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. 2026 Mar 20;26(3):10. doi: 10.1167/jov.26.3.10

Correction of saccadic decisions during active visual search in the monkey

Anna E Ipata 1,1, James W Bisley 2,2, B Suresh Krishna 3,3
PMCID: PMC13020116  PMID: 41860311

Abstract

Prior research has shown that during tasks with very salient saccadic targets, like popout visual-search and double-step saccade tasks in both humans and monkeys, two-saccade sequences occur, where an initial saccade to a distractor is followed soon after by an ultra-short-latency (USL) second saccade to the popout target. This indicates that the USL second saccade uses visual information obtained before the first saccade and corrects the erroneous decision to make the first saccade to the distractor. Here, we demonstrate that such USL second saccades also often occur when monkeys perform active visual search without a popout visual target. Unlike in prior studies where the search target was extremely salient and therefore all second saccades showed a high accuracy toward the target, in our task, we show that USL saccades are preferentially directed towards the search-target and the transfer of target selection and location information across the first saccade is very accurate. USL saccades foveate the target more often than regular-latency saccades especially when the saccade starts far away from the target. We demonstrate various properties of USL saccades that reveal how the error-correction process is very accurate and how concurrent processing of saccade goals may proceed on the basis of computations in visual/oculomotor priority maps. Our results expand our understanding of saccadic targeting and information transfer across saccades during goal-directed active vision, lay the ground for future neurophysiological studies, and suggest that error-processing in different sensory and response modalities show similar patterns and may be accounted for by similar models.

Keywords: transsaccadic, visual search, monkey, timing, error-correction

Introduction

Humans and monkeys routinely make two to four saccadic eye movements per second to bring salient and/or relevant parts of the scene onto the fovea. Each saccade is based on a decision that chooses the next salient/relevant region in the visual scene from among a large number of alternatives, and is generally followed by a period of fixation on the chosen point (of at least roughly 125 ms) during which the chosen region is analyzed and the next saccade target is chosen. However, during visual search for popout targets and double-step saccade tasks where the target is very salient (Becker & Jurgens, 1979; Findlay, Brown, & Gilchrist, 2001; Johnson & Fleming, 1963; McPeek & Keller, 2001; McPeek, Skavenski, & Nakayama, 2000), two-saccade sequences have been shown to occur, where an initial saccade to a distractor is followed soon after by an ultra-short-latency (USL) second saccade to the target, with little time spent fixating the initially chosen distractor. These ultra-short latencies are less than 125 ms and often even less than the 80 ms required for express first-saccades (Boch & Fischer, 1986; Chen, Liu, Wei, & Zhang, 2013; Fischer & Boch, 1983; Schiller, Haushofer, & Kendall, 2004), elicited by a single predictable saccadic target and a gap between fixation point offset and saccade target onset. Such ultra-short latencies mean that there is little time during the interval between the first and second saccades to allow for saccade goal selection, saccade preparation and execution. Thus the USL second saccade, that corrects the erroneous decision to make the first saccade to the distractor, is at least partly based on information processing before the first saccade that is carried over across the first-saccade and used to execute the second saccade (McPeek & Keller, 2001; McPeek & Keller, 2002b; McPeek et al., 2000; Mokler & Fischer, 1999; Morrison, 1984; Theeuwes, Kramer, Hahn, & Irwin, 1998; Viviani & Swensson, 1982). Such saccades have also been observed in other circumstances like reading (Morrison, 1984), anti-saccade tasks (Amador, Schlag-Rey, & Schlag, 1998; Mokler & Fischer, 1999), modified double-step like search tasks (Murthy, Ray, Shorter, Schall, & Thompson, 2009; Sharika, Ray, & Murthy, 2009) and in response to abrupt onsets (Irwin, Colcombe, Kramer, & Hahn, 2000; Kramer, Cassavaugh, Irwin, Peterson, & Hahn, 2001; Theeuwes et al., 1998; Theeuwes, Kramer, Hahn, Irwin, & Zelinsky, 1999).

We were interested in seeing how these USL saccades manifested themselves during a more naturalistic, active visual search task without a popout visual target where eye movements were neither rewarded nor punished and the monkeys were free to move their eyes as they pleased while performing the task. We demonstrate that USL second saccades also occur frequently when monkeys perform active visual search without a popout visual target. We then characterize several novel properties of USL saccades that support the existence of accurate and precise trans-saccadic carryover of information about the location of the search target, and indicate the influence of surround suppression on saccade goal processing. Our results support the idea that more than one saccade goal can be processed concurrently and shed further light on how saccade goal selection may proceed on the basis of computations in visual/oculomotor priority maps (Bisley & Goldberg, 2010).

Methods

We used a dataset from which we have previously published results (demonstrating an extrafoveal preview benefit) in the Journal of Vision (Krishna, Ipata, Bisley, Gottlieb, & Goldberg, 2014). The work here uses the same data but does not otherwise overlap with the previous publication. The data-collection methods are identical to those described in more detail there. In brief, and to recapitulate, we implanted head restraining devices, recording chambers, and scleral search coils (Judge, Richmond, & Chu, 1980) on two male rhesus monkeys (Macaca mulatta) weighing approximately 9–10 kg using sterile surgery under ketamine and isofluorane anesthesia. The Animal Care and Use Committees at Columbia University and the New York State Psychiatric Institute approved all experimental protocols as complying with the guidelines established in the Public Health Service Guide for the Care and Use of Laboratory Animals; the work adheres to the ARVO Animal Statement. Behavioral control and data collection (eye-position traces, stimulus events, and bar-release times) were performed on a Dell Optiplex computer using the REX system (Hays, Richmond, & Optican, 1982) running on the QNX operating system. Eye movements were sampled every millisecond using scleral search coils. Visual stimuli were back-projected on a tangent screen approximately 75 cm from the monkey in a dimly lit room by a Hitachi CP-X275 LCD projector under the control of a Dell Dimension PC running the GLVEX graphics system. The background luminance measured with a Minolta LS-110 Luminance Meter was 50 cd/m2 and the projector refresh rate was 60 Hz. We calculated stimulus timing by measuring a pulse from a photocell affixed to the back of the screen and illuminated by a small square on the corner of the same video frame as the appearance of any new stimulus. The monkey could not see the photocell or its illumination square. Monkeys were trained to sit in a primate chair with their head fixed and perform the tasks used in this experiment. Rare trials where the photocell failed to register the appearance of the search array were discarded.

Behavioral task

We trained experimentally naive monkeys to perform an active visual search task (Ipata, Gee, Goldberg, & Bisley, 2006; Ipata, Gee, Gottlieb, Bisley, & Goldberg, 2006) in which they were required to report the orientation of a search target among seven, eleven or fifteen distractors (Figure 1A), with the three set-sizes presented in equal proportion (Krishna et al., 2014). Each trial started when the monkey took hold of two bars, one with each hand. A central spot (a high contrast white square of 0.35° width) appeared, and, if the animal maintained fixation for 1–1.75 seconds within a 3° × 3° window, the fixation point was extinguished and the stimulus array appeared. The monkeys had three seconds to report the orientation of the search target by releasing one of the two bars, and were rewarded with a small drop of water or juice if they released the correct bar for that trial. The monkeys were not required to fixate the search target before releasing a bar to indicate their response.

Figure 1.

Figure 1.

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(A) Schematic diagram of the main task. Monkeys fixated a central fixation point while holding two bars, one with each hand. An array of search stimuli containing one target (an upright or inverted-T) and 7, 11 or 15 distractors was then presented at an eccentricity of 10 degrees. The monkeys were free to move their eyes as they wished and were rewarded if/when they released the bar that corresponded to the target stimulus present in the array. (B) Schematic diagram of trials where monkey foveated either the search target or a distractor with the second saccade. Numbers alongside the search stimuli indicate that they were the endpoints of the respective saccades. (C) Schematic of time-course of three example trials. 1s, 2s and 3s (1e, 2e and 3e) represent the start (end) of the first, second and third saccades. RT is the release of the bar and 0 represents the appearance of the search stimuli (trial start). Fix1 and Fix2 are the first and second fixation periods.

A search target was present on each trial, and consisted of an upright or inverted T. The monkeys had to release the left bar if the upright-T appeared in the array or the right bar if the inverted-T appeared. The distractors were black cross-like stimuli, of the same width and length as the search target, but whose vertical and horizontal components crossed each other at different levels. There were two categories of distractors: difficult distractors, in which the horizontal line crossed the vertical line near its top or bottom, and easy distractors in which the horizontal line crossed the vertical line closer to its center. There were three task-types, which were run in separate blocks: the target-popout task, where the search target was easily detectible as a result of being green and brighter than the distractors which were black on a gray background, the distractor-popout task where one of the distractors was easily detectible as a result of being green and brighter than the remaining stimuli which were black on a gray background, and the non-popout task, where all the stimuli were black on a gray background with identical luminance. We only present data from the distractor-popout and non-popout tasks here. The search target and distractors were 1.0° wide and 2.9° tall, had a contrast well above detection threshold and were positioned equidistantly along an imaginary circle at an eccentricity of 10°, with one stimulus at the location x = 10°, y = 0°. The number of distractors (seven, 11, or 15), search target location and type of search target (inverted-T or upright T) varied randomly on each trial.

Eye movement measurement

We used a magnetic field coil system (Crist Instruments) to measure eye positions. We calibrated the system at the beginning of each session by having the monkeys fixate a series of fixation stimuli at different points on the screen. We further corrected our measured eye-positions offline using an iterative procedure where we first assigned each saccade endpoint to a particular stimulus based on the raw stimulus location, then collected together the endpoints belonging to each stimulus, used its median as the new estimate of stimulus location and repeated this procedure twice. When estimating the accuracy on USL saccade trials, we recalculated mean stimulus locations after excluding saccades that preceded USL saccades, as well as USL saccades themselves from the procedure used to determine mean stimulus locations.

Data analysis

We performed all data analysis using MATLAB (The Mathworks Inc., Natick, MA, USA) and (R Team, 2007). To plot smooth curves capturing the variation in scatter plots, we used local univariate and bivariate regression methods based on maximization of local likelihood (Loader, 1999), as implemented in the Chronux MATLAB package (http://www.chronux.org). We used a locally linear tricube kernel with a variable nearest-neighbor span. The quasi-gamma distribution was used for Figure 2, the binomial distribution for the probabilities in Figures 3 through 5 and 7, and the quasi-Gaussian distribution for Figure 6. We chose the nearest-neighbor parameter based on plots of the Akaike Information Criterion, as well as visually inspecting the fits. We used parameter values of 0.4, 0.8, 0.6, 0.8, 0.6 and 0.6 in Figures 2 through 7, respectively. Larger values for the nearest-neighbor parameter reflect greater smoothing, but our results are very robust to the choice of smoothing parameter. We present the results of the fit by plotting fitted means and simultaneous confidence bands: the simultaneous confidence bands are wider than regular confidence bands because they attempt to correct for the inherent multiple comparisons along the X axis. To test whether a single curve (plotting expected value of Y for each value of X) deviated from a horizontal line, we used a permutation test (Bowman & Azzalini, 1997) that we call the “no-effect test.” This assessed whether the effects seen could be predicted as a result of random variability arising from a “no-effect” model where the expected value of Y was the same for all values of X. We shuffled the relationship between the ordinate and abscissa values and repeated the local likelihood fit and then estimated the statistic Dlocfit defined as (−2)*log-likelihood. We calculated the same statistic (Dnoeffect) for the null “no-effect” model where the predicted ordinate value for each abscissa value was simply equal to the global mean of the ordinate values. We then estimated the ratio-statistic (Dnoeffect − Dlocfit)/Dlocfit and repeated the shuffling process 1000 times to obtain the null distribution for this ratio-statistic. By comparing the ratio-statistic from the actual data to the percentiles of this null distribution, we obtained a p value for the likelihood that a ratio-statistic as large as that in the actual data could be obtained from the null no-effect model. We also used another permutation test to assess whether fitted curves were significantly different for two different groups with the same X and Y parameters, but with different cofactors associated with each group (Bokil, H.S. and Mitra, P.P; personal communication); we refer to this as the co-factor test. We defined a maximum absolute deviation statistic MAD = max(|f(x) − g(x)|) where f and g are the two fitted curves computed from groups 1 and 2, respectively; in general f arises from m samples and g arises from n samples. We then pooled the two samples into one large m + n–sized sample, randomly assigned m of these to group 1 and n to group 2 and recomputed MAD. We repeated this process 1000 times to estimate the null distribution for MAD, under the assumption of “no-group effect” (i.e., f = g). We then compared the actual observed value of MAD to the percentiles of this null distribution to obtain a p value for the possibility that a maximum absolute deviation between the two curves as large as the one observed could arise entirely by chance.

Figure 2.

Figure 2.

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Both monkeys make second-saccades with ultra-short latencies. (A) Larger-amplitude regular-latency saccades show a tendency towards shorter second-saccade latencies, as seen in the plot of second-saccade latency (ordinate) vs. second-saccade amplitude (abscissa) and the superimposed means and 95% simultaneous confidence bands estimated via local regression (see Methods). The local regression here only included regular-latency saccades. The horizontal line shows the rough boundary at 75 ms between regular-latency and USL saccades. (B) Frequency polygon of second-saccade latencies (black) and first-saccade latencies (gray) with vertical line at the 75 ms boundary separating USL second saccades from regular-latency second saccades. Data from monkey Z in the left column and data from monkey R in the right column.

Figure 3.

Figure 3.

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The probability that a regular-latency second saccade (gray lines) will foveate the search target drops when the first saccade ends further from the target. However, for USL second saccades (black lines), this probability increases (monkey Z, left column) or remains constant (monkey R, right column) when the first saccade ends further from the target. Curves represent means and 95% simultaneous confidence bands estimated via local regression. Abscissa: Distance of first-saccade endpoint from target. Ordinate: Probability that second saccade leads to target-foveation. Frequency polygons at the bottom show the discretized probability distribution of first-saccade endpoint distances from the target for regular (gray) and USL (black) saccades, allowing a quick estimate of the relative frequency of data points used to calculate the target-foveation probability.

Figure 5.

Figure 5.

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(A) Larger-amplitude second saccades are more likely to be USL saccades. Abscissa: Second-saccade amplitude (degrees). Ordinate: Probability that the second saccade is a USL saccade. Curves represent means and 95% simultaneous confidence bands estimated via local regression. Data from monkey Z in the left column and data from monkey R in the right column. Frequency polygons at the bottom show the discretized probability distribution of second-saccade amplitudes. The frequency polygons in the insets are the discretized probability distributions of saccade amplitude for regular-latency (gray) and USL second saccades (black), and confirm that USL saccades have a greater tendency to have larger amplitudes than regular-latency saccades. (B) First saccades that end further from the target are more likely to be followed by a USL second saccade. Format similar to A, except that instead of second-saccade amplitude, the distance of the first-saccade endpoint from the search target is plotted in the abscissa, as well as in the insets.

Figure 7.

Figure 7.

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Shorter-latency second saccades are preceded by smaller-amplitude (hypometric) first saccades. Abscissa: Second-saccade latency (ms). Ordinate: Amplitude of the preceding first saccade. Curves represent means and 95% simultaneous confidence bands estimated via local regression. Data from monkey Z in the left column and data from monkey R in the right column. Frequency polygons at the bottom show the discretized probability distribution of second-saccade latencies.

Before analyzing the eye-position data, we first filtered eye-position traces using a rectangular boxcar filter of 5 ms duration, and then detected saccades using an eye-velocity threshold criterion. The threshold was set at a level slightly above the random noise in the eye-position signal to catch small within-stimulus saccades while keeping the false-positive rate from a purely noisy signal close to zero. We defined saccade onset as the time at which eye-velocity crossed the threshold in an upward direction, and defined the end of the saccade as the time at which the eye-velocity re-crossed the threshold in a downward direction. Additionally, saccades had to be at least 5 ms long to qualify. This algorithm performed robustly under our conditions and we verified this by visually inspecting the eye-position traces and the detected saccades. Varying the threshold over reasonable values (based on different definitions for what constituted the end of a saccade) only led to small changes of less than around 10 ms in the inter-saccadic interval, and we verified that our results remain robust when varying the threshold.

As in a previous study of active visual search in monkeys (Motter & Belky, 1998), the monkeys moved their eyes on every trial, directing most saccades to one of the objects in the array and rarely towards blank regions of space in between or away from stimuli. Both monkeys fixated the search target on the first saccade on about half the trials and the monkey fixated the search target with the second saccade in the majority of the remaining multi-saccade trials (see below). Although the task did not require the monkeys to fixate the target, overall, both monkeys fixated the target on over 99% of trials on upright-T trials. Monkey R showed a very similar behavior during inverted-T trials, again fixating the target on over 99% of trials, but monkey Z fixated the inverted-T target on only 54% of the trials; this difference was strongly significant (p < 10−12, test of proportions). However, despite not fixating the inverted-T target on a substantial proportion of trials, monkey Z actually performed slightly better overall on inverted-T trials (99.8%) compared to upright-T trials (92.9%), whereas monkey R performed at more than 99% correct on both trial types. Detailed analysis that we have presented previously (Krishna et al., 2014) shows that both monkeys were aware of the significance of the two types of T-targets and searched for them during the task. However, while monkey R only released the bar only after finding (and foveating) the search target, monkey Z used the inverted-T response as a default response when he had not identified the search target within some time. The strategy was successful because monkey Z was very good at detecting the upright-T stimulus and therefore if the monkey failed to locate a search target stimulus within a few saccades, the monkey could be quite certain that the display contained an inverted-T rather than an upright-T (because there were no catch trials). Because of this differential behavior on inverted-T trials in monkey Z, we only present data from upright-T trials for this monkey. For monkey R, we pooled the data from upright and inverted-T trials because we did not find any meaningful differences for our results between these two trial types. We present the data after omitting trials where the first saccade was made to the popout distractor to avoid contaminating the results with patterns that are specific to popout distractor processing (Ipata, Gee, Gottlieb, et al., 2006), but our results remain substantially unchanged even if these trials are included. Few subsequent saccades were directed to the popout distractor (5.2% in monkey Z and 3.8% in monkey R; the values were 3.5% in monkey Z and 3.2% in monkey R for the second saccade).

Although most saccades landed near one of the stimuli in the search array, both monkeys made “within-stimulus” saccades that started and ended close to the same stimulus. These saccades were usually made after foveating the target (25.3% of target-foveating saccades compared to 5.4% of distractor-foveating saccades for monkey Z and 44.4% compared to 2.9% for monkey R), consistent with results from human subjects (Findlay et al., 2001). In our data, this may be related to the fact that mean fixation times on the target (224 ± 0.6 ms in monkey Z and 261 ± 0.9 ms in monkey R) were substantially longer than those on the distractor (145 ± 0.6 ms in monkey Z and 119 ± 0.7 ms in monkey R) and the probability of a within-stimulus saccade was very highly correlated with the length of the fixation time on that stimulus (p < 10−16 for both monkeys in a generalized linear model fit). These within-stimulus saccades were small in amplitude (1.15° ± 0.02° in monkey Z, 0.9° ± 0.01° in monkey R) and comparable to the dimensions of the stimuli (1.0° wide and 2.9° tall). We did not include these small within-stimulus saccades when counting the number of saccades or when calculating intersaccadic intervals; only saccades that moved the eye from one stimulus to another were included. The latency of a following saccade was thus calculated from the end of the last saccade that was not a within-stimulus saccade. In any case, this decision did not have any practical effect on our conclusions, because as mentioned above, only a small proportion of distractor fixations contained a within-stimulus saccade.

Results

We collected data from two monkeys. We first present some baseline summary data about the monkeys’ search strategy and scanpath in order to provide context to our analyses. A more detailed description of the general properties of the dataset has been presented previously (Krishna et al., 2014). Both monkeys released one of the two bars after search array onset on every trial they initiated successfully. The monkeys moved their eyes on every trial, directing most saccades to one of the objects in the array. Saccades were stimulus-directed and rarely directed towards blank regions of space in between or away from stimuli, as in a previous study of active visual search in monkeys (Motter & Belky, 1998). Because our results were generally similar across the two kinds of blocks (distractor-popout and non-popout) or at different set-sizes, we merged the data across the two task-types and three set-sizes and present results for the combined dataset. Monkey Z performed a total of 13,159 trials over 10 sessions (6116 distractor-popout and 7043 non-popout), whereas Monkey R performed a total of 6057 trials over five sessions (2758 distractor-popout and 3299 non-popout). Monkey Z released the correct bar on over 96% of trials and monkey R on more than 99% of trials. Both monkeys fixated the search target on the first saccade on about half the trials and then released the correct bar, a rate well above chance (56.5% for upright-T trials in monkey Z; 56.7% and 42.1% for upright-T and inverted-T trials respectively in monkey R; p < 10−12, binomial test). On the remaining trials, they made additional saccades to foveate the target and release the bar. We focus our analysis here on the second saccade in these trials, because the monkeys foveated the target with the second saccade in the majority of cases during these trials (78.6% in monkey Z and 67.0% in monkey R). Focusing on the second saccade latency (first inter-saccadic interval; Figure 1C) also increases interpretability by limiting the number of potential influences from preceding saccades on the observed effects: trans-saccadic processing would imply the use of information gathered during the first fixation before the eyes had moved and when all search stimuli were roughly at the same retinal eccentricity.

Both monkeys made their first saccade with short latencies (mean = 132 ms and SEM = 0.1 ms in monkey Z, mean = 152 ms and SEM = 0.2 ms in monkey R; gray frequency polygons in Figure 2B), consistent with results from a previous study of active visual search in monkeys (Motter & Belky, 1998). Over 90% of saccade endpoints lay within 2° of the center of the nearest stimulus for both monkeys at all three set-sizes, a distance comparable to the stimulus dimensions themselves (1.0° wide and 2.9° tall).

Regular and USL second saccades

Both monkeys made numerous USL second saccades (with ultra-short intersaccadic intervals between the first and second saccade: Figure 2). we roughly categorized the second-saccade latencies into USL and regular-latency groups using a categorical boundary of 75 ms. This was done based firstly on theoretical considerations: the fastest known stimulus-evoked saccades (express saccades) have a minimum latency of about 75 to 80 ms (Bibi & Edelman, 2009; Fischer, Boch, & Ramsperger, 1984), and this is also consistent with the known latencies of neural activities in relevant cortical areas of the monkey (Bisley, Krishna, & Goldberg, 2004). 75 ms is therefore a conservative choice for determining USL saccades. Additionally, based on the data itself, the distribution of second saccade latencies (black lines, Figure 2B) seems to show a clear rise in probability at around 75 ms, and in the scatter plot of second-saccade latencies versus second-saccade amplitudes (Figure 2A), a line at 75 ms seems to separate two groups of saccades (especially in monkey R). We do not mean to imply that the value of 75 ms forms a strict boundary that separates saccades with different properties; in fact, the plot in Figure 4 seems to suggest that a boundary of 100 ms may be where target foveation probability starts to drop for monkey R. We show that the boundary setting of 75 ms was not critical for the results in the rest of this paper: using a value of 100 ms did not change the results meaningfully (Appendix Figure A1). Using a boundary of 75 ms, 16.2% and 10.2% of second saccades were USL saccades in monkeys Z and R, respectively. Regular-latency second saccades showed a weak tendency to have shorter latencies at larger second-saccade amplitudes (linear regression for regular-latency second saccades alone: slope ± SEM = −0.04 ± 0.005 and −0.05 ± 0.005, r2 = 0.03 and 0.04 for monkeys Z and R respectively; p < 0.0001).

Figure 4.

Figure 4.

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When the first saccade ends further away (more than 10°) from the target, USL second saccades are more likely to foveate the target. This is not true when the first saccade ends within 10° of the target. Abscissa: Second-saccade latency (ms). Ordinate: Probability that the second saccade will foveate the target. Curves represent means and 95% simultaneous confidence bands estimated via local regression. Data from monkey Z in the left column and data from monkey R in the right column. Frequency polygons at the bottom show the discretized probability distribution of second-saccade latencies. Second-saccade latencies below the 2nd percentile and above the 98th percentile omitted from analysis.

USL saccades are associated with trans-saccadic information about search target location and identity

Despite the reduced fixation time preceding them, USL saccades did not show a reduced overall probability of foveating the search target compared to regular-latency saccades: the overall probability that a USL saccade would foveate the search target was either similar to that for regular-latency saccades (monkey Z: 79.5% for USL and 78.5% for regular-latency, p = 0.7590, test of proportions) or greater than it (monkey R: 79.6% for USL and 65.2% for regular-latency, p < 0.0001, test of proportions). However, a clearer difference emerged when the distance of the first-saccade endpoint from the search target was taken into account. Regular-latency second saccades were less likely to foveate the search target if the preceding first saccade ended further away from the search target (Figure 3, solid curves; p < 0.0001 for both monkeys, no-effect test). This is expected if the monkey used visual information gathered during the second fixation while choosing the goal of the second saccade, because the amount of available information during the second fixation is much lower when the first saccade ends far away from the search target. In contrast, the probability that a USL second saccade would foveate the target either slightly increased (monkey Z, p = 0.0100, no-effect test) or stayed constant (monkey R, p = 0.3220, no-effect test) because the preceding first-saccade ended further away from the search target (Figure 3, dotted curves). Despite the brief intersaccadic interval between the first and second saccade, USL second saccades accurately foveate the target even when the target is highly eccentric on the retina during the intersaccadic interval. Note that during the first fixation, the eye was near the center and so when the eye landed further than around 10° from the target on the first saccade, it was further from the target than it was during the first fixation. These results thus indicate that the targeting of USL second saccades is not influenced by visual information gathered during the second fixation but is rather based on information obtained during the first fixation interval and carried over across the first saccade.

As mentioned above, USL and regular-latency saccades are not discrete populations—instead, the influence of information gathered during the first fixation, relative to that gathered during the second fixation, is likely to drop with the duration of the second fixation. To show this, we replotted the same data as in Figure 3, but as a continuous function of second-saccade latency while instead categorizing the first-saccade endpoint point distance as near (within 10° of the search target) and far (further than 10° from the search target). When the first saccade landed far from the target, the probability that the second saccade would foveate the search target smoothly decreased for longer second-saccade latencies (p < 0.0001, no-effect test; Figure 4). In other words, USL second saccades had a higher probability of target-foveation when the first saccade ended far away from the search target. On the other hand, when the first saccade ended close to the search target, the probability of target foveation either increased (monkey Z, p < 0.0001, no-effect test) or slightly decreased (monkey Z, p = 0.0360, no-effect test) at longer second-saccade latencies.

When the first saccade ends far from the target, USL saccades have a higher probability of target-foveation than regular-latency saccades (Figure 3), despite the shorter time for processing during the second fixation (Monkey Z: 82.94% (USL) vs. 71.48% (regular-latency), p = 0.0003, test of proportions; Monkey R: 79.76% (USL) vs. 57.38% (regular-latency), p < 0.0001, test of proportions). The increased probability of target-foveation for USL saccades in this condition again suggests the presence of additional information about target location obtained during the first fixation. Importantly, the time required to release the correct bar after foveating the search target was also shorter for USL saccades, compared to regular-latency saccades, when the first saccade ended far from the search target (Monkey Z: 320.86 ± 6.07 ms (USL) vs. 344.19 ± 3.30 ms (regular-latency), p = 0.0008, t-test; Monkey R: 268.58 ± 6.16 ms (USL) vs. 290.46 ± 3.19 ms (regular-latency), p = 0.0060, t-test). This suggests that in addition to information about target location, trials with USL saccades were also aided by information about target identity or impending target appearance that was transferred across the first saccade (see Discussion). In contrast, when the first saccade ended close to the target, Monkey Z showed a significant reduction in target-foveation probability for USL saccades (Figure 3; 68.35% [USL] vs. 89.08% [regular-latency], p < 0.0001, test of proportions), accompanied by a small, statistically non-significant increase in the time to release the bar after target-foveation (316.57 ± 13.53 ms [USL] vs. 297.09 ± 3.88 ms [regular-latency], p = 0.1485, t-test). Monkey R did not show a significant difference for either measure (79.17% [USL] vs. 78.14% [regular-latency], p = 0.9578, test of proportions; 237.70 ± 9.34 ms [USL] vs. 230.61 ± 3.95 ms [regular-latency], p = 0.1485, t-test). We suggest that when the first saccade ends close to the target, the search target is close enough to the fovea during the second fixation period of regular-latency saccades for search target location and identity information to be obtained during this period, thereby eliminating the advantage for USL saccades.

Confirming a previous report from humans (Findlay et al., 2001), USL second saccades almost never followed first saccades that foveated the saccade target. Of the first saccades that foveated the target, only 0.20% in monkey Z and 0.14% in monkey R were followed by a USL second saccade. In contrast, of the first saccades that foveated a distractor, 18.64% in monkey Z and 10.71% in monkey R were followed by a USL second saccade. Because the USL saccade has to be based on visual information obtained during the first fixation (preceding the first saccade), this suggests that information about the target/distractor identity of the first saccade goal must be available in the visual information gathered before the first saccade, so that if the first saccade goes towards the search target, a following USL second saccade is not planned (see Discussion).

USL second saccades are more likely to be larger, and to follow short-latency first saccades that end further from the target

In the scatter plot of second-saccade latency vs. second-saccade amplitude in Figure 2, the relative sparseness of USL saccades (with second-saccade latencies below 75 ms) at small saccade amplitudes is visible (compared to the density at large saccade amplitudes). The likelihood of a second saccade having an ultra-short latency increases with second-saccade amplitude (Figure 5A; p < 0.0001 in both monkeys, no-effect test). Because we have also shown that USL saccades show a high probability of foveating the target (Figures 3 and 4), this would suggest that the first saccades preceding USL second saccades should also show a tendency to end further away from the search target. Such an effect was indeed found: the likelihood of a second saccade being a USL saccade increased when the preceding first saccade ended further away from the search target (Figure 5B; p < 0.0001 in monkey Z and p = 0.0050 in monkey R, no-effect test). We note that these two measures analyzed here in Figure 5 are not redundant: the distance of the first saccade and the amplitude of the second saccade are only highly correlated for second saccades that actually go to the target.

We also found that first saccades with shorter latencies were more likely to be followed by USL second saccades (Figure 6; p < 0.0001 in both monkeys, no-effect test). The dependence of USL probability on these two explanatory variables related to the first saccade (first-saccade latency and first-saccade endpoint distance from target) remained significant when both these variables were included in a multiple-regression (all p-values < 0.005). This was true even though there was a weak, but statistically significant, correlation in monkey R between the two explanatory variables for first saccades that did not foveate the target (r2 = 0.6%, p = 0.0001).

Figure 6.

Figure 6.

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Shorter-latency first saccades are more likely to be followed by USL second saccades. Abscissa: First-saccade latency (ms). Ordinate: Probability of a USL second saccade. Curves represent means and 95% simultaneous confidence bands estimated via local regression. Data from monkey Z in the left column and data from monkey R in the right column. Frequency polygons at the bottom show the discretized probability distribution of first saccade latencies. First-saccade latencies below the 1st percentile and above the 98th percentile omitted from the plots.

Together, these data show that USL second saccades are preferentially directed to the search target. They are also more likely to be triggered when the first saccade is of shorter-latency and ends further away from the search target, and when the second saccade is of larger amplitude than regular-latency saccades. These effects may serve as an important constraint for models of concurrent saccade-goal processing during the first fixation.

Shorter second saccade latency is associated with less accurate first saccades

Finally, second saccades with shorter latencies were preceded by first saccades with smaller amplitude (less than 10 degrees); these were also more hypometric given that the search stimuli were all around 10 degrees eccentricity during the first fixation and assuming that the first saccade was directed towards one of these search stimuli (Figure 7; p < 0.0001 in both monkeys, no-effect test). This is consistent with prior reports of USL saccades in monkeys and humans (McPeek & Keller, 2001; McPeek et al., 2000) and also with results from double-step tasks (Becker & Juergens, 1979).

Discussion

During visual-search and double-step saccade tasks in both humans and monkeys, intersaccadic intervals between the first saccade and the following second saccade (i.e., the second-saccade latency) can be much shorter than the usual latency for saccades based on retinal information (Findlay et al., 2001; Johnson & Fleming, 1963; McPeek & Keller, 2001; McPeek et al., 2000). Such USL saccades have also been observed in other circumstances like reading (Morrison, 1984), anti-saccade tasks (Amador et al., 1998; Mokler & Fischer, 1999), modified double-step like search tasks (Murthy et al., 2009; Sharika et al., 2009) and in response to abrupt onsets (Theeuwes et al., 1998). Conceptually similar results have since been reported from the reach system and this has initiated a research program focused on what is characterized as a “change-of-mind” (Resulaj, Kiani, Wolpert, & Shadlen, 2009; Song & Nakayama, 2008; Song, Takahashi, & McPeek, 2008). Such ultra-short latencies mean that there is little time during the interval between the first and second saccades to allow for saccade goal selection, saccade preparation and execution. Thus the USL second saccade, which corrects the erroneous decision to make the first saccade to the distractor, is at least partly based on information processing before the first saccade that is carried over across the first-saccade and used to execute the second saccade (McPeek & Keller, 2001; McPeek & Keller, 2002b; McPeek et al., 2000; Mokler & Fischer, 1999; Morrison, 1984; Theeuwes et al., 1998; Viviani & Swensson, 1982).

Here, we demonstrate that USL second saccades also occur frequently when monkeys perform active goal-directed visual search without a popout visual target. Unlike regular saccades with longer latencies, USL saccades preferentially foveate the target even when they start farther away from the search target, confirming that their targeting is dominated by information obtained during the first fixation when all stimuli are equidistant from the fixation point. They almost never occur when the first saccade is directed towards the search-target, thus demonstrating that the error-correction process is very accurate. Specifically, unlike in prior studies where the search target was extremely salient and therefore all second saccades showed a high accuracy towards the target, in our task, USL saccades have a target-foveation probability that is greater than that for regular-latency saccades when the saccade starts far away from the target. Regular-latency saccades on the other hand show a systematic drop of target-foveation probability when they start further away from the search target, consistent with the reduced target-detection probability at higher eccentricities that has been observed before in both humans (Findlay et al., 2001) and monkeys (Motter & Belky, 1998) as a result of the well-known decrease in conspicuity for eccentric stimuli (Makela, Whitaker, & Rovamo, 1993; Virsu, Nasanen, & Osmoviita, 1987). The boundary of approximately 75–100 ms at which this transition in properties between USL and regular-latency saccades was observed may correspond to the amount of time required for newly acquired visual information (from the second fixation) to be able to guide second-saccade targeting. Different studies have used different boundaries to arbitrarily categorize saccades into ultra-short and regular-latency saccades, with values including 90 ms (Findlay et al., 2001), 100 ms (McPeek & Keller, 2001; McPeek et al., 2000) and 125 ms (McPeek & Keller, 2002b). We used a boundary of 75 ms here based on the shape of the second-saccade latency histogram (Figure 2) and because this value represents a lower limit for saccades based on visual information obtained during the preceding fixation: the shortest saccadic latencies in response to a visual stimulus in monkeys are about 70 to 80 ms for express saccades (Boch & Fischer, 1986; Chen et al., 2013; Fischer & Boch, 1983; Schiller et al., 2004). However, our data show that this boundary is not a strict one, and there is a smooth transition in properties as the second-saccade latency becomes longer and is based more on visual information obtained during the preceding second fixation. In fact, a study using the technique of classification images has shown that even regular-latency second saccades in visual search under some conditions can be driven by information obtained mainly during the fixation preceding the first saccade (Caspi, Beutter, & Eckstein, 2004).

We note that these data were collected soon after the performance of the monkeys had stabilized on the task and before any physiological data collection on this task, and so the monkeys were not as over-trained as is often the case in physiology studies. Further, we have found very similar results in humans performing a variety of non-popout visual search tasks with only minimal practice [unpublished data, as well as our recent preprint (Jurewicz, Liao, & Krishna, 2023)]. We are therefore confident that the presence of USL saccades is not an artifact of overtraining of monkeys performing behavioral tasks.

Available physiological data about USL saccades support the idea that they are based on information transferred across saccades (McPeek & Keller, 2002b). In a target-popout task, USL second saccades were associated with enhanced target-elicited neural activity in the superior colliculus prior to the first saccade; this activity was not found before regular-latency second saccades. McPeek and Keller (2002b) proposed that this enhanced target-related activity in the superior colliculus indicated selection of the popout target before the first saccade, but too late to cancel the impending saccade to the distractor. Under such circumstances, they proposed that this target-related activity was remapped to account for the intervening eye movement (Mays & Sparks, 1980; Sparks & Porter, 1983; Walker, Fitzgibbon, & Goldberg, 1995) and then triggered a spatially accurate USL second saccade to the popout target. In our task where the search target does not pop out, both search targets and distractors could trigger USL second saccades; however, search targets were far more likely to trigger them. Following the proposal of McPeek et al. and similar models from the motor change-of-mind literature (Atiya, Rano, Prasad, & Wong-Lin, 2019; Evans, Dutilh, Wagenmakers, & van der Maas, 2020; Resulaj et al., 2009), we propose that USL saccades reflect the selection of a new saccade goal in the “dead time” (Ludwig, Mildinhall, J. W., & Gilchrist, 2007) between the selection of the first saccade goal and the actual execution of the saccade. Our data indicate that this new saccade goal is most likely to be the search target as a result of its enhanced “top-down” priority, and this is consistent with the fact that monkeys are very efficient at foveating the search target on the first saccade. The new saccade goal is likely to be represented in the visual/oculomotor priority map representations of areas like the superior colliculus, frontal eye field and lateral intraparietal area: available physiological data support such a representation in the superior colliculus (McPeek & Keller, 2002a) and also in the frontal eye field (Murthy, Thompson, & Schall, 2001). This representation of the second saccade goal must then be remapped to compensate for the intervening first saccade. Because USL saccades are often made after intersaccadic intervals of less than 50 ms, they must depend upon anticipatory or predictive remapping of the saccade goal as has been found in the lateral intraparietal area (LIP) (Duhamel, Colby, & Goldberg, 1992; Mirpour & Bisley, 2012), superior colliculus (SC) (Walker et al., 1995), frontal eye field (FEF) (Umeno & Goldberg, 1997) and extrastriate cortex (Nakamura & Colby, 2002). Indeed, some neurons in the FEF signal the compensated (remapped) saccade even before the end of the first saccade (Murthy et al., 2007). The high probability of target foveation for USL saccades indicates that saccade goal remapping is sufficiently fast and accurate to account for the intervening saccade. Finally, our data indicate that on trials with USL second saccades, the monkeys needed less time to make a response after fixating the target (compared to trials with regular-latency saccades). This suggests that the monkeys needed less time to process the target and execute the response when fixating the target with a USL second saccade; such an effect could emerge if information about target identity was obtained during the first fixation and transferred trans-saccadically to speed up bar-release after the second saccade. This identity information could either be (a) target versus distractor, thus marking the second saccade as being target-directed and speeding up the bar-release process in general via an “alerting response” that allowed a response to be prepared and executed faster or (b) could also contain information about upright versus inverted-T, thus speeding up the release of the specific bar corresponding to the target. This result, along with the extreme rarity of USL second saccades after a first saccade to the target, highlight the role of preview benefit in modulating the planning and execution of USL saccades.

We also found that compared to regular-latency saccades, USL saccades are likely to have larger amplitudes and start further away from the search target, consistent with the suggestion made in a previous study based on double-step saccades from human subjects (Becker & Jurgens, 1979). We hypothesize that these effects result from an interaction between the top-down priority of the search target stimulus and the role of lateral inhibitory influences. These amplitude effects may be potentially related to the functional concept that has emerged from a different literature looking at saccade latencies to sudden changes, that far (and small) targets require more urgent responses than near (and large) targets since far and small targets are less visible from the current fixation (De Vries, Azadi, & Harwood, 2016; Harwood, Madelain, Krauzlis, & Wallman, 2008; Madelain, Krauzlis, & Wallman, 2005). Lateral inhibitory influences are known to play a role in the superior colliculus (Meredith & Ramoa, 1998; Munoz & Istvan, 1998), frontal eye field (Schall & Hanes, 1993; Schall, Sato, Thompson, Vaughn, & Juan, 2004) and lateral intraparietal area (Falkner, Goldberg, & Krishna, 2013; Falkner, Krishna, & Goldberg, 2010). Thus the selection of the first saccade goal may exert an inhibitory influence on neighboring stimuli, preventing them from becoming prioritized during the dead time to trigger a USL second saccade. However, stimuli further away from the first saccade goal are less suppressed and can therefore be selected during the dead time and then trigger a USL second saccade. Also, search target stimuli are more likely than distractor stimuli to be selected during the dead time, because the search target stimuli are imbued with top-down priority during this task. For this reason, USL saccades are more likely when the first saccade ends far away from the search target, since under these conditions, the search target stimulus will be less suppressed by the first saccade goal. This hypothesis is supported by recent modeling findings indicating that surround suppression (i.e. lateral inhibition) appears critical to fully account for the properties of error-correcting double-responses in evidence-accumulation models (Evans et al., 2020); without surround suppression, USL saccades would become much more common than they actually are. Data from FEF during a visual search task have indicated pre-saccadic “flanking” inhibition in less than one-fourth of neurons when the search target lies just outside the response field, which itself would have covered approximately one to three stimuli (Schall et al., 2004). This pattern is quite unlike that expected based on our data, which indicate a graded inhibition extending away from the saccade goal that is more consistent with the data reported from LIP (Falkner et al., 2010). Pre-saccadic inhibition does not appear to be present in the average SC activity, though some individual neurons do show some flanking inhibition (Basso & Wurtz, 1998). Therefore, although inhibitory mechanisms are known to exist in FEF, SC, LIP and pre-frontal cortex (Suzuki & Gottlieb, 2013), a clear physiological underpinning for the presence of large, target-directed, USL corrective saccades during active visual search remains to be found.

Our finding that USL saccades do not show a reduced probability of target-foveation compared to regular-latency saccades agrees with the results of a previous study from human subjects (Findlay et al., 2001). These authors also stated (without explicitly showing the data) that the variation of target-foveation probability with target eccentricity was identical for USL and regular-latency saccades in their data, in contrast to our results (Figures 3 and 4). It is not clear which of the many task differences (or species difference) between our study and the previous one could account for the possible difference.

We found that USL second saccades were preceded by more hypometric first-saccades, as in previous studies (Becker & Juergens, 1979; McPeek & Keller, 2001; McPeek et al., 2000). First saccades with shorter latencies were more likely to be followed by a USL second saccade in our data. This is a novel result that future models of concurrent saccade goal processing would have to reproduce. Our results expand our understanding of saccadic targeting, concurrent processing and information transfer across saccades during goal-directed active vision. Consistent with a recent suggestion (Evans et al., 2020), we believe it is likely that the data presented here will provide strong constraints for models of saccadic decision-making. They also tie into a long-standing literature on error-correcting responses (Evans et al., 2020; Rabbitt, 2002; Rabbitt & Rodgers, 1977) where error-correction has been shown to be very accurate (that is, it rarely follows the correct response and is usually directed toward the correct response). This suggests that error-processing in different sensory and response modalities show similar patterns and may be accounted for by similar models.

Conclusions

USL saccades in visual search are a hallmark of trans-saccadic processing of the search array, since they are based on information obtained prior to the saccade that precedes them. They have previously been shown in popout visual search, reading and simple laboratory tasks. Here, we demonstrate that USL saccades also often occur when monkeys perform active visual search without a popout visual target. USL saccades foveate the search target with higher accuracy when starting far away from the target, and almost never follow saccades to the search target. USL saccades are thus corrections of the preceding saccadic decision, and these corrections are generally correct. Furthermore, they are based on accurate transfer of target selection and location information across the first saccade. We demonstrate additional properties of USL saccades that suggest the influence of surround suppression on the computations in visual/oculomotor priority maps that underlie the concurrent processing of saccade goals. Our results thus expand our understanding of saccadic targeting and information transfer across saccades during goal-directed active vision, lay the ground for future neurophysiological studies, and suggest that error-processing in different sensory and response modalities show similar patterns and may be accounted for by similar models.

Acknowledgments

The authors profusely thank Mickey Goldberg for generously supporting this work with resources, advice, mentorship and good humor. We thank Catherine Loader, Hemant Bokil and Partha Mitra for help with locfit. We also thank M. Osman and G. Asfaw for veterinary care, Y. Pavlova for expert assistance with animal care, G. Duncan for electronic and systems work and L. Palmer for administrative help. Preliminary experiments were performed at the Laboratory of Sensorimotor Research, National Eye Institute, Bethesda, Maryland. The research was supported by grants to Mickey Goldberg from the National Eye Institute (R01 EY014978-01, R24 EY015634-01, R01EY017039, P30EY019007), the Whitehall, James S. MacDonnell, W.M. Keck and Zegar Family Foundations, and the David Mahoney Chair at Columbia University, and to B. Suresh Krishna from the NSERC Discovery Research grant program (RGPIN-2022-05399) and Supplement (DGECR-2022-00321), as well as a computing resources grant from Calcul Quebec and the Digital Research Alliance of Canada.

Author contributions: AEI and JWB designed the experiment whose data are analyzed here; AEI performed and JWB assisted with the experiment; BSK conceived and performed the analysis and wrote the paper; all authors edited the final draft.

Commercial relationships: none.

Corresponding author: B. Suresh Krishna.

Email: suresh.krishna@mcgill.ca.

Address: Department of Physiology, McGill University, Montreal, Quebec H3G 1Y6, Canada.

Appendix

Using a larger value for the boundary produces similar results

While we used a boundary of 75 ms in our analyses to examine the properties of USL and regular-latency saccades, and this is theoretically consistent with 75 ms being the lower limit for stimulus-evoked express saccades, the data in Figure 4 suggest a smooth gradation in properties such that the reliance on information from the second fixation starts to clearly emerge after 100 ms. Below, we show the key results from Figures 3, 5 and 6, but using a boundary of 100 ms. All the effects remain clearly present, even as the proportion of USL saccades is now higher due to using a larger boundary value.

Figure A1.

Figure A1.

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The plots from Figures 3, 5, and 6 in the main text are shown here in A, B, and C, respectively, but with a boundary value of 100 ms separating USL saccades from regular-latency saccades.

References

  1. Amador, N., Schlag-Rey, M., & Schlag, J. (1998). Primate antisaccades. I. Behavioral characteristics. Journal of Neurophysiology, 80(4), 1775–1786, http://www.ncbi.nlm.nih.gov/pubmed/9772238. [DOI] [PubMed] [Google Scholar]
  2. Atiya, N. A. A., Rano, I., Prasad, G., & Wong-Lin, K. (2019). A neural circuit model of decision uncertainty and change-of-mind. Nature Communications, 10(1), 2287, 10.1038/s41467-019-10316-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Basso, M. A., & Wurtz, R. H. (1998). Modulation of neuronal activity in superior colliculus by changes in target probability. Journal of Neuroscience, 18(18), 7519–7534, http://www.ncbi.nlm.nih.gov/pubmed/9736670. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Becker, W., & Juergens, R. (1979). An analysis of the saccadic system by means of double step stimuli. Vision Research, 19, 967–983. [DOI] [PubMed] [Google Scholar]
  5. Becker, W., & Jurgens, R. (1979). An analysis of the saccadic system by means of double step stimuli. Vision Research, 19(9), 967–983, 10.1016/0042-6989(79)90222-0. [DOI] [PubMed] [Google Scholar]
  6. Bibi, R., & Edelman, J. A. (2009). The influence of motor training on human express saccade production. Journal of Neurophysiology, 102(6), 3101–3110, 10.1152/jn.90710.2008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Bisley, J. W., & Goldberg, M. E. (2010). Attention, intention, and priority in the parietal lobe. Annual Review of Neuroscience, 33, 1–21, 10.1146/annurev-neuro-060909-152823. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Bisley, J. W., Krishna, B. S., & Goldberg, M. E. (2004). A rapid and precise on-response in posterior parietal cortex. Journal of Neuroscience, 24(8), 1833–1838, 10.1523/JNEUROSCI.5007-03.2004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Boch, R., & Fischer, B. (1986). Further observations on the occurrence of express-saccades in the monkey. Experimental Brain Research, 63(3), 487–494, http://www.ncbi.nlm.nih.gov/pubmed/3758267. [DOI] [PubMed] [Google Scholar]
  10. Bowman, A. W., & Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Illustrations. Oxford, UK: Oxford University Press. [Google Scholar]
  11. Caspi, A., Beutter, B. R., & Eckstein, M. P. (2004). The time course of visual information accrual guiding eye movement decisions. Proceedings of the National Academy of Sciences, 101(35), 13086–13090. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Chen, M., Liu, Y., Wei, L., & Zhang, M. (2013). Parietal cortical neuronal activity is selective for express saccades. Journal of Neuroscience, 33(2), 814–823, 10.1523/JNEUROSCI.2675-12.2013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. De Vries, J. P., Azadi, R., & Harwood, M. R. (2016). The saccadic size-latency phenomenon explored: Proximal target size is a determining factor in the saccade latency. Vision Research, 129, 87–97, 10.1016/j.visres.2016.09.006. [DOI] [PubMed] [Google Scholar]
  14. Duhamel, J. R., Colby, C. L., & Goldberg, M. E. (1992). The updating of the representation of visual space in parietal cortex by intended eye movements. Science, 255(5040), 90–92. [DOI] [PubMed] [Google Scholar]
  15. Evans, N. J., Dutilh, G., Wagenmakers, E. J., & van der Maas, H. L. J. (2020). Double responding: A new constraint for models of speeded decision making. Cognitive Psychology, 121, 101292, 10.1016/j.cogpsych.2020.101292. [DOI] [PubMed] [Google Scholar]
  16. Falkner, A. L., Goldberg, M. E., & Krishna, B. S. (2013). Spatial representation and cognitive modulation of response variability in the lateral intraparietal area priority map. Journal of Neuroscience, 33(41), 16117–16130, 10.1523/JNEUROSCI.5269-12.2013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Falkner, A. L., Krishna, B. S., & Goldberg, M. E. (2010). Surround suppression sharpens the priority map in the lateral intraparietal area. Journal of Neuroscience, 30(38), 12787–12797, 10.1523/JNEUROSCI.2327-10.2010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Findlay, J. M., Brown, V., & Gilchrist, I. D. (2001). Saccade target selection in visual search: The effect of information from the previous fixation. Vision Research, 41(1), 87–95. [DOI] [PubMed] [Google Scholar]
  19. Fischer, B., & Boch, R. (1983). Saccadic eye movements after extremely short reaction times in the monkey. Brain Research, 260(1), 21–26, http://www.ncbi.nlm.nih.gov/pubmed/6402272. [DOI] [PubMed] [Google Scholar]
  20. Fischer, B., Boch, R., & Ramsperger, E. (1984). Express-saccades of the monkey: Effect of daily training on probability of occurrence and reaction time. Experimental Brain Research, 55(2), 232–242, 10.1007/BF00237274. [DOI] [PubMed] [Google Scholar]
  21. Harwood, M. R., Madelain, L., Krauzlis, R. J., & Wallman, J. (2008). The spatial scale of attention strongly modulates saccade latencies. Journal of Neurophysiology, 99(4), 1743–1757, 10.1152/jn.00589.2007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Hays, A., Richmond, B., & Optican, L. (1982). Unix-based multiple-process system, for real-time data acquisition and control (Electron Conventions, El Segundo, CA). [Google Scholar]
  23. Ipata, A. E., Gee, A. L., Goldberg, M. E., & Bisley, J. W. (2006). Activity in the lateral intraparietal area predicts the goal and latency of saccades in a free-viewing visual search task. Journal of Neuroscience, 26(14), 3656–3661. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Ipata, A. E., Gee, A. L., Gottlieb, J., Bisley, J. W., & Goldberg, M. E. (2006). LIP responses to a popout stimulus are reduced if it is overtly ignored. Nature Neuroscience, 9(8), 1071–1076. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Irwin, D. E., Colcombe, A. M., Kramer, A. F., & Hahn, S. (2000). Attentional and oculomotor capture by onset, luminance and color singletons. Vision Research, 40(10-12), 1443–1458, 10.1016/s0042-6989(00)00030-4. [DOI] [PubMed] [Google Scholar]
  26. Johnson, L. J., & Fleming, D. (1963). A model of model feedback control for saccadic eye movement. Proceedings of the 16th Annual Conference of Engineering in Medicine and Biology, 5, 76–77. [Google Scholar]
  27. Judge, S. J., Richmond, B. J., & Chu, F. C. (1980). Implantation of magnetic search coils for measurement of eye position: An improved method. Vision Research, 20(6), 535–538, 10.1016/0042-6989(80)90128-5. [DOI] [PubMed] [Google Scholar]
  28. Jurewicz, K., Liao, B., & Krishna, B. S. (2023). Fast and frequent corrections of saccadic decisions underlie a satisficing strategy when searching everyday scenes. OSF Preprints osf.io/8yta9_v1.
  29. Kramer, A. F., Cassavaugh, N. D., Irwin, D. E., Peterson, M. S., & Hahn, S. (2001). Influence of single and multiple onset distractors on visual search for singleton targets. Perception & Psychophysics, 63(6), 952–968, 10.3758/bf03194515. [DOI] [PubMed] [Google Scholar]
  30. Krishna, B. S., Ipata, A. E., Bisley, J. W., Gottlieb, J., & Goldberg, M. E. (2014). Extrafoveal preview benefit during free-viewing visual search in the monkey. Journal of Vision, 14(1), 10.1167/14.1.6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Loader, C. (1999). Local regression and likelihood. Berlin: Springer-Verlag. [Google Scholar]
  32. Ludwig, C. J., Mildinhall, J. W., & Gilchrist, I. D. (2007). A population coding account for systematic variation in saccadic dead time. Journal of Neurophysiology, 97(1), 795–805, 10.1152/jn.00652.2006. [DOI] [PubMed] [Google Scholar]
  33. Madelain, L., Krauzlis, R. J., & Wallman, J. (2005). Spatial deployment of attention influences both saccadic and pursuit tracking. Vision Research, 45(20), 2685–2703, 10.1016/j.visres.2005.05.009. [DOI] [PubMed] [Google Scholar]
  34. Makela, P., Whitaker, D., & Rovamo, J. (1993). Modelling of orientation discrimination across the visual field. Vision Research, 33(5–6), 723–730. [DOI] [PubMed] [Google Scholar]
  35. Mays, L. E., & Sparks, D. L. (1980). Dissociation of visual and saccade-related responses in superior colliculus neurons. Journal of Neurophysiology, 43(1), 207–232. [DOI] [PubMed] [Google Scholar]
  36. McPeek, R. M., & Keller, E. L. (2001). Short-term priming, concurrent processing, and saccade curvature during a target selection task in the monkey. Vision Research, 41(6), 785–800. [DOI] [PubMed] [Google Scholar]
  37. McPeek, R. M., & Keller, E. L. (2002a). Saccade target selection in the superior colliculus during a visual search task. Journal of Neurophysiology, 88(4), 2019–2034. [DOI] [PubMed] [Google Scholar]
  38. McPeek, R. M., & Keller, E. L. (2002b). Superior colliculus activity related to concurrent processing of saccade goals in a visual search task. Journal of Neurophysiology, 87(4), 1805–1815. [DOI] [PubMed] [Google Scholar]
  39. McPeek, R. M., Skavenski, A. A., & Nakayama, K. (2000). Concurrent processing of saccades in visual search. Vision Research, 40(18), 2499–2516. [DOI] [PubMed] [Google Scholar]
  40. Meredith, M. A., & Ramoa, A. S. (1998). Intrinsic circuitry of the superior colliculus: Pharmacophysiological identification of horizontally oriented inhibitory interneurons. Journal of Neurophysiology, 79(3), 1597–1602. [DOI] [PubMed] [Google Scholar]
  41. Mirpour, K., & Bisley, J. W. (2012). Anticipatory remapping of attentional priority across the entire visual field. Journal of Neuroscience, 32(46), 16449–16457, 10.1523/JNEUROSCI.2008-12.2012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Mokler, A., & Fischer, B. (1999). The recognition and correction of involuntary prosaccades in an antisaccade task. Experimental Brain Research, 125(4), 511–516. [DOI] [PubMed] [Google Scholar]
  43. Morrison, R. E. (1984). Manipulation of stimulus onset delay in reading: Evidence for parallel programming of saccades. Journal of Experimental Psychology: Human Perception and Performance, 10(5), 667–682. [DOI] [PubMed] [Google Scholar]
  44. Motter, B. C., & Belky, E. J. (1998). The guidance of eye movements during active visual search. Vision Research, 38(12), 1805–1815. [DOI] [PubMed] [Google Scholar]
  45. Munoz, D. P., & Istvan, P. J. (1998). Lateral inhibitory interactions in the intermediate layers of the monkey superior colliculus. Journal of Neurophysiology, 79(3), 1193–1209. [DOI] [PubMed] [Google Scholar]
  46. Murthy, A., Ray, S., Shorter, S. M., Priddy, E. G., Schall, J. D., & Thompson, K. G. (2007). Frontal eye field contributions to rapid corrective saccades. Journal of Neurophysiology, 97(2), 1457–1469, 10.1152/jn.00433.2006. [DOI] [PubMed] [Google Scholar]
  47. Murthy, A., Ray, S., Shorter, S. M., Schall, J. D., & Thompson, K. G. (2009). Neural control of visual search by frontal eye field: Effects of unexpected target displacement on visual selection and saccade preparation. Journal of Neurophysiology, 101(5), 2485–2506, 10.1152/jn.90824.2008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Murthy, A., Thompson, K. G., & Schall, J. D. (2001). Dynamic dissociation of visual selection from saccade programming in frontal eye field. Journal of Neurophysiology, 86(5), 2634–2637, http://www.ncbi.nlm.nih.gov/pubmed/11698551. [DOI] [PubMed] [Google Scholar]
  49. Nakamura, K., & Colby, C. L. (2002). Updating of the visual representation in monkey striate and extrastriate cortex during saccades. Proceedings of the National Academy of Sciences, 99(6), 4026–4031, 10.1073/pnas.052379899. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Rabbitt, P. (2002). Consciousness is slower than you think. Quarterly Journal of Experimental Psychology, 55(4), 1081–1092, 10.1080/02724980244000080. [DOI] [PubMed] [Google Scholar]
  51. Rabbitt, P., & Rodgers, B. (1977). What does a man do after he makes an error? An analysis of response programming. Quarterly Journal of Experimental Psychology, 29(4). [Google Scholar]
  52. Resulaj, A., Kiani, R., Wolpert, D. M., & Shadlen, M. N. (2009). Changes of mind in decision-making. Nature, 461(7261), 263–266, 10.1038/nature08275. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Schall, J. D., & Hanes, D. P. (1993). Neural basis of saccade target selection in frontal eye field during visual search. Nature, 366(6454), 467–469. [DOI] [PubMed] [Google Scholar]
  54. Schall, J. D., Sato, T. R., Thompson, K. G., Vaughn, A. A., & Juan, C. H. (2004). Effects of search efficiency on surround suppression during visual selection in frontal eye field. Journal of Neurophysiology, 91(6), 2765–2769, 10.1152/jn.00780.2003. [DOI] [PubMed] [Google Scholar]
  55. Schiller, P. H., Haushofer, J., & Kendall, G. (2004). An examination of the variables that affect express saccade generation. Visual Neuroscience, 21(2), 119–127, http://www.ncbi.nlm.nih.gov/pubmed/15259563. [DOI] [PubMed] [Google Scholar]
  56. Sharika, K. M., Ray, S., & Murthy, A. (2009). Attention for action during error correction. Progress in Brain Research, 176, 227–244, 10.1016/S0079-6123(09)17613-6. [DOI] [PubMed] [Google Scholar]
  57. Song, J. H., & Nakayama, K. (2008). Target selection in visual search as revealed by movement trajectories. Vision Research, 48(7), 853–861, 10.1016/j.visres.2007.12.015. [DOI] [PubMed] [Google Scholar]
  58. Song, J. H., Takahashi, N., & McPeek, R. M. (2008). Target selection for visually guided reaching in macaque. Journal of Neurophysiology, 99(1), 14–24, 10.1152/jn.01106.2007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Sparks, D. L., & Porter, J. D. (1983). Spatial localization of saccade targets. II. Activity of superior colliculus neurons preceding compensatory saccades. Journal of Neurophysiology, 49(1), 64–74. [DOI] [PubMed] [Google Scholar]
  60. Suzuki, M., & Gottlieb, J. (2013). Distinct neural mechanisms of distractor suppression in the frontal and parietal lobe. Nature Neuroscience, 16(1), 98–104, 10.1038/nn.3282. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Team, R. D. C. (2007). R: A language and environment for statistical computing . Vienna, Austria: R Foundation for Statistical Computing. [Google Scholar]
  62. Theeuwes, J., Kramer, A. F., Hahn, S., & Irwin, D. E. (1998). Our eyes do not always go where we want them to go: Capture of the eyes by new objects. Psychological Science, 9(5), 379–385. [Google Scholar]
  63. Theeuwes, J., Kramer, A. F., Hahn, S., Irwin, D. E., & Zelinsky, G. J. (1999). Influence of attentional capture on oculomotor control. Journal of Experimental Psychology: Human Perception and Performance, 25(6), 1595–1608. [DOI] [PubMed] [Google Scholar]
  64. Umeno, M. M., & Goldberg, M. E. (1997). Spatial processing in the monkey frontal eye field. I. Predictive visual responses. Journal of Neurophysiology, 78(3), 1373–1383. [DOI] [PubMed] [Google Scholar]
  65. Virsu, V., Nasanen, R., & Osmoviita, K. (1987). Cortical magnification and peripheral vision. Journal of the Optical Society of America A, 4(8), 1568–1578. [DOI] [PubMed] [Google Scholar]
  66. Viviani, P., & Swensson, R. G. (1982). Saccadic eye movements to peripherally discriminated visual targets. Journal of Experimental Psychology: Human Perception and Performance, 8(1), 113–126, http://www.ncbi.nlm.nih.gov/pubmed/6460077. [DOI] [PubMed] [Google Scholar]
  67. Walker, M. F., Fitzgibbon, E. J., & Goldberg, M. E. (1995). Neurons in the monkey superior colliculus predict the visual result of impending saccadic eye movements. Journal of Neurophysiology, 73(5), 1988–2003. [DOI] [PubMed] [Google Scholar]

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