List-strength Effect Research Articles

A context noise model of episodic word recognition.

Item noise models of recognition assert that interference at retrieval is generated by the words from the study list. Context noise models of recognition assert that interference at retrieval is generated by the contexts in which the test word has appeared. The authors introduce the bind cue decide model of episodic memory, a Bayesian context noise model, and demonstrate how it can account for data from the item noise and dual-processing approaches to recognition memory. From the item noise perspective, list strength and list length effects, the mirror effect for word frequency and concreteness, and the effects of the similarity of other words in a list are considered. From the dual-processing perspective, process dissociation data on the effects of length, temporal separation of lists, strength, and diagnosticity of context are examined. The authors conclude that the context noise approach to recognition is a viable alternative to existing approaches.

Familiarity breeds differentiation: a subjective-likelihood approach to the effects of experience in recognition memory.

With repeated exposure, people become better at identifying presented items and better at rejecting items that have not been presented. This differentiation effect is captured in a model consisting of item detectors that learn estimates of conditional probabilities of item features. The model is used to account for a number of findings in the recognition memory literature, including (a) the basic differentiation effect (strength-mirror effect), (b) the fact that adding items to a list reduces recognition accuracy (list-length effect) but extra study of some items does not reduce recognition accuracy for other items (null list-strength effect), (c) nonlinear effects of strengthening items on false recognition of similar distractors, (d) a number of different kinds of mirror effects, (e) appropriate z-ROC curves, and (f) one type of deviation from optimality exhibited in recognition experiments.

Free recall and recognition in a network model of the hippocampus: simulating effects of scopolamine on human memory function

Free recall and recognition are simulated in a network model of the hippocampal formation, incorporating simplified simulations of neurons, synaptic connections, and the effects of acetylcholine. Simulations focus on modeling the effects of the acetylcholine receptor blocker scopolamine on human memory. Systemic administration of scopolamine is modeled by blockade of the cellular effects of acetylcholine in the model, resulting in memory impairments replicating data from studies on human subjects. This blockade of cholinergic effects impairs the encoding of new input patterns (as measured by delayed free recall), but does not impair the delayed free recall of input patterns learned before the blockade. The impairment is selective to the free recall but not the recognition of items encoded under the influence of scopolamine. In the model, scopolamine blocks strengthening of recurrent connections in region CA3 to form attractor states for new items (encoding impaired) but allows recurrent excitation to drive the network into previously stored attractor states (retrieval spared). Neuron populations representing items (individual words) have weaker recurrent connections than neuron populations representing experimental context. When scopolamine further weakens the strength of recurrent connections it selectively prevents the subsequent reactivation of item attractor states by context input (impaired free recall) without impairing the subsequent reactivation of context attractor states by item input (spared recognition). This asymmetry in the strength of attractor states also allows simulation of the list-strength effect for free recall but not recognition. Simulation of a paired associate learning paradigm predicts that scopolamine should greatly enhance proactive interference due to retrieval of previously encoded associations during storage of new associations.

The list-strength effect: Strength-dependent competition or suppression?

If several items are associated with a common cue, the cued recall of an item is often supposed to decrease as a function of the increase in strength of its competitors' associations with the cue. Evidence for such a list-strength effect has been found in prior research, but this effect could have been caused both by the strength manipulations and by retrieval-based suppression, because the strengthening and the output order of the items were confounded. The experiment reported here employed categorizable item lists; some categories in each list contained strong items only, some contained weak items only, and some contained both strong and weak items. Strengthening was accomplished by varying the exposure time of the items. The testing sequence of the items from each category was controlled by the use of category-plus-first-letter cues. When the typical confounding of strengthening and output order was mimicked, list-strength effects were found, which is consistent with prior research. However, when this confounding was eliminated, the list-strength effects disappeared: The recall of neither strong nor weak items varied with the strengths of the other category exemplars. This pattern of results indicates that the list-strength effect is not the result of strength-dependent competition, but is caused by output-order biases and a process of suppression.

A model for recognition memory: REM—retrieving effectively from memory

A new model of recognition memory is reported. This model is placed within, and introduces, a more elaborate theory that is being developed to predict the phenomena of explicit and implicit, and episodic and generic, memory. The recognition model is applied to basic findings, including phenomena that pose problems for extant models: the list-strength effect (e.g., Ratcliff, Clark, & Shiffrin, 1990), the mirror effect (e.g., Glanzer & Adams, 1990), and the normal-ROC slope effect (e.g., Ratcliff, McKoon, & Tindall, 1994). The model assumes storage of separate episodic images for different words, each image consisting of a vector of feature values. Each image is an incomplete and error prone copy of the studied vector. For the simplest case, it is possible to calculate the probability that a test item is "old," and it is assumed that a default "old" response is given if this probability is greater than .5. It is demonstrated that this model and its more complete and realistic versions produce excellent qualitative predictions.

Encoding conditions and the list-strength effect.

Abstract The list - strength effect occurs when items within a list are remembered at the expense of items within that same list. The results of two experiments showed that variably encoded words were remembered better than words repeated with the same encoding context, whether memory was measured by free recall, frequency estimates, or recognition d'. However, there was little or no evidence from any of the measures that the variably encoded words were recollected in the lists at the expense of the similarly encoded words. This pattern held even though, in Experiment 2, there was a list - strength effect on free recall, when list strength was manipulated by increasing the number of presentations of a word. It was concluded that the free recall results could not be accommodated by the model of memory postulated by Shiffrin, Ratcliff, and Clark (1990) to account for the effects of list strength. Variability of Encoding and the List - Strength Effect In the past 5 years a number of studies have been carried out to investigate the list - strength effect, a term coined by Ratcliff, Clark, and Shiffrin (1990). The basic paradigm requires that items and items be presented in lists (i.e., lists including only strong or weak items respectively) and in mixed lists (which contain both strong and weak items). Items are judged to be strong or weak if recollection of the strong items is consistently and significantly better than recollection of the weak items. Typically, some items are strengthened by repeating them or giving them additional processing time relative to other items. A strong version of the list - strength effect states that the strong items are recollected at the expense of the weak, when both types occur in the same list. In other words, strong items are recollected better from a list than from a pure list while the opposite holds for weak items. A weaker version of the effect simply states that the difference in recollection between strong and weak items is greater within a list than between pure lists. Ratcliff et al. (1990) tested primarily for the presence of this weaker version, comparing the strong: weak performances as a ratio. Tulving and Hastie (1972) provided an early, partial demonstration of the list - strength effect. They gave subjects one of three types of lists to learn and recall:A+B, A+B+C, A+B+B, where A, B, and C are sets of words within a list. They found that, for the A+B+B list, recall of the B words was enhanced and recall of the A words was lowered compared to the recall of these words from either of the other two lists. This effect was replicated by Hastie (1975) for free recall but not for recognition. These experiments, though, did not test whether recall of strong items was enhanced by virtue of their presence in a list, because they did not have a pure strong condition. However, Ratcliff et al. (1990, Experiments 4 and 6) did find that recall of the strong items was generally enhanced in conditions (and recall of the weak items was generally inhibited). Most of the more recent studies of the phenomenon have measured memory with recognition (e.g., Murnane & Shiffrin, 1991b; Ratcliff et al., 1990; Yonelinas, Hockley, & Murdock, 1992). Generally speaking, the results have replicated those of Hastie (1975); i.e., they showed no effects of relative list strength when memory was measured by recognition. Shiffrin, Ratcliff, and Clark (1990) have argued that most current models of recognition have difficulty accounting simultaneously for the presence of a list - length effect on recognition (i.e., a decrease in recognition performance as the number of items in a list increases) and an absence of a list - strength effect, although this argument is open to debate (see Murdock & Kahana, 1993.) This theoretical argument seems to be the main reason for the current emphasis on recognition in studies of the list - strength effect. …

An auto-associative neural network for sparse representations: Analysis and application to models of recognition and cued recall.

The authors present the results of their analysis of an auto-associator for use with sparse representations. Their recognition model using it exhibits a list-length effect but no list-strength effect, a dissociation that current models have difficulty producing. Data on the effects of similarity and strengthening that indicate a dissociation between recognition and frequency judgments are also addressed. Receiver operating characteristic curves for the model have slopes between 0.5 and 1.0 and achieve this ratio in a novel way. The model can also predict latencies naturally. The authors' cued-recall model uses an architecture similar to that of the recognition model and where applicable the same parameters. It predicts appropriate amounts of retroactive interference, and analysis reveals an output competition process that relies on distributed representations and has not been proposed before.

TODAM and the list-strength and list-length effects: Comment on Murdock and Kahana (1993a).

B. B. Murdock and M. J. Kahana (1993a) presented a continuous memory version of the theory of distributed associative memory (TODAM) model; they claimed that this model predicts list-strength and list-length findings, including those reported by R. Ratcliff, S. E. Clark, and R. M. Shiffrin (1990) and K. Murnane and R. M. Shiffrin (1991a). This model is quite similar to one discussed by R. M. Shiffrin, R. Ratcliff, and S. Clark (1990), who rejected the model on the basis of its inability to predict both an absent or negative list-strength effect (when strength is varied by repetitions) and a present list-length effect. In this comment we elaborate the earlier discussion and demonstrate that the version of TODAM proposed by B. B. Murdock and M. J. Kahana (1993a) indeed fails for this reason. We show this first for a somewhat simplified version of the model for which derivations are obvious and then in a simulation of the complete version using the parameter values suggested by B. B. Murdock and M. J. Kahana (1993a).

Analysis of the list-strength effect.

In several articles, Shiffrin et al. (e.g,, Shiffrin, Ratcliff, & Clark, 1990) argued that their data on the list-strength effect (LSE), in conjunction with their data on the list-length effect (LLE), are counter to current global matching memory models (GMMs). This is only true if one assumes that the memory system is reinitialized after every list, which is an unrealistic default assumption present in many implementations of GMMs. By making the more reasonable assumption that memory is continuous, it is shown that TODAM (and probably other GMMs) does in fact predict the LSE and LLE data

List-strength and list-length effects: Reply to Shiffrin, Ratcliff, Murnane, and Nobel (1993).

R. M. Shiffrin, R . Ratcliff, K . Murnane, and P. Nobel (1993) claimed that TODAM (a theory of distributed associative memory) is unable simultaneously to predict an absent (or negative) liststrength effect (LSE) and a positive list-length effect (LLE). However, Shiffrin e t al. failed t o distinguish between situations in which lag (number of items intervening between study and test) is controlled and situations in which it is not. We stand by our previous conclusion; TODAM can explain why there is little or no LSE when at the same time there is an LLE when the LLE is studied under the standard conditions. To our knowledge there are no published studies where lag has been controlled. However, this simplified version of TODAM cannot explain an LLE when a scoring window i s used. Whether such a result would be inconsistent with a more complete version of TODAM remains to be seen. Murdock and Kahana (1993) derived TODAM (a theory of distributed associative memory) predictions for the liststrength effect (LSE), the list-length effect (LLE), and the d! from a study-test paradigm and showed that, with reasonable parameter values, the predicted results were in good agreement with the general pattern of data in the literature. They assumed that memory carried over from the preexperimental to the experimental situation and from list to list; they called this assumption the continuous memory assumption. Shiffrin, Ratcliff, Murnane, and Nobel (1993) claimed that they had analyzed this model and rejected it because it is unable, simultaneously, to predict an absent (or negative) LSE and a positive LLE. Shiffrin et al. reiterated this point with an informal analysis of TODAM, which was buttressed by a simulation using the Murdock and Kahana (1993) parameter values. The main problem with the analysis of Shiffrin et al. (1993) is that, for the LLE, they failed to distinguish between the situations where lag is controlled and the situations where it is not. (By lag we mean the number of items intervening between the study of an item and its test.) Shiffrin e t al.'s analysis applies to the former case, whereas our analysis applies to the latter case. It is important to understand that in studies of the LLE, lag is generally not controlled; instead, in the usual study-test paradigm all list items are tested, and there are an equal number of old and new items in the test phase. Items are randomly ordered in the test phase, so perforce lag and list length must be confounded. In fact, to our knowledge, there are no published studies of the LLE where lag is properly controlled.

Analysis of the list-strength effect.

Analysis of the list-strength effect.

TODAM and the list-strength and list-length effects: Comment on Murdock and Kahana (1993a).

TODAM and the list-strength and list-length effects: Comment on Murdock and Kahana (1993a).

Tests of the list-strength effect in recognition memory.

The list-strength effect arises when increasing the strength of some items in a list reduces memory for the remaining items. Here the list-strength effect was investigated under conditions of rapid visual presentation. Randomized and blocked formats were used for the mixed lists. Performance was measured with both yes-no and forced-choice recognition procedures. Overall no evidence for a list-strength effect in recognition was found except under conditions that may promote reverse rehearsal borrowing. Two experiments were conducted to determine why performance on the yes-no tests was greater than on the forced-choice tests.

Item and associative information in a distributed memory model

According to TODAM, a distributed memory model based on convolution and correlation, item and associative information are stored and retrieved from a common memory vector. The model also gives a possible representation of forgetting, encoding, attentional, and habituation processes. The main purpose of the present paper is to present the necessary derivations so the model can be applied to item recognition, pair recognition, and cued recall in a single-trial or list-learning paradigm. Applications to the list-strength effect, item- and pair-recognition, and paired-associative learning are briefly discussed.

Tests of the list-strength effect in recognition memory.

Tests of the list-strength effect in recognition memory.

Word repetitions in sentence recognition

When some items on a list are strengthened by extra study time or repetitions, recognition of other, unrelated, list items is not harmed (Ratcliff, Clark, & Shiffrin, 1990). Shiffrin, Ratcliff, and Clark (1990) accounted for this list-strength finding with a model assuming that different items are stored separately in memory, but that repetitions are accumulated together into a single stronger memory trace. Repeating words in the context of different sentences might cause separate storage of the repetitions of a given word, because either word or sentence traces are stored separately. Separate storage would, in effect, convert a list-strength manipulation into a list-length manipulation and thereby induce a positive list-strength effect. In Experiment 1, this result was produced for single-word recognition and for two types of sentence recognition. In Experiment 2, both words and sentences were repeated together, which should have caused repetitions to be stored in a single, stronger, trace. As expected, the list-strength effect was eliminated. A sentence trace model was fit to the data, supporting the account of Shiffrin et al. (1990) and supporting an account of word and sentence recognition in which activation is summed for representations of all list items. The results from the two studies are inconsistent with most current models of memory (as shown by the theoretical analyses of Shiffrin et al., 1990) and pose an additional challenge for theory.

Interference and the representation of events in memory.

Most current models of memory predict that the presence of increasingly well-learned, or strong, items in memory will cause increasing interference. This phenomenon, the list-strength effect, occurs as predicted when memory is tested by free recall but not when a recognition test is used. Four experiments use end-of-session testing to demonstrate that redistribution of storage time or effort from strong to weak items on mixed lists does not occur and therefore cannot be masking interference by strong items. Delay between study and test is found to cause memory loss independent of the basic list-strength findings. It is concluded that the presence of strong items in memory does not interfere with recognition performance and that interference is due to failures of retrieval rather than to composition or other forms of destructive interaction during storage.

List-strength effect: II. Theoretical mechanisms.

Ratcliff, Clark, and Shiffrin (1990) examined the list-strength effect: the effect of strengthening (or weakening) some list items upon memory for other list items. The list-strength effect was missing or negative in recognition, missing or positive in cued recall, and large and positive in free recall. We show that a large number of current models fail to predict these findings. A variant of the SAM model of Gillund and Shiffrin (1984), involving a differentiation hypothesis, can handle the data. A variant of MINERVA 2 (Hintzman, 1986, 1988) comes close but has some problems. Successful variants of a variety of composite and network models were not found (e.g., Ackley, Hinton, & Sejnowski, 1985; Anderson, 1972, 1973; Metcalfe Eich, 1982; Murdock, 1982; Pike, 1984). The results suggest constraints on the future development of such models.

List-strength effect: I. Data and discussion.

Extra items added to a list cause memory for the other items to decrease (the list-length effect). In one of the present studies we show that strengthening (or weakening) some items on a list harms (helps) free recall of the remaining list items. This is termed the list-strength effect. However, in seven recognition studies the list-strength effect was either absent or negative. This held whether strengthening was accomplished by extra study time or extra repetitions. The seven studies used various means to control rehearsal strategies, thereby providing evidence against the possibility that the findings were due to redistribution of rehearsal or effort from stronger to weaker items within a list. Current models appear unable to predict these results. We suggest that different retrieval operations underlie recall and recognition, as in the SAM model of Gillund and Shiffrin (1984), which can be made to fit the results with certain relatively minor modifications.

List-strength effect: I. Data and discussion.

List-strength effect: I. Data and discussion.