Controlling automatic imitative tendencies: interactions between mirror neuron and cognitive control systems

Abstract

Humans have an automatic tendency to imitate others. Although several regions commonly observed in social tasks have been shown to be involved in imitation control, there is little work exploring how these regions interact with one another. We used fMRI and dynamic causal modeling to identify imitation-specific control mechanisms and examine functional interactions between regions. Participants performed a pre-specified action (lifting their index or middle finger) in response to videos depicting the same two actions (biological cues) or dots moving with similar trajectories (non-biological cues). On congruent trials, the stimulus and response were similar (e.g. index finger response to index finger or left side dot stimulus), while on incongruent trials the stimulus and response were dissimilar (e.g. index finger response to middle finger or right side dot stimulus). Reaction times were slower on incongruent compared to congruent trials for both biological and non-biological stimuli, replicating previous findings that suggest the automatic imitative or spatially compatible (congruent) response must be controlled on incongruent trials. Neural correlates of the congruency effects were different depending on the cue type. The medial prefrontal cortex, anterior cingulate, inferior frontal gyrus pars opercularis (IFGpo) and the left anterior insula were involved specifically in controlling imitation. In addition, the IFGpo was also more active for biological compared to non-biological stimuli, suggesting that the region represents the frontal node of the human mirror neuron system (MNS). Effective connectivity analysis exploring the interactions between these regions, suggests a role for the mPFC and ACC in imitative conflict detection and the anterior insula in conflict resolution processes, which may occur through interactions with the frontal node of the MNS. We suggest an extension of the previous models of imitation control involving interactions between imitation-specific and general cognitive control mechanisms.

Keywords: Automatic imitation; Cognitive control; Dynamic causal modeling; Mirror neuron system; Spatial compatibility; fMRI.

Figure 1

Behavioral paradigm. (A) Example video stimuli and timing of one trial for imitative (top) and spatial (bottom) interference tasks. (B) Two example blocks are shown with time progressing from left to right and images depicting the last frame of the video for each trial. Conditions are listed under each frame (ImC = Imitate congruent; ImI = Imitate incongruent; SpC = Spatial congruent; SpI = Spatial incongruent). The congruency is defined with respect to the instructed action (lift index finger, in these examples).

Figure 2

Regional activation results. (A) Regions with greater activation for incongruent than congruent trials for imitative cues. No regions showed a significant congruency effect for spatial cues. (B) Overlap (green) of imitative congruency effect (red) and main effect of cue type (blue) demonstrate the IFGpo is both modulated by congruency and more active during action observation than observation of moving dots (C) Interaction effect showing regions where congruency effect is significantly greater for imitative than spatial cues. These regions represent the regions of interest in the DCM analysis (Green = IFGpo; Blue = ACC; Red = mPFC; Yellow = aINS). Bar graphs depict parameter estimates extracted from significant clusters, with error bars representing standard error of the mean across subjects. All contrasts are thresholded at z>2.3 corrected across the whole brain for multiple comparisons (p < 0.05 FWE).

Figure 3

Model space. (A). Schemata of parameters that made up the base models. The mirror neuron system is driven by action observation; and the three prefrontal nodes are connected by all combinations of 2 or 3 of the bidirectional connections, which are depicted by dotted lines (see Supplementary Figure 2A for expansion of 4 possible prefrontal connection models). (B). Three variations of prefrontal-MNS interactions were included. The prefrontal network was connected to the frontal node of the mirror neuron system (IFGpo) via one of the 3 prefrontal control nodes by varying the connectivity structure as shown. This allowed us to identify which prefrontal region interacts with the MNS. (C). Variations of conflict input to the system are depicted on one single connectivity structure (fully connected prefrontal network and aINS→IFGpo connection). Solid arrows show variations in the nodes receiving conflict as a driving input (from left to right: mPFC, ACC, ACC & mPFC, IFGpo). These variations test conflict detection hypotheses. Dotted lines depict conflict as a modulator of prefrontal input to the MNS. The same models excluding the modulating input were also included creating a total of 8 variations of conflict inputs. An expanded depiction of the model space showing the factorial combinations of the models depicted here can be found in Supplementary Figure 2. ACC = anterior cingulate cortex; mPFC = medial prefrontal cortex; aINS = anterior insula; IFGpo = inferior frontal gyrus, pars opercularis; AO = action observation; C = imitative conflict.

Figure 4

Behavioral Results: Mean reaction time for each condition. Error bars represent within subject standard error of the mean, calculated with Cousineau’s adaptation of Loftus & Masson’s method {Cousineau 2005, Loftus 1994}. Main effects of congruency and cue type were significant (p < 0.01), but the interaction between cue type and congruency was not.

Figure 5

DCM results. Family level inference performed on models within the fully-connected prefrontal family demonstrates exceedance probability of 0.82 for models including the aINS→IFGpo connection (top left). Model selection comparing models within this family shows only 2 models receiving any evidence (bottom left). The winning model (model 8) is shown at right. Values next to each connection or input show the mean and standard deviation (in parentheses) of the parameters across subjects. Parameters significantly different from 0 (p<0.05) are depicted with solid lines and bold parameter values. The modulation of aINS→IFGpo connection by conflict also approached significance (p=0.07).