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

reappear after a certain delay. Mean fixation times on the delayed words are extended by as much as the delay when each block of trials has fixed delay (Rayner & Pollatsek, 1981). With variable delays the extension of the fixation time with short delays is shorter than the onset delay, indicating that there is some sort of pre-programming going on. Rayner and Pollatsek (1981) therefore conclude that control of fixation duration in reading is in accordance with a mixed control model (both pre-programming and process-monitoring). However, for visual search in a stimulus onset-delay paradigm, Vaughan (1982) concludes that the effects for onset delays are in agreement with only process-monitoring.

The main evidence for pre-programming comes from several experiments (Hooge & Erkelens, 1996, 1998; Vaughan & Graefe, 1977). First, Hooge and Erkelens (1996) report the occurrence of many return saccades. Subjects were asked to find an O among C’s. They were instructed to respond by continued fixation of the O when found. Hooge and Erkelens (1996) report that after fixation of the O, the subjects often made saccades away from the O to the next C, after which they return to the O. Based on this finding they conclude that before completed analysis of the O, a saccade is programmed that cannot be stopped anymore. To investigate this phenomenon in detail, Hooge and Erkelens (1998) designed the direction-coded search task. In a hexagonal arrangement of C’s with one O (the target), subjects had to make saccades in the direction of the gap in the C. The stimulus was designed in such way that if subjects continued making saccades in the direction of the gap of the fixated C, they ended on the O. To be able to make a correctly directed saccade, visual analysis of the fixated C must be complete before the next saccade is programmed. This was often not the case, as the percentage of incorrectly directed saccades ranged from 20 to 35.

Recently, Greene and Rayner (2001) performed a direction-coded search task as well. They concluded from search in denser displays than used in Hooge and Erkelens (1998) that their results “provided evidence for a process-monitoring model of visual search”. First, they compared fixation times from their direction-coded condition with fixation times from their uncoded condition. As in Hooge and Erkelens (1998), they found longer fixation times in the direction-coded condition. We do not agree that this is evidence for process-monitoring. In the direction-coded displays the visual task differs from the visual task in the uncoded displays, not only the nature of the element (target or not) but also the direction has to be encoded by the visual system. Pre-programming models would also predict longer fixation times for a more difficult visual task as long as there are enough fixations to build up an estimate of the required fixation time. Secondly, Greene and Rayner (2001) did not see an effect on fixation duration of blocked vs mixed presentation of the coded and uncoded displays (indication for process monitoring). However, Greene and Rayner (2001) cannot rule out the possibility that pre-programming may work at relatively short time scales (for example the time scale of one trial). It even may be possible that observers use the information that there are two types of trials and only recognition of an (un)coded display may trigger the relevant fixation time settings.

In this study we investigated the dynamics of adjustment of fixation duration at the short term. To do so, we designed search displays that contain two kinds of distracters. One distracter resembled the target more than the other. This means that the display

Figure 1. Fixation times predicted by the strict process-monitoring model (a), mixed control model (b) and the strict pre-programming model (c). In the strict process monitoring model the visual analysis time of the element fixated determines the fixation time. In the strict pre-programming model, fixation time is pre-programmed for each fixation and adjusted by the estimated difficulty of the search task. In panel c the two lines overlap completely.

contained difficult (longer analysis time) and easy (shorter analysis time) non-targets. In five conditions we varied the proportion of easy and difficult stimulus elements (1.00/0.00, 0.75/0.25, 0.50/0.50, 0.25/0.75, 0.00/1.00). The two classic models for control of fixation duration yield different predictions for the fixation duration. Process-monitoring predicts that fixation duration depends only on the difficulty of the element fixated. Thus, this model predicts that fixation of easy non-targets is shorter than the fixation of difficult non-targets irrespective of the proportion of easy non-targets (Figure 1a). In contrast, pre-programming models predict fixation durations that depend on the proportions of easy and difficult non-targets (Figure 1b and c). In pre-programming models an estimate for the difficulty of the search task (the analysis time required) is used. Depending on the time used to build up this estimate, fixation times of easy and difficult elements depend on the direct fixation history and thus on the proportions of the two types of nontargets. Extreme pre-programming models would produce data as shown in Figure 1c. Here fixation time does not depend on the analysis time of an individual stimulus element; fixation times are completely determined by the proportion of easy and difficult non-targets.

1. Methods

1.1. Subjects

Six male subjects (BV, CP, EO, GJ, MN and TB; aged 22–27) participated in this experiment. EO and BV are co-authors on this paper and the others were naïve concerning the goals of the experiment.

a rectangle. This rectangle is supposed to act as a mask. The stimulus elements measured 24×24 pixels (this resembles 0 53 ×0 53 at 0.64 m). The distance between the elements was 216 pixels (4 7 ). Because we used a flat CRT-screen, we report also measures in pixels because at larger viewing angles (> 10 ) distances on the screen do not scale linearly with viewing angle.

1.3. Set up

Subjects sat in front of a LaCie Blue Electron III 22 Screen (1600 × 1200 pixels at 85 Hz) at a distance of 0.64 m. Stimuli were generated by an Apple PowerMac G4 dual 450 using a Matlab program. This Matlab program was based on routines from the Psychophysics Toolbox (Brainard, 1997; Pelli, 1997) and EyeLink Toolbox (Cornelissen, Peters, & Palmer, 2002).

Movements of the left eye were measured at 250 Hz with the EyeLink. The EyeLink is good enough for measuring fixations in a search task (Van der Geest & Frens, 2002; Smeets & Hooge, 2003). Head movements were prevented by the use of a 2-axis bite board. Data were stored on disk and were analysed off-line by a self-written Matlab program.

1.4. Procedure and task

Each of the five experimental conditions started with a calibration (9 dots standard EyeLink calibration). After successful calibration, the subject performed 100 trials. A trial started with the presentation of a fixation marker in the center of the screen. Fixation of this marker was used for on-line drift correction. Then the subjects had to push the space bar to start the presentation of the search display. Subjects were asked to find the O (the target). When found they had to push the right arrow key on the computer keyboard. There were no practice trials.

1.5. Data analysis

The velocity signal of eye movements was searched for peak velocities above 20 /s. Each peak (in the velocity signal) was considered a potential indicator of the presence of a saccade. The exact onset of the saccade was determined by going backward in time to the point where the absolute velocity signal dropped below the average velocity plus two standard deviations during the stable fixation period before the saccade. The exact offset of the saccade was determined by going forward in time to the point where the absolute velocity signal dropped below the average velocity plus two standard deviations during the stable fixation period after the saccade. This method was adopted from Van der Steen and Bruno (1995). This procedure was followed by rejection/acceptance based on a minimum saccade duration of 12 ms and a minimum amplitude of 1 5 . When a saccade was removed, fixation time before and after this saccade and the duration of the saccade were added together.

Fraction of large gap C’s

Figure 4. Mean number of fixations vs proportion of large gap C’s. Squares denote mean number of fixations. Circles denote mean number of fixations on small gap C’s (0 044 ). Triangles denote mean number of fixations on large gap C’s (0 220 ).

2.2. Fixation time

As stated in the introduction, both the process-monitoring and pre-programming models yield different predictions with respect to fixation time. Figure 5 shows fixation times on large and small gap C’s separately. Fixations on small gap C’s are 40 ms longer than fixations on large gap C’s [t5 = 7 37 p = 0 00035]. Fixation times on small gap C’s

Fraction of large gap C’s

Figure 5. Fixation times for large and small gap C’s as function of proportion of large gap C’s. Circles denote mean number of fixations on small gap C’s (0 044 ). Triangles denote mean number of fixations on large gap C’s (0 220 ).

In summary, when the observer is confronted with an easier element than during the previous fixation, fixation duration does not change; if the task is more difficult than the previous one, fixation is extended.

3. Discussion

We questioned whether fixation duration is controlled as in a pre-programming or a process-monitoring model. We engaged subjects in a search task that consisted of one target and 48 non-targets. We used easy and difficult non-targets that were present in different proportions in the displays. We validated these stimuli by checking the search times (see Figure 3). For distinguishing the O from the large gap C’s lesser time was required than for distinguishing it from the small gap C’s.

We compared our data to two extreme models. According to the process-monitoring model, the fixation time should only depend on the element fixated (i.e., longer fixation times on difficult elements). In the second model, fixation time is pre-programmed and the information required to do so is gathered during previous fixations. In our fixation time data, we found evidence for both pre-programming and process-monitoring.

1)Process-monitoring: In mixed displays, fixations on difficult elements were 20–40 ms longer than fixations on easy elements (see Figure 5). This is clear evidence that the foveated stimulus affects fixation time.

2)Pre-programming: (a) Fixation time depended on the proportion of difficult elements in the display (see Figure 5). Easy elements were fixated shorter when the displays contained many large gap C’s. (b) We also found that easy elements were fixated

longer (up to 18 ms) after fixation on a difficult element than after fixation on an easy element. This is a clear demonstration that fixation history plays a role in the control of fixation duration. The data also suggest that control of fixation duration behaves in a rather conservative way. Extending fixation occurs immediately (difficult after easy leads to extension of fixation time), whereas shortening does not occur immediately (easy after difficult does not lead to shortening of fixation time).

3.1. A new pre-programming strategy

We have a rather speculative alternative explanation of the present data. In this explanation the majority of the fixations is pre-programmed, but process-monitoring still occurs. This explanation was inspired by the new observation of rapid fixation duration increase and slow fixation duration decrease. The former may be caused by a strategy in which saccade programming starts after a fixed amount of time in parallel with visual analysis of the fixated element (McPeek et al., 2000). We will refer to this time as saccade start time (SST). SST is set in such way that analysis of a difficult non-target is possible before the eyes leave the difficult non-target (150 ms after starting saccade programming, Becker & Jürgens, 1979). When the observer fixates an easy non-target it should be efficient to shorten that fixation. However, we assume that the observer finds out that he is fixating an

easy non-target after SST. Thus the observer is too late to advance saccade programming. For the next fixation, a shorter SST is set. This explains slow fixation duration decrease. How does this explanation account for rapid fixation duration increase? When the easy non-target fixation is followed by a difficult non-target fixation, the visual system may find out that the object is not an easy non-target. Again we assume that this occurs after the new shorter SST. The saccade that is still being programmed is cancelled and a new saccade is programmed. In this way the fixation of a difficult non-target is extended. Based on the present results we prefer the first explanation instead of this alternative explanation. However, new experiments should be designed to explore this alternative model, because it is in agreement with existing data of, for example, Rayner and Pollatsek (1981) who showed that immediate extension of fixation time is possible.

3.2. Adjustment of fixation duration

Are fixations adjusted to demands of the visual task in this experiment? This is a difficult question because beforehand we do not know the demands of the search task (i.e., distribution of visual analysis times). However, we can compare performance on easy and difficult C’s. If the adjustment of fixation duration is similar for easy and difficult C’s (e.g., start programming a saccade when there is a certain level of certainty about the nature of the element fixated (Rayner & Pollatsek, 1981)), we expect inspection of easy and difficult C’s to be equally efficient. This means, for example, that the relative number of fixations for the easy and difficult C’s has to be equal. The relative number of fixations was computed by dividing the number of fixations on easy or difficult C’s by their respective numbers. Figure 7 shows that the relative number of fixations on easy C’s is lower than the relative number of fixations on difficult C’s (The difference is 0.150;

Fraction of large gap C’s

Figure 7. Relative number of fixations. Circles denote fixations on small gap C’s; triangles denote fixations on large gap C’s. The relative number of fixations is computed by dividing the number of fixations (at large or small gap C’s) by the number of elements (with large or small gap).

t5 = 4 20 p = 0 0043). This can be interpreted as follows: Search of easy elements was more efficient than search of difficult elements (despite the longer fixation time on difficult elements). This may indicate that fixation times on easy C’s are differently adjusted than fixation times on difficult C’s, which we see as an argument against processmonitoring. For, what would be the purpose of process-monitoring if its results do not appear in the efficiency of search? However, we also have three alternative explanations.

1.Despite the masks around the stimulus elements, we cannot exclude the possibility that observers have some preview of neighbour elements. If this occurs, it may be possible that in a mixed display difficult C’s are more likely to be fixated than easy C’s because they resemble the target more. This may explain the higher relative number of fixations on difficult C’s.

2.Another explanation is a possible floor effect for fixation time. If the observers had been able to fixate shorter on easy C’s they would have done that (at the expense of a higher number of fixations on easy C’s). They probably could not search faster on easy C’s.

3.Fixation of easy C’s is longer when difficult C’s are present (see Figure 5). This longer fixation time may cause preview benefits because time is left to analyse neighbours. The effect of preview is described above.

3.3.The role of the return saccade

Is accurate adjustment of fixation duration required for successful search? In our opinion, perfect adjustment of fixation time to the demands of the immediate visual task is not necessary because observers always have the possibility to make a return saccade. They can make a return saccade if they have information that the previous fixation was too short to analyse the previously fixated object. At the cost of one additional fixation they may fixate for too short a duration. In search and viewing tasks, observers make many return saccades (Hooge, Over, van Wezel, & Frens, 2005). In fact it would be a smart strategy to decrease fixation time during search until a return saccade is needed to re-inspect a specific part of the scene. The occurrence of a return saccade then may act as a signal to increase fixation time. At the moment this issue is investigated in our lab.

3.4. Interpreting reaction times

The majority of the visual search experiments are done with reaction times only. This experiment clearly shows that stimulus properties (such as target/non-target dissimilarity) are not sufficient to explain search times in detail. Figure 3 shows that search time drops for higher fractions of easy non-targets. This drop can be understood if fixation times are taken into account (see Figure 5) because as a consequence of the conservative fixation behaviour, easy non-targets are fixated longer in the presence of difficult nontargets than when presented without the presence of difficult non-targets. The drop in Figure 3 is mainly due to this effect. There is another factor that influenced search times.