Ординатура / Офтальмология / Английские материалы / Eye Movements A Window on Mind and Brain_Van Gompel_2007

that is consistent with prior disambiguating context or – in the absence of such context – whatever meaning happens to be the most common one. The specific meaning-dominance values of the supported meanings were estimated using the conditional probabilities of giving one or the other meaning of an ambiguous word as the dominant meaning of the word in the absence of biasing context. These conditional probabilities were taken directly from the norms collected by Duffy et al. (1988) for each of the different con-

ditions of that experiment: p meaningsupported = 0 57 for balanced ambiguous words in and out of context; p meaningsupported = 0 07 and 0.93 for biased ambiguous words in and out of context, respectively; and p meaningsupported = 1 for the unambiguous control words. The second term of Equation 4 can thus be interpreting as representing the fre-

quency with which the contextually supported (or in the absence of prior disambiguating context, the dominant) meaning of an ambiguous word occurs in printed text.

Table 2 shows the results of Simulation 2. If one compares the results of this simulation to the results reported by Duffy et al. (1988; the “observed” values that are also shown in Table 2), then it is clear that the frequency-based mechanism can predict at least some of the cost that is observed with gaze durations when biased ambiguous words follow disambiguating context: 12 ms vs 21 ms for the simulated vs observed costs, respectively. The model simulates this subordinate bias effect because the sentence context in this condition is consistent with the less frequent, subordinate meanings of the ambiguous words, so that these words take longer to identify than their frequency-matched control words. Of course, the model is completely silent about how context selects the appropriate meaning of the ambiguous word. For example, what happens to the dominant meaning of the ambiguous word in this condition? Is it accessed but then “ignored”, or does the context instead somehow inhibit the dominant meaning? A complete (process model) account of the Duffy et al. results seemingly requires some type of explanation for what happens to the meanings of ambiguous words that are not congruent with prior disambiguating context.

Table 2 also shows the second simulation also produced the correct pattern of gaze durations for the two no-conflict conditions. In the balanced-prior-context condition, this is due to the fact that the ambiguous words have meaning frequencies that are comparable to those of the unambiguous control words. Similarly, in the biased-no-prior-context condition, the dominant meanings (which is supported by the context) of ambiguous words are also nearly as frequent as those of the control words. Finally, it is not surprising that the model failed to simulate the cost (1 ms) that was observed (18 ms) in the balanced- no-prior-context condition. This failure was not surprising because the model effectively handles balanced ambiguous words the same way both in and out of context: In both cases, the dominant meanings are assumed to be accessed, with no cost associated with having two alternative, “competing” word meanings. This failure thus indicates that the simple frequency-based retrieval mechanism is not sufficient to account for the full pattern of results that were reported by Duffy et al. (1988).

The results of Simulation 3 are also shown in Table 2. As can be seen there, the model nicely captured the pattern of results that were reported by Duffy et al. (1988). That is, it predicted a 21 ms cost for the biased ambiguous words when they followed disambiguating context, a 19 ms cost for the balanced ambiguous words without context, and no costs for the ambiguous words in the two “no conflict” conditions. This final simulation thus demonstrates that the E-Z Reader model, when augmented with a few fairly simple assumptions about how meaning dominance and disambiguating context influence the ease with which the alternative meanings of ambiguous words are accessed, can explain the pattern of fixation durations that were observed by Duffy et al. (1988). (Our simulation would also qualitatively predict that there would be later disruption in the biased-no-prior-context condition because the dominant meaning would be accessed and thus would be inconsistent with the subsequent context.)

Perhaps not surprisingly, the assumptions that are encapsulated in Equation 5 (above) are similar to the assumptions of the re-ordered access model, and are similar to – but not identical with – the assumptions that were used in the simulations by Duffy et al. (2001). Their simulation used an interactive-constraint model adapted from Spivey and Tanenhaus (1998) in which the weights for meaning dominance and context are adjusted after each cycle, and the predictions are in terms of the number of cycles it takes for a meaning to exceed a predetermined threshold. Their simulation did not predict the pattern of results all that closely. It did predict virtually no difference in the number of cycles between the ambiguous words and the control words in the two no-conflict situations and a substantial difference in the two conflict situations. However, it predicted almost three times as many cycles in the biased-prior-context condition as in the balanced-no-prior- context condition, even though the observed differences in the cost in the gaze duration data were not very different in the two conflict conditions. In the final section of this chapter, we will use these simulations as frameworks for evaluating various assumptions about how context is affecting the word identification process, and we will return to the question of what L1 and L2 correspond to.

4. Discussion

In our final simulation, the disambiguating sentence context was allowed to influence the times that were needed to identify ambiguous words. We did this by allowing context either to increase or to attenuate the relative disparity between the “strengths” of alternative word meanings and to thereby affect the amount of competition that resulted from the two interpretations (see Equation 5). To be a bit more precise, the basic mechanism tacitly assumed in Equation 5 for the effect of context is that it enhances the strength of the representation of meanings that are consistent with the prior text relative to the strength of the representation of meanings that are inconsistent with the prior text. This assumption, by itself, is a satisfactory explanation of the Duffy et al. (1988) results. In the balanced-prior-context condition, the constraint provided by the prior disambiguating context largely eliminates the competition that would otherwise

slow lexical processing, making the time for completing L1 about as rapid as for the control word. Conversely, in the biased-prior-context condition, the disambiguating context increases the competition between alternative word meanings, thereby slowing lexical processing and making the time that is needed for completing L1 longer than the control word. In the biased-no-prior-context condition, the dominant meaning of the word is readily available and there is minimal competition between meanings, which results in the rapid completion of L1 and a rapid eye movement off of the word. Finally, in the balanced-no-prior-context condition, the two word meanings are equally available, which results in competition between meanings, the slower completion of L1, and a less rapid saccade off of the word.

As already noted, the assumptions of Simulation 3 (Equation 5) are similar to those of the re-ordered access theory; both assume that the overall meaning of the sentence can change the order in which alternative meanings of a word become available. Other interesting points of contrast can be gained by comparing the simulations in this chapter to the core assumptions of the theories that have been proposed to explain lexical ambiguity resolution. For example, although both the selective access theory and the frequencybased retrieval mechanism of Simulation 2 (Equation 5) can account for the subordinate bias effect by positing that the frequency of subordinate meaning of an ambiguous word in context is effectively equal to that of low-frequency control word (Sereno et al., 1992), neither account can say why cost is observed in the balanced-no-prior-context condition. The cost for ambiguous words in this condition seemingly requires some type of competition between more-or-less equally matched word meanings, such as that provided by the competition mechanism in Simulation 3.

Similarly, one might be inclined to implement a variant of the integration model (Rayner & Frazier, 1989) by first adding some type of post-lexical integration process to the frequency-based retrieval mechanism (Equation 4) and then assuming that this integration process can only begin when the word meaning that is consistent with the prior sentence has been accessed5. Although such a model would undoubtedly predict more cost in the biased-prior-context condition and thereby cause the simulated subordinate bias effect to be more in line with the effect size reported by Duffy et al. (1988), this model would still fail to predict the cost that is observed in the balanced-no-prior context condition. The reason for this is that any frequency-based retrieval mechanism will still make the dominant meaning of a balanced ambiguous word available for integration about as quickly as the meaning of the unambiguous control word. This again suggests the need for some type of active competition between alternative interpretations of ambiguous words.

Finally, although the competition-between-meanings mechanism of Simulation 3 (Equation 5) is admittedly a very coarse way of handling the effect of disambiguating

5 Of course, this model would also require very rapid word identification and integration times, such as the duration of L1 in E-Z Reader (e.g., 117–172 ms). An alternative assumption that post-lexical processing corresponds to some later stage of processing (e.g., L2 in E-Z Reader) would not work because fixation durations on ambiguous words would not be affected by the integration process.

context, it is conceivable that some combination of syntactic, semantic, and/or pragmatic constraints are sufficient to influence the processing of ambiguous words, making whatever meaning of the word that is being supported more or less accessible than it otherwise would be by decreasing or increasing the amount of competition between alternative word meanings. Within the framework of the E-Z Reader model, this implies that these contextual constraints are rapid enough to influence the earliest stage of lexical processing – L1. How reasonable is this assumption? A recent study by Rayner, Cook, Juhasz, and Frazier (2006) demonstrated that these constraints can also be produced rapidly: an adjective immediately preceding a biased ambiguous word that disambiguated it resulted in longer fixations on the target word when the subordinate meaning was instantiated.

A recent event-related potentials (ERP) experiment also provides some evidence that context can influence the early stages of word identification (Sereno, Brewer, & O’Donnell, 2003). In this experiment, ambiguous words were embedded in sentences contexts that were either neutral or supported the words’ subordinate meanings. This manipulation affected an early ERP component (the N1, which occurs 132–192 ms poststimulus onset) that has been interpreted as an index of early lexical processing (Sereno, Rayner, & Posner, 1998). In the condition involving neutral sentence context, the N1 component that was associated with processing ambiguous words resembled the N1 component that was associated with processing high-frequency unambiguous control words. However, in the condition involving subordinate-biasing context, the N1 associated with ambiguous words resembled the N1 for low-frequency unambiguous control words. The presence of prior disambiguating context thus affected an early electrophysiological signature of lexical processing, suggesting that context can influence the stage of word identification that, in the E-Z Reader model, corresponds to L1. Moreover, it is worth noting that this early effect of context on lexical processing occurred even in a paradigm in which there was no parafoveal preview of the word because the words were displayed one at a time.

The competition mechanism may also provide a useful way of thinking about how other aspects of higher-level linguistic processing affect word identification. In addition to explaining the lexical ambiguity phenomenon, the competition assumption also seems like an obvious mechanism to explain the increased fixation durations when a word is anomalous given the prior context (Rayner et al., 2004). However, what needs to be explicated in a more serious model is how this mechanism would work. That is, such a model should specify what set of words would be excited by or inhibited by prior context and offer a plausible mechanism for it. For example, such a model should explain why gaze durations on the anomalous word are lengthened but that the effect of context for words that are only implausible given the prior context occurs only on “spillover” fixations. Another open question is whether one has to posit that alternative meanings compete with each other such that one meaning needs to be “significantly stronger” than its competitors in order for meaning access to take place. This would be similar to the competition assumed in many interactive-activation models of word identification

(McClelland & Rumelhart, 1981)6. The above discussion suggests that such an assumption may not be necessary if one allows prior context to inhibit certain meanings sufficiently.

This challenge brings us to the question of what L1 and L2 – the core theoretical constructs in the E-Z Reader model – actually correspond to. Answering this question will not only allow us to further refine how we conceptualize our model, it may provide a basis for evaluating all of the various models that have been developed to explain eye-movement control in reading (for a review, see Reichle et al., 2003). Given all of this, what can be said about L1 and L2 based on this modeling exercise?

At this time, it seems most likely that L1 corresponds to some type of rapidly available “sense” of word familiarity that is sensitive to both orthographic and semantic information. L1 has to be rapid because it has to be completed early enough to affect decisions about when to move the eyes to a new viewing location. L1 has to be sensitive to orthographic information if the E-Z Reader model is to explain how variation in the orthographic form of a word can influence the initial fixation on the word but not the amount of spillover that is observed (Reingold & Rayner, 2006). Similarly, it has to be sensitive to the meaning of the word and how this meaning is related to higher-level linguistic information if the model is going to handle the host of effects related to such information, including the effects of word predictability (Balota, Pollatsek, & Rayner, 1985; Ehrlich & Rayner, 1981; Kliegl, Grabner, Rolfs, & Engbert, 2004; Rayner et al., 2004; Rayner & Well, 1996), thematic role assignment (Rayner, Warren et al., 2004), and the resolution of lexical ambiguity (Dopkins et al., 1992; Duffy et al., 1988; Pacht & Rayner, 1993; Rayner & Duffy, 1986; Rayner & Frazier, 1989; Sereno et al., 1992, 2006), to name just a few (for a recent review, see Rayner, 1998). However, this leaves open the question of what information L1 is directly responding to. Given the available data, there are two possibilities. The first is that it is only sensitive to access the meaning of a word but that the access of the meaning is dependent on completion of prior stages of orthographic and phonological access (i.e., a serial stage model). The second is an interactive model, in which familiarity is being computed from an evaluation of the relative completion of various types of processing (e.g., orthographic, phonological, semantic; for an example of how this might work, see Reichle & Laurent, 2006). One way to test this would be to factorially vary various factors, such as lexical ambiguity and visibility. If these factors have roughly additive effects on gaze durations, then it would be evident that L1

6 Mutual inhibition among lexical representations has also been incorporated as a central part of the Glenmore model of eye-movement control during reading (Reilly & Radach, 2003, 2006). In this connectionist model, word units that are within a fixed window of attention provide mutual inhibition to each other, and this word-level activation feeds back to letter units, which then influence the activation of a “saliency map” that determines where the eyes move. Because the activation of the letter units can also inhibit impending saccades, it is conceivable that the Glenmore model would provide a natural account of the Duffy et al. (1988) if the model were modified so that top-down effects of higher-level sentence processing are allowed to modulate the activation of the word units. If the model were successful explaining the effects of ambiguity resolution on eye movements, then this success would be paradoxical because it would violate the basic spirit of the model by allowing higher-level cognitive processes to exert a very rapid influence on the decisions about when to move the eyes during reading.

is only sensitive to later, meaning, stages of word processing, but that these stages are in turn dependent on the completion of earlier stages. On the other hand, if the effects are strongly interactive, then it would suggest that L1 is directly influenced by both earlier and later stages. However, we should point out that such additive factors logic (Sternberg, 1969) can not literally be applied to gaze duration as gaze duration is not simply equal to L1 duration. Nonetheless, such a pattern would be strongly suggestive of an interactive mechanism, and one could further evaluate this hypothesis through simulations using the E-Z Reader model as a framework.

Acknowledgments

This research was supported by a grant R305H030235 from the Department of Education’s Institute for Education Sciences and grant HD26765 from the National Institute of Health. We would like to thank Wayne Murray and two anonymous reviewers for their helpful comments on an earlier version of this chapter.

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