Twenty-nine healthy, right-handed individuals (15 male and 14 female) between 15–35 years of age (mean = 25; SD = 6) performed oculomotor and manual motor SSTs. Handedness was confirmed using the Annett Hand Preference Questionnaire (Annett, 1970). The lower age limit was determined based upon our prior work showing that inhibitory control abilities reach adult levels by age 15 years old (Luna et al., 2004). Tests including only GO trials also were administered to determine baseline reaction times for each effector for each individual. Participants were free of caffeine for 24 h prior to testing and they did not use nicotine for 1 h prior to testing. Among our participants, thirteen individuals identified as not drinking caffeine regularly, 13 as light drinkers (1–2 cups per day), and three as moderate drinkers (3–4 cups per day). With regard to daily nicotine intake, 26 participants identified as non-smokers and three as light smokers (0–10 cigarettes). Because our participants were not able consume caffeine or nicotine prior to testing, it is possible that they may have experienced acute withdrawal during the study; however, no participants reported signs of acute withdrawal during testing. All adult participants provided written consent and minors provided assent in addition to written consent from their legal guardian.

For both oculomotor and manual motor tests, visual stimuli (i.e., white dot) subtending 0.5–1° of visual angle were presented on a black monitor in the horizontal plane at eye level. A centrally located white crosshair was presented prior to each trial. During eye movement testing, all participants sat in a darkened black room 60 cm from a 101.6 cm anti-glare LCD screen monitor with a resolution of 1920 × 1060. Participants were positioned in a chin rest and eye movements were monitored using infrared sensors that detected saccades with amplitudes ≥0.20–0.25° (Model 310, Applied Science Laboratories, Inc, Bedford, MA, USA). Fixation of static targets across the horizontal plane was used to calibrate eye movement recordings. Blinks were monitored using electrodes placed above and below the left eye linked to an AC-coupled bioamplifier. Eye movement data were digitized at 500 Hz with a 12 bit A/D converter (DI-720 from Dataq Instruments, Akron, OH, USA). Digital finite impulse response filters with non-linear transition bands were applied prior to analyses of the eye movement data. For manual motor testing, participants were seated in front of a 50.8 cm monitor with a resolution of 1680 × 1050 (Dell 1905FP). Participants used a custom-made button box that recorded finger presses through a USB port with a sampling rate of 125 Hz. Stimuli for oculomotor and manual motor testing were presented using Adobe Flash software (Flash MX Actionscript 2).

Table 1 shows stopping accuracy for the SSTs and reaction times on GO trials for Stop Signal and baseline reaction time tests. All variables were normally distributed (ω²’s > 0.96, p’s > 0.24). As predicted, our mixed-effects logistic regression revealed that stopping accuracy decreased with increasing SSDs for both oculomotor (Z = -7.55, p < 0.001) and manual motor testing (Z = -10.79, p < 0.001; Figure 2). The SSD at which each subject’s accuracy on STOP trials equaled 50% was greater for manual than oculomotor responses [hand = 227 ms (SD = 56); eye = 178 ms (SD = 71); t(24) = 3.09; p = 0.005], consistent with the hypothesis that participants show a faster decline in performance with increasing SSD for eye compared to hand movements. Overall, participants were more successful inhibiting manual responses than oculomotor responses when analyses were restricted to SSDs used in both hand and eye movement tasks (Z = 2.63, p = 0.009). SSRT was longer for manual compared to oculomotor responses [hand = 221(SD = 43); eye = 146(SD = 75); t(24) = 4.49, p < 0.001]. The percentage of correctly inhibited STOP trials was not related to SSRT for either oculomotor or manual motor performance [eye r(24) = 0.11, p = 0.30, hand r(29) = -0.25, p = 0.20).

Manual reaction times were longer than saccade reaction times during baseline GO trials [Figure 3A; hand = 303 ms (SD = 32); eye = 208 ms (SD = 34); t(27) = 10.43, p < 0.001]. Manual reaction times also were longer than saccade reaction times on Stop Signal task GO trials [Figure 3B; hand = 448 ms (SD = 31); eye = 327 ms (SD = 42); t(27) = 18.85, p < 0.001]. The degree to which manual reaction times were slower than oculomotor reaction times was greater for Stop Signal GO trials compared to baseline trials [Task × Effector interaction: F(1,25) = 7.81; p = 0.01].

During the SSTs, participants slowed both their manual and oculomotor reaction times relative to their baseline reaction times [hand: F(1,28) = 247.78; p < 0.001; eye: F(1,28) = 338.40; p < 0.001]. They slowed their manual reaction times more than their eye movement reaction times [Figure 3C; hand = +145 ms (SD = 45); eye = +119 ms (SD = 33); F(1,28) = 9.71; p < 0.01]. The amounts that individuals strategically slowed their reaction times during manual motor and oculomotor SST GO trials were correlated [Table 2; r(27) = 0.44, p = 0.02]. Participants’ reaction times during manual GO trials were not related to their baseline reaction times [r(30) = -0.03, p = 0.89]. In contrast, Stop Signal oculomotor reaction times were related to baseline reaction times [r(27) = 0.65, p < 0.01]. Manual motor and oculomotor reaction times during baseline GO trials were not related to each other [r(30) = -0.14, p = 0.45], but reaction times for manual and oculomotor responses during GO trials were related [r(27) = 0.57, p = 0.002].

As seen in Figure 4, greater reaction time slowing from baseline to GO trials was associated with an increased percentage of successful STOP trials for both the manual [r(27) = 0.64, p < 0.001] and oculomotor tests [r(27) = 0.61, p < 0.001]. GO trial reaction times also were associated with an increased percentage of successful STOP trials on both manual [r(27) = 0.87, p < 0.001] and oculomotor tasks [r(27) = 0.39, p = 0.04]; however, the relationship was stronger for manual compared to eye movement responses (Z = 3.32, p < 0.01). Thus, slower reaction time during the SST was a greater predictor of stopping success for manual compared to ocular motor responses. Baseline reaction times were not predictive of either oculomotor or manual stopping accuracy [hand r(29) = -0.16, p = 0.40; eye r(28) = -0.27, p = 0.28]. We found no relationship GO trial reaction time slowing and SSRTs for either the eye or hand, consistent with the hypothesis that GO and STOP processes are independent [eye r(24) = 28,p = 0.19; hand r(29) = -0.12, p = 0.53].

For both the manual and oculomotor Stop Signal tasks, reaction time slowing during GO trials varied according to the type of the preceding trial. Participants slowed their reaction times following STOP trials more than following GO trials [Table 3; hand: F(1,28) = 5.63, p = 0.03; eye: F(1,28) = 79.49, p < 0.001], and this difference was greater for oculomotor compared to manual responses [Figure 5; Effector × Previous Trial Type interaction: F(1,28) = 5.28, p = 0.03]. During the manual task, reaction times following STOP [r(27) = 0.81, p < 0.001] and GO trials [r(27) = 0.56, p < 0.001] were both associated with increased accuracy on STOP trials. However, for eye movements, increased stopping accuracy was only related to increases in reaction times following GO trials [r(27) = 0.46, p = 0.01], and not following STOP trials [r(28) = 0.27, p = 0.15].

The extent to which reaction times were adjusted during GO trials also depended on the accuracy of the previous response. Participants slowed their responses to a greater degree following inaccurate STOP trials compared to accurate STOP trials [F(1,25) = 7.83, p = 0.01]. When comparing oculomotor and manual motor responses, there was no difference in the degree to which participants preferentially slowed their responses for inaccurate versus accurate STOP trials (p = 0.11). Increases in reaction time following STOP trial failures were associated with increases in manual stopping accuracy [r(27) = 0.50, p = 0.01] but not oculomotor stopping accuracy [r(26) = 0.17, p = 0.41]. Participants did not alter their response times according to the accuracy of previous GO trials (p’s > 0.70).

Accuracy for manual motor and oculomotor STOP trials was modestly correlated [Table 2; r(27) = 0.41, p = 0.03]. Manual motor and oculomotor SSRTs were not significantly correlated with each other [r(24) = 0.11, p = 0.66]. The SSDs at which participants’ eye and hand stopping accuracy equaled 50% also were not related [r(25) = 0.17, p = 0.41].

Greater stopping accuracy [r(28) = 0.58, p < 0.01] and greater delays [r(28) = 0.40, p = 0.04] in GO trial responses during the manual motor SST were associated with higher Full Scale IQ (Figure 6). No significant relationships between IQ and oculomotor stopping accuracy [r(26) = 0.04, p = 0.86] or slowing were seen [r(27) = 0.08, p = 0.69]. SSRTs were not related to IQ for either effector [eye: r(22) = 0.26, p = 0.27; hand: r(27) = 0.34, p = 0.08].

Our finding that individuals were better able to inhibit manual compared to oculomotor responses likely reflects the more reflexive nature and reduced inertia of eye movements relative to limb movements (Leigh and Zee, 2006; McDowell et al., 2008). Reflexive movements are made in direct response to novel external stimuli and primarily involve sensorimotor processes, whereas volitional movements involve both sensorimotor and higher-order cognitive processes. Our results are consistent with several previous reports showing higher rates of successful manual motor compared to oculomotor stopping during SSTs (Logan and Irwin, 2000; Boucher et al., 2007b) as well as alternative inhibitory control paradigms (Bekkering et al., 1994). Yet, the majority of previous studies suggest that oculomotor and manual motor stopping abilities are similar during SSTs (Logan and Irwin, 2000; Boucher et al., 2007b; Leung and Cai, 2007; Cai et al., 2014; Gulberti et al., 2014). Differences between these latter studies and ours highlight the strong influence of task-related factors that show greater impact on stopping ability of eye compared to hand movements. For example, Boucher et al. (2007b) documented that oculomotor and manual motor stopping are comparable when target behaviors are elicited by visually presented peripheral cues at shorter SSDs. Yet, they also found that oculomotor stopping is more difficult than manual motor stopping when target behaviors are elicited by peripheral cues at longer SSDs, consistent with our results. Similarly, Logan and Irwin (2000) documented comparable levels of oculomotor and manual stopping ability when central GO and STOP cues were used, but worse oculomotor compared to manual motor performance when peripheral GO and STOP cues were used. Peripheral visual cues evoke more reflexive responses than central visual cues, and reaction times for eyes are faster than for hands (Posner et al., 1980; Wijnen and Ridderinkhof, 2007). Thus, eye movements are more difficult to inhibit because faster oculomotor reaction times would reduce the probability of oculomotor STOP processes finishing prior to the completion of the GO process. This is be particularly true at longer SSDs, when the STOP process begins later.

Consistent with previous studies, we found that GO trial reaction times increased during the SSTs compared to baseline suggesting that individuals delay the start of the GO processes when task demands are uncertain (Logan and Burkell, 1986; Boucher et al., 2007a; Mirabella et al., 2008; Verbruggen and Logan, 2009; Bissett and Logan, 2011; Federico and Mirabella, 2014). Strategic delays in reaction timing also were associatd with improved stopping ability as has been documented previously (Emeric et al., 2007; Verbruggen and Logan, 2009; Bissett and Logan, 2011). We document the novel finding that strategic adjustments to reactions times are greater for hand compared to eye movements and more effective in improving stopping ability for hand compared to eye movements (predicting 64 vs. 16% of variance in STOP trial accuracy). These findings suggest that the increased capacity to stop hand compared to eye movements may reflect differences in the abilities to strategically delay hand and eye movements in uncertain conditions. Increases in reaction times prolong the finishing time of the GO process, thus providing increased opportunity for STOP processes to interrupt manual compared to oculomotor responses. Therefore, we propose that differences in the abilities to stop unwanted oculomotor and manual motor behaviors may reflect underlying differences in the degree to which reaction time adjustments can be made for different effectors.

Consistent with this idea, it has been hypothesized that reflexive movements driven by external sensory stimuli and occurring on a more rapid time scale show less amenability to top-down control processes (Leigh and Zee, 2006; Wijnen and Ridderinkhof, 2007; McDowell et al., 2008). Saccades are faster to initiate than limb movements due to their reduced inertia and torsional effects, and manual movements involve greater mechanical loads and a higher degree of inertia (Leigh and Zee, 2006). Further, eye movements cannot be interrupted once they have been initiated, whereas hand movements may be interrupted at multiple stages, including both prior to and following the initiation of action potential firing in the activated muscle (De Jong et al., 1990; Scangos and Stuphorn, 2010). Once an oculomotor response has been initiated, there is a so-called “point of no return” in which responses will be executed regardless of top-down processes attempting to inhibit that motor response (Hanes and Schall, 1996; Hanes et al., 1998). In fact, during successfully inhibited eye movements, extraocular muscles do not show any increase in firing rates or amplitude (Godlove et al., 2011). These physiological findings are consistent with our behavioral data showing that oculomotor response timing is more difficult to delay than manual motor response timing, and that delaying response times has less of an impact on overall stopping ability for eye compared to manual behaviors. Further, we found that oculomotor, but not manual motor, reaction times during GO and baseline trials were associated with each other, suggesting that the underlying processes involved in strategically delaying manual reaction times are relatively independent of those processes associated with basic motor speed.

Our findings are consistent with the majority of prior reports that also have found that response timing adjustments vary according to the type and the accuracy of preceding trials (Rieger and Gauggel, 1999; Cabel et al., 2000; Kornylo et al., 2003; Mirabella et al., 2006; Emeric et al., 2007; Verbruggen and Logan, 2008, 2009). We provide novel evidence that greater response delays following inaccurate STOP trials are related to increased stopping ability for hand movements but not eye movements. These findings suggest that while subjects may delay the onset of their behavioral responses following negative feedback across motor systems, these adjustments are not sufficient to improve stopping ability for eye movements. The lack of effectiveness in oculomotor reaction time slowing following inaccurate STOP trials may reflect a lack of consistency in reaction time adjustments across trials, or the inadequacy of smaller adjustments for allowing STOP processes to interrupt rapid eye movements. As we did not see an increase in the variability of oculomotor reaction time slowing relative manual motor reaction time slowing (p > 0.05), it appears more likely that the magnitude of eye movement timing adjustments made following inaccurate STOP trials was too small to significantly impact stopping ability.

Consistent with previous studies, we also found that SSRTs were shorter for eye compared to hand movements (Logan and Irwin, 2000; Boucher et al., 2007b; Leung and Cai, 2007; Cai et al., 2014; Gulberti et al., 2014). Although shorter SSRTs often are interpreted as an indication of superior stopping ability, shorter SSRTs do not appear to be sufficient for canceling eye movements at a rate similar to manual movements, and they are not associated with better stopping ability. Instead, the more reflexive nature of rapid eye movements and reduced capacity for strategically delaying these movements override the ability of individuals to stop them more rapidly than hand movements once they are cued. Results indicating that eye movements show shorter SSRTs than hand movements but still are more difficult to inhibit also suggests that the use of SSRTs to quantify inhibitory control ability may not capture the multiple distinct motor and cognitive processes involved in successfully inhibiting unwanted responses.

Differences in oculomotor and manual motor Stop Signal performance likely reflect separations at both peripheral and central levels (Aron and Poldrack, 2006; Boucher et al., 2007a). Inhibition of oculomotor behaviors involves interactive excitatory and inhibitory cells within frontal eye fields, dorsal caudate nuclei, and midbrain nuclei (Hikosaka et al., 1989; Pare and Hanes, 2003; Pouget et al., 2011). More specifically, GO commands impact a pre-existing balance of interacting and reciprocally inhibitory gaze-shifting (movement) and gaze-holding (fixation) neural units at cortical and subcortical levels. Single-cell recordings show increased firing rates within movement neurons in the frontal eye fields, caudate nucleus, superior colliculus, and brainstem following a GO cue; however, after the appearance of a STOP cue, the firing rates of these cells rapidly decreases and fixation neurons within the frontal eye fields and superior colliculus increase their firing rate as part of a process that is believed to cancel the movement (Hanes et al., 1998; Schall et al., 2000; Schall, 2002). Thus, central processes controlling eye movements cannot be interrupted after movement cells have reached a firing rate above a certain threshold (Lisberger and Fuchs, 1978; Fuchs et al., 1985; Hanes et al., 1995; Lauwereyns et al., 2002; Kaneko, 2006; Jantz et al., 2013).

For hand movements, a “stopping” circuit that is distinct from the GO circuit has been proposed involving right inferior frontal cortex (IFC), the internal segment of the globus pallidus (GPi) and subthalamic nuclei (STN; Aron and Poldrack, 2006; Chambers et al., 2006; Coxon et al., 2006; Mirabella et al., 2011; Mattia et al., 2012). A ‘hyperdirect’ IFC-STN pathway also allows more rapid interruption of movement generating signals and appears to enhance action of the stopping pathway (Aron et al., 2003; Aron and Poldrack, 2006; Jahfari et al., 2011). Thus, hand movements may be canceled before the movement is fully executed via indirect processes involving pre-supplementary motor cortex and inferior frontral gyrus, or hyperdirect processes involving STN activation (Mink, 1996; Nambu et al., 2002; Aron and Poldrack, 2006), allowing for a greater time window for successful manual motor stopping compared to oculomotor stopping.

Direct comparisons of oculomotor and manual motor inhibitory control neurophysiological processes further suggest that these brain systems are spatially and mechanistically different. ERP data has suggested that manual motor inhibitory control systems show a more posterior distribution compared to oculomotor inhibitory control systems, which show a more frontomedial voltage distribution (Reinhart et al., 2012). Consistent with these findings, fMRI studies show more posterior activation of pre-supplementary and supplementary motor areas during manual motor compared to oculomotor inhibitory control tasks (Aron and Poldrack, 2006), as well as more ventral inferior frontal gyrus activation during manual compared to oculomotor stopping (Leung and Cai, 2007).

The central mechanisms involved in strategically delaying motor responses also are different for the eye and hand. Pre-supplementary motor, supplementary motor, and anterior cingulate cortices play an important role in supporting strategic timing adjustments and actively suppressing the inferior frontal gyrus-striatal pathway that controls the timing of movement initiation (Aron and Poldrack, 2006). However, cells of the inferior frontal gyrus involved in stopping manual behaviors appear to be located more ventral than those involved in stopping eye movements (Leung and Cai, 2007). Also, medial prefrontal cortices including the supplementary eye fields and dorsal portions of caudal cingulate cortex appear to be selectively involved in error monitoring during eye movement tasks in a manner parallel to medial prefrontal cortical circuits that are involved in strategic timing adjustments of hand movements (Stuphorn et al., 2010; Reinhart et al., 2012). Although our current study cannot confirm the contribution of differences at the cellular and circuitry level on our observed behavioral findings, these results suggest that cognitive processes involved in slowing responses of eye and hand movements are distinct and thus may contribute to our behavioral differences during test of eye and hand movement inhibitory control.

Although our results indicate relatively distinct inhibitory processes for the eye and hand, it is unlikely that these processes are completely separate. For example, both manual motor and oculomotor behaviors become more difficult to inhibit as SSD increases consistent with the independent race model (Logan, 1994; Boucher et al., 2007a). We also found that strategic slowing of reaction times contributed to greater stopping abilities for both hand and eye movements. Furthermore, central mechanisms responsible for inhibitory control of the eyes and hands also share common structures (Verfaellie and Heilman, 1987; Curtis et al., 2005; Aron and Poldrack, 2006; Li et al., 2006; Leung and Cai, 2007). Thus, as previously suggested by Logan and Irwin (2000), inhibition of oculomotor and manual motor responses likely follow similar guiding principles (e.g., independent race model), but are controlled by distinct neurocognitive and neurophysiological mechanisms.

While our study provides novel evidence that control processes involved in inhibiting oculomotor and manual motor behaviors are different, several limitations of this study should be noted. First, it is possible that our findings showing important differences between oculomotor and manual motor stopping systems may not generalize to all types of eye or hand movements. By testing inhibition across a wide range of SSDs, we were able to examine the maximum delays at which inhibitory control processes could effectively stop reflexive oculomotor and manual motor behaviors. It is possible that inhibitory control systems used to stop slower or more voluntary motor responses, such as self-initiated or central saccades and manual movements, may be more similar in terms of their effectiveness or timing across effectors. Further, we chose to directly compare stopping of the two movement types across the same SSDs results may have differed if SSDs were chosen relative to reaction times for eyes compared to hands, or if comparisons of reaction time delays were made relative to individual baseline reaction times. Also, because we sampled SSDs more frequently across a broader range, estimates of the probability of stopping behaviors at each SSD were based on a relatively small number of trials (3–8). This approach allowed us to sample more SSDs but also may have reduced the stability of our estimates of probability functions and SSRTs. The use of different monitors across the hand and eye tasks also may have affected the inhibition functions. For example, because the refresh rate of the eye movement monitor was greater than the hand movement monitor, SSDs were presented over smaller intervals and repeated for fewer trials. Based on the large difference in the slopes of the probability of stopping functions for the hand and eye, it appears unlikely that the difference in the monitor refresh rates could account for differences in stopping rates between hands and eyes. Also, future studies should monitor fixation during manual motor testing to insure that differences in stopping accuracy across effectors are not due to differences in attention to stimuli. Additionally, we chose to use a reduced ratio of GO:STOP trials compared to several prior studies (Logan and Irwin, 2000; Boucher et al., 2007b; Leung and Cai, 2007; Cai et al., 2014; Gulberti et al., 2014) in order to more densely sample stopping abilities and reaction time slowing after STOP trials. It remains unclear whether differences in oculomotor and manual motor stopping and slowing vary in magnitude across different GO:STOP trial ratios. Last, we chose to analyze raw delays in reaction time as opposed to delays calculated as a percentage of baseline reaction times because the two tasks utilized identical maximum reaction times and thus the same amount of slowing was allowed for both movement types. We believe this approach was more informative for direct comparisons with prior studies and in order to first determine whether eyes and hands are controlled differently under similar conditions.