review, 102, Bahrick, H. P. (1970). Two-phase model for prompted recall. Psychological Review, 77, Bahrick, H. P., Bahrick, P. C., & Wittlinger, R. P. (1975). Fifty years of memories for names and faces: A cross-sectional approach. Journal of Experimental Psychology: General, 104, Balota, D. A., & Neely, J.H. (1980). Test expectancy and word frequency in recall and recognition. Journal of Experimental Psychology: Human Learning and Memory, 6, Boring, E. G. (1957). A history of experimental psychology. New York: Appleton-Century-Crofts. Bower, G. H. (1970). Analysis of a mnemonic device. American Scientist, 58, Chang, T. M. (1986). Semantic memory: Facts and models. Psychological Bulletin, 99, Collins, A. M., & Loftus, E. E (1975). A spreading-activation theory of semantic processing. Psychological Review, 82, Cowan, N. (1988). Evolving conceptions of memory storage, selective attention, and their mutual constraints within the human information processing system. Psychological Bulletin, 104, Cowan, N. (1995). Attention and memory: An integrated framework. New York: Oxford University Press. Craik, E I. M., & Tulving, E. (1975). Depth of processing and the retention of words in episodic memory. Journal of Experimental Psychology: General, 104, Cramer, P. (1968). Word association. New York: Academic Press. Deese, J. ( 1965 ). The structure of associations in language and thought. Baltimore: Johns Hopkins University Press. Eich, J. M. (1982). A composite holographic associative recall model. Psychological Review, 89, Ericsson, K. A., & Kintsch, W. (1995). Long-term working memory. Psychological Review, 102, Ericsson, K. A., Krampe, R. T., & Tesch-Romer, C. (1993). The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100, Fischer, B., & Glanzer, M. (1986). Short-term storage and the processing of cohesion during reading. Quarterly Journal of Experimental Psychology, 38A, Gee, N. R. (1997). Implicit memory and word ambiguity. Journal of Memory and Language, 36, Gernsbacher, M. A., & Faust, M. E. ( 1991 ). The mechanisms of suppression: A component of general comprehension skill. Journal of Experimental Psychology: Learning, Memory, and Cognition, 17, Glanzer, M., Fischer, B., & Dorfman, D. (1984). Short-term storage in reading. Journal of Verbal Learning and Verbal Behavior, 23, Gillund, G., & Shiffrin, R. M. (1984). A retrieval model for both recognition and recall. Psychological Review, 91, Gorman, A. M. (1961). Recognition memory for nouns as a function of abstractness and frequency. Journal of Experimental Psychology, 61, Grier, J.B. (1971). Nonparametric indexes for sensitivity and bias: Computing formulas. Psychonomic Bulletin, 75, Hintzman, D.L. (1986). "Schema abstraction" in a multiple trace memory model. Psychological Review, 93, Howe, E.S. (1972). Number of different free associates: A general measure of associative meaningfulness. Journal of Verbal Learning and Verbal Behavior, 11, Humphreys, M. S., Bain, J. D., & Pike, R. (1989). Different ways to cue a coherent memory system: A theory for episodic, semantic, and procedural tasks. Psychological Review, 96, Humphreys, M. S., & Galbraith, R. C. (1975). Forward and backward associations in cued recall: Predictions from the encoding specificity principle. Journal of Experimental Psychology, 1, Jacoby, L. L. (1983). Perceptual enhancement: Persistent effects of an experience. Journal of Experimental Psychology: Learning, Memory, and Cognition, 9, Jacoby, L.L. (1991). A process dissociation framework: Separating automatic from intentional uses of memory. Journal of Memory and Language, 30, Joelson, J. M., & Herrmann, D. J. (1978). Properties of categories in semantic memory. American Journal of Psychology, 91, Keppel, G., & Underwood, B. J. (1962). Retroactive inhibition of R- S associations. Journal of Experimental Psychology, 64, Kintsch, W. ( 1988 ). The role of knowledge in discourse comprehension: A construction-integration model. Psychological Review, 95, Kiss, G. R. (1968). Words, associations, and networks. Journal of Verbal Learning and Verbal Behavior, 7, Kucera, H., & Francis, W. N. (1967). Computational analysis of present-day American English. Providence, RI: Brown University Press. Landauer, T. K., & Dumais, S. T. (1997). A solution to Plato's problem: The latent semantic analysis theory of acquisition, induction, and the representation of knowledge. Psychological Review, 104, Logan, G. D. (1989). Automaticity and cognitive control. In J. S. Uleman & J. A. Bargh (Eds.), Unintended thought (pp ). New York: Guilford Press. Logan, G. D., Taylor, S. E., & Etherton, J.L. (1996). Attention in the acquisition and expression of automaticity. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22, McClelland, J. L., & Rumelhart, D. E. (1986). A distributed model of human learning and memory. In J. L. McClelland & D. E. Rumelhart (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition (Vol. 2, pp ). Cambridge, MA: MIT Press. McDermott, K.B. (1996). The persistence of false memories in list recall. Journal of Memory and Language, 35, McEvoy, C. L., & Nelson, D. L. (1982). Category name and instance norms for 106 categories of various sizes. American Journal of Psychology, 95, Murdock, B. B. (1982). A theory for the storage and retrieval of item and associative information. Psychological Review, 89, Neely, J. H. (1991). Semantic priming effects in visual word recognition: A selective review of current findings and theories. In D.