Psychonomic Bulletin & Review 2000, 7 (3), 550-558
Interference in memory by process or content? A reply to Neath (2000) DYLAN M. JONES and SÉBASTIEN TREMBLAY Cardiff University, Cardiff, Wales The approach to the irrelevant sound effect by Neath (2000) is discussed in terms of the contrast between content-based and process-based interference. Four themes are highlighted: First, problematic features of the feature model are highlighted; second, results not considered by Neath are presented; third, empirical underpinnings of the feature model not related to the irrelevant-sound effect are questioned; last, the parsimony of the feature model is questioned. The balance of the evidence seems to be in favor of a process-based approach, on the grounds that it provides a comprehensive account of acoustic and taskbased factors within the irrelevant sound effect, for both speech and nonspeech sound.
In the 25 years or so since Colle and Welsh (1976) first demonstrated it, the irrelevant sound effect has become an increasingly important element in theories of shortterm recall. The paper by Neath (2000) signals a coming of age for the study of irrelevant sound. It represents a significant advance in the integration of the irrelevant sound effect into a coherent theoretical framework, a step that is to be both welcomed and admired. Generally, the target paper gives a reasonably accurate account of the major findings in the area; however, it gives a less than full account of the changing state hypothesis (Jones, 1993; see also Jones, Beaman, & Macken, 1996) by failing to mention a raft of studies, some of which particularly raise problems for the feature model. The analytic force of these studies will here be discussed in detail. In this reply, we will concentrate on three facets of Neath’s paper. First, we list some problematic characteristics of the feature model as it applies to irrelevant sound effects. Second, we discuss results not considered by Neath. This is followed by a critical account of the empirical underpinnings of the feature model. Finally, we question whether the feature model is a sufficiently parsimonious account of memory. Before dealing with the characteristics of the feature model, we make a few clarifications in relation to the changing state hypothesis, which is a key feature of a more general model known as the object-oriented episodic recMost of the work reported here was supported in the form of project grants to D.M.J. from the U.K.’s Economic and Social Research Council and the Defence Evaluation and Research Agency (Farnborough, Hants), Centre for Human Sciences. Thanks are due Karen Anderson Howes, Alaistair Nicholls, and Robert Houghton for critical readings of the manuscript. Correspondence concerning this article should be addressed to D. M. Jones or S. Tremblay, School of Psychology, Cardiff University, P.O. Box 901, Cardiff CF10 3YG, Wales (e-mail:
[email protected] or
[email protected]).
Copyright 2000 Psychonomic Society, Inc.
ord (O-OER; see Jones, 1993) model. The first relates to a possible source of confusion in nomenclature. In the paper of Neath (2000), the term irrelevant sound relates to the action of nonspeech stimuli. Hence, in that context, irrelevant sound and irrelevant speech refer to mutually exclusive categories (which are also exhaustive). The term irrelevant sound as used in this paper (and in all the publications arising from our laboratory) is intended to cover both irrelevant speech and irrelevant nonspeech. Given that within the irrelevant sound paradigm, there is ample evidence that speech and nonspeech are functionally identical in their action, use of the term irrelevant sound to cover all types of effects seems more appropriate. Broadly, the O-OER model proposes that forgetting within short-term memory can best be understood by cognitive streaming—namely, the action of organizational factors that result in the partitioning of information into streams. This analysis may apply to the partitioning of successive events into groups (to be found in such paradigms as exploring memory for pitch [Jones, Macken, & Harries, 1997] or the effect of a stimulus suffix [Nicholls & Jones, 1999]) or to the partitioning of concurrent events into streams, within such settings as the irrelevant sound paradigm. Although in most cases the partitioning is largely stimulus driven—it is automatic and obligatory for unattended sound, for example—in others, such as the rehearsal of items in memory, the partitioning is volitional, or schema driven. The deployment of these schemadriven processes is not modality dependent; they apply as much to auditory sequences of to-be-remembered events as to visually presented sequences. The typical irrelevant sound paradigm represents the interference between two concurrent streams of information: One, the stream comprising the rehearsal of the to-be-remembered sequence, is the product of schema-driven processes; the other, the irrelevant sound stream, is the product of preattentive automatic processing (see Macken, Tremblay, Alford, & Jones, 1999, for a discussion). Critically, according to this perspective, the process of cognitive streaming has consequences for the representation of the order of events. In the case of irrelevant sound, the processing of sound involves the mechanism of stimulus mismatch. Successive events within the stream are compared, and the degree of physical mismatch between them determines the degree of information about order (or seriation1) that is embodied within the stream. Originally, it was supposed that any type of change was sufficient and that the larger the change, the greater the disruption (hence, an effect of changing state). Recently, it has been shown that not all physical attributes contribute to mismatch and, more important, that the relation between the degree of mismatch and seriation is nonmonotonic. It has been demonstrated that as the degree of mismatch increases from zero, so does the amount of seriation, with a
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NOTES AND COMMENT stream retaining its coherence, but only up to a point. Beyond that point, the mismatch becomes so great that events partition into streams. So, for example, in a sequence of tones, as the pitch difference between alternating tones is increased, the degree of seriation increases, but beyond a certain point the differences become so great that two streams, each of repeated pitch, are formed. At this point of fission, the degree of seriation drops markedly; now, two streams are formed, and within each there is little mismatch and, hence, little seriation (see, e.g., Jones, Alford, Bridges, Tremblay, & Macken, 1999). The irrelevant-sound effect is the result of a conflict of order information (seriation) from two sources; from the rehearsal of the to-be-remembered sequence and from the preattentive processing of sound into streams. One basic generic prediction follows from this proposition—namely, that any factor that increases the likelihood of seriation, either in the task or in the irrelevant sequence, will increase the degree of disruption in the memory task. A number of secondary predictions also flow from this, among them that speech and nonspeech have functionally similar effects, since the derivation of seriation is simply a product of physical mismatch. Also, the effects of irrelevant sound should be manifest even after the to-be-remembered list has been presented, when its contents are being rehearsed in memory in the absence of any stimuli. Uniquely, the model predicts that similarity of the identity of the to-beremembered sequence to that of the irrelevant sequence is not a critical determinant of disruption; only the relationship of events within each of the sequences is important. The key empirical referent of the O-OER model is the changing state effect; that is, repeated-item sequences are less disruptive than changing-item sequences. Another clarification relates to the claim by Neath (2000) that the effect will be observed only in tasks that have a serial order component or that induce serial processing. This is not entirely correct. Generally speaking, the O-OER model predicts that interference between concurrent tasks is the result of similarity of process, not of content. This means that it is possible to find effects other than those on serial recall tasks; generally speaking, in these cases the effects of the sound will also be different. For example, it is reasonably well established that irrelevant sound produces effects on the semantic processing of text. This effect is somewhat different from that produced in serial recall, in that semantic features of the irrelevant sound seem to determine the disruption (Jones, Miles, & Page, 1990; Martin, Wogalter & Forlano, 1988; Oswald, Tremblay, & Jones, 2000; see also Neely & LeCompte, 1999). This is entirely in line with the generalization that interference is based on similarity of process— in this case, concurrent semantic processes. However, from the standpoint of task sensitivity, it is true to say that for tasks involving list learning, the degree of disruption is a function of the degree of rehearsal (equated with seriation) in the memory task. Lists of unrelated items, for which semantic processing is difficult, are particularly liable to disruption by the action of seriation. Different requirements of retrieval
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produce different degrees of disruption by irrelevant speech. Specifically, if serial recall is emphasized, the disruptive effect is more pronounced. For example, if participants are required to produce a missing item from a closed set (such as days of the week), disruption is less than if the list is followed by a probe with the requirement to report the item that follows it in the list (Beaman & Jones, 1997; Jones & Macken, 1993). Crucially, it is not only those tasks that are nominally ones of serial recall that have rehearsal as the dominant mnemonic strategy. For example, rehearsal may be used in tasks for which the instructions are for free recall; moreover, the effect of irrelevant speech in free recall is on order errors, not item errors (Beaman & Jones, 1997, 1998). The target article acknowledges that a series of recognition tasks used by LeCompte (1994) could have contained a serial order component: “It is possible that even though a task has no serial component, subjects are nonetheless engaging in serial rehearsal or serial recall” (Neath, 2000, p. 405). There is a relatively large number of studies showing that tasks without a serial component are relatively insensitive to irrelevant sound (Baddeley & Salamé, 1986; Boyle & Coltheart, 1996; Burani, Vallar, & Bottini, 1991; Richardson, 1984; Salamé & Baddeley, 1990). In addition, increasing the weight of serial processing through rehearsal makes the disruption by irrelevant sound more likely (Banbury & Berry, 1998; Beaman & Jones, 1997, 1998; Morris & Jones, 1991). Within the target article, this leaves a single study, by Surprenant (1998), as the sole basis for claiming that memory tasks without any serial order component are sensitive to marked disruption by irrelevant sound. This work has yet to achieve archival publication, and from the available information, the results seem rather odd and without precedent in the irrelevant sound literature: The effect of irrelevant speech is on the incidence of false alarms. The feature model (Nairne, 1990; Neath, 2000) shares an assumption with many models of memory in supposing that forgetting is a product of item interference—that is, forgetting is induced by the presence of irrelevant events in memory similar in identity to the to-be-retrieved (or relevant) events. For the feature model, in the case of irrelevant sound, the contents (the similarity of items) in the relevant and irrelevant material determines the disruption. This is in very marked contrast to the O-OER model, in which disruption is a result of similarity of process (the degree of seriation in both activities). A number of predictions should flow from this emphasis within the feature model on similarity of content—among them, that the effect will be restricted to speech (by definition, if the to-be-remembered materials are verbal, only verbal material can be similar to them), that the degree of reliance on seriation within the memory task is not important (item identity is important, but not, for example, rehearsal strategy), and so forth. Problematic Characteristics of the Feature Model In this section, we deal briefly with some doubts in relation to the general form of the feature model, as ap-
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plied to the irrelevant-sound effect, coupled to a consideration of Neath’s (2000) simulations. Testability of feature adoption. A core prediction of the O-OER model is that the effect of interference is not the result of the similarity between the to-be-remembered material and the irrelevant sound. This was first demonstrated empirically by Jones and Macken (1995b). Such a result would seem to be at odds with the idea that forgetting is a product of feature adoption. One might expect on the basis of feature adoption that the more alike they are, the more likely it is that the sound and the tobe-remembered material would interfere. But in the conceptual replication of the Jones and Macken (1995b) study by LeCompte and Shaibe (1997), digits were used as the to-be-recalled materials, and irrelevant speech either rhymed with the digits (e.g. ton, gnu, tee, etc.) or did not rhyme (e.g., wick, tip, dub, etc.). As would be expected from the O-OER model, each type of irrelevant speech produced roughly similar degrees of disruption. On the face of it, given the principle of feature adoption, this result would seem to raise problems for the feature model. The model escapes the predicament by supposing that the content of the irrelevant speech was not synchronized with the contents of the list. This means that the degree of feature adoption is roughly the same for the rhyming and the nonrhyming irrelevant sound. The requirement for synchrony (to-be-remembered items coupled specifically to similar irrelevant items) has not been met by any experiment to date; thus, this critical characteristic of the feature model remains untested. A test for the idea that the same effect is produced with items while
they are being rehearsed seems implausible; there would be no way of checking on the degree of synchrony. A simple test using the marriage of similar irrelevant events to items at presentation is complicated by the tendency for some list items to be rehearsed while others are being presented, and hence, pure synchrony might be an elusive empirical goal. Rather than suggest that similarity between irrelevant and to-be-remembered sequences is important, coupled to the tortuous synchrony requirement, the available evidence supports a contrary proposition—namely, that similarity within irrelevant sequences dictates the degree of disruption. Take the study of Jones and Macken (1995b). This study used eight auditory conditions in all (see Figure 1). Two types of list were used: one of low similarity (F, K, L, etc.), the other of high similarity (B, C, D, etc.). Two types of irrelevant sequences were used: those rhyming and those not rhyming within sequence. Within each category, sequences were of two types: rhyming with a low-similarity list or not rhyming with a list. Hence, a sequence could form a rhymed sequence and also rhyme with a high-similarity list (sea, flea, key, etc.) or not rhyme with any type of list (door, war, more, etc.). In addition, a sequence could form a nonrhyming sequence and rhyme with a low-similarity list (deaf, pay, bell, etc.) or not rhyme with any type of list (hat, cow, nest, etc.). The results are clear-cut. There is a distinct effect of irrelevant sound only with low-similarity lists; both nonrhyming irrelevant sequences produced marked disruption, markedly greater than the rhyming sequences. According to the feature model, the condition with the greatest shared features
Figure 1. Serial recall errors of lists with syllables of low similarity (left panel) and of high similarity (right panel) in relation to serial position, contrasting a quiet control and four auditory conditions: words rhyming within the irrelevant speech sequence but not with any to-be-remembered lists (e.g., door, war, more), nonrhyming within sequence or with any lists (e.g., hat, cow, nest ), rhyming within sequence and potentially rhyming with a high-similarity list (e.g., sea, flea, key), and nonrhyming within sequence but potentially rhyming with a low-similarity list (e.g., deaf, pay, bell ).
NOTES AND COMMENT (Lists B, C, D, etc., with irrelevant speech tokens sea, flea, key, etc.) should show the greatest disruption, but this is one of the conditions in which the least disruption occurs. Speech and nonspeech effects. According to the feature model, feature adoption occurs with the modalityindependent features of the irrelevant items. This means that changes in pitch of irrelevant sound could never be subject to feature adoption, particularly if that change of pitch occurred in speech. However, there is adequate evidence that pitch changes in both speech and nonspeech stimuli produce marked disruption (e.g., Jones, Alford, et al., 1999; Jones & Macken, 1995a). In addition, the feature model cannot account for disruption produced by nonspeech. Rather, it supposes that feature adoption is not extensible to irrelevant nonspeech, preferring instead to believe that it constitutes a different effect, for which there is no specific provision in the model. However, there are several reasons to suppose that irrelevant nonspeech is functionally equivalent to irrelevant speech. Analytically powerful evidence on this point comes from parametric studies in which irrelevant nonspeech is shown to share the same functional relationship to disruption as speech. For example, both speech and tones show the same relation between disruption and token set size: There is an increase in error as the number of tokens increases from one to two, but there is very little increase in error as set size increases thereafter (Tremblay & Jones, 1998). Nonmonotonic functions are also found with both irrelevant tones and speech in studies of the modulating effects of organizational factors, such as streaming (e.g., Jones, Alford, et al., 1999) and auditory restoration (Jones, Macken, & Murray, 1993). For instance, the effect of acoustic variation on serial recall seems to be mediated by the phenomenon of streaming by pitch, whether tones or vowels are used as irrelevant sound (Jones, Alford, et al., 1999; see also Bregman, 1990). Other factors shown to influence the irrelevantsound effect apply to both nonspeech and speech: the stability of the effect over blocks of trials (e.g., Tremblay & Jones, 1998), the insensitivity of the irrelevant sound effect to variation in intensity (Tremblay & Jones, 1999), and the similarity of the function relating stimulus degradation to disruption of serial recall (Jones, Alford , Macken, Banbury, & Tremblay, in press). Neath, Surprenant, and LeCompte (1998) claimed to have found a functional differentiation between the action of speech and nonspeech—namely, that irrelevant speech eliminates the word length effect but irrelevant tones do not. However, we have recently tried to replicate this effect and found that neither irrelevant speech nor irrelevant tones abolish the effect of word length (Tremblay, Macken, & Jones, 2000; see also Longoni, Richardson, & Aiello, 1993). Taken together, these results show that nonspeech and speech produce strikingly similar trends. The exclusion of irrelevant nonspeech from the feature model on the grounds that it is a separate phenomenon is questionable, therefore.
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Changing state. A key feature of the O-OER model is the effect of changing state (see, e.g., Jones, Madden, & Miles, 1992; LeCompte, 1995). The difference between repeated- and varying-item sequences in terms of disruptive potency is modeled using the attentional parameter a. The logic employed here is embodied in the following quotation: “A single, repeating item will be easier to ignore than a stream of changing items. The easier an item is to ignore, the less attention will be diverted from the main task at hand” (Neath, 2000, p. 414). That the irrelevant speech effect is due to attention’s being diverted by the incoming sound is one appealing and parsimonious account for the robust phenomenon that repeated tokens produce less disruption. On the basis of the concept of the orienting response (see Sokolov, 1963), it has been suggested that irrelevant sound recruits attention away from the task at hand but repetitive stimuli cause much less disruption because the attentional response to the sound attenuates over time (see Cowan, 1995). However, a number of lines of empirical evidence suggest that habituation plays little or no role in the irrelevant-sound effect (see Jones, Macken, & Mosdell, 1997; Tremblay & Jones, 1998). A key prediction common to the habituation framework and the feature model (based on the model’s attentional parameter a) is that the degree of disruption should increase as the number of different tokens in the irrelevant sound sequence increases. With few tokens in a sequence, each token is repeated more often than if there are many, and therefore, habituation should be brought about more rapidly. Given that in Simulation 3 (that of the changing state effect), the attentional parameter for a repeated token is set at +8 and the parameter for a changing sequence of four tokens (as in the data simulated; Jones et al., 1992) is set at +2, a changing sequence containing two or three tokens should be allocated parameters set at some value between +8 and +2. Consequently, such a simulation would result in a linear function relating degree of disruption and token set size. However, it has been shown that disruption increases sharply when the set of distinct tokens increases from one to two but that, beyond two tokens, the degree of disruption does not increase significantly (Tremblay & Jones, 1998). Results Not Considered by Neath (2000) The paper of Neath (2000) fails to mention a number of phenomena that are prominent features of the O-OER model. Generally, a fairly substantial and internally consistent body of work shows that the composition of the events within the irrelevant sound are only of consequence for disruption inasmuch as they provide information about order. Other phenomena, such as the nonmonotonic relation between disruption and token set size, stem directly from the O-OER model. Streaming by location. Factors related to the organization of the irrelevant sound are key determinants of the degree of disruption by irrelevant sound. There are by now several demonstrations that this is so (Jones & Macken,
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1995a; Jones et al., 1993; Jones,, Alford, et al., 1999; Jones, Saint-Aubin, & Tremblay, 1999). That these are not mentioned in the target article is unfortunate, particularly given that they provide strong evidence against the feature model. One subclass of studies particularly raises problems for the feature model. These studies have the common feature that the identity of items in the irrelevant sequence is fixed in all conditions in which irrelevant sounds are present and that perceptual organization of the sequence is changed by some factor other than identity. An example of one such technique uses spatial location. Consider the case in which a sequence of three tokens—v, j, and x—is presented repeatedly in a fixed order to form an apparently seamless sequence. When presented monaurally, so as to form the impression that the sequence arises from the same point in space, the disruption of serial recall is quite marked. If the identical sequence of tokens is partitioned instead by stereophonic recording, so that each token comes from a different point in space (forming three streams: the v from the right ear, the j from the left ear, and the x from the middle of the head), serial recall error is significantly reduced (Jones & Macken, 1995a; Jones, Saint-Aubin, & Tremblay, 1999). Identity remains fixed, yet changing the perceptual organization of the sound may modify the degree of disruption readily and appreciably. It is difficult to understand how the feature model can account for this phenomenon, given that the stimuli are identical in content in each case. The features of the irrelevant sound are f ixed; therefore, the degree of disruption should remain fixed, according to the feature model. Streaming, mismatch, and seriation. The feature model cannot account for a range of other phenomena related to streaming. One is how the process of mismatch within the irrelevant stream relates to disruption. In this process, a calculation is made of the extent to which successive events share acoustic attributes (save for intensity; see Tremblay & Jones, 1999). Streaming information (i.e., the partitioning of the auditory scene into a number of temporally extended objects) is derived from this mismatch, and order information (seriation) is also derived. Importantly, the relation between the degree of mismatch and the degree of disruption of serial recall is nonmonotonic. That is, as the degree of mismatch is increased, the degree of seriation also increases (attended by increased disruption), but only up to a point; thereafter, seriation (and disruption) decreases. This point of inflection in the nonmonotonic function is that level of mismatch at which events in a sequence are perceived as belonging to two (or more) objects, rather than to one. Take the concrete example of an irrelevant stream in which a cycle of two alternating tones is repeated to form a sequence. As the pitch change is increased from 0 semitones to 2 and then to 5 semitones, the disruption increases. But when the change in pitch increases beyond that, to 10 semitones, the disruption decreases significantly (Jones, Alford , et al., 1999). Interestingly, the point of inflection corresponds roughly to that point, in listening studies, at
which a tone sequence with small changes in pitch, perceived as a single (albeit changing) entity, comes to be perceived as two (unchanging) entities: Fusion gives way to fission (see Bregman, 1990; Van Noorden, 1975, 1977). The O-OER model explains the reduction in disruption by the fact that one changing state stream gives way to two steady state streams. Critically, a change in the identity of the tokens is not a necessary condition for this effect. The feature model has difficulty in predicting that there is a nonmonotonic relationship between mismatch and disruption by irrelevant sound, let alone the point of inflection of the function. Seriation and discriminability. From the standpoint of the feature model, similarity in terms of acoustic features (modality-dependent features, as they are termed in the feature model) and also in terms of modality-independent features is assumed to be related to recall performance; discriminability helps recall, whereas similarity is detrimental. Perhaps contrary to intuition, a sequence of very distinct environmental sounds is very difficult to recall in order (see, e.g., Warren & Obusek, 1972). Consider the case in which a series of very distinct sounds is played in sequence (tone, noise burst, vowel, buzz): Memory for order is no better than chance, even though listeners are able to distinguish and name the components very well. However, given a sequence of sounds in which the changes are appreciable but, nevertheless, share a common carrier or ground (such as having a voice or timbre in common), memory for order improves dramatically (Warren, Obusek, & Farmer, 1969; see also Broadbent & Ladefoged, 1959). The same seems to apply to the case of semantic similarity (similarity of modality-independent features). For example, Saint-Aubin and Poirier (1999; see also Poirier & Saint-Aubin, 1995) showed that serial recall of semantically similar items is better than that of semantically dissimilar items. The foregoing examples illustrate that the feature model is incorrect in supposing that feature adoption takes place in the irrelevant sound paradigm. At the same time, these studies show that the perceptual organization of the sound is important. The O-OER model—through the concept of mismatch—incorporates factors responsible for the perceptual organization of sound, the encoding of order (which is a by-product of this organization), and the conflict of order cues between the relevant and the irrelevant streams. From a single premise, a range of phenomena is encompassed. The Empirical Underpinnings of the Feature Model The feature model rests on a particular set of assumptions; a number of these are subject to question. We argue that these assumptions are grounded only precociously on empirical evidence. Modality and discriminability. The feature model is heavily reliant on the relation between discriminability in different modalities and likelihood of recall. At least two objections can be raised about the way this is done in the
NOTES AND COMMENT model. The first relates to the claim that there is a natural hierarchy of sensory modalities with respect to discriminability, with audition at the top and vision at the bottom. However, the available empirical evidence suggests that this is an oversimplified view. For example, there is some reason to doubt that the claim about the superiority of the auditory modality is true. We need go no further than Neath’s (2000) paper for contrary data. The study on which Simulation 4v is based (Colle & Welsh, 1976; Figure 4 of the target paper) shows auditory serial recall to be very much inferior to visual presentation (based on Surprenant, Neath, & LeCompte, 1999; Figure 5 of the target paper). A second objection relates to the assumption that within a modality, memory for order is superior when the items are more discriminable. There is ample evidence to show that this is not true generally. As was mentioned in the earlier section, serial memory for a sequence of very distinct environmental sounds is poor, but if the sequence is made less distinct, memory for order improves (see, e.g., Broadbent & Ladefoged, 1959; Warren & Obusek, 1972). This finding is at variance with the discriminability assumption of the feature model, which supposes that performance should get worse when the discriminability of the sequence decreases, not better. Modality and recency. Within the feature model, the general effect of discriminability and modality is extended to the particular case of recency. First, it is claimed that recency is a characteristic of the auditory modality. However, there is clear-cut evidence of recency in the visual domain, as marked as any found in the auditory domain. If individuals are required to recall the order in which a sequence of spots appeared on a screen, their recall will show marked recency (Farrand & Jones, 1996; Jones, Farrand, Stuart, & Morris, 1995; see also Avons, 1998; Avons & Mason, 1999). Second, it is argued from the standpoint of the feature model that auditory recency arises because of the speechlike nature of encoding for auditory verbal lists. But there is at least one case in which the memory for the order of sounds in auditory space shows marked recency, and this is an effect shown with noise bursts, not with speech stimuli (Parmentier & Jones, 2000). Part of the discussion within Neath’s (2000) paper in relation to discriminability is misleading insofar as the evidence is drawn from recognition studies (e.g., Broadbent & Broadbent, 1981), not from serial recall. Arguably, making things more discriminable may make them easier to identify and, indeed, to recognize, but it does not necessarily make them easier to recall in order. Generally, therefore, some evidence points to the fact that the relationship between recency and modality is less clearcut than the feature model would have us believe. Suffix effects. The suffix effect—loss of auditory recency brought about by presenting a to-be-ignored item at the end of the list (e.g., Crowder & Morton, 1969)— is explained in the feature model by overwriting. It is, of course, possible to find recency within a list, which seems to argue against the overwriting argument. Incidence of
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this type of recency can be produced either by getting the person to rehearse material in groups or by presenting the to-be-recalled material in groups. Generally, there is marked recency at the end of each group (Frankish, 1985, 1989). Given that all but one of the groups in a list will be followed by a list item, it seems unlikely that there is appreciable overwriting. The evidence for an alternative possibility, that the suffix effect is due to grouping (Kahneman, 1973), is more credible. The feature model suggests that suffix effects occur when the suffix overwrites the modality-dependent features of the final item (see Greene, 1992, for a critique of feature overwriting accounts). This predicts correctly that a visual suffix will have less impact on an auditory list (see, e.g., Morton, Crowder, & Prussin, 1971; Rowe & Rowe, 1976), but the same mechanism cannot account for cross-modal suffix effects, such as the effect of an auditory suffix on lip-read recency (e.g., Campbell & Dodd, 1984). Also, the feature model predicts a similar effect for every modality. Therefore, it might be expected that a visual suffix has an effect on recency in a visual list, but this does not appear to be the case (e.g., Greene, 1987). The O-OER model assumes that the effect of the suffix is one of grouping. Anything by way of perceptual organization that dislodges the suff ix from the end of the list will reduce its impact on recency. One technique is to “capture” the suffix by using a competing (but irrelevant) stream (Nicholls & Jones, 1999). Parsimony and the Feature Model Whether verbal or computational, a cardinal requirement of a good theory is parsimony. A theory is not superior merely by being computational if there are very many dubious assumptions, unjustified exclusions, and untestable outcomes. In retrospect, it is easy to forget how productive the O-OER model has been, at least as far as the domain of irrelevant sound is concerned, and this with relatively few assumptions. In judging the competing models, parsimony is a relevant assumption. In judging the scope of the O-OER model, circumspection should not be confused with poverty of ambition. Complexity of the feature model. As compared with the O-OER model, the feature model makes a very large number of assumptions. It assumes two memory systems and a large number of parameters that are free to vary. As Baddeley (2000) rightly points out, with so many factors free to vary within the model, it becomes rather difficult to make predictions. For example, the dissociation between primary and secondary memory is nowhere justified fully. After the list has been presented, primary memory will have a set of partially degraded cues, and secondary memory will contain undegraded representations. Recall begins by sampling each cue to determine which one was most likely to be in the first position, and this is used to sample from secondary memory. Why secondary memory should be free from any sort of degradation, and particularly so from the retroactive interference to which primary memory is prey, seems rather arbitrary.
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Both stores could have the same attributes. Obviously, if both had the properties of secondary memory, there would be no errors. But secondary memory could hold both item and order information and be subject to retroactive interference. Attentional factor, a. The general effect of attention is incorporated within the model: “The parameter a is a scaling parameter than can be mapped onto the overall level of attention or available resources” (Neath, 2000, p. 408). The major difficulty with this parameter is that it is not grounded in any clearly articulated mechanism within the model or, indeed, coarticulated with any other characteristic of the model. Importantly, the attentional factor has no clear empirical referent, except the depression in performance that is the prior sign of its action. Alas, not all cases of performance impairment mean that the factor responsible was the attentional factor. The model does not give a rationale for this. The attentional parameter is largely arbitrary, ther & efore. How order errors occur. The feature model acknowledges a key prediction of the O-OER model—namely, that irrelevant sound brings about order errors (Jones, 1993; see also Beaman & Jones, 1997, 1998). There are two ways, within the feature model, in which order errors arise. Irrelevant sound has an effect only on one of these— namely, feature adoption. That is, the effect on order is via that on items. Specifically, “item errors, failing to correctly match a primary cue with a secondary memory item, can result in order errors” (Neath, 2000, p. 408). The process of feature adoption creates problems of retrieval; these in turn bring about order errors. The other way in which order errors occur is through perturbation, but irrelevant sound does not have an effect on perturbation. Generally, perturbation acts to produce baseline levels of error, such as the ones produced as a function of serial position, in quiet. That there should be two mechanisms of order error production within one model seems to lack parsimony, especially since the perturbation mechanism could well enough serve in isolation as the basis of the irrelevant-sound effect. Retrieval. It is not clear why the model needs so many stages. Consider the process of retrieval as described by Neath (2000). Positional uncertainty gradients are used to decide which cue is to be matched with secondary memory, and on that basis an item is recalled. But why is it not possible for the primary memory representation alone to serve as the basis for item production? The feature model assumes that there is never enough information in the primary memory cue for it to serve (in conjunction with the positional uncertainty gradient) as a basis for retrieval. Why, a priori, this complication is necessary is not clear; certainly, it does little for the parsimony of the model. Conclusions The feature model provides an extremely useful counterpoint to the O-OER model. This counterpoint is highly productive from the standpoint of suggesting a range of
empirical tests, particularly in relation to the irrelevant sound effect. The O-OER model lacks the comprehensiveness and the computational instantiation of the feature model. Nevertheless, the model has been productive and has generated many novel lines of evidence. The achievement of the feature model in attempting to encompass such a wide range of phenomena should not be understated. However, there are still points at which the model is at variance with the available evidence. Primarily, this is evident in the treatment of the irrelevant speech effect. Perhaps most critical in this regard is the failure to explain why disruption changes when the identity of the irrelevant sound remains fixed and the perceptual organization changes. This has been demonstrated sufficiently often and in so many different ways that it cannot be regarded as a detail. The partitioning of the effects of nonspeech sounds as some class of phenomenon not subject to feature adoption is also a great weakness of the feature model. A third weakness of the feature model is the lack of specificity about task sensitivity to interference by irrelevant sound. The O-OER model is frank on this matter, whereas the feature model is somewhat coy; the feature model needs to specify with clarity the principles that govern task sensitivity and to assemble a supporting empirical case. Doubtless, this will not be the last occasion on which the models will clash. Hopefully, improvements both to the articulation of the theories and to the sophistication of the experimental tests will follow this particular exchange. REFERENCES Avons, S. E. (1998). Serial report and item recognition of novel visual patterns. British Journal of Psychology, 89, 285-308. Avons, S. E., & Mason, A. (1999). Effects of visual similarity on serial report and item recognition. Quarterly Journal of Experimental Psychology, 52A, 217-240. Baddeley, A. D. (2000). The phonological loop and the irrelevant speech effect: Some comments on Neath (2000). Psychonomic Bulletin & Review, 7, 544-549. Baddeley, A. D., & Salamé, P. (1986). The unattended speech effect: Perception or memory? Journal of Experimental Psychology: Learning, Memory, & Cognition, 12, 525-529. Banbury, S., & Berry, D. C. (1998). The disruption of speech and office-related tasks by speech and office noise. British Journal of Psychology, 89, 499-517. Beaman, C. P., & Jones, D. M. (1997). The role of serial order in the irrelevant speech effect: Tests of the changing state hypothesis. Journal of Experimental Psychology: Learning, Memory, & Cognition, 23, 459-471. Beaman, C. P., & Jones, D. M. (1998). Irrelevant sound disrupts order information in free as in serial recall. Quarterly Journal of Experimental Psychology, 51A, 615-636. Boyle, R., & Coltheart, V. (1996). Effects of irrelevant sounds on phonological coding in reading comprehension and short-term memory. Quarterly Journal of Experimental Psychology, 49A, 398-416. Bregman, A. S. (1990). Auditory scene analysis: The perceptual organization of sound. Cambridge, MA: MIT Press. Broadbent, D. E., & Broadbent, M. H. P. (1981). Recency effects in visual memory. Quarterly Journal of Experimental Psychology, 33A, 1-15. Broadbent, D. E., & Ladefoged, P. (1959). Auditory perception of temporal order. Journal of the Acoustical Society of America, 31, 1539. Burani, C., Vallar, G., & Bottini, G. (1991). Articulatory coding and
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