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Getting it right? Using aphasicnaming errors to evaluatetheoretical models of spoken wordrecognitionLyndsey Nickels aa Department of Psychology , Birkbeck College, University ofLondon , London, UKPublished online: 13 Dec 2007.
To cite this article: Lyndsey Nickels (1995) Getting it right? Using aphasic naming errors toevaluate theoretical models of spoken word recognition, Language and Cognitive Processes,10:1, 13-45, DOI: 10.1080/01690969508407086
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LANGUAGE AND COGNITIVE PROCESSES, 1995,lO (l), 13-45
Getting it Right? Using Aphasic Naming Errors to Evaluate Theoretical Models of Spoken Word
Recognition
Lyndsey Nickels Department of Psychology, Birkbeck College, University of London,
London, UK
Different models of spoken word production make different predictions regarding the extent of effects of certain word properties on the output of that model. These predictions are examined with regard to the effect of these variables on the production of semantic and phonological errors by aphasic subjects. Thus the production of semantic errors is found to be affected by imageability and the production of phonological errors by word length, but not vice versa. It is argued that this pattern of variables affecting the production of semantic and phonological errors is better explained by models which require strictly sequential stage-by-stage processing (e.g. Levelt et al., 1991a; Morton, 1970; 1979) than by multi-layer perceptron (e.g. Plaut & Shallice, 1991; 1993) or interactive activation models (e.g. Dell, 1986; 1989).
INTRODUCTION Current models of spoken word production may be classified into two main types: (1) strictly sequential stage models, where semantic and phonologic- al aspects of lexical access are represented by separate, temporally distinct stages (e.g. Garrett, 1980; 1984; Levelt, 1983; Morton, 1970; 1979), and (2) connectionist or spreading activation models (e.g. Dell, 1986; 1989; Stemberger, 1985), where any activation spreads continuously through the network and, unlike in stage models, processing need not be complete at one level before activation is transmitted to the next. This latter type of
Requests for reprints should be addressed to Lyndsey Nickels, Department of Psychology, Birkbeck College, University of London, Malet Street, London WClE7HX, UK.
I would like to thank Tim Shallice and David Howard for helpful discussions during the preparation of this paper. Gerry Altmann and two anonymous reviewers gave useful comments on an earlier draft of the manuscript. The work was carried out while the author was supported by MRC Project Grant No. G8903890N.
0 1995 Lawrence Erlbaum Associates Limited
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model can be subdivided into those models where the spread of activation is primarily by feed-forward (i.e. multi-layer perceptron models; e.g. Plaut & Shallice, 1991; 1993), and those where activation reverberates around the network, by both feed-forward and feedback connections (i.e. interac- tive activation models; e.g. Dell, 1986; 1989; Stemberger, 1985). The purpose of this paper is to evaluate the extent to which these models can account for patterns of errors found in aphasic picture-naming.
Lexical access is commonly considered to occur in two stages (Butter- worth, 1989; Dell, 1986; 1989; Fromkin, 1971; Garrett, 1980; Kempen & Huijbers, 1983; Levelt et al., 1991a), which may or may not overlap depending on the type of model employed. The first of these stages involves access of the lexically specific semantic representation or lemma (Kempen & Huijbers, 1983), which is then (in the second stage) used to access the phonological representation for that lexical item, which in turn will be used for the preparation of articulatory codes for output.
Levelt et al. (1991a) argued for a “discrete two-stage model” of lexical access where phonological activation follows selection of the target item and is restricted to that item. They cited Morton’s (1970; 1979) logogen model as an example of a stage model.’ This model represents lexical items as logogens. Those logogens used for speech production are the logogens located in the phonological output lexicon. The cognitive system is a store of conceptual-semantic information which sends this information, regard- ing the concept to be expressed, to the phonological output lexicon. (In picture-naming, this conceptual-semantic information will correspond to that activated by the results of visual processing of the stimulus picture.) Each logogen in the lexicon is a device which accumulates semantic information that corresponds to that specified within the defining set for that logogen. Each logogen has a threshold, and when the count of accumulated evidence exceeds the threshold the logogen “fires”, making an appropriate response available. Thus, the accumulation of conceptual- semantic information (corresponding to the first, semantic, stage of lexical access, i.e. activation of the lemma) leads to the firing of a specific Iogogen, which sends a phonological code (corresponding to the second, phonolo- gical, stage of lexical access) to the response buffer where it is held until a vocal response is required.
Although Levelt et al. (1991a) described the logogen model as incorpor- ating two stages of lexical access, at no point in this model is the lemma
‘Although the logogen model does allow some interaction between levels (for example, between the auditory input logogens and the cognitive system), this is not the case for t he processes involved in spoken word production, and therefore this model satisfies the criterion for a discrete stage model.
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EVALUATING MODELS OF SPOKEN WORD RECOGNITION 15
explicitly available (“accessed”). However, Butterworth (1989) described a model which employs the same basic principles as the logogen model but which includes a stage which could be considered equivalent to lemma retrieval. Thus in this model semantic information addresses the semantic lexicon which is a transcoding device-taking the semantic code as input and delivering as output a phonological code for a specific lexical item. It is this stage which can be considered equivalent to retrieval of the lemma. The phonological code or address comprises the input to the phonological lexicon which delivers the phonological form as output. In contrast, connectionist models have levels but do not have discrete
stages of processing, with each level containing different types of nodes. Thus, Dell’s (1986; 1989; Dell & O’Seaghdha, 1991; 1992) model has a series of word-level nodes (corresponding to lemmas) and also a series of nodes at a phoneme level (where the pattern of activation corresponds to the phonological form of a lexical item). In this interactive activation (IAA) type of model, as in the logogen model, conceptual-semantic information activates all word-level nodes representing that information. Each activated word node sends some proportion of its activation to every node it is connected to. As all connections in Dell’s model are excitatory and two-way, word nodes will send activation both forward to the phoneme nodes representing the phonemes of that word, and back to the semantic level to the semantic features (or equivalent) of that word. In turn, the phoneme nodes will send activation back to all those word nodes to which they are connected (resulting in activation of words phonological- ly related to the target). Thus, activation reverberates around the network as a whole. As highly activated nodes will have large effects on other nodes, while less highly activated nodes will have smaller effects, differ- ences in activation at one level are reflected at other levels. The word that is most highly activated over the network as a whole is that which is selected for output after a predetermined (and variable) number of time steps by the addition of a “jolt” of further activation to this node. The phonological nodes which are most active are selected and linked to construct “word frames” for output. Other IAA models differ slightly in the precise details of the network architecture, although this influences the overall characteristics of the models relatively little. Thus, unlike Dell, Stemberger (1985) proposed weighted links’ between levels and inhibitory links within levels, although both models employ the bidirectional flow of information between levels.
’Although Dell’s model does employ weights in the between-level connections, in the implemented versions (Dell, 1986; Dell and O’Seaghdha, 1991; 1992) every connection has an equivalent weight.
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A second type of connectionist model are those models which, like stage models, involve only feed-forward processes, but unlike stage models (but like IAA models) do not require processing to be complete at one level before processing can begin at the next. Plaut and Shallice’s (1993) multi-layered perceptron model is a feed-forward connectionist model in which activation is transmitted in cascade from semantic units to intermedi- ate units to phonological units.3 This model was developed to simulate the performance of deep dyslexic patients, and was an extension of the work by Hinton and Shallice (1991), including abstract words in the net’s “vocabulary” (Plaut & Shallice, 1991; 1993) and adding an output network (Plaut & Shallice, 1993). It is the output portion of the model which concerns us here. This involves the transmission of activation from seman- tic units (sememe units) to phoneme units via intermediate units.
Of course, connectionist models can incorporate elements of both strictly feed-forward connections between some levels and interactive activation between others. An example of this is Harley’s (1993; Harley & MacAndrew, 1992) three-layer model, which comprises semantic, lexical and phonological units. In this model, the connections are purely feed- forward between semantic and lexical units, but are interactive (both feed-forward and feedback) between lexical and phonological units. This model also differs from the others described above in that it incorporates both inhibitory and excitatory connections between levels (as well as inhibitory connections within levels).
Each of these types of model have their strengths and weaknesses and can account for aspects of normal performance with greater or lesser ease. For example, IAA models can account with ease for the lexical bias effect in phonological speech errors (Dell, 1986; 1989; Stemberger, 1985), where- as stage models have to invoke an editing mechanism to account for the occurrence of phonologically related errors which are also real words (rather than nonwords) at a rate greater than chance (Levelt, 1989).4 However, this is very often because some models have been developed specifically to be able to account for particular phenomena, and clearly a model that has been specifically developed to be able to account for a particular pattern of data will be better able to do that than one that has
’Although some versions of this network do incorporate some feedback between the phonological units and the intermediate units, the crucial feature of the model for the purposes of this paper (and the reason that it has been used as an example of a feed-forward connectionist model) is the lack of feedback to the semantic level from phonology.
%is editor may be equated with the comprehension system (Levelt et al., 1991a; 1991b), or may reflect a (non-parsimonious) additional system which checks output before production (Motley, Camden, & Baars, 1982). Butterworth (1981) proposes a mechanism in which lexical selection is run through twice and the results compared for identity (and hence, accuracy).
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EVALUATING MODELS OF SPOKEN WORD RECOGNITION 17
not. Therefore, a more powerful test of these models is to derive a set of predictions regarding the performance of each type of model and test those predictions experimentally.
In two recent papers, Levelt, Schriefers and their colleagues (Levelt et al., 1991a; Schriefers, Meyer, & Levelt, 1990) attempted to do just that. Schriefers et al. (3990) used a picture-word interference paradigm to study the time-course of lexical access, and, with the same aims, Levelt et al. examined reaction times in a lexical decision task performed during object-naming at different stimulus onset asynchronies (SOAs). Both tasks aimed to examine the extent of semantic and phonological activation at different periods during the production process, and found evidence for an early stage of exclusively semantic activation and a late stage of exclusively phonological activation, with no evidence for a late rebound of semantic activation. They argued that this pattern of activation is that predicted by stage models of lexical access and not by cascade or interactive accounts (as these predict overlapping semantic and phonological activation of the target, and, for IAA models, a late rebound of semantic activation).
In a reply article, Dell and O’Seaghdha (1991) argued that Levelt and co-workers’ data can be reconciled with interactive spreading-activation theories. They proposed an adaptation of Dell’s (1986) model, which is globally modular but locally interactive. Thus, Dell and O’Seaghdha’s model has feedback “one-level-up” but little or no feedback “two-levels- up”. The pattern of performance of their model is greatly influenced by external inputs, which enforce “linguistic rules at each level by determining when a selected unit is realised at a lower level” (Dell & O’Seaghdha, 1991, p. 610). Levelt et al. (1991b) argued that although Dell and O’Seagh- dha’s model may be able to simulate (at least some of) the experimental findings of Levelt et al. (1991a), it remains to be demonstrated that, using the same parameters, their model could also simulate the patterns found in speech error data (cf. Harley, 1993).
In an earlier study, Levelt (1983) examined error and self-repair patterns from spontaneous descriptions of visual designs (coloured circles con- nected by lines). He found that naming errors tended to be examples of anticipations and that these tended to be semantically but not phonologi- cally related to the targets (e.g. “from the pink, I mean the red circle, you go up to the pink one”), suggesting that word errors which are meaning- based occur independently of the retrieval of the word’s phonological form. However, these results, which once again support a sequential stage approach to word production, were only partially replicated by Martin, Weisberg and Saffran (1989). Using a similar paradigm, Martin et al. found that semantic and phonological similarity (of targets in the visual designs) interacted to increase the probability of a substitution error. They there- fore argued that retrieval of semantic and phonological representations
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does not occur in two independent stages, but that the results of their experiments were most consistent with an interactive model. They sug- gested that the hallmark of such a model is that “the influences of linguistic variables are non-independent” (Martin et al., 1989, p. 484). It is this feature which will be exploited in order to derive and test predictions from the different types of model using neuropsychological data.
A number of studies have demonstrated that connectionist models can replicate some aspects of neuropsychological data from aphasic subjects (e.g. Hinton & Shallice, 1991; Patterson, Seidenberg, & McClelland, 1990; Plaut & Shallice, 1991; 1993), and others have argued for the ability of one model rather than another to account for the pattern of breakdown in a particular patient (e.g. Ellis, Miller, & Sin, 1983; Martin & Saffran, 1992; Pate, Saffran, & Martin, 1987). However, few have attempted to test predictions made by the models using data from aphasic subjects. These data test the models in rather a different way to the experiments of Levelt et al. (1991a); rather than examining the time-course of the models directly, .it allows a test of the consequences of different time-courses on performance of models when “lesioned”.
Lesioning of connectionist networks can take many different forms: noise can be added to weights on the connections (either throughout the network or between particular levels) or to the resting levels of activation of nodes (again either throughout the network or at a particular level); a subset of nodes and/or connections can be removed; decay rates (in those models which employ them) can be pathologically increased or decreased; or spreading rates of activation can be raised or lowered. Plaut and Shallice (1993) and Harley and MacAndrew (1992) examined the effects of diffe- rent types of lesioning. Harley and MacAndrew found that their model was extremely resistant to disruption. Increased decay rate, increased random noise in the resting levels of activation and loss of inhibitory connections all still resulted in the target being the most highly activated; it was only with lesioning by adding noise to the (excitatory) semantic-to- lexical connections that an error occurred. This illustrates one of the features of connectionist models which is often described as an advantage for the description of neuropsychological data-graceful degradation (Rumelhart & McClelland, 1986). That is, when a network suffers damage, it does not catastrophically fail, but rather shows a slow decline in perform- ance which is proportional to the degree of damage. With the currently limited data from simulations with lesioned models, it is difficult to predict the effects of different lesions on performance. While different lesions may produce different effects (see Martin & Saffran, 1992, for a discussion, although without simulation), Plaut and Shallice (1993) demonstrated that different lesions each produced the full range of error types (although the relative proportions varied). Most importantly, the effects of a lesion (in
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IAA models) will spread to affect the network as a whole as activation reverberates between levels.
There have been several studies which have examined the effects of a number of properties of words on the accuracy of aphasic naming. For example, Howard et al. (1984) studied the naming of 12 aphasics and found effects of word frequency and length (number of phonemes) on naming performance, Kay and Ellis (1987) found effects of frequency on naming success for their patient E.S.T. (but no effects of length), and Pate et al. (1987) demonstrated that patient N.U.3 naming success was affected by position of stress in a word and word length (number of syllables). However, few authors have applied the same analysis to the nature of the errors produced. Are, for example, semantic errors more likely to be produced when attempting to name words of lower frequency, and might this also hold for phonological errors? What is proposed here is to use the patterns of variables that affect the production of semantic and phonologic- al errors in aphasic patients to test predictions regarding the expected patterns from the different classes of models.
Three variables will be considered: word imageability , word frequency and word length. Each of these variables can be considered to have loci at different points in models of word production. Thus, imageability is considered to be a characteristic of the semantic system. Some authors have equated it with “richness” of semantic representations [for example, Martin and Saffran (1992) for Dell’s (1989) model; Plaut & Shallice (1991; 1993)] which results in high-imageability words generally being less suscep- tible to error. On the other hand, frequency is generally accepted to be a feature of the lexical level of representation (Wingfield, 1968). Many models equate frequency with differences in the resting level of activation of a lexical item or node, with high-frequency items having higher resting levels of activation (or lower thresholds in the case of the logogen model) and therefore needing less additional activation to be selected. Finally, word length can be considered a feature of the phonological encoding and output systems; for example, if there is a certain probability of error on any one phoneme, then the more phonemes a word has the more likely an error is to occur during these procedures.
Because stage models such as the logogen model require that processing is complete at one level before processing begins at the next level, the effects of a particular variable are confined to the level at which it is located. In contrast, because connectionist models have activation cascad- ing throughout the network (and in interactive models this is both feed- forward and feedback), the effect of a variable at one level will be “felt” throughout the network (Martin et a]., 1989). This is due to the fact that each node transmits activation to those nodes to which it is connected, which in turn reinforce its level of activation by transmitting activation
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back. For example, lexical nodes which represent high-frequency words have higher resting levels of activation. Thus, when a high-frequency node is activated, its level of activation will be higher than low-frequency competitors (even if they received the same initial input). This will then be reflected in the fact that the phoneme nodes to which it is connected will correspondingly receive greater activation than those connected to low- frequency competitors, and similarly the semantic nodes will receive higher levels of feedback. Both the semantic and phoneme levels will in turn feedback to the lexical level, and reinforce the advantage. Thus, the frequency effect, while located at the lexical level, is transmitted and “felt” throughout the network.
First, then, I shall review briefly the accounts that have been proposed regarding the source of semantic and phonological errors and the predic- tions that each of these accounts make regarding the effects of frequency, imageability and length. In particular, I shall address the different predic- tions that might result from the implementation of these different accounts of the source of the errors within the different classes of models of language production; that is, models with discrete stages, such as Morton’s (1970; 1979) logogen model and Butterworth’s semantic lexicon model, versus connectionist models where activation is only feed-forward but cascades through the system and IAA models where activation reverber- ates between levels.’
The Production of Semantic Errors Responses which are semantically related to the target word have been observed in both aphasic speech errors and those produced by normal subjects. Fromkin (1973) noted a number of examples from normal spontaneous speech, for example “don’t bum your fingers” + “. . . your toes”, “there are a lot of questions” + “. . . of answers-I mean ques- tions” (pp. 262-263). Most models of speech production can deal in a straightforward way with the production of semantically related speech errors in normal subjects, and this mechanism vanes little from theory to theory despite the fundamental differences between the models.
’Some might argue that the logogen model is a poor choice as an example of a stage model; certainly Levelt’s (1989) model is specified to a greater level of detail, and both this and Butterworth’s (1989) model are more explicit in the incorporation of two stages of lexical access. However, the logogen model is used here as it has been the most widely used for the description of neuropsychological data and therefore is the most explicit in terms of the sources of semantic and phonological errors within the model. Furthermore, it makes very clear the loci of the effects of imageability and frequency within the model.
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In the logogen model (Morton, 1970), for instance, the output logogens accumulate conceptual-semantic information until the count of accumu- lated evidence exceeds the threshold for a particular logogen, at which point it fires making a response available. A range of semantically related items will be activated at any one time and semantically related errors may result when random noise within the system causes another logogen to reach threshold before the target. This random noise may be due, for example, to temporary lowering of a threshold due to recent firing of a logogen, or partial activation of a logogen from interfering stimuli (e.g. distracting stimuli in other sensory modalities). Butterworth (1989) also argued that within the semantic lexicon model normal semantic errors can be the result of random errors in the semantic system (caused by interfer- ing stimuli).
In the interactive activation models of Dell (1986; 1989) and Stemberger (1985), errors arise in much the same way, because in these models conceptual-semantic information also activates all words that ever repre- sent that information. A word will reach a sufficient level of activation (across the network as a whole) to be produced by summing activation from large parts of its semantic structure. Once again, random noise within the system can result in a non-target being selected for production. Noise can be from external sources as discussed above (Dell, 1986), or from random variation in the resting level of activation of units and the system- atic spread of activation to non-target units from other semantically related units.
All of these sources of semantic errors in normal subjects interact with the frequency of the target, and there is a strong prediction from all these models that errors on high-frequency targets are less common (Harley & MacAndrew, 1992; Stemberger, 1985). This is because high-frequency items have higher resting levels of activation and therefore need less additional activation to “win out” and be selected. The logogen model makes precisely the same predictions of fewer errors on high-frequency items when the source of the error is “noise”, as it assumes lower thresholds for high-frequency items.
The semantic errors produced by aphasic subjects may also be caused by these same error mechanisms, with the system simply being “noisier” and therefore more error-prone (Harley & MacAndrew, 1992). Indeed, one of the ways connectionist models are “lesioned” is by the addition of noise to the network (e.g. Harley & MacAndrew, 1992; Hinton & Shallice, 1991). Alternatively, the semantic errors produced by aphasic subjects may be due to different causes to those that result in the production of errors by normal subjects, such as specific damage to the component processes of spoken word production. The loci of such deficits can be divided into deficits at the semantic level and deficits at other levels of processing.
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Semantic Deficits. Howard and Orchard-Lisle (1984) argued that the semantic errors produced by their patient J.C.U. were the result of a semantic deficit which resulted in the use of semantic representations that are in some way deficient to retrieve the phonological form in the lexicon (within the logogen model). This underspecification of the semantic address to the lexicon predicts a frequency effect in semantic errors just as in the normal case above. The underspecified semantic representation Will activate a range of semantically related items, and the item with the highest frequency is most likely to reach threshold. (In the logogen model, there would seem to be a need for an automatic or strategic lowering of all thresholds if no logogen fires, in order to enable a response even when no one logogen has fulfilled its complete semantic criterion and reached threshold; cf. Morton and Patterson, 1980.) Therefore, even if the address is underspecified, high-frequency targets may be successfully accessed, whereas for low-frequency targets a semantically related word of higher frequency may be produced instead.
A semantic deficit also might predict an effect of “semantic” variables such as imageability on naming and the production of semantic errors. It is well documented that the majority of aphasic patients who have semantic comprehension deficits show a greater impairment with words of lower imageability/concreteness (Franklin, 1989; cf. Warrington & Shallice, 1984), and the same phenomenon has been observed in the reading responses of deep dyslexic patients (Coltheart, Patterson, & Marshall, 1980). Plaut and Shallice (1991; 1993) have implemented concreteness effects within their model, with abstract words being represented by fewer features at the semantic level. This implementation successfully reproduces the principal effects of abstractness on deep dyslexic reading.
Non-semantic Dejicits. The report of Caramazza and Hillis (1990) is one of the few which describe patients with non-semantic deficits leading to the production of semantic errors. They suggest a deficit in activating the representation within the phonological output lexicon as the cause of the semantic errors produced by their two patients, R.G.B. and H.W., who were argued to have intact comprehension. Their proposal is that when the target phonological representation is inaccessible, the most highly activated semantically related item is output instead. They “remain silent on whether the impairment is in transmission of information from the semantic system, in access to the output lexicon, or within the output lexicon itself” (Caramazza & Hillis, 1990, p. 114, footnote 7).
Functionally equivalent theoretical accounts would be transmission errors or raised thresholds, which were suggested by Morton and Patterson (1980) when discussing the origins of semantic errors produced by some deep dyslexic patients. In a transmission error, the correct semantic
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information is generated but is in some way degraded during its transmis- sion to the output logogens or word-level nodes. In contrast, the raised threshold account argues that a logogen has a temporarily raised threshold blocking an output and therefore the logogen nearest threshold is selected. This could also be implemented within an IAA model by assuming unusually low resting levels of activation. As noted by Caramazza and Hillis, these accounts are in practice difficult to dissociate from each other.
As with semantic deficits, frequency effects are predicted for semantic errors produced as a result of these deficits. However, stage and connec- tionist models appear to differ in their predictions regarding imageability effects as a result of non-semantic deficits. Stage models predict no effect as the deficit is subsequent to the level at which imageability is represented in the model (the semantic level).
Plaut and Shallice (1991; 1993) and Harley (1993; Harley & MacAn- drew, 1992) have incorporated imageability into the architecture of their models. As described above, both models equate imageability with “rich- ness” of semantic representations such that high-imageability words have more semantic units (representing features) than low-imageability words (Martin and Saffran, 1992, also equate imageability with “richness” of semantic representations in Dell’s, 1989, IAA model). Both these models predict that imageability effects will occur in the production of semantic errors when the network is lesioned, even if this is subsequent to the semantic level. Thus, Harley and MacAndrew (1992) demonstrated that the effect of weakening the semantic-to-lexical connections was “moder- ated . . . by the richness of the underlying semantic representation for each item”, and concluded “this further predicts that high imageability words should also be preferentially preserved in . . . aphasia” (p. 392). Any network which implements imageability in this way should make the same prediction-as activation spreads through the network, the “support” will be greater for high-imageability words in comparison with low-imageability words, and this support should continue to give an advantage under conditions of damage.
The Production of Phonological Errors
Phonological errors are those responses which share phonemes with the target; these may be real words (e.g. soldier + shoulder) or nonwords (e.g. tulip + /tju:pDl/). As with semantic errors, there are two main sources of phonological errors within stage models: “lexical” and “post-lexical” impairments. Lexical deficits are characterised as an impairment in retriev- ing the stored phonological form of a word, which may be more severe for low-frequency items. This is the deficit that Kay and Ellis (1987) propose to account for the phonological errors produced by their patient E.S.T.
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Post-lexical deficits may be due to impaired phonological encoding procedures. These are the processes by which a phonetic (motor) plan for articulation is derived from the stored phonological form, for example by the use of a device like Shattuck-Hufnagel’s (1979; 1987) slot-and-filler model. Thus, in Dell’s (1989) IAA model, phonological errors arise when the wrong phoneme is more active than the correct one and is selected. Effects of length are predicted from this type of deficit, because if each phoneme or syllable has a certain probability of error, then the more phonemes or syllables there are in a word the greater the overall likelihood of error. Deficits of a response buffer or articulatory buffer may also lead to phonological errors. If this deficit consists of abnormally fast decay rates or capacity limitation on the amount of information that the buffer can contain at any one time, effects of length may result (Miller & Ellis, 1987).
Thus, stage models predict that both frequency (for lexical level deficits) and length (for post-lexical deficits) may have an effect on the production of phonological errors, but that there will be no effect of imageability. Connectionist models also predict effects of frequency and probably length, but for both sources of phonological errors (although no model has yet simulated length effects). However, imageability (when implemented) might also be expected to influence the production of phonological errors. This is due to the fact that high-imageability words have greater numbers of semantic units associated with them (Harley & MacAndrew, 1992; Plaut & Shallice, 1991; 1993), and therefore even when lesioning occurs else- where in the network, the pattern of activation associated with these words is likely to be more stable than for words of low imageability.
Summary of Predictions
The variables which are predicted to have an effect on the production of different types of error depend in part on the underlying deficit which is hypothesised to be the cause of that error for that patient, as well as on
TABLE 1 Summary of Predictions Regarding the Effects of Frequency, Imageability and Word
Length on the Production of Semantic and Phonological Errors in Naming ~~ ~
“Stage” Models “Connectionist” Models
Semantic Phonological Semantic Phonological Varinble Errors Errors Errors Errors
Imageability Yes No effect Yes Yes Frequency Yes Yes Yes Yes Length No effect Yes Yes Yes
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EVALUATING MODELS OF SPOKEN WORD RECOGNITION 25
the type of model proposed (Table 1). However, it is important to note that for both types of model, the predictions might be altered by changes in the precise details of their architecture.6 Thus, frequency can affect production of both semantic and phonological errors for both stage and connectionist models. (But stage models do not necessarily predict fre- quency effects for all of these errors, the precise predictions depending on the locus of the deficit. For example, no effect of frequency is predicted for phonological errors of a post-lexical origin.)
In contrast, the predictions of the two types of model differ greatly in terms of effects of imageability and length. In sequential stage models, imageability will only affect the production of semantic errors (and only those semantic errors that are the result of a central semantic deficit), and length the production of phonological errors. However, in interactive activation models (Dell, 1989; Stemberger, 1985), where activation re- verberates throughout the model, an effect which arises primarily from one level in the model (e.g. imageability at the semantic level; frequency at the word level) will be reflected throughout the system. The relative invulner- ability of words which are of high imageability, high frequency and have few phonemes does not depend on the level at which lesioning occurs. Therefore, “those words that are most robust under noisy conditions are going to be the more frequent and imageable words in the language”
61t is particularly difficult to predict the performance of “connectionist” models due to the complexities of the architecture. The training procedure, the nature and number of connec- tions and representations, and the equation used for interpretation of the output can all have marked effects on the patterns found. Additionally, many of these models only implement part of the output system (e.g. Dell, 1989, does not have any “semantic” level represented; but cf. Dell & O’Seaghdha, 1991) and within that do not attempt to model the effects of many variables known to affect aphasic performance; for example, although Plaut and Shallice (1991; 1993) model the effects of abstractness, they do not attempt to simulate effects of frequency or length. These facts cause problems for anyone who attempts to test predictions from these models, as a small change to the architecture of a network can cause unpredictable changes in its properties. Thus, it can never be said that a particular model cannot producelpredict a particular pattern of results, merely that a particular architecture of a particular model cannot. Nevertheless, there remain certain properties of the models which are inherent to the basic structure of each model. Thus, for example, as Monsell(l991) argued, an effect of frequency is an inevitable result of learning weights using a back- propagation algorithm. Thus, it is these inherent properties of the models which are used in the attempt to derive predictions from these models regarding how they would behave when “lesioned”. As they stand, the models can account for particular aspects of (normal and neuropsychological) data, but the question addressed here is whether the same architecture would be able to account for the aphasic data presented here. If this proves not to be the case, then it is a challenge to their architects to “fine tune” these models in such a way that they can account for all the data currently available regarding language production.
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26 NICKELS
(Harley & MacAndrew, 1992, p. 392) and similarly also the shorter words. (This is also assumed to hold for lesions other than noise, as it is an integral feature of the architecture.) That is, effects of imageability will be expected not only in the production of semantic errors but also in the production of phonological errors. Similarly, effects of length can be expected not only for the phonological errors but also in the production of semantic errors. Moreover, if a patient shows a significant effect of a particular variable on the production of one type of error (e.g. phonological errors), then he or she should also show a significant effect of this variable on the other type of error (e.g. semantic errors).
In feed-forward (multi-layered perceptron) models, where activation continuously cascades from one level to another but where there is no interaction, an effect of an “early” variable such as imageability would also have an effect later on in the system. Thus, because a high-imageability word has stronger representation, it will be less vulnerable than low- imageability words to the effects of lesions later in the system (Plaut & Shallice, 1993). Therefore, effects of imageability would be expected for phonological errors as well as for semantic errors. Similarly, if activation is reduced for a target (and raised for a semantically related competitor) due to a semantic deficit, longer targets will be more susceptible to error than shorter targets (if it is assumed that as a property of the network, longer words are more susceptible), and a shorter semantically related error may be successfully produced instead. Thus, there may also be effects of length on semantic errors, and the predictions from cascade models are the same as those from IAA models-every variable has an effect on every type of error.
By comparing the pattern of variables which significantly predict the Occurrence of semantic and phonological errors in aphasic naming to the predictions made by the different types of model, conclusions should be able to be drawn regarding the time-course of lexical access in word production. This is the aim of the remainder of this paper.
METHOD
Subjects The subjects in this study comprised 15 aphasic patients aged 28-86 years, all of whom were literate and had received schooling at least until the age of 14. Twelve of the subjects had suffered a unilateral, left-hemisphere cerebrovascular accident (CVA) 2-19 years prior to this investigation. The other three patients had received head injuries. All 15 subjects were relatively unselected except that they had to fulfil the following criteria:
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EVALUATING MODELS OF SPOKEN WORD RECOGNITION 27
the patient was reported to make some (no matter how few) “phonolo-
0 the patient was willing to be involved in the study and had sufficient
0 the patient was stable neurologically and in reasonable health; 0 the patient reported no difficulty with hearing or eyesight (corrected); 0 the patient reported no speech or language deficits prior to the onset of
0 the patient had some speech output (however limited).
gical” errors in naming or repetition;
attentionkoncentration to be able to do so;
aphasia; and
Seven of the patients involved in the study had previously been used as research subjects: A.E.R. (Black, Nickels, & Byng, 1991; Nickels, Byng, & Black, 1991); E.M.M., L.A.C. and E.A.A. (Black et al., 1991); J.G. (Black & Byng, 1986; Byng, 1988; Byng & Black, 1989; Funnel], 1987); T.R.C. (Nickels, 1992a); and M.K. (Franklin, 1989; Howard & Franklin, 1987; 1988; 1990). The remaining eight patients had either been referred to Birkbeck for further assessment by their local speech and language therap- ists (C.K., C.T.J., S.A.G. and C.I.) or were sought out specifically to fulfil the criteria for this study (W.J., W.M., C.H. and R.K.).
The patients’ language skills cover a broad range in terms of both comprehension and production. Eight of the patients can be broadly classified as having “fluent” speech. Goodglass and Kaplan (1983, p. 75)
TABLE 2 Case History Details of the Patients Involved in the Study
Age Years (years) Sex Aetiology Post-onset Speech Output
R.K. 86 M CVA 3 Fluent (neologistic) T.R.C. 46 M CVA 3 Fluent . M.K. 72 M CVA 8 Fluent C.I. 40 F CVA 3 Fluent C.T.J. 28 M HI 5 Fluent C.K. 75 F CVA 2 Fluent E.A.A. 70 M CVA 3 Fluent W.J. 59 M CVA 4 Fluent L.A.C. 65 F CVA 19 Non-fluent J.G. 65 M CVA 5 Non-fluent A.E.R. 70 M CVA 5 Non-fluent E.M.M. 78 F CVA 3 Non-fluentlapraxic W.M. 68 M CVA 4 Non-fluentlapraxic C.H. 47 F HI 3 Apraxic S.A.G. 35 M HI 1 Apraxic
‘CVA, cardiovascular accident; HI, head injury.
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28 NICKELS
define the “fluent” forms of aphasia as having “many long runs of words in a variety of grammatical constructions, in conjunction with word-finding difficulty for substantives and picturable action words”; this is broadly true of these patients. However, the five “non-fluent” patients do not necessari- ly follow Goodglass and Kaplan’s definition, which necessarily involves awkward, effortful articulation. While their speech may be limited in quantity and grammatical structure, three of these patients (A.E.R., J.G., L.A.C.) show no obvious effort in articulating the words they do produce. Those patients who do seem to have effortful articulation (E.M.M., W.M.) were classified by their therapists as having some degree of apraxia of speech in addition to their aphasia. The production difficulties of the remaining two patients (C.H. and S.A.G.) were classified by their therap- ists as being primarily due to apraxia of speech. These broad divisions capture little of the diversity and subtleties of the patients’ production, which can only really be adequately described on the basis of each individual patient and comparisons between patients, but they are suf- ficient for the purposes of &is study, where it is the characteristics of the errors produced which is under investigation not the characteristics of the patients.
Materials and Procedure A set of high-imageability words was compiled as the basis for a picture- naming task. All items were taken from those that appear in the Medical Research Council (MRC) psycholinguistic database (Coltheart, 1981). The words all had rated imageability values greater than 500 and the set was systematically varied in terms of frequency (high- vs low-frequency words) and length (one, two and three syllables). The high-frequency items had a frequency of occurrence greater than 20 per million (KuiSera & Francis, 1967), and the low-frequency items a frequency of less than 15 per million. Further details of the matching data for these lists and an intercorrelation matrix are given in the Appendix.
Lists were matched within each syllable length for number of phonemes, letters and clusters in each word. Additionally, only words were used that were not homographs, homophones or compounds, that were unaffixed, that had primary stress on the first syllable and that were nouns (although some items also can be other parts of speech). The pictures used for naming were a combination of black-and-white line drawings from the Cambridge pictures (Howard et al., 1985; Patterson, Purell, & Morton, 1983), those of Carolyn Bruce (pers. comm.) and others drawn specifically for this study. All pictures had at least 90% name agreement with normal control subjects.
This list provides a 130 item frequency X length set for picture-naming
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EVALUATING MODELS OF SPOKEN WORD RECOGNITION 29
which was presented in a pseudo-random order for naming by the aphasic subjects. No time limit was imposed for a response. The patients’ re- sponses were transcribed at the time and also tape-recorded. These taped records of naming responses were subsequently used to verify the original transcription. A strict scoring criterion was adopted with only a correct initial response being accepted as correct , although subsequent responses were noted. For the purpose of this study, errors were classified as semantically related or phonologically related. Semantically related errors were those that showed a clear semantic relationship to the target, which could be associative (e.g. desk 4 school) or shared feature (e.g. tiger + lion, daffodil + flower). Phonologically related errors were defined as responses where at least 50% of the phonemes in the response appeared in the target in approximately the same order (based on Morton & Patter- son’s, 1980, criterion for visual errors in deep dyslexia). All remaining errors were classified as “other” responses. These included no responses, visual errors, perseverations, unrelated words and nonwords, and morpho- logical errors.
Analysis The naming performance of the 15 patients when presented with the set of 130 pictures to name provides the data to test the predictions outlined above. The effects of imageability,’ frequency and length on the semantic and phonological errors produced by the patients are examined in two main analyses. As these variables can be highly intercorrelated (although the initial design of the word set aimed to avoid this as far as possible), simple correlations between a variable and the production of semantic or phonological errors may not give a true picture of the effect of that variable. Thus, in order to deconfound the variables, a simultaneous multiple-regression analysis is used for the data from the group. This enables identification of the unique effects of any one variable when the effects of other variables with which it is correlated are accounted for.
Although the aphasics described here form a heterogeneous group, analysis of the group data remains a valid tool. This is because the analysis is of the effects of parameters on types of errors and not types of patients or deficits. Thus the assumption is being made that similar errors arise from similar deficits. [In fact, this assumption leads to the hypothesis that similar
’As this is a picture-naming task, all the stimuli are inevitably imageable. However, there is nevertheless a range (albeit narrow) of imageability ratings associated with these items, which makes it possible to examine the effects of this variable on performance.
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30 NICKELS
errors (e.g. semantic errors or phonological errors) will be affected by similar variables; this is found to be supported by the data.]
Nevertheless, it is important to establish whether group effects hold for individual subjects within that group, so a similar analysis is performed for each patient individually. The method of analysis employed for this was “discriminant analysis”, which is similar to multiple regression in so far as they both seek to predict performance on the basis of a linear combination of a number of (predictor) variables. Where the two analyses differ is that discriminant analysis uses this equation to classify cases into groups, in other words to predict whether a particular item (with a particular com- bination of variables associated with it) will result in, for example, a semantic error or not.
RESULTS
Correct Naming Performance Although this paper aims to examine the predictions made by the models by looking at the patterns of significant variables affecting the production of semantic and phonological errors, in order to give the reader some context, the pattern of variables affecting correct naming performance will briefly be outlined.
The patients showed a wide range of accuracy in picture-naming (see Table 3) and variety in the relative proportions of semantically and phonologically related responses produced. For the group as a whole, significant simple correlations were found between naming performance (number of correct responses per item across subjects) and imageability [r(128) = 0.215, P < 0.011 and familiarity [ratings taken from the MRC database: r(128) = 0.345, P < 0.0011, but no correlation was found with frequency [from Kukra and Francis, 1967: r(128) = 0.095, P > 0.051. As a consequence of this result, familiarity is the measure of frequency used in all subsequent analyses (Gernsbacher, 1984), and is taken to represent subjective spoken word frequency (the rating was on the basis of how often subjects believed they used the word). There were also significant negative correlations with two measures of length-number of phonemes [r(128) = -0.563, P < 0.001] and number of syllables [<128) = -0.487, P < 0.0011. As these two measures of length are highly intercorrelated [r(128) = 0.8561 and it is not important to distinguish their effects in this experiment, only one-number of phonemes-is included in the subsequent analyses. When imageability, familiarity and number of phonemes were entered into a simultaneous multiple-regression analysis, all three variables continued to be significant predictors of performance (imageability: t = 2.253, P = 0.026; familiarity: t = 3.161, P = 0.002; phonemes: t = 7.659, P C 0.001).
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TAB
LE 3
P
ropo
rtio
n C
orre
ct o
n P
ictu
re-n
amin
g fo
r H
igh-
and
Low
-freq
uenc
y W
ords
of
One
, Tw
o an
d Th
ree
Syl
labl
es
Hig
h-fr
eque
ncy
Low
-fre
quen
cy
Erro
rs“
Tota
l I
2 3
1 2
3 C
orre
ct
Sem
. Ph
on.
R.K
. T
.R.C
. M
.K.
C.I
. C
.T.J
. C
.K.
E.A
.A.
W.J
. L
.A.C
. J.
G.
A.E
.R.
E.M
.M.
W.M
. C
.H.
S.A
.G.
- X
0.16
0.
20
0.56
0.
92
0.76
0.
88
0.92
0.88
0.
16
0.56
0.
56
0.08
0.
40
0.48
0.
84
0.56
0.12
0.
13
0.00
0.
12
0.13
0.
04
0.36
0.
40
0.48
0.
80
0.33
0.
80
0.72
0.
67
0.64
0.56
0.
67
0.80
0.
88
0.73
0.
80
0.80
0.
67
0.92
0.
12
0.00
0.
24
0.56
0.
33
0.64
0.44
0.
60
0.64
0.08
0.
00
0.04
0.
28
0.13
0.
20
0.16
0.
00
0.52
0.
72
0.47
0.
92
0.45
0.35
0.51
0.20
0.
16
0.60
0.
52
0.60
0.
80
0.60
0.
80
0.40
0.
56
0.52
0.
20
0.32
0.
28
0.68
0.48
0.00
0.
12
0.23
0.
12
0.07
0.
16
0.45
0.
10
0.13
0.
45
0.37
0.
06
0.13
0.64
0.08
0.
20
0.60
0.68
0.15
0.
02
0.53
0.73
0.
11
0.12
0.
40
0.75
0.
14
0.10
0.
47
0.78
0.
06
0.11
0.
00
0.18
0.
31
0.19
0.
27
0.53
0.
32
0.10
0.
33
0.54
0.
31
0.02
0.
00
0.08
0.
27
0.35
0.07
0.
25
0.09
0.
42
’
0.00
0.
25
0.04
0.
57
0.20
0.68
0.02
0.
18
0.21
0.
45
0.20
0.
18
~ ~~
“Sem
., se
man
tic; P
hon.
, pho
nolo
gica
l.
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TAB
LE 5
R
esul
ts o
f a
Sim
ulta
neou
s M
ultip
le-r
egre
ssio
n Ana
lysi
s E
xam
inin
g th
e E
ffect
s of
Fam
iliar
ity, I
mag
e-
abili
ty a
nd N
umbe
r of
Pho
nem
es o
n th
e P
rodu
ctio
n of
Sem
antic
and
Pho
nolo
gica
l Err
ors
Imag
e0 bi
lity
Fam
iliar
ity
Phon
emes
Sim
ple
corr
elat
ion
r P
r P
r P
Sem
antic
err
ors
-0.2
12
<0.0
5 -0
.119
N
S 0.
139
NS
Phon
olog
ical
err
ors
0.08
7 N
S -0
.251
<0
.01
0.504
<0.0
01
Sim
ulto
neou
r
Sem
antic
erro
rs
2.24
<0
.03
0.55
NS
1.52
NS
mul
tiple
reg
ress
ion
t P
t P
t P
Phon
olog
ical
err
ors
1.83
N
S 2.
80
<0.0
06
6.26
<0
.001
r-va
lues
with
1 a
nd 1
28 d
egre
es o
f fre
edom
; r-v
alue
s with
1 a
nd 1
26 d
egre
es of
fre
edom
. NS,
non
-sig
nific
ant
(P >
0.05
).
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EVALUATING MODELS OF SPOKEN WORD RECOGNITION 33
Thus, this group of patients is more likely to produce a correct response when a target is of higher imageability, higher familiarity and has fewer phonemes.
When the pattern of variables affecting performance is considered for each of the 15 patients individually, it can be seen that no one patient shows the same pattern as the group (Table 4). That is, no patient shows significant effects of all three variables in a multivariate analysis. However, each of the three variables are significant predictors of performance for at least one patient.
Error Production The same analyses were then performed for the semantic and phonological errors produced. For the group as a whole (Table 5) , both the simple correlation and simultaneous multiple regression showed significant effects of imageability alone on the production of semantic errors, such that the lower the imageability of the target the greater probability there was of a semantic error being produced. However, the production of semantic errors was not affected by the familiarity of the target or by its length in phonemes. Similarly, for phonological errors, familiarity and number of phonemes showed significant effects in both the simple correlation and the simultaneous multiple regression, but there was no effect of imageability. Thus, phonological errors are more likely to occur on targets that are of lower familiarity and/or have more phonemes.
However, as shown above for correct responses, individual patients may not show the pattern that is true of the group and therefore the results of the discriminant analyses for every patient must also be considered. Table 6 summarises the pattern of significant factors for each patient for semantic and phonological errors where all three variables (imageability , familiarity and length in phonemes) are included in the analysis. Every factor that reached significance in the simultaneous analysis was also significant in the univariate analysis. There were very few instances where a variable was significant in the univariate analysis but failed to reach significance in the multivariate analysis, but those that were are detailed below. Analyses were not performed when the patients produced less than 2% errors of a particular type (five errors), because analysis of this limited amount of data would be particularly unreliable. The discriminant analyses for semantic errors compared variables affecting semantic errors and any other response on the grounds that a target-related semantic error is only possible if no other error has occurred prior to that point. For phonological errors, the pattern of significant variables was compared for the production of phono- logical errors versus correct responses. This decision was made on the grounds that if a semantic error had resulted from a deficit at “earlier”
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W
P
TAB
LE 5
R
esul
ts o
f a
Sim
ulta
neou
s M
ultip
le-r
egre
ssio
n Ana
lysi
s E
xam
inin
g th
e E
ffect
s of
Fam
iliar
ity, I
mag
e-
abili
ty a
nd N
umbe
r of
Pho
nem
es o
n th
e P
rodu
ctio
n of
Sem
antic
and
Pho
nolo
gica
l Err
ors
Imag
ea bi
lify
Fam
iliar
ity
Phon
emes
~
~
Sim
ple
corr
elat
ion
r P
r P
r
P
Sem
antic
erro
rs
-0.2
12
e0.0
5 -0
.119
NS
0.13
9 N
S Ph
onol
ogic
al er
rors
0.
087
NS
-0.2
51
co.0
1 0.
504
<0.0
01
Sim
ulta
neou
s m
ultip
le re
gres
sion
I
P I
P
I P
Sem
antic
erro
rs
2.24
~
0.0
3
0.55
N
S 1.
52
NS
Phon
olog
ical
erro
rs
1.83
N
S 2.
80
co.006
6.26
<0
.001
~
~~
~~
r-va
lues
with
1 an
d 12
8 deg
rees
of f
reed
om; t
-val
ues w
ith 1 an
d 12
6 de
gree
s of f
reed
om. N
S, n
on-s
igni
fican
t (P
> 0.
05).
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TABL
E 6
Dis
crim
inan
t Ana
lyse
s of
the
Err
ors
Prod
uced
by
Each
Pat
ient
~~
~
Sem
antic
Err
ors
Phon
olog
ical
Err
ors
IMA
G
FAM
PH
ON
IM
AG
FA
M
PHO
N
F P
F P
F
P
F P
F
P
F P
df
R.K
. 0.07
NS
0.002
NS
1.31
NS
<0.001
NS
0.75
NS
0.75
NS
13
T.
R.C
. 7.36
0.00
8 0.
25
NS
1.40
NS
CO.001
NS
0.30
NS
2.75
NS
13
M
.K.
0.66
NS
0.92
NS
1.34
NS
0.73
NS
6.75
0.012
4.88
0.031
1,63
C.I.
0.33
NS
1.16
NS
0.82
N
S 0.36
NS
7.75
0.00
6 28.52
<O.oOOl
1,105
NS
1.11
NS
-
-
-
-
-
-
-
C.T
.J.
3.38
NS
2.77
C.K
. 2.20
NS
0.10
NS
0.45
NS
0.84
NS
1.17
NS
6.67
0.011
1,107
E.A
.A.
0.50
NS
2.88
NS
0.33
NS
0.17
NS
5.89
0.017
9.27
0.003
1,106
W.J.
0.91
NS
0.87
NS
0.92
N
S 0.24
NS
0.49
NS
5.68
0.019
1,112
L.A
.C.
0.26
NS
0.88
N
S 1.48
NS
0.81
NS
0.11
NS
9.33
0.00
4 1,44
J.G
. 6.54
0.012
0.20
NS
2.37
NS
0.14
NS
1.17
NS
10.37
0.00
2 1,78
NS
1.70
NS
-
-
-
-
-
-
-
A.E
.R.
3.70
NS
0.09
E.
M.M
. 4.27
0.041
0.32
NS
0.99
NS
0.56
NS
0.14
NS
3.65
NS
1.51
W.M
. 0.11
NS
0.99
NS
0.38
NS
5.17
0.02
6 0.
86
NS
3.32
NS
1,84
C.H
. 1.90
NS
0.30
NS
0.11
NS
0.35
NS
0.76
NS
25.77
CO.oOO1
1,103
S.A
.G.
-
-
-
-
-
-
0.37
NS
0.14
NS
24.04
<O.OOOl
1,107
IMA
G, i
mag
eabi
lity;
FAM
, fam
iliar
ity; P
HO
N, n
umbe
r of
phon
emes
. P-
valu
es re
pres
ent t
he s
igni
fican
ce of
F(1,126)
for
sem
antic
err
ors
in a
sim
ulta
neou
s ana
lysi
s with
all t
hree
var
iabl
es e
nter
ed, d
egre
es o
f fr
eedo
m
(df)
giv
en in
divi
dual
ly fo
r eac
h pa
tient
for
pho
nolo
gica
l err
ors.
NS,
non
-sig
nific
ant (
P >
0.05
). B
lank
s are
left
in th
e ta
ble
whe
re a
n an
alys
is w
as n
ot
perf
orm
ed b
ecau
se th
e pa
tient
pro
duce
d le
ss th
an fi
ve e
rror
s of
a pa
rticu
lar t
ype.
.
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levels of processing, then a target-related phonological error would no longer be possible and therefore the potential set would be reduced.'
The pattern shown by the group as a whole can be seen to be broadly replicated in this analysis of individual patient performance. No significant effects of familiarity on the production of semantic errors were found for the group as a whole and no patient showed a significant effect in this analysis, although one patient (C.T.J.) did show a significant effect in the univariate analysis [F(1,128) = 5.55, P = 0.0201. However, C.T.J. also showed a significant effect of imageability on performance in the univariate analysis [F(1,128) = 5.370, P = 0.0201. As imageability and familiarity are intercorrelated [r(128) = 0.253, P C 0.0051, when the variables are entered in the analysis together neither reaches significance, indicating that there is no (significant) independent effect of either variable. It is therefore dif- ficult to draw any firm conclusions regarding the presence or absence of an effect of imageability or familiarity for C.T. J.
The lack of an effect of length on the production of semantic errors held for the individual patients as well as for the group as a whole. Even in the univariate analysis, no patient showed a significant effect of length on the production of semantic errors. However, despite the significant effect for the group as a whole, not every patient showed a significant effect of imageability on the production of semantic errors (and it was only C.T.J. that had an effect of imageability which reached significance in the univari- ate analysis while failing to reach significance in the multivariate analysis). Nevertheless, it is important to note that as the quantity of data was smaller in the individual analyses compared with the group analysis, the power of the analysis was not as great and the chance of an effect reaching significance was correspondingly reduced. Additionally, as this experiment involved a picture-naming task, the range of imageability values was highly compressed; indeed, it might be considered remarkable to have found effects of imageability at all with such a restricted range.
For phonological errors, the majority of patients showed a significant effect of length on performance. [E.M.M. also showed a significant effect of length, but only in the univariate analysis: F(1,54) = 4.203, P = 0.045).] An effect of familiarity, however, was not as common as might be predicted from the group data, with only three patients showing significant
*While it is clear that the decision to use these comparisons for the analyses makes certain (non-theory independent) assumptions, in reality the choice makes no difference to the effects found. Further discriminant analyses comparing phonological errors with all responses, and semantic errors with correct responses or correct responses and phonological errors, show the same basic pattern of results. The use of these particular comparisons allows for the maximum amount of data to be included in the discriminant analysis.
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EVALUATING MODELS OF SPOKEN WORD RECOGNITION 37
effects in the discriminant analysis (both univariate and multivariate). Finally, only one patient showed a significant effect of imageability on the production of phonological errors.
Across the two types of error, no patient showed significant effects of imageability or length for both types of error. Similarly, no patient showed effects of familiarity for both semantic and phonological errors, whereas two showed effects of familiarity on phonological but not semantic errors. (C.T.J., who may have an effect of familiarity on the production of semantic errors, made too few phonological errors to allow statistical evaluation.)
DISCUSSION The results of the analysis of the group data show a pattern whereby imageability effects are found for semantic errors but not phonological errors, and length effects are found for phonological errors and not for semantic errors. This pattern conforms precisely to the predictions of a sequential stage model. In contrast, it is incompatible with the predictions from current connectionist models involving either interactive activation or purely feed-forward (cascade) processes, which predict effects of both variables on both types of error.
The results of the analysis for the individual patients within the group confirm this pattern. No patient showed an effect of length on the produc- tion of semantic errors, whereas the majority did show such an effect on the production of phonological errors. Similarly, no patient who showed an effect of imageability on the production of semantic errors also showed an effect of imageability on the production of phonological errors, as would be predicted by current connectionist models.
However, while all these data are compatible with the predicted patterns of significant variables from a sequential stage model and incompatible with the patterns predicted by IAA or cascade models, one patient, W.M., showed a pattern which does not conform precisely to the predictions of stage models. He showed a significant effect of imageability on the production of phonological errors. This effect could perhaps be a statistical accident (1 in 20 analyses will be significant by chance at the 0.05 level). In other words, perhaps it is just chance that those items that result in phonological errors are of lower imageability. However, perhaps a more satisfactory account can be provided within stage models by examining the nature of W.M.3 naming responses.
It is clear that W.M. employs many strategies when naming and may not be naming by the usual direct route from the semantic system to the phonological output lexicon. In particular, he often uses his good written naming skills (64% correct on the same set of words) when spoken naming.
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This is clear on occasion when he overtly refers to the written form of the word (e.g. for the target “triangle” he spelt with his finger the letters “TRIA”). Thus he may be able to use this orthographic information to assist his spoken naming by converting the output graphemic form to an input orthographic code via an output-to-input link (Howard & Franklin, 1988) which can then be read aloud. However, W.M.’s reading success is influenced both by imageability (although the reading errors he makes are phonological and not semantic) and regularity of spelling-to-sound corres- pondences; thus it is likely that his reading responses are the result of a combination of information from sublexical and lexical reading routines (cf. Howard & Franklin, 1988). Thus, the effect of imageability on W.M.’s phonological errors may reflect a complex interplay between “direct” spoken naming and naming via (internal) written naming and reading aloud. There is also evidence to suggest that W.M. also uses monitoring and pre-articulatory editing processes on his speech output (Nickels, 1992b), which clearly could also affect the pattern of significant variables shown in his errors.
Thus, while W.M.3 pattern does not fit easily into the predictions of a stage model, it can be seen that a plausible, but complex, account can be formulated within this type of model for the pattern of variables seen. This contrasts with the patterns shown by the other patients (and the group data), which are not compatible with the predictions of IAA and multi- layer perceptron models, and attempts to reconcile their data to these models using similar individual analyses of naming performance have met with failure. It is, therefore, argued that the data presented here support a sequential stage model of language production. Are the effects of familiar- ity found here also consistent with this conclu~ion?~
The group data are somewhat misleading in terms of the effects of familiarity on the production of each type of error, showing no significant effect on the production of semantic errors, whereas one patient did show a significant effect of familiarity on the production of semantic errors, at least in the univariate analysis. For phonological errors, once again only some patients showed effects of familiarity on their production. The fact
9As mentioned in the Results section, there was no significant effect of frequency on the correct naming performance of these aphasic patients. However, there was found to be a significant effect of rated familiarity on performance. Gernsbacher (1984) argues that fre- quency values are especially unreliable for low-frequency items. Inevitably in a set of picturable stimuli, there is a bias towards words of lower frequency, and thus this may be a possible source of error leading to the lack of a significant effect of frequency on naming for these items. Gemsbacher suggests that rated familiarity may be a better measure of spoken word frequency for low-frequency items than traditionally used measures of written word frequency. Thus, familiarity has been used here as a measure of spoken word frequency.
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EVALUATING MODELS OF SPOKEN WORD RECOGNITION 39
that effects of familiarity (taken as a measure of spoken word frequency) on phonological errors occur for some patients and not for others, cannot easily be explained within connectionist models where lexical and post- lexical sources of phonological errors both predict effects of frequency. Indeed, as Stemberger (1985) argued, higher resting levels of activation for higher-frequency words should result in pervasive effects of frequency in error production. However, in sequential stage models, while a lexical deficit (as the source of phonological errors) predicts a frequency effect, a post-lexical (phonological encoding or buffer) deficit does not. Thus the presence of familiarity effects for some of these patients and not others may reflect the difference between lexical and post-lexical sources of phonological error within a stage model.
In contrast, both models predicted frequency effects for semantic errors whatever the source, which did not appear to be the case for these data (Nickels and Howard, 1994, discuss this issue further). This suggests that there may be sources of semantic error which do not result in an effect of frequency as well as those which do. One possibility is that a “raised threshold” account-where all the thresholds for output from the lexicon were raised equally regardless of frequency-might have this effect. Alter- natively, the selection of the wrong (but complete) semantic specification from the semantic system to address the output lexicon should not result in a frequency effect in a stage model.
However, in models of lexical retrieval which, unlike the logogen model, allow for explicit retrieval of the lemma, another explanation may be possible depending on the level at which frequency is represented. While many authors locate frequency effects at the lexical level corresponding to retrieval of the lemma (Dell, 1989; Harley & MacAndrew, 1992), Levelt (1983) argued that frequency is associated with retrieval of the phonologic- al form of the target. If this were the case within an explicit two-stage retrieval model such as Butterworth’s semantic lexicon model, no effect of frequency would be predicted in the production of semantic errors but an effect would be expected for phonological errors. This is precisely the pattern found here. However, for IAA models such as that of Dell (1989), even when frequency is represented at the level of retrieval of the phonolo- gical form (phoneme level) rather than that of lemma retrieval (lexical level), frequency effects will still be predicted for both types of error because of the spread of activation both forward and back through the network.
It is unclear whether another account could be produced within the connectionist models (as they stand) for the production of semantic errors which would allow for no frequency effect. Even if this were possible, these models would still have difficulty in accounting for the fact that there are some patients who showed a familiarity effect in one type of error and not
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in the other, as it is the clear prediction of these models that, when a frequency effect occurs, it should affect both error types. However, Schriefers et al. (1990), when considering the results of their experiments, pointed out that it might be possible to reset the parameters of these models in such a way as to accommodate their data regarding a stage of pure semantic activation and a stage of pure phonological activation. Dell and O’Seaghdha (1991) argued that a model which is globally modular but locally interactive can simulate these results (but see Levelt et al., 1991b). Similarly, Harley (1993) demonstrated that his model, which combines purely feed-forward processes from the semantic to lexical levels and interactive links between lexical and phonological levels, can simulate Levelt and co-workers’ results. Despite this finding, Harley, at least (as discussed above), argued that frequency will nevertheless be a powerful predictor of whether or not a semantic error will occur (Harley & MacAn- drew, 1992).
However, it is always difficult (or impossible) to predict precisely how connectionist networks are going to perform when lesioned unless the simulations are actually performed, and consequently it is not possible to reject the models outright. What is necessary are simulations which examine the effects of imageability, frequency and length on the produc- tion of semantic and phonological errors just as performed here (and under different types of lesioning). Although Plaut and Shallice (1993) demons- trated that the results of lesioning a connectionist model may vary with its formal architecture, we can nevertheless conclude, that on the basis of data reported from connectionist models to date, they would appear to be unable to account for the data presented here. As Schriefers et al. (1990, p. 100) state, “the crucial question is whether these parameters can be set in such a way that the present set of data as well as other evidence . . . receive a satisfactory explanation”.
It is the presence of significant effects of a variable on the production of one type of error but not on the production of the other type of error which provides the strongest evidence regarding the discontinuity of levels of processing and is the most diflicult for connectionist models to account for. The fact that a significant effect has been found for one type of error avoids many of the difficulties usually associated with rejection of a null hypoth- esis (e.g. the lack of a significant effect cannot be argued to be due to problems of restrictions of range). It remains to be seen whether connec- tionist models can meet the challenge these data present.
Thus, these data seem to support a strict sequential serially ordered model of spoken word production such as the logogen model (Morton, 1970), where processing is completed at one level before activation is passed to the next. The pattern of variables affecting the production of semantic and phonological errors in aphasic picture-naming did not s u p
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port the predictions made on the basis of the capabilities of current connectionist models of spoken word production (Dell, 1986; 1989; Plaut & Shallice, 1991; 1993; Stemberger, 1985).
Manuscript received November 1992 Revised manuscript received March 1994
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Higb
-hrq
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labl
e f f SD
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APP
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ts of s
timul
i
Log
FR
EQ
F
RE
Q
IMA
G
LETT
PHON
CLU
ST
1.66
f 0
.24
1.30
-2.1
0
1.65 f 0
.25
1.38
-2.2
2
1.66 f 0
.22
1.32
-2.0
8
0.51
f 0.46
-0.3
0-1.
11
0.58
& 0
.40
-0.30
-1.15
0.56
f 0
.44
-0.3
0-1.
04
52.8
8 * 3
0.54
20
-125
53.5
2 f 3
8.25
24
-165
52.0
0 f 2
9.31
21
-120
4.98
f 3
.80
0.5-
13
5.30
f 3
.60
0.5-
14
5.33 f 3
.49
0.5-
11
595.
28 f 3
0.50
50
5-63
3
587.
88 f 3
0.39
51
3-63
3
601.
33 r
t 28
.83
55 1 -
630
591.
40 f 2
1.44
52
6-63
0
588.
12 f 3
3.22
51
3-64
2
593.
07 k
27.
70
517-
624
4.76
? 0
.59
4-6
5.92
-+
0.84
4-
8
7.80
f 1
.42
5-9
4.32
f 0
.88
3-6
6.04
5 0
.87
5-8
8.07
f 1
.18
6-10
3.56 f 0
.50
3-4
4.84
f 0
.78
3-6
6.80
f 0
.91
5-8
3.56
f 0
.57
3-5
5.00 f 0
.85
3-7
7.27
f 1
.00
5-9
0.68
k 0
.47
0-1
0.44
f 0
.57
0-2
0.53
f 0
.62
0-2
0.68
f 0
.47
0-1
0.36
f 0
.56
0-2
0.67
f 0
.70
0-2
Log
FREQ
, log
(bas
e 10)
of w
ritte
n wo
rd fr
eque
ncy;
FR
EQ
, writ
ten
word
freq
uenc
y; IM
AG
, rat
ed im
agea
bilit
y; L
ETT,
num
ber
of le
tters
; PHON, nu
mbe
r of
phon
emes
; CLU
ST, n
umbe
r of
cons
onan
t clu
ster
s.
Dow
nloa
ded
by [
New
Yor
k U
nive
rsity
] at
17:
30 3
0 N
ovem
ber
2014
EVALUATING MODELS OF SPOKEN WORD RECOGNITION 45
Intercorrelation matrix
FAM IMAG PHON ~~~~~~~~~ ~~
FREQ 0.578' 0.064 -0.064 FAM 0.253b -0.148" IMAG 0.003
FFtEQ, written word frequency; FAM, familiarity;
"P < 0.05; 'P < 0.005; 'P < O.ooO1. IMAG, imageability; PHON, number of phonemes.
Dow
nloa
ded
by [
New
Yor
k U
nive
rsity
] at
17:
30 3
0 N
ovem
ber
2014
