Monday, August 14, 2017

Guest blog post from Dial & Martin on Dual Stream models -- More discussion

[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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