Edited by:
Reviewed by:
*Correspondence:
This article was submitted to Cognition, a section of the journal Frontiers in Psychology
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Voluntary inhibition of unwanted behavioral responses is a central component of executive control, and it is necessary for flexibly adapting behavior to changing environmental demands. Determining the cognitive and neural mechanisms involved in voluntary response inhibition is critical for understanding behavioral development in health as well as in psychiatric, developmental and neurological conditions associated with inhibitory control impairments (
Stop Signal tests (SSTs) are the most widely used laboratory measure of response inhibition (
Studies of Stop Signal performance have indicated that the race model can be applied to inhibitory control processes across multiple effectors and behaviors, including reaching movements (
In contrast to these behavioral findings, neurophysiological evidence indicates that brain networks supporting inhibitory control of oculomotor and manual motor systems show important distinctions. Specifically, inhibition of oculomotor behaviors involves interactive excitatory and inhibitory cells within frontal eye fields, dorsal caudate nuclei, and midbrain nuclei (
A closer analysis of prior SST behavioral studies provides evidence that there may be important task-dependent differences in stopping mechanisms for oculomotor and manual motor behaviors. For example, individuals had reduced abilities to stop eye compared to hand movements are seen only when responses are peripherally cued (
One additional difference between oculomotor and manual motor stopping behavior may be the extent to which these distinct systems involve top-down cognitive control strategies. Prior SST studies have shown that participants strategically delay the onset of their oculomotor and manual motor responses in order to determine if a STOP cue will be presented, and that these reaction time adjustments are associated with improved stopping ability (
In the present study, participants completed SSTs of saccadic eye movements and manual button pressing designed to be as similar as possible. Our primary aim was to characterize differences in the abilities to stop peripherally cued oculomotor and manual motor behaviors over a large range of SSDs. It was hypothesized that eye movements would be more difficult to inhibit than hand movements, particularly when the task became more difficult as SSDs were increased. We also aimed to determine the extent to which strategic reaction time adjustments were used to support oculomotor and manual motor stopping. Given the increased level of reflexivity of eye compared to hand movements, it was expected that reaction time adjustments would be smaller and would have less effect on stopping ability during the oculomotor compared to the manual motor SST. Last, we aimed to determine the degree to which oculomotor and manual motor inhibitory control were related. Based on evidence that the neural systems involved in oculomotor and manual motor stopping show distinct functional characteristics and anatomical distribution (
Twenty-nine healthy, right-handed individuals (15 male and 14 female) between 15–35 years of age (mean = 25;
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).
Trials requiring saccadic eye movements began while participants fixated their gaze on a central crosshair. Participants had to maintain fixation within
In order to ensure that participants responded to GO trials without waiting indefinitely to determine if a STOP cue would be presented, two steps were taken. First, if a response to GO trials did not occur within 650 ms, a red “X” immediately appeared in place of the green target, along with the word “faster” below the “X” for 2500 ms in order to ensure that participants processed feedback. Second, every third GO trial in which subjects did not respond within 650 ms was repeated at a later random trial during the task.
During ‘STOP’ trials, a red STOP cue replaced the central fixation cue at varying SSDs after the GO cue was presented. Participants were instructed to avoid shifting their eye gaze when the STOP cue appeared. SSDs were sampled continuously in 13.33 ms intervals (matching the monitor refresh rate of 75 Hz) between 50–200 ms. For each of the 11 SSDs, 4–5 trials were included. Eight participants were tested on a separate monitor with a different refresh rate (120 Hz) and therefore completed trials with SSDs sampled continuously in 8.33 ms intervals. For each of these 18 SSDs, three to four trials were included. The monitor used for testing was switched for the final eight participants due to technical issues; however, analyses performed using monitor as a covariate did not alter the results, so testing data from both monitors were combined.
The order of SSDs was randomized, and different trial types were presented in a pseudorandomized order; no more than three consecutive trials of the same type (GO or STOP) were administered. If a participant responded on a STOP trial, a red ‘X’ was displayed centrally for 1000 ms immediately following their error. Incorrect STOP trials were not repeated. Four blocks of 63 trials [38 GO (60%) and 25 STOP (40%)] were administered consistent with GO:STOP trial ratios used in prior studies (
The manual motor SST was designed to parallel the oculomotor SST as closely as possible. Task stimuli and timing were similar, and we chose to study a manual behavior (i.e., button pressing) that was as rapid and simple as possible to closely match the reflexive nature of saccadic eye movements. During this test, participants rested their thumbs on left and right buttons corresponding to the locations of the peripheral targets. They were instructed to press the correct button as quickly as possible on all GO trials while maintaining their fixation on the central crosshair. STOP trial SSDs were sampled between 50–283 ms to match the refresh rate of the monitor specifically used for the manual motor version of this task (60 Hz, or every 16.67 ms). Seven to eight trials were included for each of the 14 SSDs. The maximum SSD for the manual motor SST was higher (283 ms) than that for the oculomotor test (200 ms) based on prior studies showing continued ability to stop manual responses at these longer SSD intervals (
In order to assess participants’ reaction times during a condition in which they would have no bias to strategically delay their responses, oculomotor and manual motor baseline reaction time tasks including only GO trials were administered. Baseline reaction time tests included 30 GO trials (15 rightward, 15 leftward) presented in the same format as GO trials in the SSTs. A small number of baseline trials were used due to minimal variability in reaction times of basic saccadic movements and manual button presses. These baseline reaction time tasks always preceded the SST of the same effector.
The Wechsler Abbreviated Scales of Intelligence (WASI;
We conducted
In order to compare strategic reaction time slowing in eyes and hands, a 2 (effector: eye vs. hand) × 2 (task: baseline vs. Stop Signal task) ANOVA was conducted to predict reaction time. Pearson correlations were performed to determine relationships between reaction time slowing and percentage of accurate STOP trials, p50, and SSRT within each effector. Fisher
To determine the extent to which oculomotor and manual motor processes were related to each other, Pearson correlations were used to assess associations between the percentage of correct STOP trials, p50, SSRT and reaction times across oculomotor and manual motor tests. Also, in order to clarify the relationship between Stop Signal performance and general cognitive ability, we calculated Pearson correlations between Stop Signal performance and Full Scale IQ estimates from the WASI.
Oculomotor and manual motor stopping performance and GO reaction times during baseline and Stop Signal tests.
Oculomotor | Manual Motor | ||
---|---|---|---|
Accuracy (%correct)1 | 62 (14) | 84 (11) | <0.001 |
SSRT (ms) | 146 (75) | 221 (43) | <0.001 |
p50 | 178 (71) | 227 (56) | 0.005 |
GO baseline trials | 208 (34) | 303 (32) | <0.001 |
GO SST trials | 327 (42) | 448 (31) | <0.001 |
Manual reaction times were longer than saccade reaction times during baseline GO trials [
During the SSTs, participants slowed both their manual and oculomotor reaction times relative to their baseline reaction times [hand:
Relationships between oculomotor and manual motor reaction times and stopping accuracy.
Oculomotor | Manual motor | |||||
---|---|---|---|---|---|---|
GO RT | STOP trial accuracy | Baseline RT | GO RT | STOP trial accuracy | ||
Baseline GO RT | 0.65∗∗∗ | -0.10 | -0.14 | 0.11 | 0.18 | |
GO RT | – | 0.39∗ | -0.21 | 0.39∗ | 0.44** | |
STOP trial accuracy | – | -0.25 | 0.20 | 0.41* | ||
Baseline GO RT | – | -0.03 | -0.07 | |||
GO RT | – | 0.87*** | ||||
As seen in
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 [
GO trial reaction times during the Stop Signal test for eye and hand movements separated by whether trials followed a GO trial or a STOP trial and by whether trials followed accurate or inaccurate trials on both GO and STOP trials.
Previous trial type | |||
---|---|---|---|
GO trial | STOP trial | STOP-GO Difference | |
Oculomotor RT | 299 (45) | 353 (45) | +54 ms |
Manual motor RT | 429 (49) | 450 (39) | +21 ms |
Oculomotor RT | 317 (43) | 312 (80) | -5 ms |
Manual motor RT | 441 (47) | 438 (63) | -3 ms |
Oculomotor RT | 366 (41) | 374 (81) | +8 ms |
Manual motor RT | 452 (42) | 486 (42) | +34 ms |
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 [
Accuracy for manual motor and oculomotor STOP trials was modestly correlated [
Greater stopping accuracy [
The present study documents three key differences between inhibitory control processes involved in stopping oculomotor and manual motor behaviors. First, eye movements were more difficult to inhibit than manual responses, and oculomotor stopping ability deteriorated more rapidly than manual motor stopping ability as SSDs increased. Second, the extent to which participants delayed their reaction times during GO trials relative to baseline trials was greater for hand than eye movements, and it was more strongly associated with stopping ability for hand compared to eye movements. Third, stopping abilities for eye and hand movements were only modestly correlated, and oculomotor and manual motor SSRTs were not related. Overall, these findings indicate that oculomotor responses are under less volitional control and are less amenable to strategic adjustments of reaction timing than manual motor behaviors. Our finding that manual motor but not oculomotor stopping ability is related to general cognitive ability provides further evidence that response inhibition of these two effector systems involves different underlying cognitive processes.
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 (
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 (
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 (
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 (
Consistent with previous studies, we also found that SSRTs were shorter for eye compared to hand movements (
Differences in oculomotor and manual motor Stop Signal performance likely reflect separations at both peripheral and central levels (
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;
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 (
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 (
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 (
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 (
In summary, we document greater top-down inhibitory control over manual motor compared to oculomotor responses suggesting that the level of influence of peripheral and central stop commands on effector systems is unique to the type of movement that they control. We demonstrated that healthy individuals are more likely to inhibit an unwanted behavioral response if they strategically delay its onset. However, our findings that strategic biases in response timing are greater overall and have a greater impact on STOP trial performance for hand than eye movements suggest that top-down mechanisms controlling eye movements are less susceptible to cognitive biases used to improve performance, perhaps due to the more reflexive nature of peripheral eye movements. Analyses of the relationship between cognitive control strategies and response inhibition success, and how these relationships differ across behaviors and effectors will be important for understanding the cognitive and neural mechanisms that support behavioral response inhibition in both health and disease, and determining optimal teaching and intervention strategies for improving inhibitory control in children and patient populations.
This study was approved by the Institutional Review Board of University of Illinois at Chicago and its procedures conformed to the Declaration of Helsinki. University of Illinois at Chicago Institutional Review Board Prior to testing, each adult participant signed an informed consent form and minors provided oral assent and their parents provided written consent. Minors were involved in this study. Minors provided oral assent and their parents provided written consent.
LS was involved in analyzing and interpreting data as well as drafting the manuscript. JS was involved in the conception and design of the experiment, providing consultation for data interpretation and critically revising the manuscript. LA was involved in collecting and analyzing the data as well as design of the experiment and initial manuscript drafts. MM provided consultation for data interpretation and was critically involved in revising the manuscript.
JS has consulted to Takeda, Lilly and Roche and has received grant support from Janssen. All the other authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The reviewer LC and the handling Editor declared their shared affiliation, and the handling Editor states that the process nevertheless met the standards of a fair and objective review.