[Note: for backstory on this discussion, see here]
To indicate which comment each response corresponds
to, we have copied the first line of the comment.
In response to “I agree but we were vague on
purpose 17 years ago because we simply didn’t know what the relation was
between brain areas and acoustic/linguistic levels of representation” and
“These are good points and I both appreciate DM’s frustration with our lack of
clarity regarding the level of processing we are talking about and laud their
interest in being more precise”:
We
appreciate the clarification of your stance regarding speech-specificity (or
linguistic-specificity) and agree that specificity of processing within a
neuroanatomical region is not necessarily a prerequisite for identifying
neuroanatomical levels of processing. This being said, we still feel it is
important when discussing the neural basis of speech processing to be as
specific as possible in the claims regarding the underlying cognitive model. We
would, thus, stand by our claim that in a model of speech processing, there is
necessarily an abstract, speech-specific sublexical processing level (see
figure below). However, we agree that there is no convincing evidence that
“linguistic levels of representation will map neatly onto individual brain
regions” and that there may not be a region that is “linguistic or level
specific,” at least to the extent that current technology allows for the
investigation of such questions (i.e., at the level of large populations of
neurons).
In response to
“’most often’ is a fair statement but one that ignores the fact that not
all of the studies showing this dissociation were unmatched”:
We discussed
directly in our paper the fact that in the Miceli et al. (1980) study, the
phonological differences in their picture-word matching task were greater than
those in the sublexical task, where the
items differed in one distinctive feature of one phoneme. We stated on p. 193, “Although their (Miceli et al.’s) picture-word matching task
included phonologically related distractors, the phonological lures differed
from the target by one or more phonemes (picture-word matching task described
in detail in Gainotti et al. (1975)) and when the difference was only one
phoneme, the phoneme might differ by more than one distinctive feature from the
target.”
We did not
discuss Bishop et al. (1990) in our paper because it is less often cited in papers on aphasia,
as their subjects were children with SLI.
Nonetheless, their findings certainly bear on the general issue of the
relation between sublexical and lexical perception. In the Bishop et al. paper,
Study 1 compared phoneme discrimination (with one distinctive feature
difference) to picture-word matching on a British version of the PPVT, which does not systematically include
phonologically related distractors. Study 2 did provide a close comparison
between performance on phoneme discrimination and lexical processing using a
word judgment task in which subjects judged whether a spoken stimulus matched
the name of a picture. On the
non-matching trials, the stimulus was, according to the examples, a nonword
differing by one distinctive feature of one phoneme (e.g., “voy” for a picture
of a boy). This comparison is similar to
that in our Experiment 2a, where we contrasted
syllable discrimination and picture word matching where the stimulus on
the non-matching trials was a word differing by one distinctive feature of one
phoneme (e.g., “beach” for a picture of a peach). In Experiment 2a, we found that controls as a
group performed better on picture-word matching than on syllable discrimination
and for the patients, though the group difference was not significant, two patients
showed significantly better performance on picture-word matching. As we discuss
in the paper, we hypothesized that picture-word matching might have been easier
for some individuals because it allows the subjects to internally generate a
phonological representation of the name of the picture against which to compare
the spoken input. To address this, we
carried out Experiment 2b, where the sublexical task involved matching a spoken
syllable to a written syllable, where subjects could generate a target from the
written syllable. With this change, now
controls were significantly better at the sublexical than the lexical task and no
patient scored significantly better on the lexical than the sublexical task. Thus, when task demands were better equated there
was no evidence of a dissociation between sublexical and lexical performance.
In response to “Here’s the key point that you are
missing”:
We showed
that patient performance on a difficult visual working memory task (where
performance was equated to that for syllable discrimination) was not correlated
with performance on either the syllable or word discrimination tasks (page 201),
suggesting that neither cognitive control nor working memory is the driver
behind the correlation.
In response to “Still highly correlated which would
argue against my point above, but a closer look at the data reveals a different
picture”:
The figures
that you have created in response to our comment do not contain the data we
reference (from Experiment 2a) and instead plot data from Experiment 1a
(titled: sublexical and lexical perception with unmatched stimuli, p.195, with
data shown in Table 2). Experiment 1a
was created for the express purpose of showing that dissociations between
sublexical and lexical performance could be shown when the stimuli were not
closely matched (i.e., when some of the lexical contrasts differed by more than
1 feature). As we noted in the text, in
this experiment, there are notable dissociations with some patients showing better
lexical performance under these conditions, which is particularly evident in
your figure with the outlier removed. We refer you instead to Figure 6 (p. 202),
which shows the data for picture-word matching and syllable discrimination for
the matched stimuli in Experiment 2a.
These are the data we were referring to in saying the correlation was
.88 even though 2 patients did show significantly better performance on
picture-word matching than syllable discrimination. For the data in Fig 6, there are no evident
outliers (which is confirmed by statistical tests such as Mahalanobis distance
or Cook’s D).
In response to “We have to ask why comprehension
tasks are easier”:
Simply
because we do not perform discrimination tasks “in the wild” does not make the
task invalid. We could further argue that individuals don’t typically spend
their time naming pictures, or selecting a picture from a set after hearing a
single spoken word, but we don’t believe this discredits the tasks. Our main
argument, on which it seems we actually don’t disagree, is that you have to
match the task in order for the linguistic manipulations to be the driving
factor behind the results.
In terms of
the generation of a phonological code, it would be difficult to do that in
advance for all the pictures in a 6 item set, as in Miceli et al. (1980).
However, we again refer to the fact that Miceli et al. (1980) did not closely
match the phonological distractors in this task to the CCVC task, making it
easier to begin with. And, in our task, generation of a phonological code would
be possible as only a single picture is presented. In terms of not having to
maintain two items in memory for comparison, we are in agreement as to why the
picture-word matching task may be easier.
In response to “’Almost all of the patients’ is in
fact 6 of 8 meaning that 25% of your now rather small sample failed to improve”:
We agree
that the small sample size makes our claim regarding AWSM and syllable
discrimination tenuous in the patient sample, but we note that the controls
also did better on the AWSM (M = 3.77) than the syllable discrimination
task (M = 2.68). Even so, our claim that the PWM task was easier than
the syllable discrimination task is the more important of the claims, because
we were arguing that syllable discrimination was harder than PWM, necessitating
a matched sublexical task. In fact, the controls did significantly better on
PWM (M = 3.42) than syllable discrimination. The mean performance on the
PWM task was much closer to the mean for the AWSM in our normal control
population, suggesting these tasks are more closely matched than the PWM and
syllable discrimination tasks.
In response to “The way you make syllable
discrimination predict auditory comprehension performance is to impose the same
kinds of artificial task demands on auditory comprehension”:
As indicated
in the paper and all that we have discussed above, syllable discrimination is
an excellent predictor of lexical performance when stimuli and task demands are
matched. We would also note that
syllable discrimination predicted auditory lexical decision in Experiment 1b at
a high level (r = .74). Also, even though the comparison of picture-word
matching and syllable discrimination in Experiment 2a revealed two patients who
did better on picture-word matching than syllable discrimination, the
correlation between the two tasks was quite high (r = .78). The results
from Experiment 2b suggest that one can create a syllable discrimination task
using spoken to written syllable matching that also correlates highly with
picture-word matching, but where no subject does better on the picture-word
matching task. A limitation of this
latter task is that many aphasic patients have difficulty reading nonsense
syllables, which will limit those who can be tested. On the other hand, there is a clear
limitation to using picture-word matching for assessing speech recognition in
that poor performance can result from a disruption of semantic knowledge rather
than from difficulty processing the speech input. Thus, all tasks have their
advantages and disadvantages in assessing particular cognitive functions. It is
only the pattern of performance across a set of converging tasks that can
provide strong evidence regarding the source of any deficit. We maintain that the use of sublexical tasks
like syllable discrimination may provide a valid indicator of an individual’s sublexical
speech processing abilities and that the use of this task may be useful in predicting
word recognition abiltiies.
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