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

findings related to lexical ambiguity and how its resolution affects eye movements during reading, it is first important to provide a brief overview of the E-Z Reader model.

1. E-Z Reader

Figure 1 is a schematic diagram of the E-Z Reader model. As the figure shows, word identification begins with a pre-attentive stage of visual processing (labeled “V” in Figure 1) that allows the visual features from the printed page to be propagated from the retina to the brain. This early stage of processing is “pre-attentive” in that information is extracted in parallel from across the entire visual field; however, the quality of this information decreases the further it is from the fovea. The low-spatial frequency information (e.g., word boundaries) that is obtained from this stage is used to select the targets for upcoming saccades. The high-spatial frequency information (e.g., letter shapes) that is obtained from this stage can serve as fodder for lexical processing on whatever word is being attended. In contrast, it is assumed that (a) subsequent lexical processing requires attention and

(b) that attention is allocated serially to one word at a time. Finally, although this early stage of pre-attentive visual processing takes some time to complete (i.e., 50 ms, or the duration of the “eye-to-brain lag”; Clarke, Fan, & Hillyard, 1995; Foxe & Simpson, 2002; Mouchetant-Rostaing, Gaird, Bentin, Aguera, & Pernier, 2000; Van Rullen & Thorpe, 2001), the effects of this delay are predicted by the model to be negligible in most normal reading situations because lexical processing continues during each saccade using

Figure 1. Schematic diagram of the E-Z Reader model of eye-movement control during reading. “V” corresponds to the pre-attentive stage of visual processing; “L1” and “L2” correspond to the first and second stages of lexical processing, respectively; and “M1” and “M2” correspond to the labile and non-labile stages of saccadic programming.

whatever information was obtained from the preceding viewing location. (For a complete discussion of how this pre-attentive stage of visual processing is related to attention and lexical processing, see Pollatsek et al., 2006.)

The second stage of word identification in the E-Z Reader model (i.e., the first stage after the visual stage), called the “familiarity check” or L1, ends when processing of word n has almost been completed, and provides a signal to the oculomotor system to program a saccade to move the eyes to word n + 1. (Note that here and elsewhere, we will refer to the word attended at the beginning of a fixation as word n; this is usually also the fixated word except if there has been a saccadic error in targeting the word.) Processing of word n then continues until the word has been identified to the point where attention can be disengaged from it and reallocated to the next word. When this final stage of word identification, “completion of lexical access” or L2, is finished, a signal to shift attention from word n to word n + 1 is sent to the attentional system; thus, the shifting of attention is decoupled from the programming of saccades. In the current version of the model, the mean times required to complete L1 and L2 on a word, t L1 and t L2 , are an additive function of that word’s frequency of occurrence in printed text (freq) and its predictability within its local sentence context (pred), as specified by Equations 1 and 2. For L1, there is some probability (p = pred) that the word will be “guessed” from it’s context, so that no time is needed to complete L1 on that word; however, for most words and in most

In Equation 1, the free parameters 1 = 122 ms , 2 = 4 ms , and 3 = 10 ms are the best-fitting values that have been used with all of the simulations involving E-Z Reader 9 (Pollatsek et al., 2006). The frequency with which a word occurs in printed text is determined through corpora norms (e.g., Francis & Kucera, 1982) and its predictability is set equal to the mean probability of guessing the word from its prior sentence context, as determined using cloze-task norms. Both equations define the mean times to complete L1 and L2, respectively. The actual times for a given Monte Carlo run of the model are found by sampling random deviates from gamma distributions having means equal to the values specified by Equations 1 and 2, and standard deviations equal to 0.22 of their means.

Note that the time to complete L1, t L1 , is also modulated by visual acuity, resulting in t L1 (see Equation 3, below). The free parameter = 1 15 modulates the effect of visual acuity, which in our model is defined in terms of the mean absolute distance (i.e., number of character spaces) between the current fixation location and each of the letters in the word being processed (N is the number of letters in the word). The value of was selected so that the rate of L1 completion would decrease by factors of 1.15, 1.32, and 1.52 when the first letter of 3-, 5-, and 7-letter words (respectively) is fixated (relative to when a 1-letter word is directly fixated). It thus takes more time to identify long words and words that are farther from the fovea (i.e., the current fixation location). Both of these predicted outcomes are consistent with empirical results; word identification is slower and

landing site increases. Finally, the model includes an assumption that, upon fixating a word, the oculomotor system makes a “decision” about whether or not to initiate a saccade to move the eyes to a second viewing position on the word. This decision is based on the distance between the initial landing position and the center of the word, under the assumption that initial fixations near the beginnings and endings of words afford a poor view of the word (due to limited visual acuity) and hence are more likely to result in a corrective refixation to move the eyes to a better viewing location. Because this decision is based on the quality of the visual information from the initial viewing location, the decision can only be made after enough time has elapsed to provide feedback about the distance between the initial landing position and the center of the intended word target. These assumptions about corrective refixation saccades allow the model to account for some of the findings related to the inverted optimal viewing position effect (Vitu, McConkie, Kerr, & O’Regan, 2001), although additional assumptions will be necessary to completely explain these phenomena.

With this overview of the E-Z Reader model, it is now possible to demonstrate how it can be used as a framework to examine the different theories that have been developed to explain the patterns of eye movements that are observed when readers encounter lexically ambiguous words in text. Before we do this, however, we will first briefly review what has been learned about how lexical ambiguity affects readers’ eye-movement behavior, and then briefly review the various accounts of this behavior.

2. Lexical Ambiguity

Most words in English are polysemous and have two or more (sometimes subtly) different meanings (Klein & Murphy, 2001). Many words are even more ambiguous in that they have two or more distinct meanings (Duffy et al., 2001). Often, the meanings of these words differ dramatically in terms of their meaning dominance, or the frequency with which the different meanings are encountered in written or spoken language. For example, with a balanced ambiguous word such as case, the two meanings of the word (one related to legal proceedings, the other related to containers) are approximately equally prevalent in the language. In contrast, with biased ambiguous words, like port, the dominant meaning (the one that is a synonym of harbor) is much more prevalent or common in the language than its second, subordinate meaning (the one that is a type of wine).

Given that two or more quite distinct meanings can be associated with a single word form, the question then becomes one of trying to understand which meaning or meanings of an ambiguous word are encoded when it is encountered in text. For example, when an ambiguous word is encountered when the prior text does not bias or support a particular interpretation of the word (e.g., “Actually the port ”), do readers access both meanings of the word, or is only one of the meanings (likely the most dominant one) accessed? Similarly, when the ambiguous word is preceded by context that supports one meaning of the word (e.g., “Even though it had a strange flavor, the port ”), how does this context influence or bias the meaning of the word that is accessed? These questions are

Table 2

Mean observed (Duffy et al., 1988) and simulated target-word gaze durations as a function of meaning dominance (balanced vs biased) and disambiguating context (present vs absent)

Note: Times are in milliseconds.

words in the balanced-prior-context condition have two meanings, there is no “conflict” between these meanings because the disambiguating context somehow lends an advantage to one of those meanings.

In the second “no conflict” condition (biased-no-prior-context), the biased ambiguous words were not preceded by disambiguating context, and the gaze durations were again about the same as those on the unambiguous control words (mean difference = 2 ms). One interpretation of this result is that, because the dominant meaning of the ambiguous word is rapidly available, the time that is needed to assign a meaning to the word is about the same as with the frequency-matched control word. However, in this case, when the subsequent context instantiates the subordinate meaning, there is quite a large cost in processing when this context is reached, indicating that the “wrong” meaning had been initially accessed and further processing was necessary to repair the error.

The two remaining conditions of the Duffy et al. (1988) experiment can be considered “conflict” conditions. That is, in the condition where the biased ambiguous words were preceded by context that supported the subordinate meaning of the words (i.e., biased- prior-context), the gaze durations were longer than those on the control words (mean difference = 21 ms). Similarly, in the condition where the balanced ambiguous words were not preceded by disambiguating context (balanced-no-prior-context), the gaze durations were also longer than on the control words (mean difference = 18 ms). One interpretation of both of these findings is that they result from a “conflict” that arises because the two word meanings are somehow competing or interfering with each other. In the biased-prior- context condition, the supporting context favors the subordinate meaning of the word,

which – because it is less common and hence less well represented – takes longer to be retrieved from memory. The increased difficulty associated with retrieving the subordinate meanings of these words thus results in longer gaze durations. In the balanced-no-prior- context condition, both meanings of the ambiguous words are equally common and hence equally well represented in memory. Because both meanings are equally available, and because there is context to lend an advantage to one of these meanings, the two meanings somehow interfere with each other. The conflict that results from having two competing meanings available results in longer gaze durations. We will examine the assumptions of this model in greater detail below. Before doing so, however, we will first briefly review the theories that have been proposed to explain lexical ambiguity resolution (for a more comprehensive review, see Pacht & Rayner, 1993).

Theories of lexical ambiguity resolution fall along a continuum with respect to the role that higher-level linguistic processing is posited to play in lexical access. On one end of this continuum, autonomous access models posit that all meanings of words are automatically and exhaustively accessed at a rate that is proportional to their frequency of occurrence, irrespective of the context in which they occur. At the other end of the continuum, selective access models posit that sentence context plays a very pronounced role in lexical access, and that only the contextually appropriate meanings of ambiguous words are accessed, again at a rate that is proportional to their frequency of occurrence. Two other models are situated between these two extreme views. The first is the re-ordered access model, in which higher-level linguistic processing can influence the order in which the meanings of ambiguous words are accessed (Duffy et al., 2001). The second is the integration model, which can be viewed as being an extension of the autonomous access models in that higher-level linguistic processing does not guide lexical processing (which is assumed to be exhaustive), but instead only affects the speed of post-lexical processing (e.g., how rapidly a word’s meaning can be integrated into the overall meaning of a sentence; Rayner & Frazier, 1989).

To further examine how well each of these theories can explain the pattern of results reported by Duffy et al. (1988), we attempted to insert what we took to be the key assumptions of the theories into the word-processing component of the E-Z Reader model. One goal in doing this was to determine whether such a model is sufficient to explain the pattern of results that were observed by Duffy et al. (1988) experiment. A second goal is to provide a conceptual “scaffolding” for thinking about the role that ongoing sentence processing plays in influencing the time course of lexical processing and the decision about when to move the eyes from one word to the next.

3. Simulations

With the above goals in mind, we completed three simulations in which various assumptions of the re-ordered model were added into the basic framework of the E-Z Reader model. Each simulation was completed using 1000 statistical subjects and the 48 sentences from the Schilling et al. (1998) corpus. These sentences were used as “frames” to examine

how differences in meaning dominance and the presence vs absence of prior disambiguating context would influence the viewing times on the ambiguous words embedded within these sentences. In the simulation, the ambiguous target words were assigned to the ordinal word positions of the highand low-frequency word targets that were used by Schilling et al. We attempted to make the simulation as true to the Duffy et al. (1988) experiment as possible2. For example, the frequency of the target words was set equal to the mean values of the targets used by Duffy et al. (1988). For the balanced condition and its unambiguous control, the frequency was set equal to 94 per million (based on the norms of Francis & Kucera, 1982); in the biased condition and its unambiguous control, the frequency was set equal to 61 per million. The length of the target words was set equal to five letters for all of the conditions, and the predictability was set to zero in the all of the conditions.

Finally, it is important to note that all of the simulations are predicated on the assumption that the variables that have been shown to affect how long readers look at ambiguous words (i.e., meaning dominance and whether or not the words are preceded by disambiguating sentence context) influence the duration of L1 in the E-Z Reader model. This assumption was necessary because, in the model, the duration of L1 largely determines how long a given word is fixated3. One implication of this assumption is that the completion of L1 corresponds to some aspect of the processing of a word’s meaning because both meaning dominance and its interaction with overall sentence meaning (such as is posited to happen in the integration model) are by definition related to the processing of meaning. Although we have preferred to remain agnostic about the precise interpretation of L1 (see Rayner, Pollatsek, & Reichle, 2003 for three possible interpretations of the distinction between L1 and L2 , our new assumption that the variables that influence fixation durations on ambiguous words do so by influencing the duration of L1 forces us to refine our conceptualization of L1 and to acknowledge that – at a minimum – this stage of lexical processing has something to do with the processing of word meaning. We will return to this issue and the larger question of what the two stages of lexical processing (L1 vs L2 in E-Z Reader correspond to in the last section of this chapter.

2 We did not attempt to find optimal model parameter values for the simulation because doing so is prohibitively labor intensive, requiring one to calculate a number of different dependent measures for each word, and well as each word’s frequency, length, and mean cloze-task predictability. We therefore opted to use the same parameter values (unless otherwise noted) that have been used in our previous simulations (Pollatsek et al., 2006; Reichle et al., 2006) and to limit our efforts to making predictions about the target words in the various conditions of the Duffy et al. (1988) experiment.

3 Other factors can also affect fixation durations in the model. For example, fixations on words preceding or following skips tend to be longer than those preceding or following other fixations (Pollatsek, Rayner, & Balota, 1986; Rayner et al., 2004; Reichle et al., 1998; cf. Kliegl & Engbert, 2005). The fixation duration on a word can also be affected by properties of the preceding or following word in cases involving saccadic error (i.e., cases where the eyes either undershot or overshot their intended targets; Rayner, White, Kambe, Miller, & Liversedge, 2003). We will ignore these factors in our discussion because their effects on fixation durations are negligible in comparison to properties of the words themselves.

3.1. Simulation 1

The first simulation was completed using the “standard” version of the E-Z Reader model that was described in the preceding section and has no additional assumptions. As Table 2 shows, the model failed to capture the effect of ambiguity or its interaction with disambiguating context; the only effect that was predicted was a small effect of word frequency that was due to the small difference in the mean frequency of the target words in the balanced vs biased conditions. Although all of the predicted gaze durations were shorter than those that were actually observed, this discrepancy is due to the fact that the model’s parameter values were not adjusted to provide the best fit to the Duffy et al. (1988) data (see Footnote 2). If one ignores this minor discrepancy, then the results of Simulation 1 can be used as a “benchmark” to evaluate the consequences of including additional theoretical assumptions (Simulations 2 and 3) that have been proposed to explain the Duffy et al. results.

3.2. Simulation 2

The next simulation was done to evaluate one potential explanation for the ambiguity effects – that the effects reflect a simple retrieval mechanism that is based on the meaning dominance or frequency of a particular word meaning. For example, for a biased ambiguous word, the frequency of the dominant meaning is close to that of the unambiguous control word, whereas the frequency of the subordinate meaning is a lot less frequent than that of the control. Thus, from this kind of frequency analysis, it qualitatively makes sense that gaze durations on biased ambiguous words will be similar to those of the unambiguous controls when given no prior context (assuming the dominant meaning is accessed) but longer than the controls given prior context instantiating the subordinate meaning. (This raises the question of how the dominant meaning would be suppressed so that it isn’t retrieved; we will return to that later.) By the same logic, for balanced ambiguous words, each meaning has a frequency somewhat less than the meaning of the unambiguous control word, so that one would expect gaze durations on the ambiguous words to be somewhat longer than those on the control words. Finally, although it is not clear that anything in such a meaning-frequency mechanism can explain why prior context reduces the size of the ambiguity effect with balanced ambiguous words, it is still of interest to see how well a model that relies solely on an access mechanism where retrieval time is based solely on the frequency of the particular word meaning can explain the lexical ambiguity data that were reported by Duffy et al. (1988).

To evaluate a simple meaning-frequency-based mechanism, Simulation 2 was completed using Equation 4 (which is a modification of Equation 1, above). In Equation 4, the amount of time that is needed to identify a given word is modulated by two variables. The first variable is the word’s frequency of occurrence in printed text (freq); that is, its (orthographic) token frequency as tabulated in the Francis and Kucera (1982) norms. The second variable is the meaning dominance of the contextually appropriate or “supported” meaning of the word, p meaningsupported , where the supported meaning is the one