From Movement to Action: An EEG Study into the Emerging Sense of Agency in Early Infancy

Dr. Lorijn Zaadnoordijk (left) & Prof. Sabine Hunnius (right)

by Dr. Lorijn Zaadnoordijk1 & Prof. Sabine Hunnius2
1Trinity College Dublin, Trinity Inst. of Neurosciences (TCIN),
Dublin (Ireland
2Donders Institute for Brain, Cognition and Behaviour, Radboud University,
Nijmegen (The Netherlands)


In our publication “From movement to action: An EEG study into the emerging sense of agency in early infancy” we investigated whether 4-month-old infants build causal action-effect models, a prerequisite for a sense of agency. Using behavioral and neural measures of violation of expectation, we found evidence for causal models only in a subset of infants. Thus, the sense of agency is beginning to emerge at this age.


As adults, we take our sense of agency — the feeling of controlling one’s actions and their consequences (Haggard & Chambon, 2012) — for granted and are readily able to predict the effects of our actions. However, it is unknown how infants come to experience their own agency. Infants’ ability to detect sensorimotor contingencies has been shown by an increase in the movement frequency of an action that yields an effect, which has previously been taken as evidence for the presence of a sense of agency in early infancy (Gergely & Watson, 1999; Rochat & Striano, 2000; Watanabe & Taga, 2011). Computer simulation work, however, showed that this behavioral pattern does not provide evidence for infants having a causal model of their actions and effects, which is required for the sense of agency (Zaadnoordijk, Otworowska, Kwisthout, & Hunnius, 2018). Here, we set out to investigate whether 3- to 4.5-month-old infants build such causal action-effect models. We used a paradigm (Rovee & Rovee, 1969) in which infants’ movement for a brief period triggers an audiovisual effect. We focused on infants’ neural and behavioral response upon discontinuation of the audiovisual effect. If infants display indications of violation of expectation as shown by a mismatch negativity (MMN) in the EEG data and an extinction burst in the movement frequency (i.e. a temporary additional increase) this points at infants predicting the consequences of their movements and thus building the model necessary for a sense of agency.

Methods & Results

Experimental procedure

Sixty-five full-term infants (MAge = 115.06 days, SDAge = 12.47; 29 male) were invited to the lab. Ethical approval was granted by the regional medical ethics committee, (NL39352.091.12, CMO 2012/012).

During the experiment, infants’ limb movement and EEG data were recorded concurrently. Four accelerometer bracelets were attached to the infants’ limbs. One accelerometer triggered an audiovisual effect upon detecting movement and was always fastened around one of the infant’s wrists. EEG was recorded from 32 active Ag/AgCl electrodes referenced online to the left mastoid (TP9), using infant-sized actiCAP caps (Brain Products GmbH, Gilching, Germany) following the international 10-20 system. Data were sampled with a BrainAmp DC amplifier (Brain Products GmbH, Brain Products GmbH, Gilching, Germany) via BrainVision Recorder Software (Vers. 1.21.0004, Brain Products GmbH, Gilching, Germany) with a sampling frequency of 500Hz.

Once the infant was capped and accommodated to the set-up, the experiment was initiated. The image of a mobile toy was presented during three phases in a fixed, uninterrupted sequence: baseline, connect, and disconnect. During the baseline and disconnect phases (2 minutes each), the image was static. In the connect phase (3.5 minutes), movement of the trigger arm elicited the audiovisual effect (see Figure 1). The experiment ended with the completion of the third phase or if the infant repeatedly showed signs of fussiness or discomfort.

From Movement to Action: An EEG Study into the Emerging Sense of Agency in Early Infancy - Fig. 1

Figure 1. The visual display used in the three phases. A. Baseline: For 2 minutes, the static image was shown. B. Connect: For 3.5 minutes, the image wiggled and a sound was played when the infant moved the trigger arm. C. Disconnect: For 2 minutes, the static image was shown again.

Data Acquisition

Movement was registered for each limb whenever the change in the limb’s velocity exceeded a threshold value that was kept constant across infants. EEG data were sampled at 500Hz, applying 0.016Hz high-pass and 125Hz low-pass filters online. We strived to keep the impedances below 50kΩ. The signal was re-referenced offline to the mastoid average (TP9, TP10). Experimental sessions were filmed to monitor the experimental process on-site.

Data Preparation

The behavioral data were pre-processed using Excel (Microsoft Office Professional Plus 2013) and IBM SPSS Statistics Version 21.0, and  the EEG data were pre-processed with the open-source Matlab toolbox Fieldtrip (, Oostenveld, Fries, Maris, & Schoffelen, 2011).

The experiment was segmented into 45 time-bins by computing the movement frequency over 10-second intervals. The behavioral data were modeled using multilevel time series analyses (Vossen, Van Breukelen, Hermens, Van Os, & Lousberg, 2011). All infants who completed the connect phase were included in the analyses.

Each movement of the trigger arm was considered an EEG trial. Thus, the onset of each trial was defined by a marker sent to the EEG system upon movement of the trigger arm. The MMN analyses and artifact rejection were conducted on the frontal sites (F3, F4), where the MMN’s morphology has shown to be most pronounced in 3- to 4-month-olds (He, Hotson, & Trainor, 2007; Trainor et al., 2003). A 0.5 – 20Hz bandpass filter was applied and the mean signal of each trial was subtracted from the data. The continuous output was segmented into 600-ms movement-locked epochs, including a 100-ms pre-movement baseline (henceforth PMB to disambiguate the pre-movement EEG baseline from the experiment’s baseline phase); correction was set at the mean amplitude over the PMB. High-amplitude artifacts were rejected manually; as a rule of thumb, trials with measured activity exceeding 50μV during the PMB and 150μV during the epoch were rejected.

All infants who had at least five artifact-free trials in the baseline and in the disconnect phase were entered in the analysis. To form difference waves, the averaged disconnect phase waveforms were subtracted from the averaged baseline phase waveforms. The mean amplitudes for the 200-350-ms window, within which we expected the MMN-response (Basirat, Dehaene, & Dehaene-Lambertz, 2014; Trainor, et al., 2003), were derived from the averaged signal over 20-ms data segments.

Data Analysis

Thirty-six infants completed the connect phase and were included in the behavioral analysis (MAge = 117.56 days, SDAge = 12.18 days). Similar to previous studies, during the connect phase, infants responded to the contingent effect by linearly increasing their overall movements (B = .65, SE = .19, t(206.5) = 3.45, p = .001, 95% CI [-0.28, 1.02]). However, the infants’ behavioral response to the absence of the audiovisual effect did not follow the hypothesized quadratic trend in the disconnect phase (B = .-12, SE = .09, t(382.82) = -1.29, p = .20), suggesting no evidence for a group-level extinction burst (see Figure 2).

Twenty-two infants had sufficient data for the ERP analysis. No significant MMN component was found 200-350ms after onset (t(21) = -1.05, p = .847, one-tailed; see Figure 3), which is known to be the M latency at this age (Basirat, Dehaene, & Dehaene-Lambertz, 2014; Trainor, et al., 2003). Thus, as a group, the 3- to 4.5-month-old infants also showed no evidence of differential neural processing during the disconnect and baseline phases.

From Movement to Action: An EEG Study into the Emerging Sense of Agency in Early Infancy - Fig. 2

Figure 2. Movement frequency over time. Until the disconnect phase, 36 infants are included; the number of infants after that point are indicated in the figure. The average movement frequency over all limbs is given in black. Additionally, the movement frequency of each individual limb (trigger arm, contralateral arm, and the legs ipsilateral and contralateral to the trigger arm) is shown in the plot. Error bars, shown for the average of all limbs and for the trigger arm, reflect one standard error around the mean.

From Movement to Action: An EEG Study into the Emerging Sense of Agency in Early Infancy - Fig. 3

Figure 3. ERP results of the MMN analysis (shaded area reflects one standard error around the mean). We found no evidence for a difference between the disconnect and baseline phases in the time window of interest (200-350ms after trigger arm movement).

However, Trainor and colleagues (2003) showed that infants between 2 and 6 months of age transition from showing a positive waveform to an adult-like negative MMN in a mismatch paradigm. To rule out the possibility of the group-level result being caused by averaging two distinct ERP morphologies, infants’ MMN responses were classified either in the positive waveform group, or in the MMN group (see Figure 4). The split was based on the deflection of the averaged activity over the window of interest. The two subgroups did not differ in age (t = 0.966, p = .346). We extended the behavioral model into a growth model, allowing us to assess the phase-specific behavioral patterns for each ERP waveform group (Curran, Obeidat & Losardo, 2010; Kwok, et al., 2008).

We found that the infants in the mismatch negativity relative to the positive waveform group moved their trigger arm more frequently than the contralateral arm in the connect (B = 2.46, SE = 0.70, t(1154.72) = 3.52, p < .001, 95% CI [1.09, 3.83]) and disconnect (B = 5.45, SE = 0.96, t(1157.44) = 5.71, p < .001, 95% CI [3.57, 7.32]) phases. Those infants also showed a more pronounced extinction burst in the movement frequency of the trigger arm relative to that of the contralateral arm compared to the infants in the positive waveform group (B = 0.26, SE = 0.06, t(1248.13) = 4.32, p < .001, 95% CI [0.14, 0.39]; see Figure 4). Importantly, there was no significant difference between the groups regarding the linear increase during the connect phase (B = -0.24, SE = 0.12, t(1183.76) = -0.21, p = .836, 95% CI [-0.25, 0.20]).

From Movement to Action: An EEG Study into the Emerging Sense of Agency in Early Infancy - Fig. 4

Figure 4. The ERP waveforms per group and the corresponding behavioral movement frequency patterns (one standard error around the mean indicated by shaded area (ERPs) and bars (behavioral data)).


We investigated whether 3- to 4.5-month-old infants build a model of the effects of their own movements, a prerequisite for the sense of agency. We obtained electrophysiological and behavioral measures to inform us about infants’ action-effect models. Specifically, we investigated infants’ violation of expectation upon discontinuation of the effect that was previously triggered by their action. We hypothesized that if infants built a causal action-effect model, we would observe this in the data in two ways: an MMN in the EEG data after the effect was discontinued and an extinction burst in the movement frequency. We found that only a subset of infants showed an MMN to a violation of expectation of their actions’ consequences. Notably, these infants had a more pronounced extinction burst for the arm that had triggered the effect as compared to the contralateral arm, indicating that these infants had not only established the causal connection between their action and the effect, but had also learned which specific limb triggered the effect. The other infants did not show an MMN. Moreover, they did not demonstrate limb specificity during the disconnect phase. Our findings suggest that only some infants were able to build an action-effect model and that the sense of agency is only beginning to emerge in infants between 3 and 4.5 months of age.


Basirat, A., Dehaene, S., & Dehaene-Lambertz, G. (2014). A hierarchy of cortical responses to sequence violations in three-month-old infants. Cognition, 132, 137-150.

Curran, P. J., Obeidat, K., & Losardo, D. (2010). Twelve frequently asked questions about growth curve modeling. Journal of Cognition and Development, 11, 121-136.

Gergely, G., & Watson, J. S. (1999). Early socio-emotional development: Contingency perception and the social-biofeedback model. Early Social Cognition: Understanding Others in the First Months of Life, 60, 101-136.

Haggard, P., & Chambon, V. (2012). Sense of agency. Current Biology, 22, R390-R392.

He, C., Hotson, L., & Trainor, L. J. (2007). Mismatch responses to pitch changes in early infancy. Journal of Cognitive Neuroscience, 19, 878-892.

Kwok, O., Underhill, A. T., Berry, J. W., Luo, W., Elliott, T. R., & Yoon, M. (2008). Analysing longitudinal data with multilevel models: An example with individuals living with lower extremity intra-articular fractures. Rehabilitation Psychology, 53, 370–386.

Oostenveld, R., Fries, P., Maris, E., & Schoffelen, J. M. (2011). FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Computational Intelligence and Neuroscience, 2011, 1.

Rochat, P., & Striano, T. (2000). Perceived self in infancy. Infant Behavior and Development, 23, 513-530.

Rovee, C. K., & Rovee, D. T. (1969). Conjugate reinforcement of infant exploratory behavior. Journal of Experimental Child Psychology, 8, 33-39.

Trainor, L., McFadden, M., Hodgson, L., Darragh, L., Barlow, J., Matsos, L., & Sonnadara, R. (2003). Changes in auditory cortex and the development of mismatch negativity between 2 and 6 months of age. International Journal of Psychophysiology, 51, 5-15.

Vossen, H., Van Breukelen, G., Hermens, H., Van Os, J., & Lousberg, R. (2011). More potential in statistical analyses of event‐related potentials: a mixed regression approach. International Journal of Methods in Psychiatric Research, 20, e56-e68.

Watanabe, H., & Taga, G. (2011). Initial-state dependency of learning in young infants. Human Movement Science, 30, 125-142.

Zaadnoordijk, L., Otworowska, M., Kwisthout, J., & Hunnius, S. (2018). Can infants’ sense of agency be found in their behavior? Insights from babybot simulations of the mobile-paradigm. Cognition, 181, 58-64.

BrainVision Recorder (Vers. 1.21.0004) [Software]. (2019). Gilching, Germany: Brain Products GmbH. 

BrainAmp DC [Apparatus]. (2019). Gilching, Germany: Brain Products GmbH.

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