**Saccades shift the gaze rapidly every few hundred milliseconds from one fixated location to the next, producing a flow of visual input into the visual system even in the absence of changes in the environment. During fixation, small saccades called microsaccades are produced 1–3 times per second, generating a flow of visual input. The characteristics of this visual flow are determined by the timings of the saccades and by the characteristics of the visual stimuli on which they are performed. Previous models of microsaccade generation have accounted for the effects of external stimulation on the production of microsaccades, but they have not considered the effects of the prolonged background stimulus on which microsaccades are performed. The effects of this stimulus on the process of microsaccade generation could be sustained, following its prolonged presentation, or transient, through the visual transients produced by the microsaccades themselves. In four experiments, we varied the properties of the constant displays and examined the resulting modulation of microsaccade properties: their sizes, their timings, and the correlations between properties of consecutive microsaccades. Findings show that displays of higher spatial frequency and contrast produce smaller microsaccades and longer minimal intervals between consecutive microsaccades; and smaller microsaccades are followed by smaller and delayed microsaccades. We explain these findings in light of previous models and suggest a conceptual model by which both sustained and transient effects of the stimulus have central roles in determining the generation of microsaccades.**

*saccades*around 1–3 times per second. Large saccades are typically produced when attention is shifted from one location on a scene to another; smaller saccades, called

*microsaccades*, are produced during periods of fixation (Martinez-Conde, Macknik, & Hubel, 2004) and when fine details are being explored (Ko, Poletti, & Rucci, 2010). Saccades and microsaccades form an oculomotor continuum, as they share the same kinematic properties and are controlled by the same neural structures (Martinez-Conde et al., 2004; Hafed, Goffart, & Krauzlis, 2009). Specifically, oculomotor maps in the superior colliculus are involved in generating saccades of all sizes, as they represent saccade amplitudes continuously: Smaller saccades are represented closer to the rostral pole and larger saccades are represented more caudally (Robinson, 1972; Hafed et al., 2009). During fixation, activity is strongest in the rostral pole, which is the center of the map (Munoz, Dorris, Pare, & Everling, 2000). A saccade is elicited once activity in a certain area of the map surpasses a threshold (Carpenter & Williams, 1995; Hanes & Schall, 1996).

*M*±

*SD*] = 23.7 ± 2.8), 12 in Experiment 2 (seven women, five men; age = 25.9 ± 3.2), 11 in Experiment 3 (eight women, three men; age = 23.7 ± 4.2), and 13 in Experiment 4 (six women, seven men; age = 24.0 ± 2.6). The experimental protocol was approved by the ethical committees of Tel Aviv University and the School of Psychological Sciences, and experimental sessions were conducted with the written consent of each participant. The eye-tracking data from Experiment 2 were included in a previous publication, focusing on different questions and analysis methods (Amit et al., 2017).

^{2}) in Experiment 3. We ran Experiment 3 last, but to improve readability we report it here prior to Experiment 4. The reason for the two different setups is that the original CRT monitor was replaced with a newer LCD screen at the end of the (chronologically) third experiment. Eye-tracker calibration was applied at the beginning of each experimental session and then once every four blocks (or more often when needed). In the darkness condition, calibration was performed on a black background after shutting down the lights but before turning off the monitor. A tone was sounded to alert participants when their gaze diverted more than 1.5° away from the central cross for more than 1 s.

*M*= 13.5 s) from vertical to horizontal or vice versa, replacing the black transients of the previous experiments. The new orientation remained until the next flip or the end of the block, and data were analyzed on these segments according to the presented orientation. Participants were asked to count the orientation flips and report the number at the end of each block.

*r*) between the logs of velocity and amplitude (Zuber, Stark, & Cook, 1965; Bahill, Clark, & Stark, 1975).

*μ*representing the mean of its Gaussian component. These

*μ*values were used to estimate the duration of the SRP.

*μ*values of the fitted ex-Gaussian distributions) and saccade amplitudes were compared between conditions using repeated-measures analyses of variance (ANOVAs) or paired-sample

*t*tests. The assumption of sphericity was tested, when applicable, using Mauchly's test. When Mauchly's test was found significant (

*p*< 0.05), the Greenhouse–Geisser corrected

*F*and

*p*values are reported, along with the original degrees of freedom and the epsilon value. All the statistical tests performed were two-tailed.

*t*test, and

*p*values were corrected for multiple comparisons (according to the number of examined participants and conditions) using the false discovery rate (Yekutieli & Benjamini, 1999). The corrected

*p*values (

*p*FDR) are reported. Grand average correlations across participants were calculated by taking the Fisher's transform of each individual

*r*coefficient, averaging them, and applying the inverse Fisher's transform on the result (Silver & Dunlap, 1987). When

*r*correlation coefficients are compared across participants, the comparison is made on the Fisher-transformed coefficients. For this correlation analysis we excluded a few outlier pairs of microsaccades that were separated by long intervals. Since the IMSI distribution is typically centered around ∼250 ms, we chose to exclude IMSIs that were longer than 1 s and consequently at the extreme end of the distribution's exponential tail. Choosing this threshold resulted in the exclusion of 12.7% ± 8.6% (

*M*±

*SD*) of microsaccades.

*n*to be the number of microsaccades in the smaller bin among the two conditions. In each iteration of the bootstrap procedure,

*n*microsaccades were sampled with return from each bin, and these microsaccades were pooled together to create a new distribution with equalized amplitudes. We then fitted an ex-Gaussian to the remaining IMSIs and extracted its

*μ*parameters for each condition, as was already described for the main analysis. These difference values were sorted and the low and high 2.5% values (in places 25 and 975 of the sorted array) were reported as the 95% confidence interval around the mean difference. When zero was outside this confidence interval we concluded that there was evidence supporting the existence of condition effects that are independent of microsaccade amplitudes.

*L*×

*L*. The walker represents the gaze position, changing over time, and the grid represents the visual field. In each time step

*t*, the walker moves from its current location (

*i*,

*j*) to a new location. This motion is driven by the movement potential on the grid. This movement potential

*E*(

*i*,

*j*,

*t*) at position (

*i*,

*j*) and time point

*t*is defined as the sum of two values: the potential map

*u*(

*i*,

*j*), a constant map with a symmetrical trough at the center of fixation (

*i*

_{0},

*j*

_{0}) to simulate the fixation task; and the activation map

*h*

_{i}_{,}

*(*

_{j}*t*), a map simulating activity in the motor map of the superior colliculus. The potential map is defined as

*a*represents the steepness of the trough at fixation.

*i*,

*j*), activation at this position is increased by 1 and activation in all other positions decays:

*E*(

*i*,

*j*).

*E*(

*i*,

*j*) at the current position is below a critical value

*E*

_{crit}, the walker will move to the position with the lowest movement potential among the four adjacent positions to its current position on the grid (

*i*±1,

*j*±1). This produces a slow drift. However, when

*E*(

*i*,

*j*) is larger than

*E*

_{crit}, the walker jumps to the position of the global minimum of the movement potential. This simulates a microsaccade.

*E*(

*i*,

*j*) with a time-dependent factor,

*a*(

_{p}*t*) and transiently increasing the critical threshold

*E*

_{crit}:

*t*is the time since the last saccade and

*E*

_{crit}is constant and does not change after each saccade, and potential

*E*(

*i*,

*j*) is not multiplied by

*a*. Alternatively, a saccadic refractory period

_{p}*T*is sampled from a normal distribution

*N*(

*μ*,

*σ*). The mean

*μ*of this distribution is estimated as the sum of two parameters:

*μ*

_{0}, which represents the individual observer's SRP, and

*μ*

_{ret}, which represents the effect of stimulus properties (e.g., SF) on the SRP. Separating these two parameters is necessary to account for the observation that in addition to the stimulation effects there are substantial individual differences in the durations of the SRP. Together, these two parameters define

*μ*:

*μ*

_{ret}parameter was set to 0 in the low-SF condition and fitted to the data for the high-SF condition (see later). These changes reduced the number of parameters by two relative to the original model: instead of

*E*

_{crit},

*λ*

_{1},

*ρ*

_{1},

*λ*

_{2}, and

*ρ*

_{2}, we now have only

*E*

_{crit},

*μ*, and

*σ*, fitted for each observer and condition. Relaxation rate

*ε*and potential well steepness

*a*were set to 0.001 and 1, respectively, as in the original model (Sinn & Engbert, 2016).

*E*

_{crit}≤ 7.99, with intervals of 0.01; 2 × 10

^{−4}≤

*ρ*

_{1}≤ 4 × 10

^{−3}, with intervals of 4 × 10

^{−4}; 1 × 10

^{−6}≤

*ρ*

_{2}≤ 2.5 × 10

^{−5}, with intervals of 1 × 10

^{−6}; 1.4 ≤

*λ*

_{1}≤ 4.3 with intervals of 0.2; and 1 ≤

*λ*

_{2}≤ 3 with intervals of 0.1. For the revised model, the tested parameters were 7.88 ≤

*E*

_{crit}≤ 7.99 with intervals of 0.01; 50 ≤

*μ*≤ 400 with intervals of 5; 10 ≤

*σ*≤ 120, with intervals of 10. These parameter spaces were chosen to make sure that any possible parameter set is well within the tested range. Goodness of fit (GOF) for each parameter set was calculated by a chi-square test between the resulting model's IMSI distribution and the original one. IMSIs between 0 and 1,000 ms were divided into

*k*equiprobable bins. The value of

*k*was chosen to be the integer nearest to 1.88 ×

*n*

^{2/5}, where

*n*is the number of samples in the original IMSI distribution according to a standard procedure (D'Agostino, 1986). The parameter set resulting in lowest chi-square statistic was chosen for each data set.

*r*> 0.86 for all data sets and all saccade sizes;

*r*> 0.85 for microsaccades alone). In the fixation task, nearly all saccades (>93%) were defined as microsaccades (smaller than 1.5°), indicating that observers complied with the fixation instruction. In contrast, in the free-view task only 45.2% of saccades were defined as microsaccades. To avoid the effects of large retinal displacements, all the following analyses were performed on microsaccades only and on the fixation task only, unless indicated otherwise. The average number of analyzed microsaccades for an individual data set was 5,356 (

*SD*= 1,360, range = 3,153–8,292).

*M*= 4.95 × 10

^{4},

*SD*= 2.41 × 10

^{3}), compared with an exponential distribution (

*M*= 5.1 × 10

^{4},

*SD*= 2.49 × 10

^{4},

*p*= 0.00041), a Gaussian distribution (

*M*= 5.26 × 10

^{4},

*SD*= 2.59 × 10

^{4},

*p*= 0.00028), and a gamma distribution (

*M*= 5 × 10

^{4},

*SD*= 2.4 × 10

^{4},

*p*= 0.00079). We conclude that the intervals between microsaccades do not follow an exponential distribution, and therefore microsaccades are interdependent and are not a pure Poisson process.

*μ*parameter of the ex-Gaussian distribution reflects the mean of its Gaussian component. Considering the ex-Gaussian pattern of an inhibition followed by a rebound,

*μ*represents the duration of the inhibition, or the SRP. To test our hypothesis that the duration of the SRP is affected by the display, we compared the fitted

*μ*values across the display conditions.

*μ*as a dependent measure. There was an effect (Figure 1A–1C) of display,

*F*(2, 20) = 10.73,

*p*< 0.001, caused by a smaller

*μ*for darkness relative to blank,

*F*(1, 10) = 5.45,

*p*= 0.042, and a smaller

*μ*for blank relative to scene,

*F*(1, 10) = 5.25,

*p*= 0.045. When analyzing all saccades instead of only microsaccades, we find the same pattern of results: a significant display effect,

*F*(2, 20) = 16.17,

*p*< 0.0001, with

*μ*smaller for darkness than for blank,

*F*(1, 10) = 8.13,

*p*= 0.0172, and smaller for blank than for scene,

*F*(1, 10) = 9.96,

*p*= 0.010.

*μ*value are mediated by differences in microsaccade rate between the conditions: There might have been more microsaccades for a less intense display, and this may have induced shorter IMSIs and consequently shorter SRPs. We find no evidence for this. In the fixation condition there was no significant difference in the rate of microsaccades between the displays,

*F*(2, 20) = 0.25,

*p*= 0.78. This indicates that the IMSIs in the ex-Gaussian distribution are characterized by a “heavy” exponential tail, resulting from many long IMSIs. Consequently, the

*μ*parameter of the IMSIs in the ex-Gaussian distribution is substantially smaller than the mean IMSI. Moreover, a previous study (McCamy, Otero-Millan, Di Stasi, Macknik, & Martinez-Conde, 2014) has shown that there are more, rather than fewer, microsaccades on more informative regions of a display. Therefore, the prediction on the relation between the display and microsaccade rate should have been the opposite: shorter IMSIs (more microsaccades) for more intense stimuli.

*F*(2, 20) = 47.9,

*p*< 10

^{−5}. Average microsaccade size (

*M*±

*SD*) was 0.52° ± 0.12° for darkness; 0.38° ± 0.10° for blank, and 0.38° ± 0.10° for scenes. Planned contrasts showed a significant difference between darkness and blank,

*F*(1, 10) = 85.7,

*p*< 10

^{−5,}but none between scene and blank—scene > blank:

*F*(1, 10) = 0.086,

*p*= 0.76 (Figure 2A).

*r*= 0.28,

*t*(10) = 6.9,

*p*< 10

^{−4}; blank: mean

*r*= 0.30,

*t*(10) = 9.62,

*p*< 10

^{−5}; scene: mean

*r*= 0.34,

*t*(10) = 10.74,

*p*< 10

^{−6}(see individual correlations in Table 1).

*r*= 0.19,

*t*(10) = 4.78,

*p*< 10

^{−3}; blank: mean

*r*= 0.20,

*t*(10) = 5.07,

*p*< 10

^{−3}; scene: mean

*r*= 0.24,

*t*(10) = 8.64,

*p*< 10

^{−5}. We conclude that consecutive microsaccades are correlated in amplitude and that this correlation cannot be explained by corrective saccades alone.

*z*scores. The correlations were found to be significantly negative across participants in the blank and scene conditions—blank: mean

*r*= −0.24,

*t*(10) = 7.15,

*p*< 10

^{−4}; scene: mean

*r*= −0.21,

*t*(10) = 10.08,

*p*< 10

^{−5}(Figure 2C)—but not in the darkness condition: mean

*r*= −0.10,

*t*(10) = −1.87,

*p*= 0.09 (see individual correlations in Table 1). This shows that smaller microsaccades tended to induce longer IMSIs than larger microsaccades in light conditions (blank and scene), but not in darkness (Figure 2C). The same qualitative effect was found after excluding SWJs—blank: mean

*r*= −0.21,

*t*(10) = 6.42,

*p*< 10

^{−4}; scene: mean

*r*= −0.16,

*t*(10) = 8.20,

*p*< 10

^{−5}.

*r*= 0.14,

*t*(10) = 4.46,

*p*= 0.0012; scene: mean

*r*= 0.12,

*t*(10) = 4.36,

*p*= 0.0014 (Figure 2D)—but not in the darkness condition: mean

*r*= −0.03,

*t*(10) = 0.97,

*p*= 0.35. The correlations were less consistent in individual participants than the negative correlations reported for IMSIs: They were positive in 10/11 and 11/11 participants in the blank and scene conditions, respectively, and significant for 4/11 (

*p*FDR < 0.05) in both conditions (7/11 when collapsing the two conditions). In the darkness condition, only 5/11 correlations were positive, and none were significant.

*r*> 0.87 for all data sets). The average number of analyzed microsaccades for an individual data set was 2,897 (

*SD*= 1,046, range = 1,429–4,613).

*M*= 1.55 × 10

^{4},

*SD*= 6.58 × 10

^{3}), compared with an exponential distribution (

*M*= 1.62 × 10

^{4},

*SD*= 7.3 × 10

^{3},

*p*= 0.001), a Gaussian distribution (

*M*= 1.63 × 10

^{4},

*SD*= 7.07 × 10

^{3},

*p*= 0.0001), and a gamma distribution (

*M*= 1.57 × 10

^{4},

*SD*= 6.9 × 10

^{3},

*p*= 0.003).

*μ*values (Figure 3B–3C) were higher for the high-SF condition compared to the low-SF condition,

*t*(11) = 3.36,

*p*= 0.0064. It is unlikely that the microsaccade rate mediated this effect, because there was no significant difference in the rate of microsaccades between the two display conditions—high SF:

*M*= 1.31 Hz; low SF:

*M*= 1.33 Hz;

*t*(11) = −0.3,

*p*= 0.76.

*t*(11) = 3.81,

*p*= 0.0029), with smaller amplitudes (

*M*±

*SD*) for the high-SF condition than the low-SF condition—high: 0.36° ± 0.14°; low: 0.44° ± 0.17°. As in Experiment 1, the correlations between the sizes of consecutive microsaccades were significantly positive across participants in both conditions—high SF: mean

*r*= 0.22,

*t*(11) = 4.80,

*p*< 10

^{−3}; low SF: mean

*r*= 0.27,

*t*(11) = 7.18,

*p*< 10

^{−4}(see individual correlations in Table 1). Smaller microsaccades tended to be followed by smaller microsaccades (Figure 3F). As in Experiment 1, correlations between consecutive saccade sizes were lower but still significant after SWJs were excluded from the analysis—high SF: mean

*r*= 0.13,

*t*(11) = 4.73,

*p*< 0.001; low SF: mean

*r*= 0.16,

*t*(11) = 6.10,

*p*< 10

^{−4}.

*r*= −0.16,

*t*(11) = 7.17,

*p*< 10

^{−4}; low SF: mean

*r*= −0.17,

*t*(11) = 5.95,

*p*< 10

^{−3}(see individual correlations in Table 1). This indicates that IMSIs were longer following smaller microsaccades than following larger ones (Figure 3E).

*μ*− low-SF

*μ*) in these unbiased data sets and constructed the 95% confidence interval of the effect (see Materials and methods for more details). The resulting confidence interval [5.63, 14.55] did not include the zero value, supporting the existence of an SF effect on IMSI that is independent of microsaccade amplitude.

*r*= 0.96,

*r*> 0.89 for all data sets). The average number of analyzed microsaccades for an individual data set was 1,664 (

*SD*= 269, range = 1,265–2,133).

*M*= 9.92 × 10

^{3},

*SD*= 1.62 × 10

^{3}), compared with an exponential distribution (

*M*= 1.02 × 10

^{4},

*SD*= 1.74 × 10

^{3},

*p*< 0.001), a Gaussian distribution (

*M*= 1.00 × 10

^{4},

*SD*= 1.62 × 10

^{3},

*p*< 0.001), and a gamma distribution (

*M*= 1.04 × 10

^{4},

*SD*= 1.64 × 10

^{3},

*p*< 0.001).

*μ*values of the fitted ex-Gaussian were higher for the high-contrast compared to the low-contrast display,

*t*(10) = 2.53,

*p*= 0.0295 (Figure 4B). Microsaccade-rate is an unlikely explanation for this effect, as there was no significant difference in the rate of microsaccades between the two display conditions—high contrast: 1.23 Hz, low contrast: 1.25 Hz;

*t*(10) = 0.44,

*p*= 0.66.

*t*(10) = 3.42,

*p*= 0.0065, with smaller amplitudes for the high-contrast condition (0.37° ± 0.09° vs. 0.41° ± 0.11°). The correlations between amplitudes of pairs of consecutive saccades (Figure 4D) were significantly positive across participants in both conditions—high contrast: mean

*r*= 0.29,

*t*(10) = 7.03,

*p*< 0.001; low contrast: mean

*r*= 0.29,

*t*(10) = 8.78,

*p*< 10

^{−5}(see individual correlations in Table 1): Smaller microsaccades tended to be followed by smaller microsaccades. The qualitative findings were also obtained after excluding SWJs—difference between displays:

*t*(11) = 3.76,

*p*= 0.0037; amplitude–amplitude correlations—high contrast: mean

*r*= 0.20,

*t*(11) = 6.85,

*p*< 10

^{−4}; low contrast: mean

*r*= 0.18,

*t*(11) = 7.82,

*p*< 10

^{−4}.

*r*= −0.24,

*t*(10) = 7.27,

*p*< 0.001; low contrast: mean

*r*= −0.24,

*t*(10) = 9.45,

*p*< 0.001 (see individual correlations in Table 1). This indicates a tendency for longer IMSIs following smaller microsaccades and vice versa.

*μ*values between the two displays. This null effect supports the claim that the effect of display on IMSI was mediated by its effect on microsaccade amplitudes.

*μ*) would be longer for vertical than for horizontal grating orientations.

*r*= 0.95,

*r*> 0.84 for all data sets). The mean saccade amplitude across observers was 0.38° (

*SD*= 0.14°), and nearly all saccades (97.4%) were defined as microsaccades (smaller than 1.5°). Importantly for the purpose of this experiment, the microsaccades demonstrated a horizontal direction bias, reflected by more horizontal than vertical microsaccades—horizontal:

*M*= 2,301,

*SD*= 703; vertical:

*M*= 871,

*SD*= 727;

*t*(12) = 4.48,

*p*< 0.001 (Figure 5B). The average number of analyzed microsaccades for an individual dataset was 3,046 (

*SD*= 836, range = 1,286–4,215).

*M*= 8.55 × 10

^{3},

*SD*= 2.88 × 10

^{3}), compared with an exponential distribution (

*M*= 1.02 × 10

^{4},

*SD*= 1.74 × 10

^{3},

*p*< 10

^{−5}), a Gaussian distribution (

*M*= 1.00 × 10

^{4},

*SD*= 1.62 × 10

^{3},

*p*< 10

^{−5}), and a gamma distribution (

*M*= 1.04 × 10

^{4},

*SD*= 1.64 × 10

^{3},

*p*< 0.001).

*μ*values of the fitted ex-Gaussian distribution, with factors spatial frequency (high/medium) and orientation (horizontal/vertical). This analysis confirmed our hypothesis by showing a significant main effect of orientation resulting from higher

*μ*values for vertical than for horizontal gratings,

*F*(1, 12) = 5.57,

*p*= 0.036 (Figure 5C). Findings were consistent with the results of Experiment 2 in showing a main effect of SF, resulting from higher

*μ*values for high than for medium SF,

*F*(1, 12) = 10.55,

*p*= 0.007 (Figure 5D). There was no evidence for a SF × Orientation interaction,

*F*(1, 12) = 1.3,

*p*= 0.27.

*F*(1, 12) = 0.191,

*p*= 0.67, but there was a marginally significant effect of SF,

*F*(1, 12) = 4.67,

*p*= 0.051, resulting from a higher microsaccade rate for lower SF. To examine whether the SF effect found for the

*μ*parameter can be explained by this small effect of microsaccade rate, we correlated the difference between high and medium SF in saccade rates and in the

*μ*parameters across participants. The same analysis was performed for the orientation conditions (correlating the difference between vertical and horizontal in saccade rates and

*μ*parameters). There was no evidence for a correlation in either analysis—SF:

*r*(11) = −0.28,

*p*= 0.34; orientation:

*r*(11) = 0.12,

*p*= 0.69. We conclude that the effects of display condition on SRP duration (

*μ*) are unlikely to be explained by differences in microsaccade rate.

*F*(1, 12) = 7.52,

*p*= 0.018 (Figure 6A), resulting from smaller amplitudes for the vertical than for the horizontal orientation displays, and a main effect of SF,

*F*(1, 12) = 6.49,

*p*= 0.026, resulting from smaller amplitudes for the high-SF than for the medium-SF display (Figure 6B). There was no significant interaction (

*F*< 1). The correlations between the amplitudes of pairs of consecutive saccades were significantly positive in all conditions across participants (Figure 6C)—vertical, high SF: mean

*r*= 0.35,

*t*(12) = 8.04,

*p*< 10

^{−5}; vertical, medium SF: mean

*r*= 0.33,

*t*(12) = 8.03,

*p*< 10

^{−5}; horizontal, high SF: mean

*r*= 0.33,

*t*(12) = 8.18,

*p*< 10

^{−5}; horizontal, medium SF: mean

*r*= 0.34,

*t*(12) = 8.05,

*p*< 10

^{−5}(see individual correlations in Table 1).

*r*= −0.15,

*t*(12) = 3.41,

*p*= 0.0051; vertical, medium SF: mean

*r*= −0.17,

*t*(12) = 5.14,

*p*< 10

^{−3}; horizontal, high SF: mean

*r*= −0.15,

*t*(12) = 4.07,

*p*= 0.0015; horizontal, medium SF: mean

*r*= −0.16,

*t*(12) = 4.40,

*p*< 10

^{−3}(see individual correlations in Table 1). This indicates that smaller microsaccades tended to be followed by longer IMSIs.

*μ*effects prevail on a data set that is balanced for microsaccade amplitudes. We constructed 95% confidence intervals on this balanced data set, separately for the SF and orientation effects. Zero was not included in either interval (SF effect: [2.67, 9.77]; orientation effect: [0.65, 7.23]), supporting the existence of independent effects of SF and orientation on the SRP regardless of amplitude.

*M*±

*SD*) were

*E*

_{crit}= 7.89 ± 0.02,

*μ*

_{0}= 141 ± 31,

*σ*= 40 ± 23, and

*μ*

_{ret}= 21.6 ± 24.5 (in the high-SF condition). The revised model showed a significantly better fit to the empirical data than the original model (revised model: mean

*χ*

^{2}= 0.181,

*SD*= 0.12; original model: mean

*χ*

^{2}= 2.08,

*SD*= 0.83),

*t*(22) = 7.21,

*p*< 10

^{−6}.

^{4},

*SD*= 520) than the gamma distribution (mean BIC = 5.28 × 10

^{4},

*SD*= 529),

*t*(11) = 47.8,

*p*< 10

^{−14}, resembling the empirical data. In contrast, the simulation data sets produced by the original model showed the opposite effect: a better fit to the gamma distribution (mean BIC = 7.25 × 10

^{4},

*SD*= 359) than the ex-Gaussian distribution (mean BIC = 7.266 × 10

^{4},

*SD*= 368),

*t*(11) = −11.2,

*p*< 10

^{−6}. This suggests that the revised model is more consistent with the empirical data than is the original model.

*more*probable, this smaller central activity may render the production of microsaccades

*less*probable. This is reflected in our finding of longer intervals until the next microsaccade can be produced. Therefore, the amplitude of a preceding microsaccade is expected to induce opposite effects on the timing of subsequent large saccades and microsaccades. Consistent with this interpretation, in Experiment 1 we show that the amplitude of a microsaccade is negatively correlated with the duration of time until the next microsaccade (in a fixation task) and positively correlated with the duration of time until the next large saccade (in a free-view task).

*cause*but also the

*consequence*of the sizes of the microsaccades that are being produced.

*Vision Research*, 118, 25–30.

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