Exploring neurophysiological markers of visual perspective taking: Methodological considerations

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International Journal of Psychophysiology 161 (2021) 1–12

Available online 31 December 2020

Exploring neurophysiological markers of visual perspective taking:

Methodological considerations

Linn Sofie Sæther

^a^,^b^,^*

, Daniel Roelfs

^a^,^b

, Torgeir Moberget

^a^,^c

, Ole A. Andreassen

^a^,^b

, Torbj ø rn Elvsåshagen

^a^,^b^,^e

, Erik Gunnar J ¨ onsson

^a^,^b^,^d

, Anja Vaskinn

^a^,^b

aNORMENT Centre for Mental Disorders Research, Oslo University Hospital, Oslo, Norway

bInstitute of Clinical Medicine, University of Oslo, Oslo, Norway

cDepartment of Psychology, University of Oslo, Oslo, Norway

dDepartment of Clinical Neuroscience, Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Sciences, Stockholm Region, Stockholm, Sweden

eDepartment of Neurology, Oslo University Hospital, Oslo, Norway

A R T I C L E I N F O Keywords:

Visual perspective taking Mentalizing

Theory of Mind ERP EEG

A B S T R A C T

Rationale: For visual perspective taking (VPT) using the avatar task, examinations of neural processes using event related potentials (ERP) indicate a distinction between an early posterior perspective calculation process (P3) and a later frontal process (LFSW) managing perspective conflict. While it is unknown if these neural processes are affected in clinical populations, it is unclear if the avatar task can be applied to this group, due to the long duration and sensitivity to data loss. Thus, we performed a methodological study of the avatar task, testing the feasibility of a shortened experimental paradigm.

Objective: To investigate whether previously reported behavioural and ERP effects in the avatar task can also be seen if analysing all trials (matching/non-matching) jointly, and whether they remain robust if only a subset of the data is analysed.

Method: Healthy individuals (n =20) completed the avatar task with ERP measurement. ERP components (P3, LFSW) and behavioural data were investigated by A) comparing use of only matching trials (n =384) versus all trials (n =768), and B) examining if reduced duration of assessment, by analysing only a subset of the data, impacts ERP findings.

Results: We observed minimal differences when analysing data from only matching trial types compared to all trial types. Further, ERP amplitudes and latency findings were replicated when analysing only a subset of the data.

Conclusions: The duration of the avatar task can be reduced to avoid long testing times, thus making it better suited for use in clinical populations.

1. Introduction

The ability to understand that other individuals may perceive the environment differently from their perspective compared to our own is important for everyday social interactions. By taking other people’s perspectives we gather valuable information that helps us to draw in- ferences about the mental state of others - a process referred to as mentalizing, or theory of mind (Apperly, 2012; Frith and Frith, 2012).

One way of gathering such information is through visual observation of someone else’s perspective of the environment, so-called Visual

Perspective Taking (VPT). Traditionally, VPT has been regarded as a deliberate and slow process, often requiring observers to engage in mental rotation in order to envision how someone else perceives an object (Flavell et al., 1986; Kessler and Rutherford, 2010; Michelon and Zacks, 2006; Surtees et al., 2013). However, research has suggested that processing of basic visual information, such as tracing someone else’s line of sight, may be related to implicit forms of mentalizing (Samson et al., 2010). This has been shown in a number of behavioural and neuroimaging studies, sometimes using paradigms that require long testing times and with suboptimal use of trial types, i.e. by excluding a

* Corresponding author at: Oslo University Hospital, Division Mental Health and Addiction, Psychosis Research Unit/TOP, PObox 4956, Nydalen, 0424 Oslo, Norway.

E-mail address: [email protected] (L.S. Sæther).

Contents lists available at ScienceDirect

International Journal of Psychophysiology

journal homepage: www.elsevier.com/locate/ijpsycho

https://doi.org/10.1016/j.ijpsycho.2020.12.006

Received 3 July 2020; Received in revised form 4 November 2020; Accepted 23 December 2020

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substantial number of trials (Ferguson et al., 2018; McCleery et al., 2011). Methodological investigations of whether such mentalizing paradigms can be adapted to settings where shorter testing times are necessary, have been lacking.

A basic form of VPT was first demonstrated in a series of experiments by Samson et al. (2010) with the “avatar” task. In this task, observers have to verify the number of discs appearing on the walls of a three- dimensional room. These judgments are made either from their own (self) or an avatar’s (other) visual perspective, which can be the same (consistent) or different (inconsistent). Samson et al. (2010) found that healthy adults were influenced by what the avatar saw when judging their own perspective (altercentric interference), and by their own perspective when judging the avatar’s perspective (egocentric interference). In other words, when a different number of discs could be seen from the two perspectives (i.e. inconsistent viewpoints) observers experienced difficulty ignoring the irrelevant perspective, resulting in increased response times. The egocentric bias was the largest and most robust effect, a common finding also in other studies of mentalizing (Riva et al., 2016; Samuel et al., 2018). The apparent automatic and uncontrolled interference from the irrelevant perspective, i.e. “consistency effect”, was interpreted as evidence that perspective taking does not always occur in a deliberate and slow manner (Samson et al., 2010).

It was therefore suggested that the avatar task could measure implicit forms of mentalizing, which has been characterized as unconscious and rigid attribution of mental states to others (Apperly and Butterfill, 2009;

Frith and Frith, 2012; Van Overwalle and Vandekerckhove, 2013).

Although self-other interferences on response times is a robust finding on the avatar task, the existence of an implicit form of mentalizing is under debate. It has been claimed that the observed effects reflect experimental artefacts, such as attentional cueing (Catmur et al., 2016; Cole and Millett, 2019; Heyes, 2014; Santiesteban et al., 2017, 2014). Numerous studies with elegant designs have attempted to solve this debate by using the avatar task under various conditions, such as replacing the avatar with non-social cues/inanimate objects (Kragh Nielsen et al., 2015; Samson et al., 2010; Santiesteban et al., 2017, 2014;

Schurz et al., 2015), or restricting the avatar’s view (Cole et al., 2016;

Conway et al., 2017; Furlanetto et al., 2016; Marshall et al., 2018).

Results are inconclusive, with some evidence supporting an implicit mentalizing view (effect diminished when avatar is replaced or restricted), and some supporting the attentional cueing account (com- parable results when avatar is replaced or restricted).

Since the avatar task requires rapid cognitive processing and motor response, electroencephalography (EEG) could offer insights into the precise timing of the underlying neural processes. Of particular interest for the current study are previous EEG-based investigations of event- related potentials (ERPs), which have the advantage of measuring neural activity with millisecond precision. McCleery et al. (2011) were the first to identify ERP components sensitive to the perspective and consistency effects of the avatar task, later replicated by others (Fergu- son et al., 2018; Peng et al., 2018). These studies suggest that the task can be used to identify neurophysiological markers of VPT. Although ERP studies using the avatar task have had different methodological and experimental manipulations, the main findings are similar. They indicate that VPT entails an early perspective calculation process over the posterior and temporoparietal cortices sensitive to self and other perspectives, and a later frontal process of managing conflict between perspectives (consistent and inconsistent viewpoints) during behavioural response (Ferguson et al., 2018; McCleery et al., 2011).

The perspective calculation process was demonstrated by increased behavioural response times and latency of the TP450 component (or P3 when referring to the ordinal position of the component) when taking the avatar’s perspective (other >self). Additionally, the peak amplitudes of the TP450 were sensitive to differences in consistency, with smaller amplitudes for inconsistent viewpoints. McCleery et al. (2011) therefore suggested that the TP450 (or P3) reflected the computational cost of taking someone else’s perspective. In their source estimate

analysis, this activity originated from several sources in the occipital, parietal and temporal lobes, particularly the mentalizing region temporoparietal junction (TPJ; Frith and Frith, 2006). The TPJ has been found important for calculating and representing self-other perspectives in previous neuroimaging studies (Quesque and Brass, 2019), and modulations of the P3 component have been reported for self-other processing (Knyazev, 2013).

Further, McCleery et al. (2011) found that the late frontal slow wave (LFSW) reflected the process of managing conflict between perspectives following behavioural response. The mean amplitude of this wave showed that consistent viewpoints generated more negative-going LFSW compared to inconsistent viewpoints. McCleery et al. (2011) suggested that observation of this late wave could reflect executive and inhibitory control mechanisms of the frontal cortex. Although there are variations in the exact topography and time windows defined for the LFSW, its sensitivity to different aspects of mentalizing has been reported in several studies (Jiang et al., 2016; Leng et al., 2018; Liu et al., 2009;

Meinhardt et al., 2011; Zhang et al., 2009). Both McCleery et al. (2011) and Ferguson et al. (2018) additionally identified an early occipital component (P200, or P2) where the peak amplitude was sensitive to self- inconsistent trials (self-inconsistent >all other trial types). However, this effect was attributed to selective visual attention, as self- inconsistent trials are the only condition in which observers have to attend to both walls and ignore the avatar’s perspective.

Individuals with known social cognitive deficits seem to be less influenced by the avatar’s perspective when judging their own perspective (i.e. a diminished altercentric interference effect), suggestive of implicit mentalizing deficits. This has been found in recent behavioural studies of autism spectrum disorder (Doi et al., 2020), psychotic disorders, including schizophrenia (Kronbichler et al., 2019), and populations with elevated rates of psychopathy (Drayton et al., 2018). The characteristics of the neural stages of VPT processing measured with the avatar task in individuals with social cognitive deficits are, however, unknown. Thus, an ERP investigation of VPT in such groups could offer deeper insights into the mechanisms involved in implicit mentalizing deficits.

However, several issues need to be considered before implementing the avatar task with ERP recordings in clinical populations. A general challenge in ERP research is the question of data loss, i.e. not all administered trials are used in the final analyses. A large number of trials are usually required to obtain reliable and statistically meaningful grand average ERPs (Luck, 2014). Nevertheless, trials containing behavioural errors are often not included in ERP analysis, and some trials are removed due to eye blinks or muscle movement, so-called artefact rejection. There are good reasons to evaluate how to best protect against data loss. One line of investigation is to explore if it is possible to use more of the acquired data, i.e. to optimize the use of trials, which will be of value for studies of clinical populations. First, individuals with a clinical condition may fail to respond on more trials than other individuals due to reduced processing speed, and they may commit more errors on the avatar task due to impaired social cognition. Second, some clinical populations exhibit more EEG artefacts, such as muscle movement, compared to control groups (Kappenman and Luck, 2016), which again could lead to more data loss during artefact rejection.

Previous studies using the avatar task only analysed matching trials in which the given perspective and number of discs matched the stimuli, i.e. trials requiring a “yes” response (50% of trials). Mismatching trials were not analysed due to difference in difficulty level between matching and mismatching trials. Mismatching consistent trials have been said to be easier to process compared to other trial types, because in this trial type the given number of discs are irrelevant for both perspectives.

Samson et al. (2010) therefore argued that inclusion of mismatching trials could lead to an overestimation of the consistency effect. The consistency effect has, however, been the most robust finding in most previous studies. In a transcranial magnetic stimulation (TMS) study, mismatching trials were included in order to avoid delivering twice as

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many TMS pulses and to stay within the safety limits of such stimulation (Santiesteban et al., 2017). This did not eliminate perspective or consistency effects on response times, suggesting that all trials can be included without losing or changing statistical effects. However, it is unknown whether mismatching trials have different neural generators than the matching trials, as this has only been estimated based on the latter trial type (McCleery et al., 2011).

The previous ERP studies have used paradigms with more trials than behavioural studies, with up to 768 trials in total versus N =208 in Samson et al., (2010). Consequently, the recording session is significantly longer (~80 min), increasing the risk of fatigue and diminished attention, perhaps especially in clinical groups. Excluding mismatching trials leaves 384 trials in total, only 96 trials per condition (with zero data loss). This leaves little room for dealing with a potential increase in data loss that could come when studying a clinical population. Although there is no consensus on the exact number of trials needed for ERP studies, a recent ERP study (Boudewyn et al., 2018) noted that increasing the number of trials in within-participant designs generally results in increased power, which in turn can protect against data loss.

However, while including both matching and mismatching trials for analysis can protect against data loss, long ERP recording sessions may still have undesirable consequences for study participants.

Reducing time spent on ERP paradigms will make testing more tolerable (Kappenman and Luck, 2016). It is well known that long lasting involvement in cognitive tasks can lead to fatigue and restless movement, which can influence noise level in the ERP data and decrease task performance (Boudewyn et al., 2018). Studies investigating the effect of long testing times on various visual attention tasks have shown that ERP components are differentially affected, such that the amplitudes and latencies of different components increase or decrease over time intervals (Boksem et al., 2005; Kato et al., 2009; M¨ockel et al., 2015). ERP components are also sensitive to learning and adaptation effects, which could influence both amplitudes and latencies, especially in the early phases of an experiment (Mockel et al., 2015). The exact ¨ effects of long testing times on different ERP components are likely to vary depending on type of task and length of experiments. It is currently unknown how the ERPs measured from the avatar task are affected over the course of testing. Thus, a thorough methodological investigation is required in order to find a balance in which the use of trials are optimized, while simultaneously avoiding a trade-off in data quality that could come from long testing times.

The overall objective of the present study is to investigate whether the avatar task with EEG recording can be adapted to studies of clinical populations. This involves protecting against data loss and simultaneously avoiding long recording times. The first aim of the study is to investigate whether there are substantial differences in the behavioural and ERP findings when optimizing the use of trials by including mismatching trials (i.e. all trials). The second aim of the study is to investigate whether the duration of the paradigm can be reduced, by exploring whether the ERP findings are maintained when only a subset of the data is analysed.

2. Materials and methods 2.1. Participants

A total of 20 individuals (11 female, 9 male) between the ages of 23 and 48 (31.1 ±8.81) years participated in this study. The sample size was chosen based on sample sizes of previous studies using a similar method for ERP and EEG research (Beck et al., 2018; Ferguson et al., 2018; McCleery et al., 2011). All participants provided oral consent.

Only anonymized data (gender, age) was collected. In Norway, research undertaken using anonymized data is not governed by privacy legisla- tions. The Internal Review Board at Oslo University Hospital approved that the study could be conducted without written informed consent.

This study is part of a larger project which has been approved by the

Regional Ethical Committee (ClinicalTrials.gov Identifier NCT03543553). Presumed healthy individuals were recruited through personal contact. Inclusion criteria were fluency in Norwegian, and normal or corrected-to-normal vision.

2.2. Stimuli and procedure

The visual stimuli was retrieved from the Open Access archives on Figshare (Samson and Apperly, 2015). An adapted version of the experimental paradigm for ERP research described by McCleery et al.

(2011) was used while continuous EEG activity was recorded. Written text was translated into Norwegian and auditory stimuli was recorded at a radio station in Oslo, Norway, in an isolated room with premium sound quality.

The visual stimuli of the avatar task consisted of images depicting a lateral viewpoint of a room with a realistic avatar facing either the right or left wall. Red discs would appear on both or one of the walls. As effects are observed even in the absence of gender congruency (Samson and Apperly, 2015), and since some early ERP components are influenced by low-level visual differences (Luck, 2014), the gender of the avatar was kept constant (male). The auditory stimuli was comprised of 8 different spoken sentences, either “you see X” or “he sees X”, where X described the number of discs between 0 and 3. On half of the trials, participants made judgments based on their own perspective (Self), and on the other half judgments were based from the avatar’s perspective (Other). On half of these the participant or avatar either saw the same number of discs on the wall (Consistent), or a different number (Inconsistent).

Each trial began with a centred fixation cross (600 ms), followed by the auditory cue (1800 ms), a second fixation cross (150, 250 or 350 ms), and finally the test stimuli was presented on the screen (see Fig. 1 for trial sequence). Reaction time was measured from the onset of the test stimuli, which was displayed on the screen for a maximum of 1000 ms and terminated upon response. The auditory cue provided participants with information regarding which perspective to take, and the task was to verify whether the number accompanying the perspective matched (“yes” response) or mismatched (“no” response) the test stimuli. Match and mismatch trials were equally represented. Participants were instructed to respond as quickly and accurately as possible from the onset of the test stimuli, by pressing keyboard buttons (yes =key M, no =key Z).

Prior to the experiment, participants were given detailed de- scriptions of the procedure. Following a brief practice session with 24 trials, participants proceeded with the experimental task while they underwent continuous EEG recording. The task was organised in 4 main blocks, each block containing 192 trials (768 in total), with short breaks after every 48 trials. The four conditions (Self-Consistent, Self- Inconsistent, Other-Consistent and Other-Inconsistent) were equally divided and presented in a fully randomised design within each block.

Whenever the auditory cue was “you see 0” the response was always no.

Therefore, these trials were regarded as “filler” trials and excluded from all subsequent analyses (N =48). Completion of the experiment took approximately 80 min.

2.3. Materials

The avatar task was written in MATLAB R2017a (MathWorks, 2018), using the Psychophysics Toolbox extension v3.0.15 (Brainard, 1997;

Kleiner et al., 2007; Pelli, 1997). The task was presented with Ubuntu operating system v18.04.2 (64-bit), on a AOC 240LM00010 LED monitor (screen resolution: 1920 ×1080). Participants were comfort- ably seated 0.6 m from the LED monitor, and auditory stimuli were presented using Etymotic Research ER•1 insert earphones. Participants were monitored to ensure they maintained attention and followed instructions.

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2.4. EEG acquisition

Continuous EEG activity was recorded using an ActiveTwo BioSemi system (BioSemi, B. V., Amsterdam) with 64 active Ag/AgC1 electrodes distributed on the scalp according to the international 10–20 system. An additional 8 mini bipolar electrodes, 6 electromyography (EMG) and 2 electrocardiography (ECG) electrodes, were placed at the outer canthi of each eye, suborbital and supraorbital regions, directly below the pupil, right clavicle, and over the left pelvic bone (ECG 7 and 8). The EMG and ECG electrodes were used for the detection of eye movements and cardiac interference for later artefact rejection. The EEG scalp electrodes were recorded relative to the standard BioSemi CMS and DRL electrodes using the accompanied acquisition software, ActiView version 7.05 (BioSemi, 2013). The EEG was sampled at a rate of 2048 Hz, and im- pedances were kept within ±20 KΩ (Ferree et al., 2001).

2.5. EEG signal processing

Fully automated pre-processing of EEG data was conducted offline in MATLAB R2017a (MathWorks, 2017), using the EEGLAB 14.1.2b toolbox (Delorme and Makeig, 2004). Following down-sampling to 512 Hz, the data went through the PREP pipeline algorithm using default criteria (Bigdely-Shamlo et al., 2015). The PREP pipeline 1) carefully removes line-noise, 2) robustly reference the signal to a temporary average reference, and 3) detects and interpolates bad channels relative to the computed average reference. Next, all EEG channels were re- referenced to the average of the 64 scalp electrodes and filtered using windowed-sinc Finite Impulse Response (FIR) filter (high-pass =0.3 Hz, low-pass =40 Hz).

Independent component analysis using the adaptive mixture ICA algorithm, AMICA, was then applied (Delorme et al., 2012; Palmer et al., 2011). In order to detect and reject independent components (ICs) containing artefact-related activity, a recently developed, fully automated independent component classifier plugin called ICLabel (Pion- Tonachini et al., 2019) was used. The ICLabel classifier computes probability estimates for ICs across seven categories (i.e. probabilities of ICs containing brain, muscle, eye, heart, line noise, channel noise and other). The rejection thresholds for the ICLabel computations were as follows: components were rejected if the probability of belonging to the

“brain” category was <20%, and the probability of belonging to the

“other” category was <40%. The “other” category was included since it can be comprised of a mixture of signals (including brain), and since approximately half of ICs are usually classified as having the highest probability of belonging to the “other” category. Visual inspection verified that these thresholds effectively removed clear artefacts related to vertical and horizontal eye-movements, muscle activity and cardiac activity, while keeping components primarily or partially reflecting brain activity.

The continuous EEG was then segmented into epochs beginning 100 ms before and 850 ms after the onset of visual stimuli. Epochs containing

amplitudes exceeding ±100 μV were rejected in the time window of interest for ERP analysis (− 100 to 600 ms post stimulus). A summary of trial rejection and accepted segments from pre-processing can be found in Supplementary Materials 1. Finally, artefact-free epochs were base- line corrected to the interval 100 ms prestimulus to stimulus onset and averaged for each experimental condition, yielding ERPs. Incorrect and no response trials were discarded.

2.6. ERP data extraction

The selection of ERP components was based on previously identified ERP components sensitive to Consistency and Perspective effects on the avatar task: LFSW and the P3 (Ferguson et al., 2018; McCleery et al., 2011). The mean ERP amplitude (in microvolts, or μV) of the LFSW was extracted in the time window 270–600 ms over left frontal (Fp1, AF7, AF3, F7, F5, F3, F1) and right frontal (Fp2, AF8, AF4, F2, F4, F6, F8) electrodes. The peak amplitude and latency-to-peak of the P3 was extracted over central posterior electrode sites (P1, P3, PO3, P2, P4, PO4, Pz, POz) in the time window 200–600 ms. The means, minimum and maximum values were inspected across all blocks to ensure they fell within the respective time-windows.

2.7. Data analysis 2.7.1. Aim 1

In our first aim, we investigated whether significant differences occur if mismatching trials are included. This was done for behavioural (response times) and ERP data. Behavioural data was treated separately from ERP data, therefore trials were not excluded on the basis of ERP pre-processing. For the response times, trials containing errors, lack of response, and response times <200 ms were excluded from analyses.

Due to the small combined percentage of errors/no response (3.92%), and primary focus on VPT effect on response times, analyses for response accuracy are provided in Supplementary Materials (2). The average percentage of correct trials was at 96% for both the mismatching “no” response trials and matching “yes” response trials, suggesting no difference in difficulty level between these trial types. See Supplementary Materials for a descriptive summary of the behavioural data (3). Grand averages for response times and ERPs were subjected to repeated measures analysis of variance (ANOVA), with Perspective (Self vs. Other), and Consistency (Consistent vs. Inconsistent) as within-subjects vari- ables. Hemisphere (Left vs. Right) was included as an additional within- subjects variable for the LFSW component. Both behavioural and ERP data were analysed separately for a) only matching trials, and b) all trials, allowing for comparison of potential differences and similarities in results. Additional statistical analyses were conducted on the mismatching “no” response trials for the behavioural and ERP data, which are provided in Supplementary Materials (4).

Fig. 1. Schematic overview of a trial sequence using the avatar task, with a multi-modal auditory-visual format.

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2.7.2. Aim 2

In our second aim, we examined if similar ERP effects could be obtained when analysing only a subset of the data. Behavioural data was not included, as findings have been replicated in both short (behavioural) and long (ERP) studies. The second aim was investigated in two steps. The first step was conducted by exploring changes in amplitudes and latencies over time. ERP data from all trials were divided into 5 time intervals of approximately 15 min each. Each time interval contained an average of 140 trials. Separate one-way ANOVAs were conducted to investigate the effect of time (time intervals) on the P3 (peak amplitude

& latency-to-peak) and LFSW (mean amplitude) components. If the ERPs

obtained stability before the end of the experiment, it would indicate that the paradigm could be shortened. If so, a second step would be conducted. In the second step, ERP analyses in aim 1 were repeated in order to investigate if the same VPT effects could be observed by analysing a subset of the data. Depending on the findings in aim 1, either all or only matching trials were included in analyses of aim 2.

Data tidying and statistical analyses were conducted in Rstudio, version 3.6.0 (R Core Team, 2019). Significant interactions were followed-up with paired samples t-tests. Correction for multiple comparisons were automatically computed by multiplying the p value by the number of comparisons (Bonferroni adjusted p values). Due to space constraints, only significant results are reported in the main text, but all statistical details including effect sizes (Partial eta squared and Cohen’s d) are reported in Tables 1–8.

3. Results

3.1. Aim 1: comparison of only matching trials versus all trials 3.1.1. Response times

3.1.1.1. Only matching trials. The ANOVA on response times for only matching trials revealed a significant main effect of Perspective, Con- sistency, and Perspective-by-Consistency interaction effect (Tables 1–2, Fig. 2A). The main effects reflected faster judgments on Self-trials (M = 507.91 ms) compared to Other-trials (M = 518.18 ms), and on Consistent-trials (M =496.14 ms) compared to Inconsistent-trials (M = 529.95 ms). Follow-up of the interaction effect revealed a significant Perspective effect for both Consistent and Inconsistent trials. On Consistent-trials participants were significantly faster at judging the avatar’s perspective compared to their own perspective (Other-Consis- tent =487.69 ms, Self-Consistent =504.58 ms). On Inconsistent-trials they were significantly faster at judging their own perspective compared to the avatar’s perspective (Self-Inconsistent =511.23 ms, Other-Inconsistent =548.68 ms). The effect of Consistency was significant for Other-trials (Consistent < Inconsistent, 61 ms advantage) demonstrating egocentric interference, but the altercentric interference effect failed to reach significance for Self-trials (p >.05, but Consistent <

Inconsistent).

Table 1 Response times.

df F p η_p²

Only matching trials

Response times Perspective 1,19 5.82 0.03 0.23

Consistency 1,19 40.58 <0.0001 0.68 Perspective ×Consistency 1,19 45.33 <0.0001 0.70 All trials

Response times Perspective 1,19 50.60 <0.0001 0.73

Consistency 1,19 19.00 0.0003 0.50

Perspective ×Consistency 1,19 94.40 <0.0001 0.83 Note. The table shows results from repeated measures ANOVA for response times. Separate ANOVAs were conducted for only matching trials and inclusion of all trials. p values in bold indicate significance <0.05.

Table 2

Follow-up analyses for response times.

Comparisons df t p Cohen’s

d Only matching trials

Response times Perspective × Consistency

Perspective effect Self-Consistent vs.

Other-Consistent

19 −4.239 0.002 0.95

Perspective effect Self-Inconsistent vs. Other- Inconsistent

19 5.145 0.0002 1.15

Consistency effect Self-Consistent vs.

Self-Inconsistent

19 −1.255 0.9 0.28

Consistency effect Other-Consistent vs. Other- Inconsistent

19 −7.816 <0.0001 1.75

All trials Response times

Perspective × Consistency

Other-Consistent

19 −1.214 0.96 0.27

19 10.030 <0.0001 2.24

Self-Inconsistent

19 1.113 1 0.24

19 −8.351 <0.0001 1.87

Note. Follow-up analyses of significant Perspective-by-Consistency interaction effects for response times. Separate paired samples t-tests were conducted for only matching trials and inclusion of all trials. p values in bold indicate significance <0.05 (p values are Bonferroni corrected).

Table 3

ERP amplitudes for the P3 and LFSW.

df F p η_p²

P3 Perspective 1,19 10.55 0.004 0.36

Consistency 1,19 55.55 <0.0001 0.75

Perspective ×Consistency 1,19 44.33 <0.0001 0.70

LFSW Perspective 1,19 17.47 0.0005 0.48

Consistency 1,19 34.46 <0.0001 0.64

Hemisphere 1,19 10.34 0.005 0.35

Perspective ×Consistency 1,19 23.60 0.0001 0.55 Perspective ×Hemisphere 1,19 12.04 0.003 0.39 Consistency×Hemisphere 1,19 0.61 0.44 0.03 Perspective ×Consistency ×

Hemisphere 1,19 1.50 0.24 0.07

All trials

P3 Perspective 1,19 1.46 0.24 0.07

Consistency 1,19 86.04 <0.0001 0.82

Perspective ×Consistency 1,19 18.78 0.0004 0.50

Consistency 1,19 60.20 <0.0001 0.76

Hemisphere 1,19 5.18 0.03 0.21

Perspective ×Consistency 1,19 14.18 0.001 0.43 Perspective ×Hemisphere 1,19 7.27 0.01 0.28 Consistency ×Hemisphere 1,19 0.00 0.97 0.0001 Perspective ×Consistency ×

Hemisphere 1,19 0.23 0.64 0.01

Note. The table shows results from repeated measures ANOVA on peak P3 and mean LFSW amplitudes. Separate ANOVAs were conducted for only matching trials and inclusion of all trials. p values in bold indicate significance <0.05.

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3.1.1.2. All trials. Inclusion of all trials on the response times yielded the same main effects and interactions as the ANOVA for only matching trials (Tables 1–2, Fig. 2B). Judgments were faster on Self-trials (M = 518.54 ms) compared to Other-trials (M = 537.75 ms), and on Consistent-trials (M =519.33 ms) compared to Inconsistent-trials (M = 536.96 ms). The only difference in the follow-up analyses of all trials was that the Perspective effect on Consistent trials was non-significant.

The significant comparisons had large effect sizes in both match and all trials analyses.

3.1.1.3. Response accuracy. As expected, participants made more errors for Inconsistent compared to Consistent perspectives. This was the case for only matching as well as for all trials. For only matching trials, participants made more errors for Inconsistent compared to Consistent trials when asked to take their own perspective, and vice-versa when Table 4

Follow-up analyses of ERP amplitudes (P3, LFSW).

d Only matching trials

P3 Perspective × Consistency

Other-Consistent

19 2.116 0.19 0.47

19 − 6.331 <0.0001 1.42

Self-Inconsistent

19 2.565 0.07 0.56

19 9.049 <0.0001 2.02

LFSW Perspective × Consistency

Other-Consistent

19 − 0.537 1 0.12

19 6.592 <0.0001 1.47

Self-Inconsistent

19 − 2.786 0.04 0.62

19 − 6.897 <0.0001 1.54

LFSW Perspective × Hemisphere

Perspective effect Self-Right vs.

Other-Right

19 1.941 0.26 0.43

Perspective effect Self-Left vs. Other- Left

19 4.343 0.001 0.97

Hemisphere effect Self-Right vs. Self- Left

19 − 3.438 0.01 0.77

Hemisphere effect Other-Right vs.

Other-Left

19 − 2.937 0.03 0.66

All trials P3 Perspective ×

Consistency

Other-Consistent

19 1.451 0.65 0.32

19 − 3.341 0.01 0.75

Self-Inconsistent

19 6.599 <0.0001 1.47

19 9.795 <0.0001 2.19

Other-Consistent

19 0.414 1 0.09

19 5.166 0.0002 1.15

Self-Inconsistent

19 − 5.428 0.0001 1.21

19 − 7.893 <0.0001 1.76

Other-Right

19 1.254 0.9 0.28

Table 4 (continued)

d Perspective effect

Self-Left vs. Other- Left

19 3.88 0.004 0.87

19 −2.656 0.06 0.58

Other-Left

19 −1.874 0.3 0.42

Note. Separate paired samples t-tests were conducted for only matching trials and all trials for the peak P3 and mean LFSW amplitudes. p values in bold indicate significance <0.05 (p values are Bonferroni corrected).

Table 5

Latency-to-peak for the P3 component.

df F p η_p²

P3 Perspective 1,19 21.04 0.0002 0.53

Consistency 1,19 7.28 0.01 0.28

Perspective ×Consistency 1,19 2.17 0.16 0.10 All trials

P3 Perspective 1,19 27.08 <0.0001 0.59

Consistency 1,19 1.90 0.18 0.09

Perspective ×Consistency 1,19 8.79 0.008 0.32 Note. The table shows results from repeated measures ANOVA latency-to-peak for the P3 component. Separate ANOVAs were conducted for only matching trials and inclusion of all trials. p values in bold indicate significance <0.05.

Table 6

Follow-up analyses of latency-to-peak for the P3 component.

d All trials

P3 Perspective × Consistency

Other-Consistent

19 4.356 0.001 0.97

Perspective effect Self-Inconsistent vs.

Other-Inconsistent

19 5.162 0.0002 1.15

Self-Inconsistent

19 0.359 1 0.08

Consistency effect Other-Consistent vs.

Other-Inconsistent

19 −2.557 0.07 0.57

Note. The table shows paired samples t-test for the significant Perspective × Consistency interaction on the P3 latency for all trials. p values in bold indicate significance <0.05 (p values are Bonferroni corrected).

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asked to take the perspective of the avatar. See Supplementary Materials (2) for details.

3.1.2. P3 amplitude

3.1.2.1. Only matching trials. The ANOVA for matching trials for the P3 amplitude revealed a main effect of Perspective and Consistency, and a significant Perspective-by-Consistency interaction effect (Tables 3–4, Fig. 3A). The main effects reflected a larger amplitude on Self-trials (M = 10.32 μV), compared to Other-trials (M =9.8 μV), and on Consistent- trials (M = 10.82 μV) compared to Inconsistent-trials (M =9.3 μV).

Follow-up analyses of the interaction effect revealed a significant Perspective effect on Inconsistent-trials, with larger amplitudes on Self- trials (M =10.04 μV) compared Other-trials (M =8.55 μV). There was also a significant Consistency effect when taking the avatar’s perspective, with higher amplitudes when viewpoints were Consistent (M = 11.03 μV) compared to Inconsistent (M =8.55 μV).

3.1.2.2. All trials. The ANOVA for the P3 amplitude showed a significant main effect of Consistency and Perspective-by-Consistency interaction, but no main effect of Perspective (Tables 3–4, Fig. 3B). Similar to analysis on only matching trials, the main effect of Consistency reflected larger amplitudes on Consistent-trials (M = 10.63 μV) compared to Inconsistent-trials (M =9.08 μV). The follow-up analyses revealed the same Perspective effect for Inconsistent-trials (with a smaller effect size compared to only matching trials) and Consistency effect for Other-trials (large effect size in both analyses), but there was an additional Consis- tency effect for Self-trials. The latter effect reflected larger P3 amplitudes when perspectives were Consistent (M =10.53 μV) compared to Inconsistent for Self-trials (M =9.34 μV), with a large effect size.

3.1.3. P3 latency

3.1.3.1. Only matching trials. The ANOVA for the P3 latency revealed main effects of Perspective and Consistency, but non-significant interaction effect (Tables 5–6). The main effects reflected longer P3 latency for Other-trials (M =407.85 ms) compared to Self-trials (M =391.59 ms), and for Inconsistent-trials (M =402.6 ms) compared to Consistent- trials (M =396.84 ms).

3.1.3.2. All trials. Inclusion of all trials revealed the same main effect of Perspective, but no effect of Consistency. However, there was an additional Perspective-by-Consistency interaction effect (Tables 5–6). The main effect of Perspective was in the same direction, with longer latencies on Other-trials (M =406.91 ms) compared to Self-trials (M = 390.56 ms). Follow-up comparisons of the interaction effect revealed a significant Perspective effect on both Consistent and Inconsistent trials,

both with large effect sizes. This effect was driven by longer latencies when taking the avatar’s perspective on Consistent-trials (Other- Consistent =403.25 ms, Self-Consistent =391.01 ms), as well as on Inconsistent-trials (Other-Inconsistent =410.58 ms, Self-Inconsistent = 390.1 ms).

3.1.4. LFSW amplitude

3.1.4.1. Only matching trials. The ANOVA for the LFSW amplitude for only matching trials revealed significant main effects of Perspective, Consistency and Hemisphere. In addition, Perspective-by-Consistency and Perspective-by-Hemisphere interactions were significant (Tables 3–4, Fig. 4A–C). The main effects reflected larger, more negative going amplitudes on Self-trials (M = − 2.37 μV) compared to Other-trials (M = − 1.95 μV), and on Consistent-trials (M = − 2.66 μV) compared to Inconsistent-trials (M = − 1.66 μV). Regardless of conditions, amplitudes were more negative over the left hemisphere (M = − 2.99 μV) compared to the right hemisphere (M = −1.32 μV). Follow-up analyses of the Perspective-by-Consistency interaction revealed a significant Perspec- tive effect for Inconsistent-trials, with a larger negative amplitude when taking one’s own perspective (M = − 2.12 μV) compared to the avatar’s perspective (M = −1.19 μV). There was a significant effect of Consis- tency when taking the avatar’s perspective, with larger amplitudes on Consistent-trials (M = − 2.7 μV) compared to Inconsistent trials (M =

− 1.19 μV). This effect was also significant when taking the Self- perspective, again with larger amplitudes on Consistent-trials (M =

− 2.62 μV) compared to Inconsistent-trials (M = − 2.12 μV). Finally, follow-up analyses of the Perspective-by-Hemisphere interaction revealed a significant effect of Perspective in the left Hemisphere (Self >

Other), and a significant effect of Hemisphere for both Self-trials and Other-trials (Left Hemisphere >Right Hemisphere).

3.1.4.2. All trials. Including all trials yielded the same main and interaction effects as analysis for only matching trials (Tables 3–4, Fig. 4B–D). All main effects were in the same direction, and the same comparisons were significant for the Perspective-by-Consistency interaction. The effect size was larger for the Consistency effect on Self-trials, compared to the analysis on only matching trials. However, follow-up analysis of the Perspective-by-Hemisphere interaction did not yield any significant Hemisphere effects. The Perspective effect in the left hemisphere yielded a large effect size in both analyses.

3.2. Mismatching “no” response trials

Additional statistical analyses were performed for the mismatching

“no” response trials only.

Although there were some differences compared to the analyses on only matching trials/all trials, the main behavioural and ERP findings Table 7

ERP amplitudes (P3, LFSW) and latency (P3) on the initial 3 time intervals.

df F p η_p²

P3 amplitude Perspective 1,19 0.64 0.43 0.03

Consistency 1,19 91.17 <0.0001 0.83

P3 latency Perspective 1,19 26.91 <0.0001 0.59

Consistency 1,19 5.94 0.02 0.24

Consistency 1,19 73.54 <0.0001 0.79

Hemisphere 1,19 5 0.04 0.21

Perspective ×Hemisphere 1,19 7.94 0.01 0.29

Consistency ×Hemisphere 1,19 0.26 0.61 0.01

Perspective ×Consistency ×Hemisphere 1,19 0.74 0.4 0.04

Note. The table shows results from repeated measures ANOVA on P3 amplitude and latency, and the mean LFSW amplitude. The ANOVAs were conducted on ERP data from the initial 3 time intervals (approximately 50 min of recording), and with all trials included. p values in bold indicate significance <0.05.

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were present. See Supplementary Materials (4) for details.

3.3. Aim 2: effect of reducing paradigm 3.3.1. P3 amplitude over time

The ANOVA revealed a significant difference across time intervals for the P3 amplitude (F(2.78, 52.83) =8.18, p <.001, _pη²=0.30). Follow- up analyses revealed that the first time interval differed significantly from all the other intervals (for all p <.01, d =0.71–1.12), and time interval 2 differed significantly from time interval 5 (p <.05). The P3

amplitude was less pronounced in the first time interval (M =9.2 μV), and gradually increased from interval 2 to interval 5 (M =9.92 μV - 10.36 μV). No other comparisons were significant, indicating that the P3 amplitude remained relatively stable from interval 2 onwards (Fig. 5A).

3.3.2. P3 latency over time

The one-way ANOVA revealed a significant difference across time intervals for the P3 latency (F(2.33, 44.28) =5.91, p <.01, pη²=0.24).

Follow-up analyses revealed that there was a significant difference in P3 latency from interval 1 to interval 3 (p <.05, d =0.72), and from interval 2 to interval 5 (p <.01, d =0.9). The P3 latency significantly decreased from time interval 1 (M =404.24 ms) to interval 3 (M = 394.14 ms), and from interval 2 (M =402.36 ms) to interval 5 (M = 395.64 ms). No other comparisons were significant, indicating that the P3 latency remained stable from interval 3 onwards (Fig. 5B).

3.3.3. LFSW amplitude over time

The ANOVA revealed a significant difference in the LFSW amplitude across the time intervals (F(2.49, 47.25) =5.04, p <.01, pη²=0.21).

Follow-up analyses showed that the only significant difference was between interval 1 and interval 5 (p <.01, d =0.9), in which the amplitude was less negative in the first interval (M = −1.51 μV), compared to the last interval (M = − 2.3 μV). Similar to the P3 component, the LFSW showed no other significant comparisons in the later time intervals, indicating amplitude stability (Fig. 5C).

3.3.4. Effects over time results

Results from the effect of time showed no significant changes in amplitudes or latencies from time intervals 2 and 3, suggesting stability in the ERPs from those time intervals. Based on these exploratory findings, we determined that a subset of the data (time intervals 1, 2, and 3) would be used in subsequent analyses. ERP data from time interval 4 and 5 were therefore excluded, in order to investigate if the same (i.e. results from aim 1 on all trials) effects can be obtained if the duration of the paradigm is reduced.

3.3.5. P3 amplitude effects: reduced paradigm

The only difference from the previous analyses using all trials was a lack of a significant Perspective effect on Inconsistent trials in the follow-up comparisons (Table 8). The ANOVA for the P3 amplitude on data from the 3 initial time intervals yielded the same main effect of Consistency (Consistent > Inconsistent), and Perspective-by- Consistency interaction (Table 7). The follow-up analyses yielded an effect of Consistency for both perspectives (Consistent >Inconsistent).

Effect sizes were large, same as the previous analyses on all trials.

3.3.6. P3 latency effects: reduced paradigm

The ANOVA for the P3 latency from the initial 3 time intervals revealed the same main effect of Perspective (Other > Self) and the Perspective-by-Consistency interaction effect as analysis on all trials (Table 7). However, there was an additional main effect of Consistency (Inconsistent >Consistent). Follow-up comparisons of the interaction effect revealed identical Perspective effects on Consistent and Incon- sistent trials as the analysis on all trials, with large effect sizes also being present in the analysis on a subset of the data. There was an additional Consistency effect when taking the avatar’s perspective, with longer latencies on Inconsistent trials (Other-Inconsistent >Other-Consistent).

3.3.7. LFSW amplitude effects: reduced paradigm

The results from the ANOVA on the LFSW amplitude on a subset of the data were identical to the findings in aim 1 for all trials (Tables 7–8).

This included significant main effects of Perspective, Consistency and Hemisphere, and interaction effects, all in the same direction. Follow-up analyses of the Perspective-by-Consistency and Perspective-by- Hemisphere interaction effects were identical to the initial analyses on all trials, with large effect sizes.

Table 8

Follow-up analyses of ERP amplitudes (P3, LFSW) and latency (P3) on the initial 3 time intervals.

d P3 amplitude

Perspective × Consistency

Other-Consistent

19 0.955 1 0.21

19 −2.691 0.06 0.6

Self-Inconsistent

19 6.037 <0.0001 1.35

19 9.712 <0.0001 2.17

P3 latency Perspective × Consistency

Other-Consistent

19 3.677 0.006 0.82

19 5.614 <0.0001 1.25

Self-Inconsistent

19 - 0.57 1 0.13

19 −3.728 0.006 0.83

Other-Consistent

19 0.662 1 0.1

19 6.331 <0.0001 1

Self-Inconsistent

19 −6.94 <0.0001 1.09

19 −10.679 <0.0001 1.69

Other-Right

19 1.389 0.72 0.31

Perspective effect Self-Left vs.

Other-Left

19 3.921 0.004 0.88

19 −2.704 0.06 0.6

Other-Left

19 −1.733 0.39 0.39

Note. The table shows paired samples t-test for the significant Perspective-by- Consistency interaction (P3 amplitude, P3 latency, LFSW amplitude), and Perspective-by-Hemisphere (LFSW amplitude) for all trials, on the initial 3 time intervals (approximately 50 min recording). p values in bold indicate significance <0.05 (p values are Bonferroni corrected).

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Fig. 2. Grand average response times for only matching trials (panel A) and all trials (panel B) for each condition. Error bars represent interquartile range.

Fig. 3.Grand average ERPs over central posterior electrode sites from onset of test stimuli for all conditions, reflecting the P3 component. Only matching trials (panel A) and all trials (panel B). Shaded areas show the 95% confidence interval.

Fig. 4. Grand average ERPs over left and right frontal electrode sites from onset of test stimuli for all conditions, reflecting the LFSW component. Only matching trials (panel A & B) and all trials (panel C & D). LH =left hemisphere, RH =right hemisphere. Shaded areas show the 95% confidence interval.

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4. Discussion

The present methodological study, focused on how the ERP-version of the avatar task can be optimized for clinical populations, yielded two main findings. First, the study replicated previous behavioural and neurophysiological findings both when using all trials and when using only matching trials (as has been done in most previous experiments).

These findings suggest that all trials can safely be included without eliminating any VPT effects, which can protect against data loss in future studies. Second, we showed that analysing only a subset of the data maintained these results for all trials, suggesting that the duration of assessment can be reduced.

For the behavioural data, the ANOVA on response times yielded identical main effects and interactions for only matching trials and all trials. The only difference was that the Perspective effect on Consistent trials was non-significant in the follow-up analyses for all trials. In line with previous studies using the avatar task (Catmur et al., 2016; Conway et al., 2017; Ferguson et al., 2017; Furlanetto et al., 2016; Kragh Nielsen et al., 2015; Marshall et al., 2018; Qureshi et al., 2010; Samson et al., 2010; Santiesteban et al., 2014), the important Perspective and Con- sistency effects were present in both analyses, with slower responses for Other perspective-taking and for Inconsistent viewpoints. Although the Consistency effect for Other-trials (egocentric interference) was also large in our study, the altercentric interference effect failed to reach significance in both analyses. The altercentric interference effect has

also been lacking in other studies (Santiesteban et al., 2017), suggesting that the egocentric interference is the most robust effect of the avatar task.

The primary concern of including the mismatching trials has been the risk of overestimating the Consistency effect, due to the suggestion that there might be an unbalanced difficulty level between matching and mismatching trials (Samson et al., 2010). However, the difficulty level appeared to be the same as seen in an average of 96% correct for both matching and mismatching trials. Further, there was no evidence of overestimating the Consistency effect, as in fact, the Consistency effect was non-significant in the additional analyses on the mismatching trials (Supplementary Materials 3). This suggests that the Consistency effect may be underestimated when collapsing the two trial types. Neverthe- less, the same pair-wise comparisons were significant in the follow-up analyses on the mismatching trials as the analyses on all trials. In the main analyses we were able to show that the effect sizes for the main effect of Consistency and follow-up of the Consistency effect are large both for only matching trials and all trials. Thus, Consistency effect was not overestimated when including all trials. In addition, these findings are in accordance with a previous TMS study in which inclusion of mismatching trials did not eliminate important Perspective or Consis- tency effects of the avatar task (Santiesteban et al., 2017). Another concern is that McCleery et al. (2011) estimated the neural generators of the ERP components based on only matching trials. Future studies may consider investigating whether mismatching trials have other neural generators than the matching trials.

For the ERP data, there were some marginal differences when including all trials in the analyses. For the P3 amplitude, the main effect of Perspective was not significant for all trials. However, this main effect had the weakest effect size in the analysis on only matching trials, and previous ERP studies have shown that the P3 amplitude is primarily sensitive to Consistency effects (McCleery et al., 2011; Ferguson et al., 2018). The main effect of Consistency was also present in an analysis of mismatching “no” response trials only. This suggests a robust effect on the P3 amplitude both across different trial types and when these are analysed together (see Supplementary Materials 4). The effect of Con- sistency was present with large effect sizes in both analyses (only matching trials vs. all trials), replicating the finding that Inconsistent viewpoints generates smaller P3 amplitudes. Further, in the follow-up analyses, there was an additional Consistency effect for Self-trials that was not present in the analysis on only matching trials. This finding was suggestive of an altercentric interference effect, with smaller P3 amplitudes for Inconsistent viewpoints. It was however, approaching significance for only matching trials, which may suggest that inclusion of all trials increases the statistical power to detect more effects. Overall, the Consistency effect was significant both for only matching trials and for all trials, suggesting that inclusion of all trials did not eliminate the most important VPT effects on the P3 amplitude.

In line with the previous ERP findings (Ferguson et al., 2018;

McCleery et al., 2011), the main effect of Perspective on the P3 latency was significant with large effect sizes in both analyses. This finding reflected the perspective calculation process, with longer latencies when taking someone else’s perspective. However, there were a few notable differences between the analyses on only matching trials and all trials.

First, the analysis on all trials replicated the McCleery et al., (2011) findings of a main effect of Perspective, with a Perspective-by- Consistency interaction. This interaction effect was not present in the analysis on only matching trials, where the main effect of Perspective was accompanied by a main effect of Consistency. It is possible that the presence of an interaction effect in the analyses on all trials is a result of increased power that comes with utilizing more trials in the analyses.

Indeed, this interaction effect is present in the analyses of the mismatching “no” response trials (Supplementary Materials 4). Therefore, we cannot rule out a difference in neurophysiological mechanisms between trials in which participants correctly respond “yes” compared to correctly responding “no”. However, the main finding of Perspective for Fig. 5. Grand average amplitudes (P3 and LFSW) and peak latency (P3) across

five time intervals, regardless of condition (all trials).