Incidentals that build fluency: Optimal word processing and its implications for vocabulary acquisition.

This article reviews recent research demonstrating that fluent language users are optimal language processors – their unconscious language processing systems are probability tuned to predict the linguistic constructions which are most likely to be relevant in the ongoing discourse context. The first section describes the rational analysis of language processing. The second section provides a summary of psycholinguistic evidence illustrating usage-based optimalization of language representations. The final section considers the implications of these findings for second and foreign language acquisition and instruction. In sum, sophisticated probabilistic information of form-function mappings is a necessary component of native fluency which can only be acquired from communicative usage. Yet, however necessary, these incidentals are not sufficient. Optimal word processing p. 3 Optimal word processing Consider the word processing programs you have known. You probably have strong views. Remember the one that crashed at 2am losing your only copy, the one that took ten minutes to search and replace, the one that required perverse three letter combination commands, and the one you have now that replaces spellings, styles and punctuations for you, whether you like it or not, and you still can’t figure how to get it to stop? Besides the obvious requirements for speed and reliability, an optimal word processing program would really know what you wanted to do next, and would, like Jeeves, present you with your next needed command, file, or figure, ready on a silver platter. You could, like Bertie Wooster, occasionally exert your independence and decline. But Jeeves would take this with good grace, and would remain constant, knowing just what was needed in the circumstances, providing optimal recommendations while ever allowing you the choice to think better. There are some technical developments in this direction. One relatively new feature of several programs is that of ‘Open Recent’. When a user goes to open a document, the word-processor presents a list of recently opened files to select from. If the sought after file is included in the list, the user is spared the time and effort of searching through the file hierarchy. The program is no mind-reader, it does not know the goals of the user. It simply proffers alternatives using the heuristic that the more recently a file has been opened, the more likely it is to need to be opened now. This simple rule does a surprisingly good job of putting the appropriate files on the list. It does so, as we will see, because the world works that way, and so do people. Another recent feature is that of predictive text entry. When entering options from a limited set, for example a journal name into a citation management system, we appreciate it if the system suggests the most likely completion of our first few letters, and plumps for that choice when the uniqueness point is reached. Predictive text-entry systems try to anticipate our need and to complete the word stem we have begun typing, using the predictions of a statistical language model: probable pieces of text are made quick and easy to select, improbable pieces of text (low frequency words or text with spelling mistakes) are made harder to choose. The language model learns all the time: if you use a novel word once, it is easier to write next time. These systems are the ergonomic operationalizations of the cohort theory of perception which Optimal word processing p. 4 describes how we recognize speech we will discuss this shortly. The common feature of such mechanisms is that they tune the availability of selections on the basis of past history of use. They make more likely used options more readily available. Their facility is measurable: we like them, as long as they choose correctly; there is nothing more frustrating than correcting a dumb program’s dumb guesses. Generally, these programs are reasonably successful. They work, as said before, because the world works like that. Things that were likely yesterday tend too to be likely in the same context today, as Jeeves knew full well. (1) Something which has been frequently required in the past is likely to be required now, as reliable a need as a hot bath at the end of the day. (2) Something which has been recently required is likely to be required now: that rather garish cravat that Bertie has regrettably taken a liking to. (3) Something which has been often required in this particular context is likely to be required now: the hangover pick-me-up morning after rather too jolly a night at the club. These principles have been formally analysed for information need: the ‘information retrieval problem’, as it has been investigated for borrowing books from libraries (Burrell, 1980), or accessing files from computers, can be addressed because the statistical structure of the environment enables an optimal estimation of the odds that a particular book or file will be needed. The probability that a particular piece of information will be relevant, its ‘need probability’, can be estimated using Bayesian evaluation procedures whereby the odds ratio for that particular piece of information is a product of its odds ratio given its particular history, i.e. its combined frequency and recency of occurrence, and the current context (Anderson, 1989). Taking such factors into account, an optimal estimation of an item’s need probability is possible. Using such a procedure for all items in the set, the most likely ones can then be made more available in readiness, with a cost/accuracy trade off whereby the more items suggested, the greater the chance of a hit, but also the more false positives and the longer the list of items that have to be checked and discounted. Current word-processing programs typically just use recency of usage in determining their suggestions. Information retrieval analysis suggests that it would be advantageous to take frequency of prior usage into account too – recently used items which also have a history of high frequency overall usage are more likely to be needed than those of infrequent overall history of usage. Context of use could also Optimal word processing p. 5 prove a useful guide: knowing who the particular user is and factoring that in provides a better estimate than averaging over all library or information users, as clients of Amazon will attest; similarly, knowing the user is working on a particular report makes it more likely that they will need the files pertaining to figures for that report, etc. (Schooler & Anderson, 1997). These are all ways of tuning a word processing package for optimal usage. As we will see, they are ways in which we human word processors are tuned for optimal operation too. Analysis of the ways the world works demonstrates that optimal estimation of an item’s need probability is possible. This insight has allowed, over the last twenty or thirty years or so, a sea change in our understanding of human cognition. It has stemmed largely from workers at Carnegie Mellon University, and it concerns the ways that human cognition is rational in that it optimally responds to the statistical contingencies of the world. A major characteristic of the environments that are relevant to human cognition is that they are fundamentally probabilistic (Anderson, 1991) -as William James said over a century ago, “Perception is of definite and probable things” (James, 1890, p. 82). The rational analysis of memory (Anderson, 1989, 1990; Anderson & Milson, 1989; Schooler & Anderson, 1997) builds on these foundations and on the more recent investigations of information retrieval. Its central hypothesis is that human cognition optimally solves the problems that it faces. This means that if we can find, describe, and properly characterize the problem a cognitive system is trying to solve and find the optimal solution to this problem, then the rational analysis makes the strong prediction that the behaviour of the system will correspond in form to this solution. And the research that has followed in testing this claim suggests that human cognition is indeed rational in this sense, as the last half century’s work in psycholinguistics similarly demonstrates that fluent language users are optimal processors of words. The words that we are likely to hear next, their most likely senses, the linguistic constructions we are most likely to utter next, the syllables we are likely to articulate next, the graphemes we are likely to read next, and the rest of what’s coming next across all levels of language representation, are made more readily available to us by our language processing systems. In this way our unconscious language mechanisms are like Jeeves: they present up to consciousness the construction that’s most likely to be relevant next. Their offering is usually Optimal word processing p. 6 appropriate, but consciousness, like Bertie Wooster, can decline if it has reason to think better. Fluent language users are optimal word processors. How about that! Human language learning provides the optimal solution to the various problem-spaces posed by language form and the way it relates to its functions. But accounts of language learning rarely consider these aspects of language knowledge at all. Look in the index of your favourite books on acquisition. How many of them mention ‘probability’, or ‘frequency’, or ‘statistics’? Yet language acquisition is not simply a matter of learning linguistic constructions, it requires the tuning of the availability of these for optimal operation as well. In the next section I will briefly summarise some of the psycholinguistic evidence for this claim. In the final section I will introduce some of the implications for second and foreign language learning. Probabilistic language processing Counting from 1 to 10 is early content in most second and foreign language courses and an ESL or EFL student is soon secure in the knowledge of