Abstract Almost all attention and learning—in particular, most early learning—take place in social settings. But little is known of how our brains support dynamic social interactions. We recorded dual electroencephalography (EEG) from 12-month-old infants and parents during solo play and joint play. During solo play, fluctuations in infants’ theta power significantly forward-predicted their subsequent attentional behaviours. However, this forward-predictiveness was lower during joint play than solo play, suggesting that infants’ endogenous neural control over attention is greater during solo play. Overall, however, infants were more attentive to the objects during joint play. To understand why, we examined how adult brain activity related to infant attention. We found that parents’ theta power closely tracked and responded to changes in their infants’ attention. Further, instances in which parents showed greater neural responsivity were associated with longer sustained attention by infants. Our results offer new insights into how one partner influences another during social interaction.

Author summary We are a social species. Most infants and young children spend the majority of their early waking hours in the company of others. However, almost everything that we know about how the brain subserves early attention and learning comes from studies that examined brain function in one individual at a time just because it is easier to do experiments that way. Here, we examine the neural correlates of how attention is shared between two people engaged in social interaction. We recorded brain activity from infants and parents using scalp electroencephalogram during parallel solo play with toys and during joint play. We examined the associations between attention and brain activity in each member of the dyad independently (infant attention–infant brain, parent attention–parent brain), and we also examined cross-dyad associations (infant attention–parent brain). Our findings suggested that infants’ attention is more endogenously controlled during solo play than joint play. They also suggested that parents are neurally responsive to their infants during social play, and that, when the parent is more neurally responsive, the infant is more attentive.

Citation: Wass SV, Noreika V, Georgieva S, Clackson K, Brightman L, Nutbrown R, et al. (2018) Parental neural responsivity to infants’ visual attention: How mature brains influence immature brains during social interaction. PLoS Biol 16(12): e2006328. https://doi.org/10.1371/journal.pbio.2006328 Academic Editor: Vinod Menon, Stanford University, UNITED STATES Received: April 11, 2018; Accepted: November 9, 2018; Published: December 13, 2018 Copyright: © 2018 Wass et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: Data are publicly accessible at the UK Data Reshare service by following this link: http://reshare.ukdataservice.ac.uk/853123/. Data underlying the main and supplementary figures can be found in S1 and S2 Data. Funding: Economic and Social Research Council (grant number ES/N017560/1). to VL and SW. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Economic and Social Research Council (grant number ES/N006461/1). to SW. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Nanyang Technological University (grant number Grant M4081585.SS0). to VL. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Abbreviations: EEG, electroencephalography; ICA, independent component analysis

Introduction Attention and learning are supported by endogenous oscillatory activity in the brain [1–4]. The nature of these oscillations and their relationship to behaviour develop and change from infancy into adulthood [5–9]. In infants, convergent research has suggested that theta band oscillations, which are particularly marked during early development [10], are associated with attentional and encoding processes. Theta band activity increases in infants during periods of anticipatory and sustained attention [11]; in 11-month-old infants, differences in theta band oscillations during object exploration predict subsequent object recognition during preferential looking [12]. Theta activity also increases in infants in social compared to nonsocial settings [13] and is particularly marked in naturalistic settings [13]. Although considerable previous research has investigated how brain oscillations relate to an individual’s behaviour, only a smaller body of research has investigated the neural mechanisms through which interpersonal and social factors influence behaviour [14–16]. This is despite the fact that our brains have evolved for social living [17], and most of our lives—particularly early life—are spent in social settings [18]. Understanding how social influences on attention and learning are substantiated across the brains of people engaging in social interaction, particularly during the crucial early stages of attention and learning, is an important goal for research [19, 20]. Previous work has shown that social factors influence infant attention and behaviour over short time-frames (seconds/minutes) and long timeframes (months/years). Over long timeframes, the children of parents who engage in more joint engagement during play show superior cognitive outcomes [21–23]. Over short timeframes, when an infant and social partner jointly attend to the same object during naturalistic play, infant attention is increased [24]. Recent research has contrasted two explanations for this finding: first, that social context may cause infants to be more attentive because they are more in control of their own attention behaviours. Second, that social context may offer increased opportunities for parents to scaffold their child’s attention using external attention cues—so infants are more attentive even though they are less in control of their own attention behaviours [25]. Time-series analyses conducted to evaluate these two hypotheses provided evidence more consistent with the latter hypothesis: first, infants’ rate of change of attentiveness was faster during joint play than solo play, suggesting that internal attention factors, such as attentional inertia, may influence looking behaviour less during joint play [26]. Second, adults’ attention forward-predicted infants’ subsequent attention more than vice versa [25]. These behavioural results suggest that infants’ increased attentiveness during social relative to solo play may be attributable to the presence of attention scaffolding from parents using exogenous attention cues [27]. However, to our knowledge, no previous work has examined this question from the neural perspective. Previous research has shown that ostensive social cues such as eye gaze and vocalisations can lead to increases in interpersonal neural synchrony between infants and adults [28]. Bidirectional Granger-causal influences between the brains of infants and adults engaged in social interaction were observed in the theta and alpha frequency bands, which were stronger during direct relative to indirect gaze [28; see also 29; 30]. Infants vocalised more frequently during direct gaze, and individual infants who vocalised longer elicited stronger synchronisation from the adult [28]. These findings raise the possibility that conversely, interpersonal influences between the brains of individuals engaged in social interaction may also actively drive their partners’ attentional processes and behaviour. However, in this previous research, the direct link to attention and behaviour was not examined. Here, we examined the neural and behavioural dynamics of infants’ and adults’ attention in two contexts (see Fig 1). During joint play, each dyad was presented consecutively with toy objects and asked to play together. During solo play, a 40-cm-high divider was placed between the infant and the parent, and two identical toys were presented concurrently to child and parent, who played separately (see Fig 1). Looking behaviour was videoed and coded post hoc, frame by frame, at a rate of 30 Hz. Time-lagged cross-correlations were used to assess how changes in one time series preceded or followed changes in another [31; cf. 32, 33]—an approach similar, but not identical, to Granger causality [34]. Our analyses examined whether changes in one time series ‘forward-predicted’ changes in the other. The age of the infants was selected to be 12 months because this is considered the age at which the capacity for endogenous control of attention first starts to develop rapidly [35, 36]. As is typical [e.g., 24], visual attention was coded as the presence or absence of looking behaviour towards the play object—albeit that previous research has shown the limitations of looking behaviour alone as an index of attention [37, 38, 39]. PPT PowerPoint slide

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larger image TIFF original image Download: Fig 1. Experimental overview. (a) Demonstration of experimental set-up; (b) illustration of visual coding that was applied to the data; (c) illustration of raw data. EEG data were decomposed using a Fourier decomposition, and power within continuous bins was calculated, epoched to 4 Hz; (d) cross-correlation showing the relationship between infant object looks and parent object looks [see 25]. The underlying data for this figure can be found in S1 Text. EEG, electroencephalography. https://doi.org/10.1371/journal.pbio.2006328.g001 Based on previous research [10, 13], we expected that fluctuations in infant theta activity would associate with and forward-predict fluctuations in infant attentiveness. Based on our previous research [25], we predicted that the forward-predictive relationship between infants’ own endogenous brain activity and infants’ attentiveness would be higher during solo play than joint play because of the increased prevalence of exogenous parental attention scaffolding (and capture) during joint play. Further, since previous research indicates that parental responsiveness is an influential factor for early developing cognition [40, 41], we also examined the short-term associations between infants’ attention and neural activity in the parent. We predicted, in the absence of prior investigations in this area, that a higher association between infant attention and neural activity in the parent would predict greater attentiveness from the infant.

Methods Ethics statement The study was conducted according to guidelines laid down in the Declaration of Helsinki, with written informed consent obtained from a parent or guardian for each child before any assessment or data collection. All procedures involving human subjects in this study were approved by the Psychology Research Ethics Committee at the University of Cambridge (Number PRE.2016.029). No financial inducements were offered other than the reimbursement of travel expenses and the gift of a T-shirt for participating infants. Participants Twenty-four and twenty-five parents contributed usable data for the joint play and solo play conditions, respectively; for infants, it was 21 and 25 for joint play and solo play, respectively. Paired parent–child data were available for 20 dyads for joint play (10 M and 10 F infants; mean [SE] infant age 345.1 [12.1] days; mother age 34.7 [0.8] years) and for 22 dyads for solo play (12 M and 10 F infants; mean [SE] infant age 339.2 [10.3] days; mother age 34.1 [1.0] years). All participating parents were female. It should be noted that the recruitment area for this study, Cambridge, United Kingdom, is a wealthy university town, and the participants were predominantly Caucasian and from well-educated backgrounds and so do not represent an accurate demographic sample [60]. Experimental set-up As previously reported [25], infants were seated in a high chair, which was positioned immediately in front of a table. The toys on the table were within easy reach (see Fig 1). Parents were positioned on the opposite side of the 65-cm-wide table, facing the infant. In the solo play condition only, a 40-cm-high barrier was positioned across the middle of the table (see Fig 1A). When the barrier was in place, parent and child had line of sight to one another (to reduce the possibility of infant distress), but neither could see the objects with which the other was playing. Each infant–parent dyad took part in both the joint play and solo play conditions. Presentation order was randomised between participants, but the two conditions were presented consecutively, with a short break in between. Parents were informed that the aim of the study was to compare behaviour while they were attending to objects separately from each other and when they were attending to the same object. During the solo play condition, parents played silently with the toys alone. During the joint play condition, they played silently with the toys whilst involving their infant in the play. A research assistant was positioned on the floor out of the infant’s sight. The research assistant placed the toys onto the table one at a time. In the joint play condition, one toy was presented at a time. In the solo play condition, two identical toys were presented concurrently to the infant and parent, one on either side of the barrier. The toys were small (<15 cm), engaging objects. Presentation order was randomised between conditions and between participants. Approximately every two minutes, or more frequently if the child threw the object to the floor, the current toy object was replaced with a new object. The mean (SE) duration for which each object was presented was 140.1 (17.9) seconds for joint play and 110.3 seconds (7.9) for solo play. Approximately 10 minutes of data was collected per condition from each dyad. The mean (SE) duration of play for each condition was 10.80 (0.46) minutes for joint play and 10.35 (0.33) minutes for solo play. When the infant became fussy during testing, data collection was stopped earlier; however, this occurred fairly rarely: the number of infants contributing sessions that lasted less than 8 minutes was 2/3 for the joint play/solo play conditions. Video coding and previous behavioural findings Play sessions were videoed using two camcorders positioned next to the child and parent, respectively. Further details of video coding and synchronisation are given in S1 Text. The visual attentional patterns of parents and infants were manually coded by reviewing their respective video recordings on a frame-by-frame basis (30 frames per second, 33.3 ms temporal acuity) using video editing software (Windows Movie Maker) (see Fig 1). This coding identified the exact start and end times of periods during which the participant was looking at the toy object. A previous report based on these data, which contained behavioural findings only, reported that infants showed longer look durations towards the object during joint play relative to solo play, together with shorter periods of inattention (see S1 Fig) [25]. EEG data acquisition EEG signals were obtained using a 32-channel wireless Biopac Mobita Acquisition System (Biopac Systems, Goleta, CA, USA) and 32-channel Easycap. Further details of EEG acquisition are given in S1 Text. EEG artefact rejection and preprocessing Automatic artefact rejection followed by manual cleaning using ICAs was performed. Full descriptions are given in S1 Text. Because previous analyses have shown that movement and muscle artefacts can contaminate EEGs [46, 47], data from all channels other than the two channels close to the vertex, C3 and C4, were excluded, and only frequencies between 2 and 14 Hz were examined. Analyses suggested that these frequencies show the least EEG signal distortion due to sweating, movement, or muscle artefact [46]. Prior literature [e.g. 11, 61] suggests that these frequencies were also most likely to show associations with visual attention. In S5 and S6 Fig, we also include comparison plots based on alternative anterior and posterior midline electrode groupings, which are consistent with the results reported in the main text. EEG power analysis For each electrode, we computed the Fourier transform of the activity averaged over artefact-free epochs, using the fast Fourier transform algorithm implemented in MATLAB (The MathWorks, Natick, MA, USA) (see S1 Text for full description). The FFT was performed on data in 2,000 ms epochs, which were segmented with an 87.5% (1,750 ms) overlap between adjacent epochs. Thus, power estimates of the EEG signal were obtained with a temporal resolution of 4 Hz and a frequency resolution of 1 Hz. S2 Fig compares EEG power for infants and parents between solo play and joint play; no significant between-condition differences were observed. Calculation of time-lagged cross-correlation The attention data used for the cross-correlation analysis were resampled as continuous and time-synchronised data streams at 4 Hz (to match that of the EEG power estimate). Attention data were coded as 1 and 0 (either attentive towards the play object or not). The cross-correlation calculations were performed separately for each frequency band (in 1 Hz bands) and for each member of the dyad (infant brain–infant attention and parent brain–parent attention) (Analysis 1). Then, they were calculated across the dyad (parent brain–infant attention) (Analysis 2). For each computation, the zero-lag correlation was first calculated across all pairs of time-locked (i.e., simultaneously occurring) epochs, comparing the EEG power profile with the attention data using a nonparametric (Spearman’s) correlation. In S4 Fig, we also show the results of the same tests repeated using an alternative test, the Mann–Whitney U test, for which results were identical.) The mean correlation value obtained was plotted as time ‘0’ (t = 0) in the cross-correlation. Next, time-lagged cross-correlations were computed at all lags from –10 to +10 seconds in lags of ±250 ms (corresponding to one data point at 4 Hz). For example, at lag time t = –250 ms, the EEG power profile was shifted one data point backwards relative to the attention data, and the mean correlation between all lagged pairs of data was calculated. Based on an average of 10.5 minutes of data per condition, sampled at 4 Hz and allowing for some attrition at artefact rejection due to the max-min thresholding criteria, the N of the cross-correlation was approximately 2,300 for the zero-lag correlation and up to 40 fewer for the most shifted correlation. In this way, we estimated how the association between two variables changed with increasing time lags. The individual cross-correlation series were then averaged across participants to obtain the group mean cross-correlation at each time interval and frequency band. To compare the distribution of time × frequency data between any single condition and a null distribution, a cluster-based permutation test was conducted across time × frequency data using the FieldTrip function ft_freqstatistics [62]. In comparison to other approaches to solving the family-wise error rate, this approach identifies clusters of neighbouring responses in time/frequency space [63]. In particular, corresponding time × frequency points were compared between contrast condition and null distribution with a t test, and t values of adjacent spatiotemporal points with p < 0.05 were clustered together with a weighted cluster mass statistic that combines cluster size and intensity. The largest obtained cluster was retained. Afterwards, the whole procedure, i.e., calculation of t values at each spatiotemporal point followed by clustering of adjacent t values, was repeated 1,000 times, with recombination and randomised resampling before each repetition. This Monte Carlo method generated an estimate of the p value representing the statistical significance of the originally identified cluster compared to results obtained from a chance distribution. In addition, a supplementary analysis was conducted using bootstrapping in order to further verify our results (see S1 Text). Calculation of power changes around looks Analysis 3 examined whether individual looks accompanied by higher theta power are longer lasting. To calculate this, we examined all looks to the play objects that occurred during the play session. The onset times of these looks were calculated, as described above, at 30 Hz. Then, for each look, we excerpted the EEG power for three time windows immediately before and after the onset of each look (3,000–2,000, 2,000–1,000, and 1,000–0 ms pre-look onset; 0–1,000, 1,000–2,000, and 2,000–3,000 ms post-look onset). Separately, we calculated the duration of each look towards the object. Since these were heavily positively skewed, as is universal in looking time data [64], they were log-transformed. Then, we calculated separate linear mixed effects models for each of the six windows using the fitlme function in MATLAB. For each model, we examined the relationship between EEG power within that time window and look duration, controlling for the random effect of participant. In this way, we examined whether, for example, theta power in the time window 1,000–0 ms prior to the onset of a look showed a significant relationship to the subsequent duration of that look.

Acknowledgments Thanks to John Duncan and Paul Chadderton for commenting on early versions of this manuscript.