Sense of Agency Beyond Sensorimotor Process: Decoding Self-Other Action Attribution in the Human Brain

Abstract

The sense of agency is defined as the subjective experience that "I" am the one who is causing the action. Theoretical studies postulate that this subjective experience is developed through multistep processes extending from the sensorimotor to the cognitive level. However, it remains unclear how the brain processes such different levels of information and constitutes the neural substrates for the sense of agency. To answer this question, we combined two strategies: an experimental paradigm, in which self-agency gradually evolves according to sensorimotor experience, and a multivoxel pattern analysis. The combined strategies revealed that the sensorimotor, posterior parietal, anterior insula, and higher visual cortices contained information on self-other attribution during movement. In addition, we investigated whether the found regions showed a preference for self-other attribution or for sensorimotor information. As a result, the right supramarginal gyrus, a portion of the inferior parietal lobe (IPL), was found to be the most sensitive to self-other attribution among the found regions, while the bilateral precentral gyri and left IPL dominantly reflected sensorimotor information. Our results demonstrate that multiple brain regions are involved in the development of the sense of agency and that these show specific preferences for different levels of information.

Keywords: functional magnetic resonance imaging; inferior parietal lobe; multivoxel pattern analysis; sense of agency; supramarginal gyrus.

Figure 1

Multistep processes behind the sense of agency extending from lower sensorimotor to higher cognitive level processing. This schematic is an overview combing the two influential theories of the sense of agency: 1) the comparator model (Miall and Wolpert 1996; Blakemore et al. 1998, 2000) and 2) the two-step account of agency (Synofzik et al. 2008, 2013). Considering the two theoretical models together, agency attribution is achieved through the multistep processes extending from lower sensorimotor to higher cognitive level. The hypothesis in the current study is that some brain regions represent the immediate output of sensorimotor processing, while others represent the information directly leading to agency attribution. The background yellow gradation depicts the level of the information represented in the brain from sensorimotor to cognitive level, which is the main target of the current study. In Synofzik et al. (2008), the sense of agency encompasses two levels of representation: a nonconceptual feeling and a conceptual judgment of agency. The term “agency attribution” (or “self-other attribution”) in the current study corresponds to a conceptual judgment of agency. We assume that the process leading to the conceptual judgment of agency (not including the judgment process itself) based on the lower sensorimotor information is a nonconceptual “feeling of agency.” We use the term “sense of agency” to include both a nonconceptual feeling and a conceptual judgment of agency.

Figure 2

(A) Trial timeline. After moving a cursor to the start position (shown as a square) during the 5-s ready period, participants traced a sinusoidal target-path with a cursor controlled by a joystick during the 10-s move period. Following a 6-s delay period, participants assigned a score (on a 9-point Likert scale) to their self-other attribution by pushing buttons. (B) Target path (top). Numbers were sequentially presented every second to help participants maintain the required pace of tracing. Cursor visibility (bottom). The cursor was invisible on the screen during the first 2.0 s (first cycle). Visibility linearly increased from zero to one over the next 2.0 s (second cycle). Here, zero corresponds to black (RGB: 0, 0, 0), which is the same brightness as the background, while one corresponds to white (RGB: 255, 255, 255). The cursor continued to be clearly visible during the next 4.0 s (third and fourth cycles) and then linearly became darker from 8.0 to 10 s (fifth cycle). (C) Morphing method. Cursor position on the screen (X, Y) was the weighted summation of the joystick position controlled by the current participant (self) (x, y) and a pre-recorded joystick position (other) (x’, y’). Weights were modified by a morphing ratio (). (D) Cursor trajectories. Circles labeled with numbers (1–5) illustrate how the cursor position was changed according to the five morphing ratios (): self 0% (number 1) to self 100% (number 5) at every 25% step. In the self-other mixed conditions (number 2, 3, or 4), the cursor was displayed between the position of the participant’s own joystick and the position of the other person’s joystick.

Figure 3

Self-other attribution rating scores averaged across participants for each morphing ratio. The higher the score was, the more strongly participants felt that the cursor movement was attributed to their own joystick movement. Error bars indicate standard error of the mean. A linear regression model was fitted to each participant’s rating scores. The regression lines of all participants are shown behind the bars.

Figure 4

Relationship between tracing behavior and rating score of self-other attribution of movement. (A) Schematic of the four behavioral measures whose relationships with the self-other attribution score were investigated. Blue, green and red lines in the left panel indicate the target-cursor, target-joystick, and cursor-joystick distances, respectively. Light and dark gray arrows in the right panel denote the joystick and cursor velocity, respectively. Orange line represents the cursor-joystick velocity difference. (B) Time courses of Fisher-transformed Pearson’s correlation coefficients between each behavioral measure and the self-other rating scores during the 10-s move period. Values of behavioral measures were averaged within every second. Colored shaded areas denote 95% confidence intervals. Bottom panel denotes visibility of the cursor during the move period. Hatched area denotes the period during which the cursor was invisible (i.e., cursor visibility was zero). Here, the data for self 50% condition are shown. Negative correlation indicates that the greater the behavioral measure became, the lower the score the participants gave (i.e., more other attribution). (C) Time courses of Fisher-transformed Pearson’s correlation coefficients between accumulated value of each behavioral measure and rating scores. Values of behavioral measures were averaged from movement onset to every second. Colored shaded areas denote 95% confidence intervals. Hatched area denotes the period during which the cursor was invisible. Note that the data for self 50% condition are shown. Negative correlation indicates that the greater the behavioral measure was accumulated, the lower the score the participants gave.

Figure 5

Decoding performance for self-other attribution during movement, with clusters of significant decoding accuracy (P < 0.01 FWE corrected at cluster level with a cluster-forming threshold of P < 0.0005). A searchlight decoding analysis was applied to a volume scanned every 2 s during the 10-s move period to create accuracy maps. The sinusoidal waves represent a typical cursor movement along the timeline shifted by 6 s from the actual time considering the hemodynamic response delay (HRD). All clusters larger than 50 voxels are reported. IFG: inferior frontal gyrus, IPL: inferior parietal lobe, pre CG: precentral gyrus, MOG: middle occipital gyrus, MTG: middle temporal gyrus, SMG: supramarginal gyrus, STG: superior temporal gyrus.

Figure 6

(A) Clusters showing significant decoding accuracy for cursor-joystick distance and (B) those for cursor-joystick velocity difference (blue regions; P < 0.01 FWE-corrected at cluster level with a cluster-forming threshold of P < 0.0005). A searchlight decoding analysis (Kriegeskorte et al. 2006) was applied to a volume scanned every 2 s during the 10-s move period to create accuracy maps. The sinusoidal waves represent a typical cursor movement along the timeline shifted by 6 s from the actual time considering the HRD. All clusters larger than 50 voxels are reported.

Figure 7

(A) Clusters color-coded according to the effect size of the difference between decoding performance for self-other attribution and for sensorimotor information. Warmer colors denote bias toward self-other attribution (positive value of the effect size), while colder colors denote bias toward sensorimotor information (negative value). The clusters are identical to those in Figure 5 (see Table 1 for anatomical details). The sinusoidal wave represented at the bottom is shifted by 6 s from the actual time considering the HRD. (B) Effect sizes in the 15 clusters sorted in ascending order. The number and color of the bars correspond to those of clusters in (A). (C) Decoding performance (z-score) for self-other attribution and cursor-joystick velocity difference in the right SMG in the fifth cycle (red and blue bars, respectively). Error bars show standard error of the mean.

Figure 8

Time courses of z-scores for self-other attribution (A), cursor-joystick distance (B), and velocity difference (C) at the peak voxel (x = 60, y = −34, z = 30 in MNI coordinates) in the right SMG (cluster no. 15 in Fig. 7). Error bars show standard error of the mean. Asterisks indicate z-scores that were significantly larger than zero according to two-tailed one-sample t-test (^*: P < 0.01 uncorrected, ^**: P < 0.05 Bonferroni corrected for multiple comparisons). Each time bin corresponds to a volume scanned every 2 s during the 10-s move and 6-s delay periods. Gray bars highlight the fifth bin (last cycle of the sinusoidal movement). The events denoted under the time bins are shifted by 6 s from the actual time considering the HRD.