One of the benefits of having good colleagues and a communal department printer is that you get to discover interesting stuff you would never have run across. The process (I call itresearch,” btw) is easy: go get what you have just printed out for yourself and look at the papers that your colleagues have printed out for themselves that are lying in the pick-up tray. If you are unscrupulous you steal it from the printer tray and let your colleague print another for her/himself. If you are honest you make a copy of the paper and leave the original for your collegial benefactor (sort of a copy theory of mental movement). In either case, whatever the moral calculus, there is a potential intellectual adventure waiting for you every time you go and get something you printed out. All of this is by way of introducing the current post topic. A couple of weeks ago I fortuitously ran into a paper that provoked some thought, and that contained one really pregnant phrase (viz. “encoding-induced load reduction”). Now, we can all agree that the phrase is not particularly poetic. But it points to a useful idea whose exploration was once quite common. I would like to say a few words about the paper and the general idea as a way of bringing both to your attention.
The paper is by Bonhage, Fiebach, Bahlmann and Mueller (BFBM). It makes two main points: (i) to describe coding for the structure features of language unfold over time and (ii) to identify the neural implementations of this process. The phenomenal probe into this process is the Sentence Superiority Effect (SSE). SSE is “that observation that sentences are remembered better than ungrammatical word strings” (1654). Anyone who has crammed for an exam where there is lots of memorization is directly acquainted with the SSE. It’s a well know device for making otherwise disparate information available to concoct sentences/phrases as mnemonic devices. This is the fancy version of that. At any rate, it exists, is behaviorally robust and is a straightforward bit of evidence for online assignment of grammatical structure where possible. More exactly, it is well known that “chunking” enhances memory performance and it seems, not surprisingly, that linguistic structure affords chunking. Here is BFBM (1656):
Linguistically based chunking can also be described as an enriched
encoding process because it entails, in addition to the simple sequence of items, semantic and syntactic relations between items… [W]e hypothesize that the WM benefit of sentence structure is to a large part because of enriched encoding. This enriched encoding in turn is hypothesized to result in reduced WM [working memory, NH] demands during the subsequent maintenance phase…
BFBM identifies the benefit specifically to maintaining a structure in memory, though there is a cost for the encoding. This predicts increased activity in those parts of the brain wherein coding happens and reduced activity in parts of the brain responsible for maintaining the coded information. As BFBM puts it (1656):
With respect to functional neuroanatomy, we predict that enriched encoding should go along with increased activityin the fronto-temporal language network for semantic and syntactic sentence processing.
During the subsequent maintenance period, we expected to see reduced activity for sentence material in VWM systems responsible for phonological rehearsal because of the encoding-induced load reduction.
The paper makes several other interesting points concerning (i) the role of talking to oneself sotto voce in memory enhancement (answer: not important factor in SSE), (ii) the degree to which the memory structures involved in the SSE are language specific or more domain general (answer: both language areas and more general brain areas involved) and (iii) the relative contribution of syntactic vs semantic structure to the process (somewhat inconclusive IMO). At any rate, I enjoyed going through the details and I think you might as well.
But what I really liked is the program of linking linguistic architectures with more general cognitive processes. Here, again, is BFBM (1666):
But how does the involvement of the semantic system contribute to a performance advantage in the present working memory task? One possible account is chunking
of information. From cognitive psychology, we know that chunking requires the encoding of at least two hierarchical levels: item level and chunk level (Feigenson & Halberda, 2004). The grammatical information contained in the word list makes it possible to integrate the words (i.e., items) into a larger unit (i.e., chunk) that is specified by grammatical relationships and a basic meaning representation,
as outlined above. This constitutes not only a syntactically but also a semantically enriched unit that contains agents and patients characterized, for example,
by specific semantic roles. Additional encoding of sentence-level meaning of this kind, triggered by syntactic structure, might facilitate the following stages (i.e.,
maintenance and retrieval) of the working memory process.
So, there is a (not surprising) functional interaction between grammatical coding and enhanced memory (through load reduction) through reduced maintenance costs in virtue of there existing an encoding of linguistic information above the morpheme/word level. Thus, the existence of G encodings fits well with the cognitive predilections of memory structure (in this case maintenance).
Like I said, this general idea is very nice and is one that some of my closest friends (and relatives) used to investigate extensively. So, for example, Berwick and Weinberg (B&W) tried to understand the Subjacency Condition in terms of its functional virtues wrt efficient left corner parsing (see, e.g. here). Insightful explorations of the “fit” between G structure and other aspects of cognition are rarish if for no other reason that it requires really knowing something about the “interfaces.” Thus, you need to know something about parsers and Gs to do what B&W attempted. Ditto with current work on the meaning of quantifiers and the resources of the analogue number system embedded on our perceptual systems (see here). Discovering functional fit requires really understanding properties of the interfaces in a non-trivial way. And this is hard!
That said, it is worth doing, especially if one’s interests lie in advancing minimalist aims. We expect to find these kinds of dependencies, and we expect that linguistic encodings should fit snugly with non-linguistic cognitive architecture if “well-designed.” Moreover, it should help us to understand some of the conditions that we find regulate G interactions. So, for example, Carl De Marcken exhibited the virtues of headedness for unsupervised learners (see here for discussion and links). And, it seems quite reasonable to think that Minimality is intimately connected with the fact that biological memory is subject to similarity based interference effects. It is not a stretch, IMO, to see minimality requirements as allowing for “encoding-induced load reduction” by obviating (some of) the baleful effects of similarity based interference endemic to a content addressable memory system like ours. Or, to put this another way, one virtue of Gs that include a minimality restriction is that it will lessen the cognitive memory load on performance systems that use these Gs (most likely for acts of retrieval (vs maintenance)).
Minimalism invites these kinds of non-reductive functional investigations. It invites asking: how does the code matter when used? Good question, even if non-trivial answers are hard to come by.
 Yes, I know, there are various conceptions of “good design” only some of which bear on this concept. But one interesting thing to investigate is the concept mooted here for it should allow us to get a clearer picture of the structure of linguistic performance systems if we see them as fitting well with the G system. This assumption allows us to exploit G structure as a probe into the larger competence plus production system. This is what BFBM effectively does to great effect.