How abstract phonemic categories are necessary for coping with speaker-related variation

but flexible prelexical representations thus provide the means by which listeners can deal with phonetic variability. An extreme episodic m? del, i~ whi~h word recognition entails a comparison of the current input, in al11ts detail, Wl~ previous lexical episodes, lacks this stage of abstraction, and thu~ l~cks th1s flexibility. Without prelexical abstraction, such a model cannot cap1tah~~ upon sublexical regularities in the current talker's speech during w? rd recogm~wn: A model of this type should therefore be unable to explain the lex1cal generahzat1~n data. Acoustic traces corresponding to the training trials could be stored m the lexicon but these traces should not affect the match between [do:? ] and previous tr~ces of doof and doos. We tested the validity of.this claim directly, in simulations with MINERVA-2 (Hintzman 1986), followmg the example of 100 Anne Cutler, Frank Eisner, James M McQueen and Dennis Norris Goldinger (1998). Goldinger argued that to assess the capability of episodic models, it is best to begin with "pure" models, of which MINERVA-2 is by far the best-known example. In MINERVA-2, each episode of previous experience lays down a trace in Long-Term Memory (LTM); applying this model to word recognition means that these traces are episodes of heard words. When a new input arrives, a probe activates all traces in LTM in proportion to their match to the probe's acoustic content. An aggregate echo of all activated traces is then returned to Working Memory. Echoes have two properties: echo intensity (the summed activation of all traces contributing to the echo) and echo content (the values of each element in the echo vector). For the equations used to compute these characteristics, see Appendix A of Goldinger ( 1998). The focus here will be on echo content. If MINERVA-2 receives the 20 training trials of the lexical generalization experiment, the effect will be to add traces that are very similar to existing traces for those 20 words, except that their final portion does not correspond exactly to anything anywhere in the traces already in the lexicon. Every other trace for every other word will be unaffected. When a test stimulus is presented, it is matched against all traces in memory. The activation of each trace is a function of the overall similarity between it and the input. The content of the retrieved vector is then determined by summing over all traces multiplied by their activation. It is immediately clear that the very limited number of new episodes (one per word) added during the training is unlikely to be enough to change the model's performance. The 20 critical episodes would have to compete with the effect of many thousands of existing episodes. Even infinitely many ambiguous training episodes, however, would not produce the experimental finding. Ambiguous training stimuli just add ambiguous traces to the lexicon; no benefit flows to other words. Whether or not a test word and the training words share the same ambiguous portion, the training phase will not affect the test word's interpretation. When a test episode is presented, its ambiguous phoneme will match the corresponding part of the traces of the critical training items, and this will increase the activation of those traces. Activation will increase for the entire trace, however, not just those elements corresponding to the ambiguous phoneme. The effect of these episodes will be to bias the content of the echo towards an ambiguous fricative interpretation, not towards [ f] or [ s] the training episodes thus resonate with the test input, but bias the resulting echo in neither direction. The previous episodes corresponding to each member of the minimal pairs will also have no biasing effect on echo content. A test item such as [do:? ] will match equally poorly to both sets of episodes, and thus to both possible interpretations of the test item (e.g., doof or doos). Furthermore, the activation Coping with speaker variation requires abstract phonemic categories 101 of these two sets of traces, and hence their effect on echo content, cannot be biased in either direction by the presence of the traces with ambiguous phonemes left by the training words. There is therefore nothing in the model that would make a test item more likely to retrieve a vector corresponding to the trainingconsistent interpretation of the ambiguous phoneme than one corresponding to the training-inconsistent interpretation. The model's situation is actually even worse. As already noted, adding 20 new episodes to the lexicon may have no detectable effect. But consider what would happen if the training phase of the experiment were repeated many times. In the test phase, as we saw, the ambiguous training tokens would have little effect, but additional unambiguous training tokens would indeed contribute to echo content. Recall that recognition in this model involves activation of stored traces in proportion to their match to the current input. If training includes many words ending with unambiguous [ s ], but no words ending with unambiguous [ f], [s]-final word traces would become stronger than [f]-final word traces, and thus would have more effect than [f]-final traces on echo content given a probe partially matching their content. The echo evoked by an ambiguous test word would thus be more strongly dominated by [s]-final word traces partially matching the test word than by [f]-final traces. As a result, ambiguous test words would more strongly activate the interpretation consistent with the unambiguous training phoneme. This of course is exactly the opposite pattern from that observed in the data. To confirm that this description of the model is correct, we performed simulations of the McQueen et al. (2006) experiment in MINERVA-2. These necessitated certain assumptions about the model, differing from those made in Goldinger's ( 1998) simulations of shadowing data, but still preserving MINERVA-2's core storage and retrieval mechanisms.