I've pointed out previously that embodied effects are small at best. Here's an example--a statistically significant crossover interaction--from a rather high-profile TMS study that investigated the role of motor cortex in the recognition of lip- versus hand-related movements during stimulation of lip versus hand motor areas:
Effect size = ~1-2% This is typical of these sorts of studies and beg for a theory of the remaining 98-99% of the variance.
So, let me throw out a challenge to the embodied cognition crowd in the context of well worked out non-embodied models of speech production. Let's take a common set of data, build our embodied and non-embodied computational models and see how much of the data is accounted for by the standard versus the embodied model (or more likely, the embodied component of a more standard model).
Here is a database that contains naming data from a large sample of aphasic individuals. The aim is to build a model that accounts for the distribution of naming errors.
Here is a standard, non-embodied model that we have called SLAM for Semantic-Lexical-Auditory-Motor. (No, the "auditory-motor" part isn't embodied in the sense implied by embodied theorists, i.e., the level of representation in this part of the network is phonological and abstract.) Here's a picture of the structure of the model:
Incidentally, Matt Goldrick argued in a forthcoming reply to the SLAM model paper that this fit represents a complete model failure due to the fact that the patient had zero semantic errors whereas the model predicted some. This is an interesting claim that we had to take seriously and evaluate quantitatively, which we did. But I digress.
The point is that if you believe that embodied cognition is the new paradigm, you need to start comparing embodied models to non-embodied models to test your claim. Here we have an ideal testing ground: established models that use abstract linguistic representations to account for a large dataset.
My challenge: build an embodied model that beats SLAM. You've got about 2% room for improvement.