JOURNAL OF MEMORY AND LANGUAGE
ARTICLE NO.
35, 775–800 (1996)
0040
Lexical Retrieval and Selection Processes: Effects of
Transposed-Letter Confusability
SALLY ANDREWS
University of New South Wales, Sydney, Australia
Three experiments investigated performance for words which differ from another word
only by the transposition of two letters (e.g., salt, slat). In Experiment 1, high frequency
words from transposed-letter (TL) confusable pairs were responded to more slowly than
carefully matched control words in both the lexical decision and word naming task. Low
frequency TL words were responded to less accurately than control words in the naming but
not the lexical decision task. Experiment 2 replicated the naming data of Experiment 1 and
also revealed that naming accuracy for TL word targets was reduced when they were preceded
by a brief masked presentation of their confusable mate. Experiment 3 provided a third
replication of the impaired naming performance for TL target words and demonstrated that
the effect was insensitive to concurrent dual task demands. These TL confusability effects
provide strong constraints that can contribute to evaluation and specification of current models
of visual word recognition. q 1996 Academic Press, Inc.
This research investigates the effects of a
manipulation of lexical similarity known as
transposed-letter (TL) confusability. TL confusable word pairs differ from each other only
in the order of two adjacent letters e.g., salt/
slat; trail/trial; calm/clam. The broad goal of
the research is to provide evidence that can
contribute to evaluating and refining models
of lexical retrieval. Most models of word recognition assume that lexical retrieval involves
a parallel activation process: That the representations for all words that are sufficiently
similar to the target are activated by the sensory stimulus. Precise specification of how
this process operates requires consideration of
the psychologically relevant dimensions of
lexical similarity that determine which repreThis research was supported by Australian Research
Council Grant 8932262. I am grateful to Stephen Lupker,
Jay McClelland, and Joan Gay Snodgrass for constructive
comments on earlier versions of the manuscript; Monica
Blayney and Danielle Scarratt for devoted research assistance; Ken and Jonathan Forster for providing and supporting the DMASTR package; and Mark Seidenberg for
providing me with the error scores from the Seidenberg
and McClelland (1989) simulation. Address reprint requests to Sally Andrews, School of Psychology, University of New South Wales, Sydney 2052, Australia.
sentations are sufficiently similar to the sensory stimulus to be activated, and of the mechanisms that allow the target representation to
be selected from among those for other simultaneously activated words. Investigations of
lexical similarity provide a means of evaluating the validity of current models’ assumptions regarding both of these issues.
One of the best accepted and specified accounts of the mechanisms underlying lexical
retrieval is provided by interactive activation
models (McClelland & Rumelhart, 1981; Taft,
1991). These models assume that linguistic
stimuli elicit parallel activation in hierarchically organised layers of nodes corresponding
to abstract symbolic units such as letters, letter
clusters, and words. Selection among these
candidates is achieved through the interaction
of excitatory activation between sublexical
and lexical levels, and inhibitory competition
among nodes at the same level. Excitatory activation from sublexical units activates the target word node as well as nodes for other similar words—‘‘neighbors’’ of the target. Lateral
inhibition among activated word nodes allows
selection between these candidates by dampening representations that receive less support
from lower and higher levels. Thus, together,
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Copyright q 1996 by Academic Press, Inc.
All rights of reproduction in any form reserved.
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between-level excitation and within-level inhibition allow ‘‘the network to implement a
‘best match’ strategy of choosing representations’’ (McClelland, 1987, p. 8).
The most obvious prediction of the interactive activation model for investigations of
lexical similarity is that words that are similar
to many other words will take longer to
achieve threshold than words with few neighbors because co-activated neighbors will laterally inhibit activation in the target word node.
This prediction appears to be contradicted by
recent evidence about the effects of lexical
similarity.
The measure of lexical similarity that has
received the most empirical attention is the
neighborhood size metric developed by Coltheart, Jonasson, Davelaar, and Besner (1977)
which is calculated by counting the number
of words that can be created by changing one
letter of a target word. Coltheart et al. found
that large neighborhood size was associated
with slower nonword classifications in a lexical decision task but had no effect on responses to words. More recent investigations
of the effects of neighborhood size on word
identification have confirmed Coltheart et al.’s
finding that large neighborhood size interferes
with nonword classification, but demonstrate
that neighborhood size does influence responses to words in both lexical decision and
naming tasks (Andrews, 1989, 1992; Sears,
Hino, & Lupker, 1995). However, the effects
are generally only evident for low frequency
words (but see Sears et al., 1995) and the direction of the effect of neighborhood size on
word performance is opposite to that on nonwords: Low frequency words from large
neighborhoods are responded to more quickly
than words with few neighbors. The finding
that performance is sensitive to lexical similarity supports the parallel activation assumptions of the interactive activation model, but
the facilitatory direction of the neighborhood
effect is incompatible with the competitive lateral inhibitory mechanism presumed to underlie selection among co-activated candidates.
Similarity to other words appears to benefit
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identification rather than yielding the interference predicted from lateral inhibition.
In contrast to the facilitatory effects of
neighborhood size on lexical decisions and
word naming performance, large neighborhood size has been reported to yield inhibitory
effects on both reaction time and accuracy of
identification responses to degraded stimulus
presentations, but only under conditions that
require subjects to make a single response to
a fragmented stimulus rather than successive
guesses to gradually increasing fragments
(Snodgrass & Minzer, 1993). Inhibitory effects of neighborhood size are not surprising
under degraded presentation conditions: The
probability of correctly identifying a word on
the basis of partial stimulus information will
be lower for words that are similar to many
other words than for words that are similar to
few words. However, superior performance
for degraded words from small neighborhoods
may reflect sophisticated guessing strategies
that are independent of the processes contributing to identification of clearly presented
stimuli.
Task-specific processes also appear to contribute to the apparent evidence of lateral inhibition provided by ‘‘neighborhood frequency
effects.’’ Grainger (Grainger, O’Regan, Jacobs, & Segui, 1989; Grainger & Segui, 1990)
reported that target words with a single high
frequency neighbor are responded to more
slowly than words with no such neighbors.
He interpreted this effect as evidence that the
target word representation is inhibited by the
more strongly activated high frequency neighbor. Grainger’s findings appear to conflict
with the facilitatory effects of large neighborhood size discussed above because words with
many neighbors are more likely to have a
neighbor that is of high frequency; the finding
of interference from high frequency neighbors
is therefore directly opposite to the facilitatory
effects of neighborhood size. However, the
different outcomes may reflect differences between the processes contributing to performance in different tasks. Neighborhood frequency effects are largest in a ‘‘progressive
demasking’’ task requiring identification of
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degraded stimuli (Grainger & Segui, 1990)
and may therefore reflect processes involved
in resolving ambiguous input. Although
Grainger (1990; Grainger et al., 1989) found
similar inhibitory effects of neighbor frequency in the lexical decision task using
French stimuli, both Sears et al. (1995) and
Forster and Shen (in press) were unable to
replicate this finding in English even though
they did both find robust facilitatory effects
of neighborhood size. In a word naming task,
Grainger (1990) found a tendency toward facilitatory effects of neighborhood frequency
paralleling Andrews’ (1989, 1992) findings
for neighborhood size.
Thus, investigations of neighborhood size
have consistently demonstrated facilitatory effects on performance in lexical decision and
naming tasks but tend to produce inhibitory
effects in tasks requiring perceptual identification of degraded stimuli. The latter tasks are
obviously subject to influences from whatever
strategies subjects have available to resolve
ambiguous stimulus information. Lexical decision and naming performance are less vulnerable to such influences, although neither
provides a pure measure of lexical retrieval.
However, the fact that neighborhood size exerts similar effects on both tasks implies that
it influences a common lexical retrieval mechanism (Andrews, 1989, 1992). The influence
of neighborhood size is consistent with the
general assumption of a parallel lexical retrieval mechanism because it implies that orthographically similar neighbors are activated
during target identification. However the facilitatory direction of the neighborhood size effect appears to be incompatible with the interactive activation model’s assumption of lateral
inhibition between simultaneously activated
lexical representations.
Consistent with this conclusion, initial investigations of the performance of McClelland
and Rumelhart’s (1981) computational implementation of the interactive activation model
yielded inhibitory effects of competing neighbors (Jacobs & Grainger, 1991). However, this
is not the only possible outcome of this model.
The time for a target word node to achieve
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threshold is not solely a function of inhibitory
activation from other word nodes, but reflects
a complex interplay between these competitive influences and the excitatory activation
between letter and word nodes. Activated
neighbors will inhibit the target word node but
they will also send excitatory feedback to the
nodes for their component letters. Depending
on the relative strengths of the parameters
governing lateral inhibition among word
nodes and the excitatory connections between
letter and word levels, the inhibitory influences of competing neighbors may be offset
by the excitatory feedback accruing from the
letters shared by the target and its neighbors
(Andrews, 1989, 1992). Supporting this suggestion that the general interactive activation
framework might yield either facilitatory or
inhibitory effects of lexical similarity depending on the precise parameters used in the
simulation, Coltheart and Rastle (1994) reported the results of an interactive activation
simulation using a parameter set different
from that of Jacobs and Grainger (1991) which
showed facilitatory rather than inhibitory effects of neighborhood size.
Thus, even though the interactive activation
model provides a completely explicit account
of the mechanisms underlying lexical retrieval
in the sense that it exists in the form of a
computational implementation, the model is
indeterminate with respect to effects of lexical
similarity. Varying the parameters governing
between-level excitation and within-level inhibition allows ‘‘prediction’’ of a vast array
of effects of lexical similarity. Additional empirical evidence concerning the effects of lexical similarity is necessary to further constrain
the solution space that a valid version of the
interactive activation model must satisfy.
Such data are also critical to evaluating the
validity of the more recently developed parallel distributed processing (PDP) models of
word recognition (Plaut, McClelland, Seidenberg, & Patterson, 1996; Seidenberg &
McClelland, 1989). There are two major differences between PDP models and the interactive activation framework that are critical to
lexical similarity effects. The first concerns
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assumptions about the form of lexical knowledge. In contrast to the interactive activation
framework, PDP models do not assume localist representations corresponding to lexical
or sublexical units. Lexical knowledge is contained in networks of elementary processing
units and the weighted connections linking
them. With experience, words come to be defined by unique patterns of activity across
these networks, but there is no single element
corresponding to a lexical or sublexical symbol and each element of the network participates in the patterns for many different words.
Thus rather than words being coded in terms
of symbolic units such as letters and letter
clusters, models assuming distributed representation can be described as coding ‘‘microfeatures’’ (Hinton, McClelland, & Rumelhart,
1986) or ‘‘subsymbolic’’ components of
words (Smolensky, 1989).
Distributed representations may yield a different similarity structure than the hierarchical
architecture of the interactive activation
model. In interactive activation models, similarity between words is a function of shared
symbolic constituents such as letters or phonemes. Which constituents are relevant depends on which linguistic symbols are explicitly represented in the hierarchical layers of
the model. McClelland and Rumelhart only
assumed three levels of orthographic representation: features, letters and words so the neighbors activated by a target word will be those
that have enough letters in common with the
target for their word node to be activated by
excitatory activation from the letter level.
However, Taft (1991) describes an interactive
activation framework which includes separate
levels of representation for graphemes, letter
clusters and word bodies, so words that share
any of these components with the target can
potentially be activated by the target stimulus.
In general, then, the dimensions defining similarity between words are explicit in the architecture of the interactive activation model.
Within a PDP network, by contrast, similarity is a function of the overlap between the
connections defining the patterns for different
words. The connection weights learned during
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training of the network will reflect the statistical relationships between orthographic inputs
and phonological outputs that allow optimally
efficient discrimination beween the words in
the training vocabulary. The particular relationships learned will depend on the structure
of the training vocabulary and the choice of
input and output coding scheme, but distributed representational networks allow for much
more complex patterns of similarity relationships than are possible within the interactive
activation model because no particular linguistic features are ‘‘hard-wired’’ into the
model. Different linguistic features might
come to be reflected in the patterns defining
different words as the network develops a set
of weights that optimise performance for the
complete training vocabulary.
The second difference between the interactive activation and PDP frameworks that is
relevant to lexical similarity effects concerns
the mechanisms by which a target word is
‘‘selected’’ from competing alternatives. Instead of the competitive lateral inhibition that
is critical to selection in the interactive activation framework, PDP models with distributed
representations generally rely on constraint
satisfaction procedures to achieve ‘‘selection’’ between overlapping patterns for similar
words. Presentation of a word to a trained PDP
network elicits activation that is propagated
through the network in a manner determined
by the pattern of connection weights acquired
in the course of training. Over time, this activity settles into a stable state corresponding to
the pattern for the target word. The strength
of the connections linking the elements activated by particular stimulus patterns depend
on frequency of exposure so that a stable network state is achieved more quickly for stimuli that are presented more often during training. As well as accounting for the faster
identication of high frequency words, the
strengthening of connections with more frequent experience also predicts facilitatory effects of neighborhood size. Words that share
most of their letters with many other words
will have some of their connections strengthened by experience with these neighbors: that
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is, ‘‘the neighbors of the word tend to modify
the weights in the same direction as the word
itself’’ (Seidenberg & McClelland, 1989, p.
544). Consistent with this prediction, the
Seidenberg and McClelland model successfully simulates Andrews’ (1989) evidence of
facilitatory effects of neighborhood size on
word naming.
Thus, facilitatory effects of neighborhood
size appear more compatible with the mechanisms underlying the performance of PDP
models than with the competitive selection
processes of the interactive activation framework. However, Coltheart and Rastle’s (1994)
interactive activation simulation data demonstrate that at least some parameter settings
yield better performance for words from large
neighborhoods despite the influence of lateral
inhibition. The assumption of a competitive
selection process does not, therefore, preclude
facilitatory effects of lexical similarity.
TL Confusability
The present experiments are designed to
provide further evidence about the effects of
lexical similarity on word identification that
can contribute to evaluating and refining interactive activation and PDP models. They focus
on a manipulation of similarity between words
that has received less attention than neighborhood size. This TL confusability manipulation
was first investigated by Chambers (1979)
who found that lexical decision responses to
TL words (e.g., salt-slat; trail-trial) and to
nonwords created by transposing two letters
in a word (e.g., ottal, stroe) were slower than
responses to control words and nonwords. She
interpreted the impaired performance for TL
stimuli as indicating that information about
the order of letters within a word does not
contribute to the access procedure so that presentation of slat can result in access to salt.
Chambers assumed that the interference was
specific to the lower frequency members of
TL word pairs: ‘‘that the lexical entry for a
higher frequency word differing from a lower
frequency stimulus word only by the order of
an adjacent letter pair, is accessed’’ by the
low frequency stimulus (Chambers, 1979, p.
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234). But her experiments only investigated
low frequency TL pair members, so this assumption was never validated.
Similarity effects for TL pairs are of interest
for a number of reasons. First, investigations
of such stimuli provide insight into the structure of lexical knowledge. Members of TL
word pairs will only interfere with each other
if, at some level, words are processed in terms
of position-insensitive letter identities. If each
letter position is processed completely independently, as implemented in the slot-coding
scheme used in computational versions of the
interactive activation model (Grainger & Jacobs, 1992; McClelland & Rumelhart, 1981),
then salt and slat share only two letters and
there is little reason to predict confusion between the two items. Neither does the contextsensitive ‘‘Wickelfeature’’ coding scheme as
implemented in Seidenberg and McClelland’s
(1989) PDP model predict substantial effects
of TL confusability: for example, the codes
for salt (_sa, sal, alt, lt_) do not overlap at all
with those for slat. More generally, patterns
of effect of lexical similarity provide insight
into the organization of lexical and orthographic knowledge.
Second, effects of TL confusability would
provide stronger evidence of parallel lexical
activation than effects of neighborhood size.
Large neighborhood size is defined by the fact
that the target word shares all but one letter
with many other words. In natural language
samples, this measure of lexical similarity is
inherently confounded with measures of the
frequency of sublexical constituents. Therefore, rather than reflecting co-activation of
neighboring words, differential performance
for words from large and small neighborhoods
may be a function of the relative frequency of
sublexical constituents: Perhaps words from
large neighborhoods are responded to more
quickly because they contain more frequently
occurring sublexical units that lead to faster
activation of the relevant target word. Demonstrations that effects of neighborhood size remain when stimuli are matched on measures
of bigram frequency imply that neighborhood
size effects are not solely due to orthographic
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redundancy (Andrews, 1992, Sears et al.,
1995). However, it remains possible that bigram frequency fails to capture all psychologically relevant dimensions of sublexical structure. The TL manipulation of similarity is defined by the specific confusability of two
words rather than by the overlap of a word
with the complete set of lexical items, so it is
not subject to the confounding between lexical
similarity and sublexical frequency.
Third, TL word pairs provide a further test
of the lateral inhibition mechanism assumed to
contribute to selection among similar lexical
representations in the interactive activation
model. The facilitated performance for words
from large neighborhoods appears incompatible with such a competitive mechanism, but
may reflect the fact that excitatory activation
due to the common sublexical constituents of
neighboring words outweighs the inhibition
from co-activated word nodes (Andrews,
1989, 1992). The specific confusability of TL
pairs may provide a vehicle for demonstrating
lateral inhibition.
The present experiments therefore investigated performance for TL words to provide
further insight into the organisation and retrieval of lexical knowledge. Experiment 1 established the basic phenomenon by directly
comparing lexical decision and naming responses to TL words with those for carefully
selected control stimuli. This experiment confirmed Chambers’ (1973) finding of delayed
performance for TL stimuli but, contrary to
her expectations, demonstrated that the effect
is at least as large for high as low frequency
words. The other two experiments confirm the
robustness of the TL interference effect across
different samples of subjects and different task
contexts. In addition, these experiments were
designed to provide evidence about the locus
of the TL confusability effects. Experiment 2
investigated how TL interference is modulated
by the explicit presentation of the TL competitor as a masked prime and Experiment 3 investigated whether TL effects were enhanced under dual task demands as would be expected if
they reflected attentionally mediated selection
processes.
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EXPERIMENT 1
Experiment 1 investigated performance for
TL words in both the lexical decision and the
speeded naming task. Stimuli were presented
individually so that the influence of the
‘‘mate’’ of a target word (e.g., interference
from salt on identification of slat) is inferred
from differences between performance for TL
words and carefully matched control stimuli.
Performance for TL stimuli was assessed
in both lexical decision and naming tasks to
provide evidence regarding the locus of similarity effects. Both tasks involve clear unambiguous stimulus presentations and so are less
subject to the influence of response-related
processes than tasks in which stimuli are degraded. But neither task provides a pure measure of lexical retrieval processes. In the lexical decision task, the information retrieved
from lexical memory must be used to make a
lexical classification and generate the appropriate response and it has therefore been suggested that differences between the speed and
accuracy of lexical classification might reflect
differences in these decision and response processes rather than in lexical retrieval itself
(Balota & Chumbley, 1984; Gordon, 1983).
The distinction between lexical retrieval and
decision processes is particularly important to
investigations of lexical similarity because the
binary response requirements of the lexical
decision task allow subjects to make correct
responses without having identified the exact
stimulus presented (Andrews, 1989; Snodgrass & Minzer, 1993). In the present context,
the subject might correctly respond ‘‘word’’
to the stimulus slat when the most active lexical representation is actually salt. Correct
naming performance cannot be achieved
through retrieval of the wrong TL word, but
this task introduces other problems because
subjects may be able to generate the correct
pronunciation using sublexical procedures
(Coltheart, 1980). Thus, this task can also, in
principle, be performed without lexical retrieval. Given the ambiguity of each task as a
measure of lexical retrieval, conclusions as to
the locus of effect of any variable require con-
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verging evidence from both tasks (Andrews,
1989, 1992). Effects due to the decision requirements of lexical classification or to the
assembly processes that might contribute to
word naming will be confined to a single task.
Effects due to lexical retrieval mechanisms
should be evident in both tasks.
The experiments also compare performance
for the higher and lower frequency members
of TL pairs. Chambers (1979) assumed that
TL interference would be restricted to lower
frequency TL words because the TL mate
would only become available to interfere with
target identification if it was of higher frequency than the target. Chambers’ conclusion
was derived from the serial search model of
lexical access (Forster, 1976), but larger TL
effects for low frequency words are also predicted by the parallel activation assumptions
of the interactive activation and PDP models.
Word frequency effects in the interactive activation model are attributed to differences between the resting activation levels of word
nodes. High frequency words have higher resting levels and therefore need less activation
from the sensory stimulus to achieve threshold
than low frequency words. They should therefore be less vulnerable to lateral inhibition
from co-activated neighbors, particularly
when they are of lower frequency than the
target. Similarly, the stronger connections defining the patterns for high frequency words
within the PDP framework mean that the time
to settle into a stable state should be relatively
unaffected by overlap with other words.
Method
Subjects. The subjects were 39 undergraduate university students who received course
credit for their participation.
Stimuli. TL word pairs were selected on
the criteria that the words were identical apart
from the transposition of two adjacent letters;
that the transposed letters did not include the
initial letter of the word, and that there was a
clear difference between the frequency of the
two members of the pair. In general, one member was clearly of high frequency (more than
70 in the American Heritage norms [Carroll,
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Davies & Richman, 1971]) and the other of
low frequency (less than 30), but this was not
always possible. In ten pairs, the lower frequency member had a frequency of greater
than 30 (41–130) but for these pairs the higher
frequency member occurred at least twice as
frequently (Carroll et al., 1971). The set of 32
pairs selected virtually exhaust the population
of items fitting these criteria.
For each member of the TL pair, a control
word of the same length and with the same
initial letter and phoneme as the TL word was
selected. To ensure that differences between
performance for TL and control stimuli could
not be attributed to differences in orthographic
constraints, control words always contained a
pair of letters that could be transposed to form
an orthographically legal and pronounceable
string. Control words were matched pairwise
with target words for word frequency, and the
sets of TL and control words were approximately matched on measures of neighborhood
size (Coltheart et al., 1977) and average bigram frequency (Mayzner & Tresselt, 1965).
The majority of TL and control stimuli were
pronounced regularly (Coltheart, 1980) and
the small number of irregular words were relatively evenly distributed across the four stimulus conditions. Table 1 presents summary statistics for the selected stimuli which are listed
in the Appendix. Apart from the intended manipulation of word frequency, none of the differences between the characteristics of the
stimulus sets were significant.
The word stimuli were divided into two lists
of 64 words. Each contained the higher frequency members of half of the TL pairs along
with their matched control words, and the
lower frequency TL words and controls from
the remaining 16 pairs. Thus, each list included only one member of each TL pair to
reduce intra-experimental confusion between
the two words.
Three sets of legal nonword stimuli were
generated for use in the lexical decision task.
Two of these sets were ‘‘TL nonwords’’ created by selecting a word with the same first
letter/phoneme and of the same frequency as
each TL/control word pair, and transposing
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TABLE 1
AVERAGE WORD FREQUENCY, BIGRAM FREQUENCY, AND NEIGHBORHOOD
SIZE FOR THE FOUR GROUPS OF TARGET WORDS
High frequency
Word frequency
Bigram frequency
Neighborhood size
TL
Control
TL
Control
300.6
42.6
5.2
290.2
42.7
4.9
31.0
39.6
4.1
30.1
33.6
4.4
two adjacent letters (not including the initial
letter) to form a legal pronounceable nonword
(e.g., momnet, crod). There were therefore 32
TL nonwords, half of which were derived
from higher frequency words and half from
lower frequency words. A further 32 legal pronounceable nonwords that could not be turned
into a word by letter transposition were generated to serve as control nonwords. This resulted in 64 nonwords and therefore an equal
number of word and nonword stimuli in each
lexical decision stimulus list. The three sets
of nonwords were of similar average bigram
frequency (High frequency TL Å 33.0, Low
frequency TL Å 24.9, non-TL Å 37.4).
Procedure. Subjects completed both tasks,
in counterbalanced order, within a single onehour session. In the lexical decision task they
were told that they would be presented with
word and nonword stimuli and that they
should respond to words with their dominant
hand and use their non-dominant hand to respond to nonwords. Responses were made by
pressing one of two microswitches mounted
on a hand-held box. In the naming task subjects were presented with only word stimuli
and told to read each word aloud as it appeared
on the screen and initiate their reponse as
quickly as possible while avoiding making too
many errors. Before each task, subjects completed 20 practice trials using stimuli that were
not included in the experimental lists. They
were then presented with the experimental
stimuli in an individually randomized order
and preceded by two buffer trials. Each subject was presented with the same stimulus list
in both the lexical decision and naming task
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but in a different random order. Task order
and list assignment were counterbalanced.
Testing was conducted individually in a
darkened sound-attenuated room. The DMASTR program was used to control stimulus
presentation and response collection. Stimuli
were presented on the video monitor of a personal computer in which the timing of the
display was synchronised with the video raster. All stimuli were presented in lower case
centred on the screen for 500 ms. The next
trial was initiated 1500 ms after the subject’s
reponse. The computer controlling stimulus
presentation recorded the latency and accuracy of lexical decision responses. For the
naming task, a voice activated relay detected
vocal initiation and the computer recorded the
latency from stimulus presentation. The experimenter monitored subjects’ performance
over headphones and recorded hesitations, erroneous pronunciations and trials on which
the relay was triggered by extraneous noise or
by a subject’s irrelevant vocalization.
Results
Although subjects participated in both
tasks, analyses treated task as a between-subject factor and only included the data for each
subjects’ first task to avoid contamination
from effects due to carryover between tasks
or stimulus repetition. (An identical pattern of
results was evident in the complete data set.)
Responses with latencies shorter than 200 ms
or longer than 1500 ms and naming trials on
which the experimenter had recorded that the
relay was triggered by an irrelevant noise were
excluded from further analysis. No such ex-
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TABLE 2
MEAN REACTION TIME (AND PERCENTAGE ERROR RATE)
TO
TARGET WORDS IN EXPERIMENT 1
Lexical decision task
High frequency
Low frequency
TL
Control
TL
Control
590 (2.7%)
641 (13.8%)
556 (1.5%)
653 (15.2%)
600 (4.1%)
600 (14.2%)
568 (1.3%)
619 (4.2%)
clusions were necessary from the lexical decision data and a total of 1.45% of all naming
responses were eliminated from the naming
analysis. Naming trials on which the subject
hesitated or mispronounced the word were excluded from the RT analysis and counted as
errors. The remaining data were collated to
form mean RT and error rates for each subject
in each condition and task, and mean RT and
error rate for each item in each task. The mean
RT and percentage error rate averaged over
subjects are presented in Table 2. Separate
analyses were conducted on RT and error rates
for each task. For each measure, separate analyses were conducted treating subjects and
items as random effects. The F ratios for each
analysis are reported as Fs and Fi , respectively. In all analyses, planned contrasts tested
main effects and interactions of word type
(TL/control), word frequency (high/low).
Both word type and word frequency were
treated as repeated measures in the subject
analysis but word frequency was a betweengroup factor in the analysis based on items.
Lexical decision task. High frequency
words were classified more quickly than low
frequency words (Fs(1,18) Å 53.19, p õ .01;
Fi(1,62) Å 38.66, p õ .01). Frequency also
interacted significantly with the effect of
word type, because TL words were classified
more slowly than control words when they
were the higher frequency member of the TL
pair, but low frequency words showed the
opposite effect (Fs(1,18) Å 11.11, p õ .01;
Fi (1,62) Å 4.56, p õ .05). Classifications
were more accurate for high than low frequency words (Fs(1,18) Å 44.65, p õ .01;
Fi(1,62) Å 20.25, p õ .01), but accuracy was
unaffected by TL status.
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Nonwords created by transposing letters of
high frequency words were classified more
slowly and less accurately (736 ms, 10.9%
errors) than TL nonwords created from low
frequency words (706 ms, 5.7% errors) or control nonwords (708 ms, 6.7% errors). Analysis
of these data showed that the difference between the error rates for TL nonwords created
from high vs low frequency words was significant in the subject analysis (Fs(1,18) Å
4.57, p õ .05; Fi (1,62) Å 1.25, p ú .05).
Word naming task. Naming responses were
faster to high than low frequency words
(Fs(1,17) Å 22.71, p õ .01; Fi (1,62) Å 3.63,
p ú .05) and faster to control words than to
TL stimuli (Fs(1,17) Å 5.72, p õ .05; Fi (1,62)
Å 4.63, p õ .05). Paralleling the lexical decision data, there was also a significant interaction between frequency and TL status which
arose because high frequency TL words were
responded to more slowly than control words
while low frequency words showed the opposite effect (Fs(1,17) Å 12.34, p õ .01; Fi (1,62)
Å 4.17, p Å .05). The naming accuracy data
paralleled the RT findings in demonstrating
greater accuracy for high than low frequency
words (Fs(1,17) Å 31.09, p õ .01; Fi (1,62)
Å 8.04, p õ .01), and for control than TL
stimuli (Fs(1,17) Å 17.91, p õ .01; Fi (1,62)
Å 7.60, p õ .01). The interaction between
frequency and TL status was significant in the
subject but not the item analysis (Fs(1,17) Å
7.07, p õ .01; Fi (1,62) Å 2.47, p ú .05).
However, in contrast to the reaction time data,
the interaction occurred because the reduced
accuracy for TL words was more marked for
low than high frequency words.
An analysis comparing naming and lexical
decision performance revealed no significant
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differences between tasks in the TL effects on
response time but the TL interference effect
on response accuracy was greater in the naming than the lexical decision task (Fs(1,35) Å
9.97, p õ .01; Fi (1,62) Å 5.97, p õ .05)
particularly for low frequency words (Fs(1,34)
Å 4.57, p õ .05; Fi (1,62) Å 1.25, p ú .05).
Discussion
Performance in both tasks was sensitive to
TL confusability. Words that differ from another word only in the order of two letters
were responded to more slowly and less accurately than control words. In contrast to the
common prediction that high frequency words
should be less sensitive to variations in lexical
similarity (e.g., Andrews, 1982, 1989), TL interference effects on response time in both
tasks were larger for high than low frequency
words. The results for low frequency words
were task-specific. Lexical decisions for low
frequency TL words were both faster and
more accurate than responses to control
words. Naming times to low frequency TL
words were also faster than to control words,
but the speed advantage was offset by a higher
error rate for TL words.
The lack of TL interference for low frequency words in the lexical decision task
probably reflects the insensitivity of this task
to errors involving retrieval of the wrong
member of the TL pair. Whether the subject
has retrieved salt or slat the same ‘‘word’’
response will be made. Some proportion of
subjects’ responses to low frequency TL
words may, therefore, be based on incorrect
retrieval of their high frequency mate. Including fast responses based on incorrectly retrieved high frequency TL mates in the means
for the low frequency TL condition would obscure the TL interference effect. The same
problem does not occur for nonword classifications because responses based on retrieval
of inexact lexical matches will yield erroneous
‘‘word’’ classifications. The tendency towards
a higher error rate for TL nonwords created
from high frequency words is consistent with
the possibility that lexical classifications are
sometimes based on activation of the node for
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a word that is a TL version of the presented
stimulus.
The lexical decision data are compatible
with ‘‘deadline’’ accounts of the task (Coltheart et al., 1977) which assume that nonword
classifications are made when no lexical representation exceeds threshold before some fixed
deadline expires. This view predicts that nonwords’ similarity to words will not necessarily
influence reaction time, which is determined
by the deadline, but may yield errors if an
apparently matching lexical representation
achieves threshold before the deadline expires. The pattern of RT and accuracy observed in the present lexical decision data
might be obtained if a relatively fast deadline
was imposed that occasionally expired before
the representation for a target low frequency
word had achieved threshold. This would
yield a relatively high overall error rate for
low frequency words, but incorrect ‘‘nonword’’ responses to low frequency TL targets
that were not retrieved before the deadline
would sometimes be avoided because a
‘‘word’’ response would be made on the basis
of (incorrect) retrieval of its high frequency
TL mate. Similarly, TL nonwords would
sometimes be classified as words particularly
when they were derived from high frequency
words which may achieve threshold before the
deadline expires.
Thus, the absence of TL interference effects
on lexical decision responses to low frequency
words may reflect the application of a relatively lax decision criterion that allows
‘‘word’’ responses to be made without a perfect match between sensory and lexical representations. Lexical classification tasks may,
therefore, be an unreliable index of the impact
of co-activation of similar lexical representations, particularly for low frequency words.
Naming performance provides a more appropriate measure of effects of lexical similarity
because errors involving retrieval of the
wrong TL pair member can be detected.
The naming data clearly demonstrate TL
interference effects on both RT and accuracy
for high frequency words which parallel those
observed in the lexical decision task. How-
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ever, TL effects for low frequency words remain ambiguous because the TL manipulation
had opposite effects on RT and accuracy: Low
frequency TL targets were named more
quickly but less accurately than control words.
The majority of the errors made to low frequency TL targets were pronunciations of the
higher frequency transposed word (45%) or
initiations of this response that were self-corrected by the subject (18%). These intrusion
errors appear to provide direct evidence of coactivation of the higher frequency TL word.
The results for low frequency words suggest a form of speed–accuracy trade-off. In
their effort to produce fast naming responses,
subjects sometimes initiated the pronunciation
of the co-activated higher frequency TL mate
leading to a marked effect of TL confusability
on naming accuracy. The absence of a parallel
delay in RT to low frequency TL words may
reflect the fact that incorrect naming of the
co-activated TL mate is most likely to occur
for the low frequency targets that take longest
to retrieve. Thus, the naming times for these
items do not contribute to the average correct
RT for the low frequency TL condition and
the mean RT is therefore faster than for the
control condition where the more difficult
items are named correctly and have their RT
included in the condition average.
Some caution is, however, required when
interpreting the differential patterns of performance for high and low frequency TL words
because of difficulties in stimulus selection
and matching. Because the experiments were
designed to compare the higher and lower frequency members of TL word pairs, stimuli
were selected on the basis of the relative frequency of the two members of the pair rather
than their conformity with an absolute definition of high or low frequency. There are two
consequences of this approach to selection.
The first is that the difference between the
high and low frequency word sets is less extreme than in many other investigations of
word frequency effects. In the American Heritage corpus of approximately 5 million words
that was used for stimulus selection and
matching (Carroll et al., 1971), the average
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frequencies for high and low frequency words
were approximately 300 and 30 respectively,
but in the smaller corpus of about 1 million
words contributing to the Kucera and Francis
(1967) count, the corresponding averages for
the samples of high and low frequency words
are 40.7 and 11.2. Although this is a smaller
difference in normative frequency than is
characteristic of most investigations of the the
effects of this factor, it is noteworthy that
marked frequency effects were obtained for
control stimuli in both the lexical decision (96
ms) and the naming task (51 ms).
The second, and more important, implication of the selection criteria is that the accuracy of assignment of TL items to the high
or low frequency conditions depends on the
reliability of the normative data provided by
the frequency counts. This reliability is questionable for low frequency words where sampling error exerts its greatest effect (Gernsbacher, 1984). To assess this unreliability, the
classification of words as the higher or lower
frequency members of TL pairs based on the
Carroll et al. (1971) norms was checked
against the Kucera and Francis (1967) frequency count. This comparison revealed that
4 of the 32 pairs would have been assigned
to the opposite frequency categories, and a
further 11 pairs contained words of approximately equivalent frequency according to
Kucera and Francis (1967). That is, only about
half of the TL pairs received the same frequency classification in both corpora. This
problem does not compromise the matching
of the TL and control stimuli because the frequency estimates of control words are subject
to the same sampling error (17 of the 32 pairs
of control words maintained their assignment
in both corpora). However, it may explain
why the interaction between frequency and TL
status was not reliable in the item analysis of
naming accuracy, and suggests that caution is
required when interpreting the differential TL
effects for high and low frequency words.
EXPERIMENT 2
The results of Experiment 1 provide evidence consistent with the view that both mem-
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bers of a TL pair are activated when a single
TL word is presented and that the co-activated
mate interferes with identification of the target
word. Experiment 2 is designed to confirm
these conclusions by replicating the finding of
TL interference on word naming and to provide further insight into the impact of co-activated neighbors on target identification by investigating the effects of priming manipulations. Priming paradigms provide more direct
insight into the consequences of co-activation
of TL word pairs than the single word paradigms of Experiment 1 because explicit presentation of the TL mate as a prime ensures
that it has been activated to compete with
identification of the target. Conclusions based
on priming effects have the additional advantage of allowing comparison of the effects of
different priming conditions on the same target words.
A priming paradigm that is particularly suitable to the present investigation was developed by Forster and Davis (1984). A clearly
presented uppercase target stimulus to which
subjects are required to respond is preceded
by a briefly presented lowercase prime which,
in turn, is preceded by a dummy stimulus presented for the same duration as the target. The
dummy and target stimuli act as forward and
backward masks for the prime, and the combination of the brief presentation and masking
apparently renders the prime inaccessible to
subjective awareness: Subjects are little better
than chance at judging whether the prime and
target are the same stimulus, and at chance
for judgements of prime identity (Forster &
Davis, 1984). The very brief stimulus-onset
asynchrony between the prime and target,
combined with the fact that subjects are unaware of the identity of the prime ensure, at
least, that priming effects in this paradigm do
not reflect conscious prediction or expectancy
strategies (Neely, 1991).
Most applications of this paradigm have required lexical classification of target stimuli.
However, because of the ambiguity of lexical
decision responses to TL words, the present
experiment investigated priming of word naming performance so that errors due to misiden-
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tification of the TL pair member could be detected. Forster and Davis (1991) demonstrated
that masked primes that are either identical
or similar to a target word facilitate naming
performance relative to an unrelated control
condition, but found evidence suggesting that
unrelated primes with a first phoneme different from the target induce response competition that delays naming performance. This
competition can be avoided by matching the
first phoneme of prime and target stimuli.
The specific goal of the present application
of the masked priming paradigm was to directly investigate the effects of co-activation
of TL word pairs by priming a TL target word
with its confusable mate (e.g., slat SALT).
Two comparison prime conditions were included. ‘‘Neighbor primes’’ were created by
changing one letter of the target word to form
a pronounceable nonword (e.g., saft SALT),
and unrelated word primes that shared only a
first letter with the target (e.g., spin SALT)
served as a baseline. Neighbor primes have
been demonstrated to facilitate both lexical
decision and naming performance (Forster et
al., 1987; Forster & Davis, 1991) so the comparison with TL primes, which share all letters
with the target word but in a different order,
provides a direct test of the relative effect of
these two similarity manipulations on identification of TL targets.
The same three prime conditions were presented in conjunction with control target
words (e.g., snad SAND, sant SAND, soul
SAND). These comparisons provide two
sources of evidence regarding the locus of any
effects observed for TL targets. First, the effects of TL primes on responses to control
targets will demonstrate whether interference
is due simply to the perceptual or orthographic
overlap of letters between prime and target. If
interference from TL primes is due to lexical
competition rather than to perceptual overlap,
it should affect performance for TL but not
control targets. Second, comparison of neighbor and TL priming of control targets provides
a test of the relative priming due to each similarity manipulation without the confounding
with lexical status of the two prime types that
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occurs for TL targets. That is, differential
priming of SALT from saft and slat might be
due to the lexicality of the prime rather than
the similarity relationship, but the same problem does not attend the comparison of priming
of SAND by sant and snad.
TABLE 3
MEAN REACTION TIMES (AND PERCENTAGE ERROR
RATES) TO TARGET WORDS IN THE THREE DIFFERENT
PRIMING CONDITIONS OF EXPERIMENT 2
Prime condition
Method
Subjects. The 42 subjects were from the
same population as tested in Experiment 1.
Stimuli. The target stimuli were 30 of the
matched TL and control words from Experiment 1. The design involved three factors: target status (TL/ control), word frequency (high/
low), and prime type. Each target word occurred with three different primes: a neighbor
prime—a nonword differing from the target
word in any single letter position other than
initial, an unrelated prime—a word with the
same first letter and phoneme as the target but
otherwise unrelated to it, and a TL prime—
the TL mate of the target word or, in the case
of control stimuli, a nonword created by
transposing two letters of the target word. The
limited number of TL stimuli and the nature
of the experimental design made it necessary
to present both members of the TL pair to
allow sufficient observations within each condition, for example, to present both slat SALT
and song SLAT in the same list. However, the
randomization of items in the first and second
half of the trial sequence was constrained to
ensure that the two members of a pair were
separated by 60 trials.
Three experimental lists were constructed
each containing 10 trials within each condition
and 120 trials in all. Each word occurred as a
target only once in any list, but across lists
each target occurred in all three prime conditions. Each list was presented to 14 subjects.
Procedure. Testing was conducted in the
same laboratory and under the same general
conditions as the naming task of Experiment
1. The only difference was that each trial consisted of three successive displays: a mask
consisting of as many $ symbols as the letters
in the target word presented for 500 ms, the
prime presented in lowercase for 56 ms, and
the target presented in uppercase for 500 ms.
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High frequency
TL
Control
Low frequency
TL
Control
Neighbor
prime
Unrelated
prime
TL
prime
618
(6.0%)
593
(1.4%)
628
(6.4%)
603
(1.4%)
640
(12.4%)
598
(0%)
630
(6.7%)
624
(2.4%)
635
(7.9%)
634
(2.6%)
651
(11.2%)
645
(2.9%)
Subjects were instructed to name the uppercase stimulus and to ignore the $$$$ stimuli
that preceded the target word. After 20 practice trials they were presented with two buffer
trials and the 120 experimental trials in an
individually randomized order. Data were collected and monitored in the same manner as
for Experiment 1.
Results
The results were collated and analyzed following the procedures described for the naming task of Experiment 1. Less than 1% of
spoiled trials were excluded from analysis.
The mean reaction time for correctly named
target words and the percentage error rate for
each condition, averaged over subjects are
presented in Table 3. In the subject analyses
all factors were treated as repeated measures,
but frequency was a between-group factor in
the item analyses.
The average effects of word type on reaction time paralleled those of Experiment 1.
There was a significant reaction time advantage for high frequency words (Fs(1,39) Å
46.88, p õ .01; Fi (1,58) Å 6.05, p õ .05),
TL words were classified more slowly than
control words (Fs(1,39) Å 25.16, p õ .05; Fi
(1,58) Å 16.46, p õ .01), and the TL effect
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was more marked for high than low frequency
words (Fs(1,39) Å 34.77, p õ .01; Fi (1,58)
Å 7.31, p õ .01). Analysis of naming accuracy
revealed a significant effect of TL status
(Fs(1,39) Å 58.43, p õ .01; Fi (1,58) Å 45.27,
p õ .01) but neither the frequency effect nor
its interaction with target TL status were reliable (all Fs õ 1).
Analysis of the effects of prime condition on
naming latency revealed that target words were
named significantly faster following neighbor
primes than TL primes (Fs(1,39) Å 20.02, p õ
.01; Fi (1,58) Å 5.43, p õ .05). Neighbor primes
yielded facilitation relative to unrelated primes
(Fs(1,39) Å 5.84, p õ .05; Fi (1,58) Å 1.05, p
ú .05) while TL primes yielded interference
(Fs(1,39) Å 7.75, p õ .01; Fi (1,58) Å 1.17, p
ú .05), but neither of these effects were significant in the item analysis. None of the interactions between prime condition and target type
were significant (all Fs õ 2.92).
Investigations of priming effects on accuracy showed that error rates were significantly
higher following TL primes than either unrelated (Fs(1,39) Å 8.94, p õ .01; Fi (1,58) Å
2.04, p ú .05) or neighbor primes (Fs(1,39)
Å 11.00, p õ .01; Fi (1,58) Å 3.13, p ú .05).
These main effects of prime condition were
not significant in the item analysis because the
effects of prime condition depended on the
TL status of the target word. In contrast to the
RT analysis, there were significant interactions between priming and the TL status of
the target: The interference from TL primes
on naming accuracy was greater for TL than
control words when compared to both unrelated (Fs(1,39) Å 8.76, p õ .01; Fi (1,58) Å
3.66, p ú .05) and neighbor primes (Fs(1,39)
Å 14.03, p õ .01; Fi (1,58) Å 5.06, p õ .05).
Discussion
This experiment provides two sources of
data regarding the effects of TL confusability.
The average data, ignoring prime condition,
replicate the naming data of Experiment 1 by
demonstrating that TL words were named
more slowly and less accurately (633, 8.4%
errors) than control words (616 ms, 1.8% errors). Again, the TL effect on naming speed
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was more marked for high frequency (31 ms)
than low frequency (5 ms) words. These results imply that presenting a particular TL target word activates representations of both
members of the TL pair and that this co-activation interferes with target identification.
Further evidence of co-activation is provided
by the errors made to TL targets which were
primarily intrusions of the TL mate (46%) or
spontaneously corrected initiations of such intrusions (26%).
The second source of evidence about the
effects of TL confusability is provided by
comparisons of the effects of different prime
conditions on responses to TL and control
words. The goal of the priming manipulations
was to determine whether directly activating
the TL mate of a target word by presenting it
as a masked prime would impair target identification. Such a finding would provide converging evidence for the hypothesis that parallel activation of both members of a TL pair
impairs identification of TL target words.
The pattern of priming effects provides general support for this hypothesis. Target words
were named more quickly when preceded by
neighbor primes (616 ms) than by unrelated
primes (625 ms) or TL primes (634 ms). The
priming effects on RT therefore simultaneously demonstrate the facilitatory neighbor
priming observed in earlier applications of this
paradigm (Forster & Davis, 1991) and the TL
interference expected on the basis of the results of Experiment 1. However only the overall comparison between neighbor and TL
priming conditions was significant over both
subjects and items. Although the interference
for TL compared to unrelated primes was
more marked for TL than control targets (14
ms vs 3 ms), none of the interactions between
priming and TL status of the target word were
significant.
TL priming effects on naming accuracy
were significantly greater for TL than control
targets. Almost twice as many naming errors
were made to TL words when they were preceded by a TL rather than a neighbor or unrelated prime. There was no equivalent decrease
in accuracy for control words preceded by a
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TL prime (e.g., snad SAND) implying that the
interference from TL primes reflects competition between lexical representations rather
than being simply due to perceptual or orthographic overlap. Although the comparison between TL priming of TL and control targets
is confounded with the lexicality of the prime
(e.g., slat SALT vs snad SAND), the same TL
targets were responded to less accurately following TL primes than unrelated word primes
indicating that TL primes cause interference
over and above that due to competing activation from an unrelated lexical prime. More
generally, the fact that TL naming errors to the
same target words were significantly higher
following a TL prime than either a neighbor
or an unrelated prime implies that they are not
solely attributable to perceptual or response
biases associated with TL targets, but include
a specific contribution due to direct priming
of the TL mate even under masked priming
conditions in which subjects are not consciously aware that the prime was presented.
Thus, the investigation of TL priming effects confirms the broad conclusion that coactivation of a confusable TL mate impairs
naming performance, but only the effects on
naming accuracy are specific to TL targets. It
is important to recognize that single word and
priming paradigms are not directly analogous
methods of investigating lexical co-activation.
There is no necessary reason that the activation consequent on direct, brief presentation
of a prime is equivalent to that induced by
presentation of a similar item: that is, activation of the representation of slat by the brief
presentation of the word itelf as a prime may
not be the same as the activation of the slat
representation consequent on presenting salt
under clear viewing conditions. In addition to
the temporal differences between the patterns
of co-activation consequent on single word
vs priming paradigms, the two methods have
different implications for sublexical activation. For example, within the interactive activation framework, presenting the competitor
as a prime will yield direct activation of its
component letters as well as activation of the
whole word competitor. However, when a sin-
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gle target word is presented, activation of the
transposed letters of its TL mate can only occur indirectly through feedback from the word
node. Simulations of the model would be necessary to determine whether these two procedures would be predicted to produce different
outcomes.
In summary, Experiment 2 confirms the
finding that TL target words are named more
slowly than matched control targets. It also
demonstrates that co-activating a TL competitor by priming causes interference on naming
accuracy that appears to be mediated, at least
in part, by lexical rather than perceptual processes. Although this demonstration does not
prove that the poorer performance observed
for single TL words is due to co-activation of
the representation of the TL mate, they provide converging evidence that such co-activation would yield interference. The priming
data also provide weak evidence that the effects of TL primes contrast with neighbor
primes which tend to facilitate speed of identification. This differential effect of the two
similarity manipulations will be explored in
more detail in the General Discussion.
EXPERIMENT 3
The results of both experiments are consistent with the assumption that both members
of a TL pair are activated by the target word
and that correct identification requires selection of the correct alternative. The term ‘‘selection’’ is intended to convey processes that
are inherent to the lexical retrieval process,
such as the mutual inhibition between activated word nodes in the interactive activation
model or the increase in settling time that occurs when the attractor basins for similar
words overlap in the PDP framework (Plaut
et al., 1996). But it is possible to conceive of
a more strategic selection process that is taskspecific and independent of the mechanisms
involved in discriminating between multiply
activated candidates in normal lexical retrieval. For example, subjects’ awareness of
the presence of confusable TL pairs may induce them to engage in a ‘‘post-access check’’
against the sensory stimulus whenever the re-
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trieved lexical representation is a member of
a TL pair (O’Connor & Forster, 1981). The TL
confusability effects observed in the previous
experiments might reflect a verification process implemented after lexical retrieval rather
than being due to processes inherent to lexical
retrieval of all words.
It is difficult to clearly distinguish between
lexical and post-lexical processes, particularly
once serial processing assumptions are abandoned (McClelland, 1979). One criterion that
has, however, frequently been adopted is the
sensitivity of processes to competition from
simultaneous task demands. Post-lexical processes are generally assumed to be more strategic and attentionally mediated than the autonomous processes that underlie lexical retrieval.
Experiment 3 therefore attempts to validate
the assumption that TL effects arise from autonomous lexical retrieval processes rather
than attentionally mediated selection processes by investigating the sensitivity of TL
confusability effects to dual task demands.
Subjects named TL and control target stimuli
under either standard naming conditions or
while concurrently performing a simple visual
discrimination task to flanker stimuli presented on either side of the target word (e.g.,
* salt *). If TL effects reflect post-lexical verification processes that subjects abandon when
attentional resources must be siphoned off to
the secondary task, the delay in naming latency to TL targets will be eliminated under
dual task conditions, but there will be an increase in errors involving production of the
incorrect TL mate. To allow a direct comparison with the naming data of Experiment 1,
half of the stimuli in both the single and dual
task conditions, were presented without
flanker stimuli. Thus, as well as providing a
test of whether TL confusability effects are
modulated by competing task demands, the
experiment provides a third replication of the
difference between naming performance for
TL and control target words.
Method
Subjects. The 28 subjects were solicited from
the same population as the earlier experiments.
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Stimuli. The design of the experiment required eight stimulus conditions corresponding to a 2 1 2 1 2 manipulation of frequency
(high/low), word type (TL/control) and flanking stimuli (present/absent). The symbols *
and # were used as flanking stimuli and appeared one space away from both the first and
the last letter of the word (e.g., * salt *, # salt
#). The 32 pairs of TL words and their
matched controls from Experiment 1 were organized into four lists. Each target word occurred only once in a list but across lists each
target occurred twice without flankers and
once with each flanker symbol. The two members of a TL pair occurred in different halves
of the stimulus list. The higher frequency
member occurred first in two lists and second
in the other two. Each list was presented to
an equal number of subjects.
Procedure. The presentation procedure was
identical to the naming task of Experiment 1
except for the presence of the flanking stimuli.
In the standard naming task subjects were instructed to ignore the flanking stimuli when
they were present and to name the words as
quickly and accurately as possible. To avoid
any competing response tendencies to the
flankers, standard naming was always run as
the first task. In the dual task condition, subjects were told that they should name the stimuli as they had in the standard naming task
but that, when the flankers were present, they
should also classify the flanker by pressing
one of two microswitches mounted on a handheld box and labelled with the * and # symbols. Before each task, subjects completed 20
practice trials. Two buffer trials preceded the
128 stimuli in each task condition. Each subject saw the same stimulus list in standard
and dual task conditions, but in a different
individually randomized order.
Results
The results were collated and analyzed following the same procedures as the earlier experiments. The average RT for correct naming
responses and the error rates for each condition are presented in Table 4. In the subject
analyses, all factors were treated as repeated
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TABLE 4
MEAN NAMING REACTION TIMES (AND PERCENTAGE ERROR RATES) TO TARGET WORDS PRESENTED
WITH AND WITHOUT FLANKING SYMBOLS IN SINGLE AND DUAL TASK CONDITIONS
High frequency
Single task
No flankers
Flankers present
Dual task
No flankers
Flankers present
TL
Control
TL
Control
639 (5.2%)
661 (5.4%)
603 (1.3%)
622 (0.7%)
664 (11.3%)
693 (10.7%)
663 (3.8%)
673 (3.0%)
654 (3.6%)
784 (5.6%)
616 (0.2%)
745 (1.6%)
679 (9.6%)
795 (14.5%)
645 (2.7%)
773 (5.2%)
measures but word frequency was a betweengroup factor in the analysis of items.
Analyses of the reaction time data replicated the effects of frequency and TL status
observed in Experiments 1 and 2. High frequency words were named more quickly than
low frequency words (Fs(1,27) Å 82.22, p õ
.01; Fi(1,62) Å 8.26, p õ .01) and TL words
were named more slowly than control words
(Fs(1,27) Å 64.39, p õ .01; Fi(1,62) Å 22.86,
p õ .01). The TL effect was larger for high
than low frequency words, but this effect was
only significant in the subject analysis
(Fs(1,27) Å 19.31, p õ .01; Fi (1,62) õ 1).
There were marked effects of the dual task
requirements. Responses were significantly
slower under dual than single task response
requirements (Fs(1,27) Å 24.17, p õ .01; Fi
(1,62) Å 250.89, p õ .01) and significantly
slower when flanking stimuli were present
than when they were not (Fs(1,27) Å 121.00,
p õ .01; Fi (1,62) Å 327.15, p õ .01). There
was also a significant interaction between
these two task manipulations because, as
would be expected, the effects of flanking
stimuli were much greater under dual than single task conditions (Fs(1,27) Å 73.89, p õ
.01; Fi (1,62) Å 105.19, p õ .01).
Despite the dramatic increase in RT under
dual task conditions, there was no significant
increase in the effects of frequency or TL status. The effect of word frequency was, in fact,
significantly smaller in dual than single task
conditions (Fs(1,27) Å 8.42, p õ .01; Fi(1,62)
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Å 8.37, p õ .01). The overall TL interference
was slightly larger under dual than single task
conditions but the difference was not significant (Fs(1,27) Å 3.98, p õ .10; Fi (1,62) Å
3.09). None of the other higher-order interactions among frequency, word type, and task
requirements were significant (all Fs õ 2.53).
The average effects of word type on accuracy paralleled the reaction time data. More
naming errors were made to low than high
frequency words (Fs(1,27) Å 40.81, p õ .01;
Fi (1,62) Å 8.12, p õ .01) and to TL than
control words (Fs(1,27) Å 82.03, p õ .01;
Fi (1,62) Å 14.45, p õ .01). There was an
interaction between word frequency and TL
status that was significant only in the subject
analysis (Fs(1,27) Å 10.27, p õ .01; Fi(1,62)
Å 1.47, p ú .05). In contrast to the analysis
of response time, the interaction arose because
the accuracy disadvantage for TL words was
greater for low than high frequency words.
These data replicate those of Experiment 1
and suggest that co-activation of the TL mate
delays naming of high frequency TL targets,
but leads to intrusions for the lower frequency
members of TL pairs.
There was no difference in overall accuracy
under standard and dual task conditions (both
Fs õ 1). However, a small, but significant,
reduction in average accuracy when flanker
symbols were present (Fs(1,27) Å 4.20, p õ
.05; Fi (1,62) Å 8.21, p õ .01) was due entirely to differences between accuracy with
and without flankers under dual task condi-
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tions (Fs(1,27) Å 11.34, p õ .01; Fi (1,62) Å
7.05, p õ .01). This interaction demonstrates
that naming accuracy was reduced when subjects had to concurrently classify flanker symbols. No other interactions between task and
stimulus factors significantly affected naming
accuracy (all Fs õ 2.84).
Discussion
Again the overall results of the experiment
replicate the poorer naming performance for
TL by comparison with control words and the
tendency for the TL effect on naming speed
to be more marked for high frequency than
low frequency words (38 ms vs 19 ms). Performance for low frequency words revealed
the same form of speed-accuracy trade-off as
Experiment 1 whereby co-activation of the
higher frequency mate appeared to lead to
naming intrusions rather than the delayed performance evident for higher frequency TL targets.
The dual response requirements had a dramatic effect on response speed which was entirely due to the demands of simultaneously
performing both the naming and discrimination tasks. Average naming time was almost
identical in standard and dual task conditions
(642 ms vs 648 ms) when no flanking stimuli
were present, but responses were more than
100 ms slower in dual than standard conditions when the presence of flankers indicated
that subjects should make a concurrent manual
classification response to the flanking stimuli.
The major purpose of this experiment was
to determine whether the TL confusability effect is sensitive to dual response requirements
as would be expected if it reflected some form
of post-access checking procedure. Despite
the clear disruption of performance by dual
task requirements, there was virtually no
change in the magnitude of the TL effect in
the two task conditions. If anything, the average TL effect on reaction time was slightly
larger under dual than single task conditions
(33 ms vs 24 ms) but the test of the interaction
was not significant in either analysis. Naming
accuracy was lower when subjects had to
make dual responses, but this disruptive effect
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of dual task requirements was equivalent for
TL and control words. The results therefore
provide no support for the possiblity that the
TL interference effect is due to attentionally
mediated selection processes.
GENERAL DISCUSSION
All three experiments demonstrate that
word identification is impaired for words that
differ from another word only in the order
of their constituent letters. The results extend
Chambers’(1979) original findings by demonstrating that TL confusability affects word
naming as well as lexical decision performance. Naming performance provides a more
sensitive test of co-activation of TL neighbors
because it reveals errors based on retrieval of
the incorrect member of the TL pair that cannot be detected in the lexical decision task.
TL confusability effects provide support for
models assuming parallel lexical retrieval
mechanisms because they imply that both
members of a TL pair are activated by presentation of a single TL target. The impaired performance for TL compared to control words
is compatible with the lateral inhibitory mechanism of the interactive activation model: The
co-activated TL mate appears to interfere with
identification of the correct target word. The
priming data of Experiment 2 provide converging evidence that co-activating the mate
of a TL target word interferes with naming
performance because responses to the same
TL target words were less accurate when their
mate had been co-activated by priming than
when the prime was a nonword one-letter different from the target or another word sharing
only an initial letter with the target. The reduced accuracy following TL primes was specific to TL targets so it appears to be due, at
least in part, to co-activation of the TL mate
rather than just the perceptual overlap between
the prime and target. Neither do the TL confusability effects appear to be attributable to attentionally mediated processes invoked to select between highly similar candidates because the magnitude of the TL effect did not
increase under dual task conditions.
Thus, the results replicate the performance
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difference between TL and control words in
three different samples of subjects tested in
four different task environments: lexical classification, standard speeded naming, naming
following masked primes, and naming with a
concurrent manual response task. Although
the RT results obtained for low frequency
words are somewhat variable across experiments, the latency disadvantage for high frequency TL words compared to controls is essentially constant: 34 and 32 ms in the lexical
decision and naming tasks of Experiment 1
and 31 and 38 ms in Experiments 2 and 3,
respectively.
The systematicity of this difference across
different subjects and task contexts increases
confidence that it is genuine, but it does not,
of course, validate the assumption that it is
the intended manipulation of TL confusability
that is responsible for the observed differences. Because of the limited representation
of TL words in the language, all three experiments employed the same set of TL and control target stimuli. It is therefore possible that
the different outcomes reflect systematic differences between the two word sets on dimensions other than their TL status. Control words
were selected to match the TL targets as
closely as possible on first phoneme and on
measures of word frequency, neighborhood
size, bigram frequency and regularity of
orthographic–phonological correspondence.
While it is possible that the stimulus sets differ
on uncontrolled dimensions, any alternative
hypothesis of the basis of the difference would
need to account for the systematicity of the
effect over different task and response requirements and for the fact that it is at least as
large for high as for low frequency words. For
example, two variables known to affect word
identification that have not been controlled are
age-of-acquisition (Carroll & White, 1973)
and concreteness (James, 1975), but both of
these variables usually have a more marked
effect on performance for low than high frequency words. So, although it is not possible
to conclusively demonstrate that the poorer
performance for TL words is due to their TL
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status, it seems to provide the most plausible
explanation of the observed differences.
Lexical Similarity Effects for High and Low
Frequency Words
In contrast to other manipulations of lexical
similarity like neighborhood size and phonological consistency (Andrews, 1982, 1989,
1992), the effects of TL confusability were
not confined to low frequency words. However, high and low frequency words did differ
in how TL confusability affected performance.
Correct naming responses to high frequency
TL words were delayed relative to matched
controls. However, the most consistent effect
of TL status on low frequency TL words was
an increase in naming errors. Most errors were
complete or partial pronunciations of the TL
mate.
There are some ambiguities concerning the
interpretation of the different outcomes for
high and low frequency words. The present
manipulation of frequency necessarily confounded the frequency of the target word with
the relative frequency of the target and its
mate. Moreover, as detailed in the discussion
of Experiment 1, neither manipulation was
‘‘clean.’’ Some of the words in each condition
did not satisfy conventional absolute criteria
for the high or low frequency category, and
some of the relative classifications differ
across frequency norms.
Despite this difficulty in estimating the frequency of TL pair members, the overall TL
interference effect on both speed and accuracy
was robust over both subjects and items in
all three experiments, and the interaction with
frequency was significant in both analyses of
RT for all but Experiment 2. However, the
differential effect of TL confusability on naming accuracy for high and low frequency
words was never significant in the item analysis demonstrating that it was not uniform
across all TL pairs. Thus all experiments demonstrate a clear delay in naming speed to TL
words particularly when they are high frequency, and an interference effect on accuracy
that tends to be larger for low frequency
words.
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By contrast, the interference from TL
primes observed in Experiment 2 manifested
in accuracy rather than RT for both high and
low frequency words. This pattern of effects
on speed and accuracy is consistent with the
view that naming errors occur when the TL
mate is activated more strongly than the target
word. Under the single word presentation conditions of Experiements 1 and 3, this more
frequently occurs when the mate is of higher
frequency than the target word and TL confusability effects on accuracy are therefore more
pronounced for low frequency TL targets. But
when the mate receives sensory support from
its presentation as a masked prime, even a
lower frequency TL mate may exceed the activation of a high frequency TL target. Thus,
the interference caused by TL primes is reflected in naming errors for both high and low
frequency TL targets.
On the face of it, the demonstration of robust TL confusability effects for high frequency words conflicts with current implementations of the interactive activation and
PDP models which both predict that lexical
similarity will have less effect on performance
for high than low frequency words. However,
the present stimuli were not as high in absolute
frequency as the samples used in many previous investigations of lexical similarity. Sears
et al. (1995) compared the magnitude of the
neighborhood size effect for moderately and
very high frequency words and found that
only the latter produced a signicantly smaller
effect for high than low frequency words. The
present stimuli may not be sufficiently high
in frequency to show the insensitivity to similarity predicted by the models.
However, both models need to be refined
to accommodate the differential speed and accuracy effects observed in the present data.
Most simulations of the models have relied
on a single dependent measure. Grainger and
Jacobs (in press) have added a decision mechanism to a semistochastic version of the interactivation model to allow simulation of both
speed and accuracy and Plaut et al. (1996)
report simulations of a PDP attractor network
that allows independent estimates of settling
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time and performance accuracy. The present
data provide strong constraints that can be
used to refine such attempts to more precisely
specify the processes contributing to performance. Even stronger constraints are provided
by the relationship between TL interference
effects and evidence of facilitation from lexical neighbors.
General Lexical Similarity vs Specific TL
Confusability
The present evidence of interference from
TL neighbors appears to conflict with the facilitatory effects of general orthographic overlap between words as assessed by measures
such as neighborhood size (Andrews, 1989,
1992; Sears et al., 1995). The neighborhood
size effects imply that general similarity to
many other words, defined as sharing all but
one letter, helps word identification particularly for low frequency words. By contrast,
the TL effects suggest that more specific similarity to one particular word, defined as containing exactly the same letters in a different
order, hinders identification of both high and
low frequency words. Like the present TL effects, neighborhood size effects have been observed in both lexical decision and naming
tasks, suggesting that they reflect general lexical retrieval mechanisms rather than task-specific processes.
There are two aspects of the present results
which demonstrate that facilitatory effects of
neighborhood size coexist with TL interference. The first source of evidence was provided by the results of Experiment 2, which
suggested facilitatory priming of naming
speed from a nonword neighbor of the target
under the same conditions in which TL primes
caused inhibition. However, these RT effects
were not statistically reliable and priming effects do not necessarily reflect the same mechanisms as neighborhood effects in single word
tasks.
Direct evidence of neighborhood effects
paralleling those observed in previous single
word tasks is provided by regression analyses
of the data for the naming task of Experiment
1, which are summarized in Table 5. Although
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TABLE 5
SUMMARY OF THE RESULTS OF REGRESSION ANALYSES PREDICTING REACTION TIMES AND ERROR RATES FOR THE
NAMING DATA OF EXPERIMENT 1, INCLUDING THE STANDARDIZED REGRESSION COEFFICIENT, THE PERCENTAGE OF
VARIANCE UNIQUELY PREDICTED, AND THE ASSOCIATED PROBABILITY VALUE FOR EACH PREDICTOR VARIABLE IN THE
TWO REGRESSION EQUATIONS
Reaction time
Error rate
Predictor variables
Regression
coefficient
Unique
contribution
Probability
Word frequency
Bigram frequency
Number of neighbors
TL status
Multiple R
00.351
0.097
00.213
0.133
0.434
12.1%
0.9%
4.4%
1.8%
0.000
0.250
0.010
0.110
the sets of TL and control stimuli were
matched on average neighborhood size, the
items within each set had neighborhood sizes
ranging from 0 to 19. Simple regression analyses were conducted on the average reaction
time and error rates for the set of TL and
control items using word frequency (a
weighted average of the Kucera-Francis and
Carroll et al. normative values), neighborhood
size, bigram frequency, and the dichotomous
variable of TL status as predictors. These variables were slightly better predictors of variability in RT (R2 Å .19) than accuracy (R2
Å .13). Although the analyses account for a
relatively small proportion of of overall variability in either measure, the results for this
small set of predictors compare favourably
with the regression analyses of naming data
for two very large and diverse word sample
using sets of more than 30 different predictor
variables (Treiman, Mullenix, Bijeljac-Babic, &
Richmond-Welty, 1995).
TL status accounted for significant unique
variance in accuracy, but not RT. In both analyses, TL status was associated with poorer
performance. Word frequency and neighborhood size were significant unique predictors
of both dependent variables and, critically, the
direction of the relationship with neighborhood size was facilitatory: Larger neighborhood sizes were associated with faster and
more accurate word naming. The fact that TL
status does not make a significant contribution
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Regression
coefficient
Unique
contribution
Probability
00.200
0.104
00.173
0.231
0.359
4.0%
1.0%
2.9%
5.3%
0.020
0.234
0.047
0.007
to the regression equation predicting RT, even
though the ANOVA item analysis yielded a
significant effect presumably reflects the consequences of partialling the opposing effect of
neighborhood size from the effect of TL status. This significant unique contribution to
prediction of neighborhood size replicates previous demonstrations of facilitatory effects of
neighborhood size on word naming (Andrews,
1989, 1992; Grainger et al., 1989; Sears et al.,
1995) and shows that there is no empirical
conflict between these earlier findings and the
present demonstration of TL interference.
Rather, both phenomena can be demonstrated
within the same data set.
This evidence of the co-existence of facilitatory effects of neighborhood size with inhibitory effects of TL status strongly constrains
the form of solution available to current models. Within the interactive activation model,
these two effects of similarity can, in principle, be identified with different mechanisms.
The TL interference effects might reflect lateral inhibition while the facilitatory effects of
neighborhood size can only be explained in
terms of excitatory feedback between the letter and word level (Andrews, 1989, 1992). It
remains to be seen whether it is possible to
find a single set of parameters that allow successful simulation of both phenomena.
Seidenberg and McClelland’s (1989) implementation of the PDP model simulates Andrews’ (1989) facilitatory effects of neighbor-
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hood size but examination of the phonological
error scores computed by the simulation for
the present TL and control targets reveals a
very small advantage for TL over control low
frequency words (TL Å 5.35; Control Å 5.60)
and no difference between TL and control
high frequency words (TL Å 4.68; Control Å
4.62). Thus, if anything, this simulation shows
the same facilitatory effect of lexical similarity for TL confusability as for neighborhood
size. This probably reflects limitations of the
coding scheme that are discussed below, but
may also be due to Seidenberg and McClelland’s use of a feed-forward architecture. In
fact, Seidenberg and McClelland (1989) finessed the question of selection among similar
words altogether in their computational implementation of the PDP model because, to reduce computational demands, activation was
computed deterministically after a single processing sweep rather than after the network
achieved convergence. Performance was assessed using an error score reflecting the difference between the activity at the phonological layer and the desired target with no evaluation of whether this was the pattern most
closely corresponding to the state of the network. Essentially, this means that the implemented system only contains the mechanisms
corresponding to excitatory mechanisms in interactive activation models and lacks the
mechanisms that allow a fully interactive PDP
system to converge on a single target response.
To overcome the limitations of the earlier
implementation, Plaut and McClelland introduced complete interactivity within the hidden
and output layer of the model. This interactivity results in an ‘‘attractor network’’ in which
the pattern of activity elicited by an input
‘‘gradually settles to the nearest attractor pattern’’ (Plaut & McClelland, 1993). In the present terms, it allows selection from among
overlapping lexical representations. This implementation of the model therefore provides
a better approximation to lexical selection processes, but there are, as yet, no data available
concerning its sensitivity to lexical similarity.
Systematic investigations of the model are
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necessary to determine whether the inclusion
of interactivity allows simulation of TL interference effects without changing the facilitatory effects of neighborhood size observed in
the feed-forward version of the model (Seidenberg & McClelland, 1989).
Thus, the present evidence of inhibitory effects of TL confusability in conjunction with
facilitatory effects of neighborhood size provide empirical constraints that can be used to
evaluate and refine specifications of both the
interactive activation and PDP models. Such
constraints are essential because, despite explicit computational implementation, the models are currently indeterminate. Variations in
the relationship between excitatory and inhibitory parameters in the interactive activation
model, or in the patterns of interactivity in
PDP models, can yield dramatic changes in
how lexical similarity affects performance. At
one level, the flexibility of the models can be
seen as desirable. Lexical similarity effects
do vary as a function of task and stimulus
characteristics (Snodgrass & Minzer, 1993).
Variations in model parameters might provide
a means of explaining such strategic variablity. However, the fact that ‘‘tweaking’’ the
model’s parameters can yield such different
outcomes raises doubts about the faith that
can be placed on the apparently ‘‘hard data’’
provided by any individual simulation. Systematic demonstrations that the same set of
parameters can be used to simulate a set of
relevant phenomena are necessary to determine the psychological validity of any particular implementation of the model. In the case
of lexical similarity effects, it is necessary to
determine not only whether the present inhibitory effects of TL confusability can be successfully simulated, but also whether it is possible to find a set of parameters that can
achieve this effect while at the same time being able to demonstrate facilitatory effects of
neighborhood size. Ultimately the models also
need to address more fine-grained features of
the data such as the differential TL interference effects on RT and accuracy for high and
low frequency words.
There is, however, a more fundamental fea-
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ture of the results that must be addressed by
the two models. This is the fact that the two
members of a TL pair are sufficiently similar
to cause mutual interference. Current implementations of both the interactive activation
and the PDP model cannot accommodate this,
most basic, feature of the data.
The Coding of Letters in Word
Representations
The most direct implication of the present
results is for assumptions about the coding of
letters in words. Effects of TL confusability
provide evidence about the similarity relationships between the input codes for words. Sensitivity to TL confusability implies, at least,
that the coding of letter position is approximate rather than absolute.
Following McClelland and Rumelhart’s
(1981) original assumptions, implementations
of the interactive activation model assume independent position-specific letter detectors.
By this view, salt and slat are no more similar
to each other than, say, salt and spot because
a or l in the second position are completely
unrelated to a or l in the third position. However, the absolute position-specificity built
into the implementations is not a necessary
requirement of the interactive activation
model. Position might be coded somewhat approximately so that a letter in position n also
yields some activation of the same letter in
positions n 0 1 and n / 1, for example. Alternatively, features might be translated into ‘‘location invariant representations’’ of letters or
letter clusters as in the BLIRNET component
of Mozer’s (1987) MORSEL model of object
identification.
The input coding assumptions of implementations of the PDP model are also difficult to reconcile with the evidence of TL interference.
Seidenberg and McClelland (1989) used a very
coarsely coded version of Wickelgren’s (1969)
‘‘triples’’ coding scheme which conveys information about relative but not absolute letter position (e.g., salt would be coded as -sa, sal, alt,
lt-). Since such schemes respect sequential letter
position, they are relatively insensitive to overlap between the codes for TL word pairs: four-
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letter words like salt and slat, for example, have
no common triplets and are therefore no more
similar at the input level than two words containing completely different letters. But again,
there is no necessary association between the
PDP framework and this method of coding letter
position. Plaut et al.’s (1996) revision of the
model, for example, adopts a scheme similar
to the completely position-specific localist letter
and phoneme codes used in the interactive activation model, but specifies syllabic positions
(onset, vowel, coda) rather than absolute letter
positions and reduces the redundancy of input
and output codes by using grapheme rather than
letter codes and exploiting phonotactic constraints within the syllabic segments. This coding scheme might yield confusion between
words in which transpositions occurred within
a subsyllabic unit (e.g., boast/boats), but not
when transpositions cross an onset-vowel or
vowel-coda boundary as occurs for the majority
of TL word pairs.
Thus, the evidence of similarity effects due
to letter transposition is inconsistent with the
particular assumptions embodied in the input
representations of current implementations of
both the interactive activation and PDP models.
Confusability between words sharing letters in
adjacent positions demonstrates at least, that
coding of letter position is approximate and implies that word identification might rely on location-invariant representations of letters (Mozer,
1987). Modifications could be made to the coding assumptions of either model that might
allow for sensitivity to TL similarity, so the
demonstration of TL interference does not undermine the conceptual validity of either general
framework. But the results do provide constraints that have implications for the organisation of knowledge in both interactive activation
and PDP systems. Further constraints are provided by evidence that, concurrently with inhibitory effects of TL status, the present data also
demonstrate facilitatory effects of neighborhood
size on naming speed and accuracy.
Modelers rarely provide a strong rationale for
their choice of input and output coding scheme.
For example, Seidenberg and McClelland
(1989) claimed that their ‘‘Wickelfeature’’ cod-
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ing schemes (Wickelgren, 1969) were motivated
‘‘by a desire to capture a few general properties
[of] . . . such representations’’ (p. 528) but that
they are ‘‘less theoretically relevant’’ than assumptions about the architecture and learning
rule employed by the model and are not claimed
to embody ‘‘an adequate characterization of English phonology’’ (p. 563). However, the choice
of coding scheme is a crucial determinant of the
model’s performance. Limitations of the Wickelfeature scheme are probably a major contributor to the model’s failure to successfully simulate nonword pronunciation data (Besner et al.,
1990; Seidenberg & McClelland, 1990) and the
far superior generalization capability of Plaut et
al.’s revised model is due, at least in part, to
‘‘the use of orthographic and phonological representations that make explicit the structured relationship between written and spoken words’’
(Plaut & McClelland, 1993, p. 1).
Recognizing the importance of the representational assumptions embodied in the choice of
input and output coding scheme is also important to distinguishing between interactive activation and PDP models. The fundamental difference between the two frameworks is that the
representations in PDP models do not correspond to symbolic entities such as letters and
words. However, the patterns defining particular
items at the hidden unit layer of a PDP model
are learned through exposure to the training vocabulary, and therefore depend on both the
structure of the training vocabulary and the
choice of input and output coding scheme. Distributed representational networks allow for
much more complex patterns of similarity relationships than are possible within the hierarchical structure of the interactive activation model.
But this is not a necessary consequence of the
architecture or algorithms defining the model. If
the regularities extracted by the PDP model are
well described in terms of the frequency of the
sublexical constituents that are ‘‘hard-wired’’
into the interactive activation model, then both
models may show sensitivity to the same dimensions of lexical similarity. For example, a PDP
system relying on localist inputs and outputs and
distributed hidden layer representations may be
functionally equivalent to a hierarchical sym-
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bolic model such as interactive activation (Andrews, 1994) so that the differences between
distributed and localist representations of words
are, in that sense, ‘‘merely implementational’’
(Fodor & Pylyshyn, 1989). Nevertheless, the
emergent symbols acquired at the hidden layer
of PDP models may suggest a different functional architecture than that based on symbolic
distinctions between lexical and rule-based
knowledge (Andrews, Davies, & Davis, 1995).
Systematic comparisons of the consequences of
different input and output coding schemes and
architectures are necessary to evaluate these possibilities.
APPENDIX
THE TARGET TL
CONTROL WORDS USED
EXPERIMENTS
AND
IN ALL
High frequency
Low frequency
TL
Control
TL
Control
minute
colt
wrap
grab
sung
bolt
split
cold
silver
boats
salt
forth
crops
calm
dairy
coast
fits
busy
bugle
fired
signs
scared
barn
farmer
burnt
slave
swan
carve
trail
beats
perfect
sang
modern
clap
warn
golf
slip
bait
skirt
corn
sister
broke
sand
fresh
chart
clue
dirty
chair
fold
born
breed
false
sense
seldom
bird
forget
barge
slope
slot
crest
train
burst
parties
sail
minuet
clot
warp
garb
snug
blot
spilt
clod
sliver
boast
slat
froth
corps
clam
diary
coats
fist
buys
bulge
fried
sings
sacred
bran
framer
brunt
salve
sawn
crave
trial
beast
prefect
snag
mortal
clad
wart
gnat
slab
brag
sprig
clop
scorer
barbs
slop
friar
curly
clip
delay
claim
fail
bury
broth
fiery
stern
striped
bred
florid
briar
sulks
sips
clove
treat
belts
preface
sulk
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