We have shown previously that patients with schizophrenia display difficulties in organizing and smoothly executing motor actions when those actions are sequential (Delevoye-Turrell et al., 2007). Even when performing a task as simple as lifting and gripping an object, our previous results suggest that patients with schizophrenia fail to plan their actions in advance. Contrary to controls, they are not disturbed by a secondary task during the planning phase, but use more resources than healthy volunteers during the motor execution phase of movements (Delevoye-Turrell et al., 2006). Other studies have also reported a difficulty in planning actions in a coherent way (Jogems-Kosterman et al., 2001; Zalla et al., 2006; Grootens et al., 2009). However, to the best of our knowledge, previous studies have not shown any correlation between clinical disorganization and the ability to plan sequences of motor actions. In the present study, we included a manipulation of those visual factors that are known to be affected by clinical disorganization in order to increase the sensitivity of our task to this clinical factor.

Mechanisms helping to organize visual information are diverse, and intervene at different stages of visual organization. Visual information is first extracted locally and decomposed into color, orientation, or luminance information. This information then needs to be correctly integrated or separated in order to derive the shapes of objects and the structure of the considered visual scene. Many automatic grouping mechanisms have been described, e.g., grouping by color, by similarity, by uniform connectedness (Wertheimer, 1923/1950; Palmer and Rock, 1994). These grouping mechanisms allow us to attribute object parts to the same object and to put together different objects that belong to a same group, e.g., the trees in a forest. Our results and those reported in the literature show that mildly symptomatic patients with schizophrenia benefit as much as controls from automatic grouping (Chey and Holzman, 1997; Silverstein et al., 1998; Giersch and Rhein, 2008; van Assche and Giersch, 2011), at least when grouping cues are unambiguous and when they do not lead to spurious grouping (see Kurylo et al., 2007; Silverstein and Keane, 2011 for limits to the benefits of automatic grouping in schizophrenia).

In addition to automatic grouping mechanisms, more integrated cognitive mechanisms provide a function of “re-grouping” of visual elements (van Assche and Giersch, 2011) This mechanism would give the means to the brain to explore a visual scene by foveating different objects successively, without being restrained by the fact that these objects belong to different groups of objects. We have suggested that patients with schizophrenia are in great difficulty when having to “re-group” items in visual perceptual tasks, and shown that this effect is correlated with clinical disorganization (van Assche and Giersch, 2011). To further assess the possible consequences of regrouping deficits in schizophrenia, here we question the possibility that “re-grouping” is also involved in the planning and execution of a sequence of motor actions that requires participants to point toward objects of different groups. Indeed, when pointing to a series of visual targets, these targets can be considered individually (i.e., one by one) or as a series of sub-elements of a greater whole (two adjacent targets composing a group and guiding sequential pointing actions). The grouping of adjacent targets might then affect the planning and fluent execution of the motor sequences.

The possibility that visual information can impact action planning and execution is supported by several arguments, which are presented in the next section.

A growing number of empirical findings now support the involvement of perception in action processing, and has led to the ideomotor theory (Prinz, 1987; see Nattkemper et al., 2010; Shin et al., 2010, for reviews). According to this theory, the brain would use perceptual representations of action-effects for optimal planning of motor actions. For example, if one plans to manually point toward visual targets, the sensory feedback expected to result from this action is processed in advance of action initiation, i.e., during motor planning. During action execution, participants can then check whether the actual sensory feedback corresponds to what was expected. As a consequence, the correspondence between the action and sensory feedback has a facilitation effect on action, as shown by a number of studies [review in Shin et al. (2010)]. For example, Kunde (2001) used a task during which key presses were followed by tones of varying intensity. The results showed that the correspondence between the intensity of the key presses and the loudness of the tones facilitated the responses. Even when the relationship between the action and its consequence is less straightforward, it can be learned and used to optimize performance (Elsner and Hommel, 2004). In the present study, participants had to execute pairs of pointing actions toward distinct visual targets, and we manipulated the correspondence between the organization of the motor sequence on the one hand and the perceptual organization of visual targets on the other hand. The manipulation of this correspondence was then used in the framework of the ideomotor theory to assess to what extent patients with schizophrenia are able to include visual representations for motor planning and to what extent their difficulties in organizing perceptual information may be revealed in a task that requires in addition the execution of sequences of motor actions.

In the present study, we describe a manual-pointing task in which participants were required to tap on 6 circles (the visual targets) that were presented on a tactile screen (Figure 1). The participants' task was to tap successively and clockwise on the targets following the rhythm of a series of auditory stimuli. To induce the perception of an auditory structure, we used temporal proximity, which is known as a fundamental principle of organization in auditory perception (Wertheimer, 1938). Two sounds separated by a short interval defined a pair, and successive pairs were separated by a longer interval. Since participants were instructed to tap in synchrony with the sounds, the rhythmic organization of the auditory stimuli induced a rhythmic motor execution of pairs of taps on adjacent circles, which required participants to structure the motor elements through space and through time. When participants performed the pairs of taps on 6 isolated circles (without any visual grouping cues), the motor outputs were organized in pairs but the perceptual representation of the visual targets was not. A perceptual visual structure was induced by using connectors, which linked adjacent circles together in order to create visual perceptual pairs. This visual structure globally matched the auditory structure, inasmuch both organizations led to the perception of pairs: sound pairs on the one hand, and visual pairs on the other hand. Consequently, the perceptual organization of the circles could hypothetically affect the motor output, depending on the grouping abilities of the participants. Indeed, when pairs of taps were executed on pairs of connected targets the action structure matched the automatic visual organization. On the other hand, when the pairs of taps were to be executed on unconnected targets, participants had to match their pair of taps with “re-grouped” targets (Figure 1). We hypothesized that patients would show impairments related to their difficulties in visual “re-grouping,” i.e., we expected patients to have difficulties when the task required to point to successive visual targets belonging to different groups. Inasmuch the difficulty related to “re-grouping” is related to clinical disorganization, we also predicted that this pattern of results should be observed predominantly in those patients suffering from clinical disorganization.

Twenty-two stabilized outpatients participated in the study. They met the Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition)'s criteria for schizophrenia (American Psychiatric Association, 1994). The diagnosis was based on a semi-structured interview (the Mini International Neuropsychiatric Interview, Sheehan et al., 1998), and was established by a senior psychiatrist of the University Psychiatry Department. Patients were 9 women and 13 men (mean age = 40.2 years, SD = 8.2; schooling years = 11.5, SD = 2.4).

The patients' performances were compared to those obtained in 22 controls (11 women and 11 men; mean age = 40.9 years, SD = 7.7; schooling years = 13.2, SD = 2). The two groups did not differ in age (F < 1). They did differ, however, in the number of schooling years [F_{(1, 42)} = 6.2, p < 0.05).

Regarding clinical characteristics, mean disease duration was 14.9 years (SD = 7.6). Symptoms were assessed through the use of the Positive and Negative Syndrome Scale (PANSS, Kay et al., 1987). Patients presented a mean score of 16.8 (SD = 5.9) for the Positive Subscale, 22.1 (SD = 6.3) for the Negative Subscale and 36.5 (SD = 9.8) for the Global Psychopathology Subscale, leading to a total mean score of 75.5 (SD = 17.2). Overall, these results indicated that the patients were within the normal-to-mild range, and were relatively asymptomatic.

Individual PANSS scores were also converted into a Positive factor (sum of P1, P3, G9 = 8.2, SD = 3.5), a Negative factor (sum of N1, N2, N3, N4, N5, N6, N7, G7, G13, and G16 = 20.3, SD = 6.3) and a Disorganization factor (sum of P2, N5, G10, and G11 = 9.6, SD = 3.1), according to the classification proposed by Lépine et al. (1989).

We were especially interested in the disorganization factor, and thus, we divided patients in two subgroups, one with a disorganization score above or equal to 10 (N = 10) and another group with a disorganization score below 10 (N = 12). This cutoff was set in order to divide the group of patients in two (this could not be totally achieved, due to the fact that 4 patients had a disorganization score of 9). These two groups differed in the number of schooling years [10.4 SD 1.6 for the group with a high disorganization score vs. 12.5 SD 2.5 for the group with a low disorganization score, F_{(1, 20)} = 5.1, p < 0.05]. We thus, divided the group of controls according to their schooling years also, in order to evaluate whether there was an impact of schooling years on the performance patterns. The 10 controls with the lowest number of schooling years had a similar education level as the patients from the group with a high disorganization score [10.4 in patients vs. 11.2 in controls, F_{(1, 18)} = 2.2, n.s.]. It is to note that the two subgroups of controls revealed very similar performances profiles, indicating that the education level did not impact result patterns. Our findings were in fact similar whether we divided the group of controls or not. Thus, for the sake of simplicity, the data presented in the following section corresponds to the means averaged across the two subgroups of controls.

All patients were taking medication, and doses were converted in chlorpromazine equivalents (mean dose = 272 mg, SD = 237). Patients received a neuroleptic treatment, either typical (N = 7) or atypical (N = 15). Six patients were also administered with an antiparkinsonian treatment.

All participants gave written informed consent prior the beginning of the study, consistently with the Declaration of Helsinki's recommendations. This project was approved by the local ethics committee (Comité Consultatif de Protection des Personnes dans la Recherche Biomédicale d'Alsace IV). Any history of neurological disorder (meningitis, brain injury, cerebrovascular accident), generalized anaesthesia within the past 3 months, or recent drug abuse, was considered as exclusion factors. Participants treated by benzodiazepines were also discarded from this study. In addition, four participants were excluded from the initial group of 24 participants because they did not follow instructions (2 patients and 2 controls).

Participants were seated comfortably on a chair in front of a touch screen (Elo Touch, 23 × 36 × 30 cm), which was placed on a narrow support at knee-height. The participants' task was to produce sequential-pointing movements to visual targets, with their dominant index finger, in synchrony with a series of beeps produced by the computer. The visual targets consisted in 6 equidistant outlined black circles arranged in the form of a hexagon. Participants were asked to point each circle (diameter 1.2°), one after the other, starting with the bottom-right circle (Figure 1). Participants were required to point each target (touching the screen) following a clockwise direction until they had reached the end of the trial duration (13 s). The auditory rhythmic sequence of beeps was created using Audacity software. Three different visual images were created using CorelDraw software. One was with the 6 circles but without connecters. The two others were with connecters, i.e., with line-segments linking neighboring circles by pairs (Figure 1). There were two possible locations for the connecters and both were used an equivalent number of times. Participants were instructed to be as accurate as possible both spatially and temporally. The connecters being presented right from the start of each trial, they emphasized a first grouping structure to the task, based on the perceptual principles of visual grouping.

The participants' task was to tap each visual target in synchrony with the sounds emitted by the computer. Alternating rhythmic patterns were used, i.e., inter-stimulus intervals of 300 and 600 ms. These alternating intervals imposed an auditory grouping and emphasized the need to re-group motor actions. Sounds separated by a short interval (300 ms in our paradigm) induce the perception of grouped pairs of sounds because they are close in time and separated from other pairs by a longer interval (600 ms in our paradigm). Since participants were required to tap in synchrony with the sounds, this led to the execution of pairs of taps close together in time, the two taps of a pair being ideally separated by a time interval of 300 ms, and successive pairs of taps being ideally separated by a time interval of 600 ms. The 300/600 ms intervals were chosen because when combined they are close to the preferred tempo of Inter Response Intervals of 500 ms which corresponds to the spontaneous tempo reported in the tapping literature (Repp, 2005). In addition, we knew from previous studies (Ameller et al., 2011; Turgeon et al., 2012) that patients with schizophrenia are able to perform the task at this speed. Finally, the contrast between 300 and 600 ms intervals was strong enough to yield the unambiguous perception of rhythmic grouping. It should be noted that this auditory grouping manipulation has already been applied to taps executed in one unique location; the originality of our paradigm is to ask participants to tap on distinct visual targets spread out throughout the action space and thus, requiring participants to re-group motor elements not only through time but also through space.

The 300/600 ms rhythmic pattern was used both with stimuli without and with connecters, leading to three conditions:

A neutral condition: Participants were required to tap alternating rhythmic patterns on visual targets that were not connected.

The experiment lasted about 1 h. First, all participants performed a familiarisation phase in which the different rhythms and images were presented. Participants were trained to synchronize their pointing taps with a trial requiring no visual grouping and equivalent time intervals between tones before being trained with alternating rhythms.

Each trial consisted in a listening phase (≈4.5 s), a waiting phase without sound, which gave time for the participants to get ready to point to the first circle [they prepared their finger just above the first circle (bottom right)] (≈3 s) and a test phase of 30 taps (≈13 s). Each participant performed a sum-total of 48 trials.

Participants started with the Neutral condition (without connecters) and performed 2 blocks of 4 trials each. This was followed by the Visual Grouping conditions. Each congruent and non-congruent condition included 4 blocks of 5 trials each (2 blocks with the image A and 2 with the image B, see Figure 1). These 8 blocks were randomized.

For each subject and each trial, we measured both the Inter Response Interval and the asynchrony between the tap and the sound. The spatial location of the tap was also measured allowing us to distinguish those taps directed toward connected and unconnected targets. We were in this way able to check whether participants followed the rhythm correctly.

In a first analysis, we measured the Inter Response Interval (IRI in ms), which is the time interval between the onsets of two successive taps produced by the participants, and which is a parameter that is commonly used in the tapping literature (Repp, 2005). From this IRI measure, we then calculated the mean ratio across trials for each participant and under each condition. This ratio referred to the participants' capacity to maintain the metric organization of the auditory rhythmic sequence (i.e., alternance of short and long intervals lasting 300 and 600 ms, respectively) and was calculated following the equation: IRIn/IRIn-1, where n refers to the long interval (600 ms), and n-1 to the short interval (300 ms). The ratio should thus be equal to 2. When this ratio was inverted (<1), we considered the trial as bad. Following this rule, we then calculated for each subject and under each experimental condition, the percentage of trials considered as bad (the number of bad trials divided by the total number of trials in a condition), a calculation that we will refer to in the following as “rate of errors.”

In a second analysis we aimed to evaluate the use of anticipatory mechanisms. We used here another typical parameter that is used in tapping experiments, i.e., the tap-tone synchronization accuracy (applied on correct trials only). To that aim, we calculated the asynchrony, which was taken as the time interval between the peak force of each tap and the start of the nearest tone (in ms).

A crucial aspect of such a sensorimotor synchronization task is the predictability of the external beep, which arises from its regular recurrence. It is this predictability that allows good synchronization between tap and sound. This is specifically the feature that distinguishes sensorimotor synchronization from a simple reaction time task, for which the response is made as quickly as possible after the sound and thus, is characterized by a delay >180 ms. In rhythmic tapping, the ability to anticipate the beep occurrence leads to asynchronies between tap and sound that are close to zero or even negative in healthy volunteers. Tap-tone asynchronies can thus be used to evaluate the quality of predictive timing. In addition, in the present study, our task imposed an organization of the taps by pairs, and this means that predictive timing might differ according to the order of the taps. We thus, distinguished the tap-tone asynchronies for the 1st and 2nd tap of a pair.

We used Analyses of Variance (ANOVA) with repeated measures on both error rates (rates of trials with an inverted ratio) and tap-sound asynchronies, with Group as a between-group factor and Experimental Condition as a within group factor (neutral, congruent and non-congruent conditions). For tap-sound asynchronies, an additional within-group factor was the tap Order within the sequences of two taps (first vs. second). Significant interactions were decomposed by means of Tukey HSD post-hoc tests and Sub-Analyses of Variance.

The most striking effect was the large amount of errors characterizing motor performances in the patients with schizophrenia. The interval ratio of patients was inverted, i.e., below 1, in 18% of the trials (averaged across conditions), which was significantly higher than the rate of inversions of 5% observed in the controls [F_{(1, 42)} = 11.1, p < 0.005].

The error rates were highly variable in patients, but were unusually large in half of the patients in the non-congruent condition. In this condition, 11 patients (vs. 0 control) revealed an error rate above 19%, i.e., 3SD above that measured in the controls (4.7% SD 4.7) (see distribution in Figure 2). The other half of the patients (N = 11) showed an error rate below 10% which was within the normal range (Figure 2). The two subgroups of patients differed significantly only on the score of clinical disorganization [F_{(1, 20)} = 4.7, p < 0.05]. These results were confirmed by the presence of significant correlations between tapping performances and clinical scores in the patients group as a whole. More specifically, a significant positive correlation was revealed between the score of clinical disorganization and the rate of errors in the non-congruent condition (r = 0.44, N = 22, p < 0.05). There were no other significant correlations with clinical scores, neither with the sub-scales of the PANSS nor with the positive or negative Lepine factors. All further analyses on motor performance scores were hence conducted taking into account the disorganization subgroups.

It is to be noted that when the group of patients was not divided, there was only a tendency toward a significant interaction between groups and experimental conditions [F_{(2, 84)} = 2.6, p = 0.076], which might be due to the heterogeneity of performance in the patients. Nevertheless, the Tukey HSD post-hoc analysis showed that in patients errors were more frequent in the non-congruent (22.1%) than in the congruent condition (14.8%), p < 0.05. This effect was not observed in controls (4.7 vs. 3.0%, p > 0.9).

In the following, we compared the performance levels across three groups of individuals (controls; patients with a high disorganization score; patients with a low disorganization score) as a function of the experimental conditions.

There was a significant interaction between groups and experimental conditions for the rate of errors [F_{(4, 82)} = 4.8, p < 0.005]. The HSD Tukey post-hoc analysis showed that patients with a high score of disorganization had a higher error rate in the non-congruent condition (31.6%) than in the two other conditions (18.2% in the neutral condition, p < 0.005; 19.7% in the congruent visual grouping condition, p < 0.05, Figure 3). These results were confirmed by means of sub-analyses that were conducted in each group and aimed at comparing performance levels across conditions. The sub-analyses not only confirmed the HSD Tukey post-hoc test but also revealed some effects in controls and in patients with a low disorganization score. The effect of experimental conditions was significant in the group with a low disorganization score [F_{(2, 22)} = 3.7, p < 0.05], due to a lower error rate in the congruent than in the neutral condition (10.6 vs. 17.1%, p < 0.05 in the HSD Tukey post-hoc analysis following the sub-analysis in patients with a low disorganization score). In the control group, the effect of experimental conditions tended toward significance only, F_{(2, 42)} = 3, p = 0.06. As in the group with a low disorganization score, the error rate tended to be lower in the congruent than in the neutral condition (3 vs. 7.3%, p = 0.05).

Sub-analyses were also used to compare performance levels across groups of participants in each experimental condition. In the non-congruent condition, the group effect was significant [F_{(2, 41)} = 11.8, p < 0.001]. The HSD post-hoc analysis showed that patients with a high disorganization score made more errors than both controls (p < 0.001) and patients with a low disorganization score (p < 0.05). These results consistently confirm the performance decrement in the non-congruent condition in disorganized patients relative to the two other groups.

In the congruent condition, the group effect was significant [F_{(2, 41)} = 5.6, p < 0.01] and the HSD Tukey post-hoc analysis showed that patients with a high disorganization score made more errors than controls (p < 0.01). The group effect did not reach significance level in the neutral condition, F_{(2, 41)} = 2.7, p = 0.076.

The recording of tap-sound asynchronies was aimed at checking to which extent participants benefited from the sound regularities and planned their tap in advance. Results on this parameter were identical in patients with a high and low score of disorganization, as illustrated in Figure 4 and thus, data was averaged across subgroups of patients.

A first analysis of variance conducted on tap-sound asynchronies showed a triple significant interaction between group (patients vs. controls), the tap order (first vs. second), and the experimental conditions (neutral, congruent, non-congruent), F_{(2, 84)} = 4.3, p < 0.05. In all participants, the tap-tone asynchrony was longer for the second than for the first tap, reflecting the rhythmic structure of the motor sequences. In patients, the tap-tone asynchrony increased by 41 ms between the first and the second tap, F_{(1, 21)} = 23.6, p < 0.001. In controls, it increased by a similar amount, i.e., by 41 ms, F_{(1, 21)} = 82, p < 0.001. In the following sections, we decompose the 3rd level interaction by distinguishing the first vs. second tap of a sequence of two.

There were no significant correlations between performance and the dosage of neuroleptics (in chlorpromazine equivalent). In addition, the number of patients treated with typical neuroleptics was equivalent in both subgroups of patients (4 and 3). Finally, the dosage of neuroleptics did not differ significantly between the two subgroups of patients [F_{(1, 20)} = 1.3, p > 0.25]: 219 vs. 335 mg in the groups with the low and the high scores of disorganization, respectively. The non-significant differences in dosage were due to one patient having a higher dose than all others (1200 mg). The dosage was strictly identical between groups when this patient was excluded from the analyses: 219 vs. 239 mg. In the present paper, the subject was included in all analyses because when excluded result patterns were identical in all points. For example, the error rates remained abnormally high in the patient group for the non-congruent condition (30% without vs. 31.6% with this subject).

Correlations with clinical disorganization scores have been described above. There were no other significant correlations with clinical scores.

The 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.