Abstract

Unconscious visuomotor priming defined as the advantage in reaction time (RT) or accuracy for target shapes mapped to the same (congruent condition) when compared with a different (incongruent condition) motor response as a preceding subliminally presented prime shape has been shown to modulate activity within a visuomotor network comprised of parietal and frontal motor areas in previous functional magnetic resonance imaging (fMRI) studies. The present fMRI study investigated whether, in addition to changes in brain activity, unconscious visuomotor priming results in a modulation of functional connectivity profiles. Activity associated with congruent compared with incongruent trials was lower in the bilateral inferior and medial superior frontal gyri, in the inferior parietal lobules, and in the right caudate nucleus and adjacent portions of the thalamus. Functional connectivity increased under congruent relative to incongruent conditions between ventral visual stream areas (e.g., calcarine, fusiform, and lingual gyri), the precentral gyrus, the supplementary motor area, posterior parietal areas, the inferior frontal gyrus, and the caudate nucleus. Our findings suggest that an increase in coupling between visuomotor regions, reflecting higher efficiency of processing, is an important neural mechanism underlying unconscious visuomotor priming, in addition to changes in the magnitude of activation.

Introduction

It is meanwhile well accepted that response preparation and execution can be triggered by unconsciously perceived stimuli (Schmidt et al. 2011; Ansorge et al. 2014). In line with other work demonstrating influences of unconscious stimuli on emotion, cognition, and behavior (Kiefer 2012), this phenomenon shows that stimuli presented outside of conscious awareness elicit activity in processing pathways up to the motor level (Dehaene et al. 1998). A widely used paradigm to probe unconscious response preparation is the masked visuomotor priming paradigm (Neumann and Klotz 1994; Klotz and Wolff 1995; Leuthold and Kopp 1998; Ansorge et al. 2002; Vorberg et al. 2003; Ansorge and Neumann 2005; Martens et al. 2011; Zovko and Kiefer 2013). In this paradigm, a target stimulus such as a visual shape is arbitrarily mapped to a specific response category. For instance, a rectangle requires a left-hand response, whereas a diamond requires a right-hand response. The target is preceded by a prime shape either requiring the same or a different response. The prime is masked using either a metacontrast or a pattern mask, thereby preventing its conscious identification. Although participants were not aware of the masked prime stimulus as shown in rigorous identification tests (Schmidt and Vorberg 2006; Schmidt 2007), latencies and error rates associated with the target response were lower (“subliminal visuomotor priming”) when prime and target required the same response (congruent condition) in comparison with a different response (incongruent condition).

Subliminal visuomotor priming effects are typically assumed to reflect execution of a prepared task-control representation based on the unconsciously presented prime stimulus, when the latter matches the stimulus specifications of the task-control representation (Neumann 1990; Kunde et al. 2003; Klauer et al. 2007; Ansorge et al. 2014). This view goes back to Neumann's theory of direct parameter specification (Neumann 1990). According to this account, individuals set up action plans specifying the critical stimuli and the actions that are to be executed in response to the stimuli. If the features of a stimulus resemble the relevant features specified in the action plan, the stimulus can directly activate a response, even if the stimulus is presented subliminally and outside of awareness. As a consequence, when prime and target are congruent, the response required for the target is already prepared, hence, facilitated. In contrast, when the prime preactivates an inappropriate response, cognitive reconfiguration processes need to be performed to overcome the response conflict and to give a correct target response. At a neuroanatomical level, it has been suggested that subliminal visuomotor priming involves the dorsal visual stream (Neumann 1990; Wolbers et al. 2006), similar to the intact grasping performance in the absence of awareness in patients with visual agnosia (Milner and Goodale 1995).

Although human electrophysiological studies using event-related potentials suggest an origin of visuomotor priming in parietal and frontal motor areas (Leuthold and Kopp 1998; Jaskowski et al. 2003; Martens et al. 2011; Zovko and Kiefer 2013), the functional neuroanatomy and the neural mechanisms underlying unconscious visuomotor priming have to be better elucidated. We are aware of only a few functional magnetic resonance imaging (fMRI) studies on unconscious visuomotor priming suggesting an involvement of posterior parietal, (pre-)motor, and lateral frontal areas (D'Ostilio and Garraux 2012; D'Ostilio et al. 2013) as well as the basal ganglia (Aron et al. 2003). However, interpretation of findings in terms of visuomotor priming is limited due to the use of arrow stimuli as primes and targets. Importantly, arrows are quite particular stimuli because their direction indicates the response side. It is therefore an open question whether findings can be generalized to stimuli for which assignment of response categories is entirely arbitrary and only based on instruction (Ansorge et al. 2014). Furthermore, for arrows there is an overlap between spatial stimulus and response dimensions because, for instance, a left pointing arrow is typically mapped to a left-sided response. Hence, observed priming effects are very likely confounded by spatial compatibility effects of the Simon type (Simon and Rudell 1967; Shwartz et al. 1977; Kornblum et al. 1990; Ansorge et al. 2014). Moreover, owing to the fact that prime and target arrows in these studies were identical, reported effects may reflect repetition priming (Schacter 1990) and not unconscious response preparation.

Finally and perhaps most importantly, the neural mechanisms underlying unconscious visuomotor priming are not well understood. Typically, priming is thought to be reflected by a modulation of neural activation in task-relevant areas, in particular, by a decrease in activity (Aron et al. 2003; D'Ostilio and Garraux 2012; D'Ostilio et al. 2013), although an increase in activity has also occasionally been observed (Henson 2003; Boy et al. 2010). Consequently, these studies focused on brain activation. However, facilitatory effects observed in priming studies may arise, in the first place, from increased functional coupling of neural populations into task-relevant functional brain networks, thereby enhancing the efficiency of information processing. Temporary interactions between brain regions can be assessed using functional or effective connectivity measures of brain activity (Friston 2011; Buckner et al. 2013). Recently, we have shown that subliminal semantic priming, which reflects automatic access to word meaning, depends on a temporary coupling of distributed brain regions into functional networks established by an attentional set (Ulrich et al. 2014). Similarly, demands on attentional control modulated the magnitude of unconscious visuomotor priming as reflected by a modulation of functional connectivity between motor areas (pre-supplementary motor area), visual perceptual regions (lateral occipital complex), and basal ganglia (putamen) (Wolbers et al. 2006). However, these previous studies only assessed functional connectivity profiles induced by different attentional sets, but not changes in functional connectivity profiles induced by unconscious priming itself. The hypothesis that unconscious priming is supported by increased functional coupling between task-relevant areas has not been tested so far. However, increased functional coupling between brain areas as determined by synchrony measures in magnetoencephalogram recordings has been shown to support visible priming, with lower levels of brain activity being a consequence of increased connectivity (Ghuman et al. 2008; Kujala et al. 2012).

The present fMRI study therefore investigated the neural mechanisms of unconscious visuomotor priming using brain activation and functional connectivity analyses. Unlike the earlier work on unconscious visuomotor priming, we used visual shape stimuli for which arbitrary stimulus–response (S–R) rules could be formed, and tested the prediction that priming not only induces modulation of brain activity in visuomotor regions, but also associates with increased functional connectivity within a visuomotor network. Participants were presented with 2 geometric shapes (circle and diamond) mapped to the left index finger, and 2 other shapes (square and ellipsoid) mapped to the right index finger, or vice versa. Given the arbitrary S–R assignment, priming effects would likely arise from execution of task-control representations based on the subliminal prime stimulus. We hypothesized that the motor response associated with the visual target shape is faster for congruent compared with incongruent trials. This behavioral facilitation should be accompanied by lower levels of activity in brain regions involved in visuomotor processing such as the posterior parietal cortex (PPC), frontal motor areas, and the basal ganglia, as suggested previously (Aron et al. 2003; D'Ostilio and Garraux 2012; D'Ostilio et al. 2013). Finally, priming should modulate functional temporal interactions between brain regions within the visuomotor system: Functional connectivity between regions of the visuomotor network should be higher during congruent relative to incongruent trials. Such a result pattern would thus demonstrate that a change in functional connectivity profiles is an important mechanism underlying unconscious visuomotor priming.

Materials and Methods

Participants

Thirty-one healthy, right-handed students (16 female) aged 24.5 years on average [standard deviation (SD) = 4.8] were recruited from the local university and were paid €25 for participation. Neither psychiatric nor neurological disorders nor any contraindications regarding the fMRI procedure were reported. Written informed consent was obtained prior to the experimental phase. The study has been approved by the ethics committee at the University of Ulm and was conducted in accordance with the Declaration of Helsinki.

Stimuli

The primes, targets, and masks used for the shape discrimination task were taken from previous studies (Martens et al. 2011; Zovko and Kiefer 2013). The primes and targets consisted of 4 white geometrical shapes (circle, diamond, square, and ellipsoid) presented on a black background. The 4 shapes appeared equally often as primes and as targets, but were never identical in any trial to avoid repetition priming effects. In 64 trials, the prime and the target were response congruent, that is, the response assigned to the target was compatible with the prime stimulus. In another 64 trials, the prime and the target had incongruent S–R mappings. Primes were masked by a forward and a backward line pattern mask.

The program “Optseq2” (http://surfer.nmr.mgh.harvard.edu/optseq/, last accessed 30 March, 2015; see also Dale 1999) was used to obtain a sequence of congruent and incongruent trials allowing for efficient estimation of the associated hemodynamic responses. Trial order and trial onsets as initially delivered by the program were modified such that a specific trial type did not appear >3 times in direct succession. Furthermore, onsets were jittered by randomly adding fractions of the fMRI repetition time (TR). The resulting average trial onset asynchronies were 17.1 s for congruent trials and 17.3 s for incongruent trials, not significantly differing from each other (t(124) = 0.10, P = 0.924). The mean intertrial interval (ITI) across all trials irrespective of event type was 6.2 s (range: 3.6 to 18.7 s).

Stimuli were presented on MRI-compatible video goggles (VisuaStim Digital, Resonance Technology, Inc., Northridge, CA, USA). If necessary, correction lenses were provided to ensure sufficient visual acuity. Resolution was set to 800 × 600 pixels and screen refresh frequency was set to 60 Hz (frame duration = 16.67 ms; stimulus presentation and screen refreshes were synchronized). Stimuli appeared in white (luminance: ∼6 cd/m2) against a black background in the center of the display. The masks were 105 px in height and 85 px in width corresponding to visual angles of 10.3° vertically and 6.1° horizontally. The shape stimuli were smaller. Depending on the specific shape, the height ranged from 46 to 64 px (visual angle: 4.4°–6.1°), and the width from 30 to 46 px (visual angle: 2.1°–3.3°). Visual stimulation and recording of RTs was performed using the software Experimental Run Time System 3.35 (BeriSoft Cooperation, Frankfurt/Main, Germany) running under DOS 7.1 on a standard PC.

Procedure

In the run-up to the experiment, care was taken to instruct subjects about the nature of the task but without informing them about the presence of the masked prime stimuli. In the scanner, participants laid supine, with their head resting in foam padding to reduce head movements. Prior to the main experiment outside the scanner, subjects performed a practice run of the subliminally primed shape discrimination task, consisting of 16 congruent and 16 incongruent trials. Different from the trial structure described below, additional visual feedback was given (“correct” in green, and “wrong” or “faster” in red font). In the main experiment, each trial lasted 2517 ms. Participants first viewed a fixation cross for 750 ms, which was replaced by a forward line pattern mask with 200 ms duration, randomly drawn from a set of 8 line pattern masks. The forward mask was replaced by the prime shape lasting 33 ms, followed by a backward line pattern mask shown for 33 ms, after which the target shape appeared for 500 ms. Half of the participants were asked to respond with their right index finger when the target was a circle or a diamond, and with their left index finger when it was a square or an ellipsoid. The opposite S–R assignment was administered to the other half of the participants. The response was to be given as fast and as accurate as possible within a time window of 1500 ms beginning with the target onset. After offset of the target, 3 hash marks were presented for 1000 ms (completing the trial), and they also persisted during the subsequent, variable ITI. Three hash marks were also shown at the beginning (13.2 s), and at the end (24.2 s) of the run to account for scanner equilibration, and to capture the hemodynamic response associated with the last trial, respectively.

The scanning session ended with acquisition of a T1-weighted structural image of the brain. After scanning, still inside the scanner but without MR image acquisition, participants were informed about the presence of the primes, and instructions on an additional task, which tested prime visibility objectively, were administered. The trial structure was similar to that of the main experiment in order to keep stimulation comparable (750 ms fixation cross, 200 ms forward mask, 33 ms “prime,” 33 ms backward mask, 500 ms “target,” and 1000 ms hash marks). This time, however, participants were to identify the prime between the 2 masks, and to perform the shape decision with the same response categories as in the main experiment. A decision on the stimulus at previous target position was not required, but this stimulus was presented to keep stimulation comparable. Emphasis was on accuracy over speed, and subjects were forced to give a decision on the prime. Execution of the task was self-paced to ascertain that participants felt optimally prepared before starting each trial. Apart from 10 practice trials, the visibility test consisted of 48 trials (24 congruent and 24 incongruent trials). Trials were presented in a random order.

MRI Data Acquisition

Functional images were acquired on a 3-T magnetic resonance scanner (MAGNETOM Allegra, Siemens AG, Erlangen, Germany) in combination with a single channel transmit/receive head coil (RAPID Biomedical GmbH, Rimpar, Germany). During the experiment, an echo-planar pulse sequence (EPI) was applied to measure the T2*-weighted blood oxygen level-dependent (BOLD) signal. The following parameters were used: TR = 2200 ms, echo time (TE) = 39 ms, flip angle = 90°, field of view (FOV) = 230 mm, matrix size = 64 × 64, number of slices = 34, slice thickness = 3.0 mm, interslice gap = 0.6 mm; isotropic voxel size of 3.6 mm3. Ascending slice acquisition was parallel to a tangent plane touching the inferior surfaces of the orbitofrontal cortex and the cerebellum. Scan time was 1170 s, corresponding to 530 EPI volumes. To obtain a high-resolution T1-weighted structural image for later co-registration purposes, a magnetization-prepared rapid acquisition gradient-echo sequence was employed (TR = 2080 ms, TE = 3.93 ms, inversion time = 1100 ms, flip angle = 12°, FOV = 256 mm, matrix size = 256 × 256, voxel volume = 1 mm3, slice orientation: sagittal; scan time = 467s).

Data Analysis

Behavioral Data

Responses obtained from the shape discrimination task were analyzed for RTs and errors. With regard to the RT analysis, only correct decisions were included. Individual mean RT data were fed into a 2 × 2 repeated-measures analysis of variance (ANOVA) with factors response congruency (congruent vs. incongruent prime–target pairs) and response side (targets requiring a left-hand response vs. targets associated with a right-hand response). A second ANOVA was performed on error rates calculated as the percentage of incorrect decisions per event type. From participants' responses given during the prime visibility test, d′ (Green and Swets 1966) was calculated. The subject-wise d′ values were tested for significant deviation from zero using a one-sample t-test. All statistical tests were assessed at P < 0.050.

MRI Data

Imaging data preprocessing and statistical analyses were performed with the software package Statistical Parametric Mapping (SPM8, Wellcome Department of Cognitive Neurology, London, UK). Preprocessing included discarding the first 5 and last 4 EPI volumes, slice timing correction, spatial realignment of the EPIs to the session mean, and coregistration of the individual T1 image to the mean EPI. Thereafter, the T1 image was segmented into gray and white matter using SPM8's “new segment” routine. Spatial normalization to the Montreal Neurological Institute (MNI) space was achieved by applying the resulting individual deformation field to the T1 image and the EPI images. Before smoothing all EPI images using a Gaussian kernel with 8 mm full width at half maximum, voxels were resampled to 2 × 2 × 2 mm3.

After data preprocessing, a hierarchical standard modeling approach was used. For each subject, a general linear model was set up with onsets for correct trials (beginning with the onset of the fixation cross). To account for voxels whose BOLD signal might have differed between hemispheres due to lateralized motor effects, onsets for congruent and incongruent trials were split depending on whether the target required a left- versus right-hand response (i.e., response side). Thus, 4 regressors were formed: congruent-left (CON-L), incongruent-left (INC-L), congruent-right (CON-R), and incongruent-right (INC-R). A fifth condition represented all incorrect decisions. Furthermore, the spatial realignment parameters were added to the design matrix. Resulting stick functions were convolved with the canonical hemodynamic response function and its time and dispersion derivatives. To remove low-frequency scanner drifts, data were high-pass filtered with a frequency cutoff at 128 s, and an autoregression model of polynomial order 1 was used to account for temporally correlated residual errors. Upon model estimation, contrast images were computed representing the main effects of CON-L, INC-L, CON-R, and INC-R trials versus baseline. The contrast images from each participant were then entered into a random-effects analysis, implemented as a flexible factorial design with factors response side, response congruency, and subject. After estimation, we first tested for significant interaction between response side and congruency using two-directional t-contrasts, [−CON-L + INC-L + CON-R − INC-R] and [CON-L − INC-L − CON-R + INC-R]. Owing to the lack of a robust influence of response side (see the Results section), visuomotor priming effects were averaged over left- and right-hand responses using the contrast [−CON-L + INC-L − CON-R + INC-R], hereafter abbreviated as “INC > CON.” The opposite contrast [CON-L − INC-L + CON-R − INC-R] (“CON > INC”) was also routinely inspected.

A second branch of analyses examined congruency-modulated functional connectivity with the brain regions identified by the activation analysis. As seed regions, we defined the first peak voxel of each cluster listed in Table 1. The seed regions were invariant, that is, the same coordinates derived from the group analysis were used for all subjects. A generalized form of context-dependent psychophysiological interaction analysis (gPPI v7.12, McLaren et al. 2012; http://www.nitrc.org/projects/gppi, last accessed 30 March, 2015; see also Friston et al. 1997) was performed separately for each seed. The single-subject gPPI models resembled those of the activation analysis but, furthermore, included the seed region's time course as well as the PPI regressors modeling the interactions between the time course and the main effects of CON-L, INC-L, CON-R, and INC-R. After model estimation, in analogy to the activation analysis, contrast images representing functional connectivity associated with CON-L, INC-L, CON-R, and INC-R were computed and passed on to a flexible factorial random-effects analysis with factors response side, congruency, and subject. As neither the behavioral nor the activation results had been significantly affected by response side, we did not initially test for possible modulations of functional connectivity as a function of response assignment. This time, the contrast of primary interest was “CON > INC,” reflecting stronger functional connectivity with a given seed region for congruent than for incongruent trials (irrespective of response side). A second contrast examined the opposite direction (“INC > CON”).

Table 1

Brain regions demonstrating subliminal visuomotor priming on brain activity, revealed by contrasting incongruent > congruent shape–response assignments at P < 0.001 (voxel level) and P < 0.050 (cluster level, FWE-corrected)

Region BA Number of voxels Peak voxel (MNI space)
 
x y z Z-score 
R: Inferior parietal lobule 40 341 36 −54 42 4.57 
R: Medial superior frontal gyrus 222 34 44 4.52 
L: Medial superior frontal gyrus  −4 30 50 3.70 
R: Medial superior frontal gyrus  42 44 3.33 
R: Inferior frontal gyrus 44 327 54 20 4.37 
R: Inferior frontal gyrus 45  48 22 14 4.09 
L: Inferior parietal lobule 304 −36 −58 42 4.36 
R: Caudate nucleus – 232 14 14 4.20 
R: Thalamus –  16 −10 3.45 
L: Inferior frontal gyrus 44 718 −46 20 30 4.17 
L: Insula 47  −40 18 −6 3.99 
L: Inferior frontal gyrus 45  −48 30 10 3.97 
Region BA Number of voxels Peak voxel (MNI space)
 
x y z Z-score 
R: Inferior parietal lobule 40 341 36 −54 42 4.57 
R: Medial superior frontal gyrus 222 34 44 4.52 
L: Medial superior frontal gyrus  −4 30 50 3.70 
R: Medial superior frontal gyrus  42 44 3.33 
R: Inferior frontal gyrus 44 327 54 20 4.37 
R: Inferior frontal gyrus 45  48 22 14 4.09 
L: Inferior parietal lobule 304 −36 −58 42 4.36 
R: Caudate nucleus – 232 14 14 4.20 
R: Thalamus –  16 −10 3.45 
L: Inferior frontal gyrus 44 718 −46 20 30 4.17 
L: Insula 47  −40 18 −6 3.99 
L: Inferior frontal gyrus 45  −48 30 10 3.97 

Since the brain regions revealed by the gPPI analyses hardly overlapped with the regions identified by the activation analysis, we were interested whether the former showed task-related activation. For that reason, we first created an image containing the voxels of all gPPI clusters. Then, turning to the activation analysis, 4 additional second-level contrasts were computed testing for greater brain activation associated with CON-L versus baseline, CON-R versus baseline, INC-L versus baseline, and INC-R versus baseline. Eventually, the image representing all gPPI clusters was used as an inclusive mask for testing the conjunction of those contrasts at an uncorrected voxel height threshold of P < 0.001.

All other statistical parametric maps were assessed at a voxel height threshold of P < 0.001 and a family-wise error (FWE) -corrected cluster threshold of P < 0.050.

Results

Behavioral Data

On average, 5.7% of all congruent trials and 6.0% of all incongruent trials were incorrect or missing (e.g., due to slow decisions). The difference was not significant (t(30) = 0.35, P = 0.728). Mean RTs (SD given in parentheses) associated with the shape discrimination task were 581 (88) ms for CON-L, 592 (86) ms for INC-L, 569 (91) ms for CON-R, and 583 (91) ms for INC-R targets. Subliminal visuomotor priming thus amounted to 11 ms for targets requiring a left-hand response and to 14 ms for targets requiring a right-hand response. A repeated-measures ANOVA with factors response congruency and response side yielded a significant main effect of response congruency (F1, 30 = 5.22, P = 0.030), that is, priming, whereas neither the main effect of response side nor the interaction between response side and congruency reached significance (F1, 30 = 1.97, P = 0.171 and F1, 30 = 0.21, P = 0.647, respectively). A repeated-measures ANOVA on error rates did not reveal any significant effects (all F1, 30 < 0.16, all P > 0.689).

None of the participants reported to have seen the primes between the masks. The mean overall hit rate in the prime visibility test was 52.5% (SD = 6.9%), barely not attaining a significant difference from the 50% level (t(30) = 2.02, P = 0.052). The d′ measure of prime visibility (Green and Swets 1966) was 0.03 on average (SD = 0.39) and did not significantly deviate from zero (t(30) = 0.43, P = 0.670). There were no significant correlations between RT priming and hit rate or d′ (all t(29) < 0.48, all P > 0.635).

Neuroimaging Data

Initial tests whether lower brain activity for congruent than incongruent trials was significantly influenced by response side yielded only a few small clusters that were, however, not significant at the cluster level. The biggest of these clusters, observed for the contrast [−CON-L + INC-L + CON-R − INC-R], was located in the right middle temporal gyrus: peak coordinates: 54, −12, −14; cluster size: 93 voxels; FWE-corrected cluster level P = 0.294. The second biggest cluster, obtained by the opposite contrast [CON-L − INC-L − CON-R + INC-R], emerged in the left superior parietal lobule: peak coordinates: −18, −62, 46; cluster size: 31 voxels; FWE-corrected cluster level P = 0.827.

Averaged across left- and right-hand responses, the activation analysis revealed significant subliminal visuomotor priming (INC > CON) in 5 bilateral fronto-parietal clusters as well as in the right caudate nucleus with extension into the thalamus (Fig. 1 and Table 1). In the PPC, priming effects were located ventrally along the intraparietal sulcus, that is, in the inferior parietal lobules [IPLs, Brodmann area (BA) 7/40]. Ventrolateral prefrontal cortex (VLPFC) clusters included opercular (BA 44) and triangular (BA 45) parts of the inferior frontal gyrus (IFG) bilaterally. The IFG cluster of the left hemisphere had a greater anterior extent and also encompassed a portion of the insula. Another cluster was found in the medial superior frontal gyrus (BA 8/9). Opposite effects (CON > INC) were not detected at the predefined statistical threshold.

Figure 1.

Brain regions showing significant subliminal visuomotor priming, that is, lower brain activity for trials with congruent than with incongruent shape–response assignments. Effects were overlaid on coronal sections of the group averaged T1 image using MRIcron (http://www.mccauslandcenter.sc.edu/mricro/mricron/, last accessed 30 March, 2015). White numbers refer to the y-coordinates of the coronal sections in MNI space. Averaged parameter estimates representing neural activity [arbitrary unit] associated with the 4 event types are depicted below. Estimates were extracted from and averaged across the entire respective cluster listed in Table 1. The information “left hand” versus “right hand” refers to the response side associated with the target stimulus. Error bars represent standard error of the mean (31 subjects). L: left; R: right; IFG: inferior frontal gyrus; IPL: inferior parietal lobule.

Figure 1.

Brain regions showing significant subliminal visuomotor priming, that is, lower brain activity for trials with congruent than with incongruent shape–response assignments. Effects were overlaid on coronal sections of the group averaged T1 image using MRIcron (http://www.mccauslandcenter.sc.edu/mricro/mricron/, last accessed 30 March, 2015). White numbers refer to the y-coordinates of the coronal sections in MNI space. Averaged parameter estimates representing neural activity [arbitrary unit] associated with the 4 event types are depicted below. Estimates were extracted from and averaged across the entire respective cluster listed in Table 1. The information “left hand” versus “right hand” refers to the response side associated with the target stimulus. Error bars represent standard error of the mean (31 subjects). L: left; R: right; IFG: inferior frontal gyrus; IPL: inferior parietal lobule.

Of the 6 gPPI analyses performed using the peak voxels of the clusters reported in Table 1, 3 revealed significant congruency-modulated functional connectivity between the respective seed and other brain regions. These were the left IFG seed ([−46, 20, 30]), the left IPL seed ([−36, −58, 42]), and the right caudate nucleus seed ([14, 0, 14]). Functional connectivity with the seeds was always greater for congruent than for incongruent trials (CON > INC), whereas the opposite effect (INC > CON) was not found at the predefined level of significance. The connectivity profiles associated with the 3 seed regions for the contrast “CON > INC” are summarized color-coded in Figure 2. Differential connectivity (CON > INC) with the left IFG seed was found for bilateral brain regions of the ventral visual stream, right-sided areas of the dorsal stream, and with predominantly left-sided motor areas (Table 2): the bilateral inferior and right middle occipital gyri (BA 18/19), the bilateral lingual and right fusiform gyri (BA 18/19), the precuneus (BA 5), the right superior parietal lobule and angular gyrus (BA 7), the left precentral gyrus (BA 6), the supplementary motor area (BA 6), and the middle cingulate gyrus (BA 24). There was also a cluster in the right superior temporal pole (BA 38) extending into the amygdala. A second analysis revealed differential connectivity (CON > INC) between the left IPL seed and the right inferior occipital gyrus (BA 18) and adjacent portions of the calcarine (BA 17) and cuneus (BA 18; Table 3). The third seed region, the right caudate nucleus, showed greater connectivity during CON than INC with the right IPL (BA 40) and with the right middle and superior occipital gyri (BA 19; Table 4).

Table 2

Brain regions showing greater functional connectivity with the left IFG seed voxel [−46, 20, 30] for congruent than incongruent trials at P < 0.001 (voxel level) and P < 0.050 (cluster level, FWE-corrected)

Region BA Number of voxels Peak voxel (MNI space)
 
x y z Z-score 
R: Lingual gyrus 19 341 24 −64 4.66 
R: Fusiform gyrus 19  28 −66 −10 4.10 
R: Lingual gyrus 19  18 −50 −2 3.32 
L: Precentral gyrus 212 −42 −12 58 4.61 
L: Precentral gyrus  −50 −12 50 4.39 
L: Inferior occipital gyrus 18 383 −24 −92 −8 4.49 
L: Lingual gyrus 18  −12 −62 3.89 
L: Lingual gyrus 18  −16 −90 −14 3.81 
R: Superior parietal lobule 813 32 −60 58 4.48 
R: Middle occipital gyrus 19  38 −68 28 4.41 
R: Angular gyrus  32 −64 46 4.27 
R: Superior temporal pole 38 362 40 −20 3.86 
R: Amygdala –  28 −18 3.77 
R: Precuneus 267 12 −44 52 4.10 
R: Precuneus  −52 52 3.91 
L: Precuneus  −6 −38 60 3.31 
R: Supplementary motor area 193 56 4.06 
L: Supplementary motor area  −6 54 3.62 
R: Middle cingulate gyrus 24  42 3.56 
R: Lingual gyrus 18 237 24 −82 −6 3.96 
R: Inferior occipital gyrus 19  34 −82 −2 3.78 
R: Inferior occipital gyrus 18  26 −90 −6 3.69 
Region BA Number of voxels Peak voxel (MNI space)
 
x y z Z-score 
R: Lingual gyrus 19 341 24 −64 4.66 
R: Fusiform gyrus 19  28 −66 −10 4.10 
R: Lingual gyrus 19  18 −50 −2 3.32 
L: Precentral gyrus 212 −42 −12 58 4.61 
L: Precentral gyrus  −50 −12 50 4.39 
L: Inferior occipital gyrus 18 383 −24 −92 −8 4.49 
L: Lingual gyrus 18  −12 −62 3.89 
L: Lingual gyrus 18  −16 −90 −14 3.81 
R: Superior parietal lobule 813 32 −60 58 4.48 
R: Middle occipital gyrus 19  38 −68 28 4.41 
R: Angular gyrus  32 −64 46 4.27 
R: Superior temporal pole 38 362 40 −20 3.86 
R: Amygdala –  28 −18 3.77 
R: Precuneus 267 12 −44 52 4.10 
R: Precuneus  −52 52 3.91 
L: Precuneus  −6 −38 60 3.31 
R: Supplementary motor area 193 56 4.06 
L: Supplementary motor area  −6 54 3.62 
R: Middle cingulate gyrus 24  42 3.56 
R: Lingual gyrus 18 237 24 −82 −6 3.96 
R: Inferior occipital gyrus 19  34 −82 −2 3.78 
R: Inferior occipital gyrus 18  26 −90 −6 3.69 
Table 3

Brain regions showing greater functional connectivity with the left IPL seed voxel [−36, −58, 42] for congruent than incongruent trials at P < 0.001 (voxel level) and P < 0.050 (cluster level, FWE-corrected)

Region BA Number of voxels Peak voxel (MNI space)
 
x y z Z-score 
R: Cuneus 18 446 −90 14 5.01 
R: Inferior occipital gyrus 18  30 −84 −4 4.08 
R: Calcarine 17  14 −98 3.88 
Region BA Number of voxels Peak voxel (MNI space)
 
x y z Z-score 
R: Cuneus 18 446 −90 14 5.01 
R: Inferior occipital gyrus 18  30 −84 −4 4.08 
R: Calcarine 17  14 −98 3.88 
Table 4

Brain regions showing greater functional connectivity with the right caudate seed voxel [14, 0, 14] for congruent than incongruent trials at P < 0.001 (voxel level) and P < 0.050 (cluster level, FWE-corrected)

Region BA Number of voxels Peak voxel (MNI space)
 
x y z Z-score 
R: Inferior parietal lobule 40 285 26 −52 52 4.72 
R: Inferior parietal lobule 40  28 −44 44 3.68 
R: Middle occipital gyrus 19 327 34 −74 26 4.32 
R: Superior occipital gyrus 19  28 −84 30 4.26 
R: Superior occipital gyrus 19  24 −80 38 3.61 
Region BA Number of voxels Peak voxel (MNI space)
 
x y z Z-score 
R: Inferior parietal lobule 40 285 26 −52 52 4.72 
R: Inferior parietal lobule 40  28 −44 44 3.68 
R: Middle occipital gyrus 19 327 34 −74 26 4.32 
R: Superior occipital gyrus 19  28 −84 30 4.26 
R: Superior occipital gyrus 19  24 −80 38 3.61 
Figure 2.

Congruency-modulated functional connectivity with 3 seed regions (contrast: congruent > incongruent). Depending on the specific seed region, functionally connected brain regions are depicted in red (L IFG seed, [−46, 20, 30]), in green (L IPL seed, [−36, −58, 42]), or in blue (R caudate nucleus seed, [14, 0, 14]). Overlap between red and green is displayed in yellow, and overlap between red and blue is shown in violet. Functional connectivity patterns were overlaid on the group averaged T1 image using MRIcron. White numbers refer to the y-coordinates of the coronal sections in MNI space. Averaged parameter estimates representing functional connectivity [arbitrary unit] associated with the 4 event types are depicted below. Estimates were extracted from and averaged across the entire respective cluster listed in Tables 2, 3, or 4, respectively. The information “left hand” vs. “right hand” refers to the response side associated with the target stimulus. Error bars represent standard error of the mean (31 subjects). L: left; R: right; FFG: fusiform gyrus; IFG: inferior frontal gyrus; IOG: inferior occipital gyrus; IPL: inferior parietal lobule; LG: lingual gyrus; MCG: middle cingulate gyrus; MOG: middle occipital gyrus; PrG: precentral gyrus; SMA: supplementary motor area; SOG: superior occipital gyrus; SPL: superior parietal lobule.

Figure 2.

Congruency-modulated functional connectivity with 3 seed regions (contrast: congruent > incongruent). Depending on the specific seed region, functionally connected brain regions are depicted in red (L IFG seed, [−46, 20, 30]), in green (L IPL seed, [−36, −58, 42]), or in blue (R caudate nucleus seed, [14, 0, 14]). Overlap between red and green is displayed in yellow, and overlap between red and blue is shown in violet. Functional connectivity patterns were overlaid on the group averaged T1 image using MRIcron. White numbers refer to the y-coordinates of the coronal sections in MNI space. Averaged parameter estimates representing functional connectivity [arbitrary unit] associated with the 4 event types are depicted below. Estimates were extracted from and averaged across the entire respective cluster listed in Tables 2, 3, or 4, respectively. The information “left hand” vs. “right hand” refers to the response side associated with the target stimulus. Error bars represent standard error of the mean (31 subjects). L: left; R: right; FFG: fusiform gyrus; IFG: inferior frontal gyrus; IOG: inferior occipital gyrus; IPL: inferior parietal lobule; LG: lingual gyrus; MCG: middle cingulate gyrus; MOG: middle occipital gyrus; PrG: precentral gyrus; SMA: supplementary motor area; SOG: superior occipital gyrus; SPL: superior parietal lobule.

An additional analysis confirmed that in most clusters identified by the gPPI analyses, 100% of all voxels showed significantly greater conjoint task-related activity versus baseline. The only exceptions were the right PPC with 94.5% of all voxels being significant, the precuneus cluster, and the temporal pole cluster (30.7% and 19.9%, respectively).

We also computed the spatial overlap of voxels obtained by the different gPPI analyses. Overlap between clusters resulting from the analyses of the left IFG seed and the left IPL seed was found in 137 voxels of the right inferior occipital gyrus, and overlap between clusters pertaining to the left IFG and right caudate nucleus seeds was located in the right PPC and the right middle occipital gyrus (40 voxels in total).

Discussion

In the present study, 31 healthy participants underwent fMRI to investigate the neural signature of subliminal visuomotor priming in terms of both brain activity and functional connectivity. In line with earlier work (Aron et al. 2003; D'Ostilio and Garraux 2012; D'Ostilio et al. 2013), analyses revealed lower brain activity for congruent (CON) than incongruent (INC) trials in ventrolateral and dorsomedial frontal and inferior parietal brain regions as well as in the basal ganglia. The present study thereby confirms and extends earlier findings by demonstrating an involvement of the visuomotor network in subliminal visuomotor processing even under conditions of an arbitrary S–R mapping, ruling out repetition priming and spatial congruency effects of the Simon type (Shwartz et al. 1977; Kornblum et al. 1990; Klotz and Neumann 1999; Ansorge et al. 2014). Although speculative, similar behavioral visuomotor priming effects would likely have been obtained if we had used unmasked visible primes instead of masked invisible primes at a prime–target stimulus onset asynchrony (SOA) of 67 ms as in the present experiment. This assumption is based on results of visuomotor priming experiments conducted by Vorberg et al. (2003), leading to their claim that “the invariance of priming with and without awareness is […] typically found in experiments with prime–target SOAs that are shorter than 100 ms” (Vorberg et al. 2004, p. 288). However, generalizing our previous fMRI findings on semantic priming (Ulrich et al. 2013), neural unmasked visuomotor priming effects likely would have been observed in additional brain regions associated with conscious strategic processing.

Furthermore, we found changes in the functional connectivity profile as a function of response congruency, suggesting that temporary coupling between brain regions within the visuomotor system supports subliminal visuomotor processing. Functional connectivity was greater for CON than INC trials between the left IFG and occipito-temporal, superior parietal, precentral, and supplementary motor areas, between the left IPL and posterior occipital regions, and between the right caudate nucleus and middle/superior occipital and superior parietal areas. Functional connectivity profiles overlapped in right occipital and right parietal regions across seed regions, suggesting a consistent coupling of these 2 regions with other parts of the visuomotor network identified by the activation analysis. In accordance with previous findings on visible priming (Ghuman et al. 2008; Kujala et al. 2012), an increase in functional connectivity between task-relevant brain regions seems to be a relevant neural mechanism for priming, in addition to changes in neural activity.

Brain regions showing less activation during CON compared with INC were the IPLs, the inferior frontal gyri, the medial superior frontal gyrus, and the right caudate nucleus. The PPC, to which the IPL belongs, can be conceived of as an interface between perceptual and motor regions allowing visual information to be transformed into motor output (Goodale and Milner 1992; Jeannerod et al. 1995; Caminiti et al. 1998; Andersen and Buneo 2002; Cohen 2009; Creem-Regehr 2009). More specific to the phenomenon “priming”, the PPC has recently been regarded as a “strong candidate for a neural substrate of unconscious response priming” (Koivisto et al. 2012, p. 631), in accord with previous suggestions that the PPC may represent S–R mappings (Bunge, Hazeltine, et al. 2002; Bunge et al. 2003; Brass and von Cramon 2004; Hartstra et al. 2012). This function can readily explain the present finding of greater IPL activity for incongruent than congruent trials. During congruent trials, both prime and target activated the same response, whereas during incongruent trials likely 2 different S–R mappings were represented. Moreover, the PPC implements visuospatial attention (Corbetta et al. 1993, 1995; Nobre et al. 1997; Gitelman et al. 1999) and also motor attention (Rushworth et al. 2001, 2003) directed toward an upcoming movement. Preparation of an inappropriate motor response mapping during incongruent trials very likely made it necessary to redirect attention toward the actual, relevant response, which is also in line with the broader notion that the PPC is involved in response selection (Deiber et al. 1991; Schumacher and D'Esposito 2002; Jiang and Kanwisher 2003; Schumacher et al. 2003; Cavina-Pratesi et al. 2006; van Eimeren et al. 2006; Amiez et al. 2012). Apart from the IPL, priming was also detected bilaterally in portions of the IFG often referred to as ventrolateral prefrontal cortex (VLPFC). The VLPFC is prominent for its function to implement cognitive control (Miller and Cohen 2001; Niendam et al. 2012). More specifically, the VLPFC has been shown to play a major role in learning, retrieval, and maintenance of S–R rules (Toni and Passingham 1999; Murray et al. 2000; Toni et al. 2001; Brass et al. 2003; Bunge et al. 2003, 2005; Brass and von Cramon 2004; Bunge 2004; Crone et al. 2006). Thus, its congruency-modulated activity in the present context is not surprising. Upon unconscious processing of the prime, participants had to retrieve the rule specifying the appropriate button in response to the target. During CON trials, the relevant S–R rule and the associated response were already preactivated by the prime. During INC trials, in contrast, the prime activated an inappropriate S–R mapping, and thus, the appropriate S–R rule for the target response had to be retrieved in a more elaborated manner. Retrieval demands reflected by VLPFC activity were therefore lower for CON than for INC. A related alternative explanation for the involvement of the IFG comes from studies showing that the (posterior) VLPFC is recruited in situations where competing task-representations are simultaneously activated (Bunge, Hazeltine, et al. 2002; Zhang et al. 2004; Badre et al. 2005; Moss et al. 2005; Souza et al. 2009). As mentioned above, during INC trials, the prime preactivated an S–R rule (and response) inappropriate to the upcoming target, and thus, the VLPFC might have been engaged to resolve that conflict. Previous research suggests that functions discussed above might also apply to the right caudate nucleus. Specifically, it might have been involved in representing S–R rules (Toni and Passingham 1999; Passingham et al. 2000; Toni et al. 2001; Boettiger and D'Esposito 2005) and/or selecting (Schumacher et al. 2003; Gerardin et al. 2004; Wager et al. 2005; van Eimeren et al. 2006; Amiez et al. 2012) and reprogramming (Mars et al. 2007) the relevant response appropriate to the target. These possible functions would be relied on less during CON compared with INC trials. Moreover, the caudate nucleus might have served to inhibit the prime-induced irrelevant response during INC trials (Bunge, Dudukovic, et al. 2002; Aron et al. 2003; Cai and Leung 2011; Niendam et al. 2012), possibly in concert with the medial superior frontal gyrus (Kaladjian et al. 2009; Shi et al. 2010).

Psychophysiological interaction analyses revealed congruency-modulated functional connectivity of several task-relevant visuomotor areas with any one of the seed regions. An increase in temporary coupling of brain activity was found for areas of the ventral visual stream, that is, the cuneus, the calcarine, and the inferior occipital, lingual, and fusiform gyri. We assume that these areas played a perceptual role by representing the visual shape stimuli (Haxby et al. 1991, 1994; Gauthier et al. 1997; Ishai et al. 1999; Grill-Spector 2003; Grill-Spector and Malach 2004). A second group of regions, the supplementary motor area and the precentral gyrus, was likely involved in preparing, planning, and executing the motor response (Colebatch et al. 1991; Larsson et al. 1996; Roland and Zilles 1996; Tanji 1996; Tanji and Mushiake 1996; Rizzolatti and Luppino 2001; Hanakawa et al. 2003; Witt et al. 2008). Finally, there were clusters in the right PPC including portions of the superior parietal lobule, of the IPL, and of the angular gyrus. As discussed above, these regions might have been involved in transforming visual information processed by the ventral stream into motor representations in the supplementary motor area and the precentral gyrus. Overall, the important interplay particularly between ventral and dorsal visual areas is underlined by the overlap of the different connectivity profiles in the inferior occipital gyrus and in the right PPC, respectively.

Our activation and functional connectivity findings might be best integrated by recapitulating the main difference between CON and INC trials: During CON trials, the S–R rule associated with the subliminally presented prime is compatible with the S–R mapping required for the target. That is, under CON, the target-appropriate S–R mapping is preactivated, that is, facilitated, without the presence of any interfering incompatible S–R rule or response perturbing the cognitive system. Thus, visuomotor processing associated with the target can likely be performed efficiently. We suggest that this higher efficiency of processing is reflected by an increase in functional coupling between task-relevant brain regions: Brain regions significantly activated during the task were more strongly coupled with each other during CON than INC. Moreover, in line with the assumption that efficient processing entails lower levels of neural activity (Ghuman et al. 2008; Kujala et al. 2012), several brain regions showed less activation under CON relative to INC.

Conversely, during INC trials, the prime unconsciously activates an S–R rule and the associated response which is incongruent with the target. When subsequent information associated with the target enters the system, a different S–R mapping needs to be established. The cognitive system, therefore, likely needs to discard the prime-related mapping and to retrieve and prepare the appropriate S–R mapping for the target. This reconfiguration process might be aggravated by the fact that the inappropriate response tendency induced by the prime appears to inhibit the appropriate response required for the target (Praamstra and Seiss 2005). The apparent response conflict under INC might have resulted in an increase of neural activity for INC compared with CON in several regions likely involved in functions related to task preparation, response conflict resolution, and response execution.

The finding of greater neural activation for INC compared with CON trials is also in agreement with accumulator models of perceptual decision-making (Ho et al. 2009; Mulder et al. 2014) and visuomotor priming (Vorberg et al. 2003): For INC trials, in comparison with CON trials, owing to the interfering prime, the rate of evidence accumulation required to arrive at a decision was probably lower, which has been related to greater brain activity in sensory brain regions and the PPC, among other regions (Ho et al. 2009). In a more general way, the present results are also in line with predictive coding accounts of sensory processing holding that the brain proactively anticipates future stimuli (Friston 2005; Bar 2007; Hohwy 2012; Panichello et al. 2013). From this point of view, the present visuomotor priming effects might have emerged because during CON trials the S–R rule which had to be applied to the target had been correctly predicted by the prime stimulus, probably accompanied by reduced neural activation (Kherif et al. 2011; Kok et al. 2012), whereas during INC trials the prediction was incorrect.

Alternatively, it could be argued that the presently observed subliminal-congruency effects reflect unconscious statistical learning of prime–target contingencies. In the present study, each congruent prime–target pair was presented twice as often as each incongruent prime–target pair because (1) per each target there was 1 possible congruent prime but 2 possible incongruent primes, (2) prime–target pairs were created with the constraint that primes and targets were never the same, and (3) congruent and incongruent conditions were equally frequent. Thus, the mere difference in the respective frequencies of each specific prime–target pair between congruent and incongruent trials might have produced an effect of statistical learning, mimicking a motor–congruency effect. Although statistical learning has been found to modulate activity in a variety of brain regions (Turk-Browne et al. 2009) including the caudate nucleus for which lower activation for congruent than for incongruent trials was detected in the present study, this effect of statistical learning has only been observed for patterns of visible shapes. It is therefore open, whether statistical learning can also occur for subliminal stimuli as in the present experiment. Nevertheless, in order to address this potential confound, we performed a split-half analysis on the RT data. We hypothesized that in case the difference in frequencies of congruent and incongruent prime–target pairs had an effect of mimicking visuomotor priming, this would likely yield a significantly greater priming effect in the second half than in the first half of the experiment. While the split-half analysis revealed significantly faster responses in the second half than in the first half (main effect of experimental half: F1, 30 = 8.81, P = 0.006), and an overall priming effect (main effect of response congruency: F1, 30 = 5.13, P = 0.031), the critical interaction between experimental half and response congruency was far from being significant (F1, 30 = 0.23, P = 0.632). Thus, although some learning effects might appear quickly at least with visible stimuli (Turk-Browne et al. 2009), the alternative interpretation that statistical learning of visual regularities involving the subliminal prime might have mimicked effects of visuomotor priming did not receive empirical support.

In conclusion, the present study elucidated the neural mechanisms underlying unconscious visuomotor priming by measuring both brain activation and functional connectivity profiles. Posterior parietal and inferior frontal brain regions as well as the caudate nucleus and the thalamus showed lower brain activity for primed than unprimed trials of a shape discrimination task. At the same time, these areas were more strongly functionally connected with ventral visual stream areas, premotor cortex, and PPC, probably indicating higher efficiency of visuomotor processing for primed compared with unprimed trials. In a broader context, the present study shows that temporary coupling between brain regions is an important aspect of the neural signature of unconscious priming, in addition to priming-related changes in the magnitude of brain activation.

Funding

This work was supported by a grant from the German Research Foundation within the Research Network “Neuro-cognitive mechanisms of conscious and unconscious visual perception” (PAK 270/2) to M.K. (DFG Ki 804/3-2).

Notes

We thank all volunteers for their participation in this study, Kathrin Brändle for her help in data acquisition, and Bärbel Herrnberger for assistance in fMRI data analyses. Conflict of Interest: None declared.

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