Received: 4 July 2019 Revised: 5 February 2020 Accepted: 11 February 2020 DOI: 10.1002/hbm.24966 RESEARCH ARTICLE Functional magnetic resonance imaging (fMRI) item analysis of empathy and theory of mind Matthias G. Tholen1 | Fynn-Mathis Trautwein2 Tania Singer4 | Philipp Kanske5,6 1 Centre for Cognitive Neuroscience, Department of Psychology, University of Salzburg, Austria | Anne Böckler3 | Abstract In contrast to conventional functional magnetic resonance imaging (fMRI) analysis 2 Edmont J. Safra Brain Research Center, University of Haifa, Israel 3 Department of Psychology, Leibniz University Hannover, Hannover, Germany 4 across participants, item analysis allows generalizing the observed neural response patterns from a specific stimulus set to the entire population of stimuli. In the present study, we perform an item analysis on an fMRI paradigm (EmpaToM) that measures Max Planck Society, Social Neuroscience Lab, Berlin, Germany the neural correlates of empathy and Theory of Mind (ToM). The task includes a large 5 stimulus set (240 emotional vs. neutral videos to probe empathic responding and Clinical Psychology and Behavioral Neuroscience, Faculty of Psychology, Technische Universität Dresden, Dresden, Germany 6 Max Planck Institute for Human Cognitive and Brain Sciences, Research Group Social Stress and Family Health, Leipzig, Germany 240 ToM or factual reasoning questions to probe ToM), which we tested in two large participant samples (N = 178, N = 130). Both, the empathy-related network comprising anterior insula, anterior cingulate/dorsomedial prefrontal cortex, inferior frontal gyrus, and dorsal temporoparietal junction/supramarginal gyrus (TPJ) and the ToM related network including ventral TPJ, superior temporal gyrus, temporal poles, and Correspondence Matthias G. Tholen, Centre for Cognitive Neuroscience, Department of Psychology, University of Salzburg, 5020 Salzburg, Austria. Email: matthias.tholen@sbg.ac.at anterior and posterior midline regions, were observed across participants and items. Funding information Austrian Science Fund, Grant/Award Number: FWF-W1233; Bundesministerium für Bildung und Forschung, Grant/Award Number: BMBF FKZ 01EE1409A; Deutsche Forschungsgemeinschaft, Grant/Award Number: Heinz Maier-Leibnitz Prize KA 4412/1-1; FP7 Ideas: European Research Council, Grant/Award Number: 205557 selection of the most effective items to create optimized stimulus sets that provide Regression analyses confirmed that these activations are predicted by the empathy or ToM condition of the stimuli, but not by low-level features such as video length, number of words, syllables or syntactic complexity. The item analysis also allowed for the the most stable and reproducible results. Finally, reproducibility was shown in the replication of all analyses in the second participant sample. The data demonstrate (a) the generalizability of empathy and ToM related neural activity and (b) the reproducibility of the EmpaToM task and its applicability in intervention and clinical imaging studies. KEYWORDS affect sharing, anterior insula, mentalizing, social cognition, temporoparietal junction 1 | I N T RO DU CT I O N correlates of how we feel with (affective route) and know about others (cognitive route). The affective route allows for sharing others' Aiming at elucidating the mechanisms underlying social understanding, emotions (empathy, affect sharing) (de Vignemont & Singer, 2006), for human neuroscience research has extensively investigated the brain example, when vicariously sharing another person's sadness or grief. The cognitive route enables reasoning about others' mental states Tania Singer and Philipp Kanske share senior authorship. (Theory of Mind, ToM, mentalizing) (Frith & Frith, 2005; Premack & This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2020 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc. Hum Brain Mapp. 2020;1–18. wileyonlinelibrary.com/journal/hbm 1 2 THOLEN ET AL. Woodruff, 1978), for example, when attributing another person's physical or emotional pain yielding activity in the typical empathy and belief, desire or intention. Several meta-analyses across different medial parts of the ToM related neural networks, respectively. This experimental approaches to both empathy and ToM have consistently study did not, however, compare these results with the subject-wise described two distinct neural networks related to these functions. analysis published previously, which would directly show replicability of Core regions of the empathy related network are found in the subject- and item-wise analyses (Bruneau, Pluta, & Saxe, 2012). Interest- anterior insula (AI), anterior cingulate/dorsomedial prefrontal cortex ingly, Bedny et al. (2007), who studied word class processing, found dif- (ACC/DMPFC), inferior frontal gyrus (IFG) and dorsal portions of the ferent results for subject- and item-wise analyses, demonstrating the temporoparietal junction/supramarginal gyrus (TPJ/SMG) (Bzdok potential of item analysis to make theoretically important distinctions, et al., 2012; Lamm, Decety, & Singer, 2011). The ToM related network which in that case reconciled conflicting evidence regarding the role of includes the ventral TPJ, anterior and posterior medial prefrontal the prefrontal cortex in processing nouns vs. verbs (Bedny & Thompson- cortex (MPFC), superior temporal gyrus/sulcus (STG/STS), and tempo- Schill, 2006; Davis, Meunier, & Marslen-Wilson, 2004; Shapiro, Moo, & ral poles (Bzdok et al., 2012; Schurz, Radua, Aichhorn, Richlan, & Per- Caramazza, 2006; Tyler, Bright, Fletcher, & Stamatakis, 2004). ner, 2014). Direct contrasts of both functions confirmed these In the present study, we aimed to investigate whether item- networks with functional (Kanske, Böckler, Trautwein, & Singer, 2015) analyses of empathy and ToM replicate the neural networks observed and structural neuroimaging (Eres, Decety, Louis, & Molenberghs, with subject-wise analyses. To this end, we applied a previously vali- 2015; Valk et al., 2017; Valk, Bernhardt, Bockler, Kanske, & Singer, dated fMRI paradigm that assesses both functions (EmpaToM) 2016). These studies show that empathizing and mentalizing engage (Kanske et al., 2015). Empathy is probed via video stimuli with brief distinct neural networks. Furthermore, brain regions also differ in cor- autobiographical narrations that are highly emotionally negative or tical thickness according to the subjects' capacity to share emotions neutral. The negative emotional narrations included such diverse or to reason about mental states. Importantly, even though both func- issues as traffic accidents, involuntary pregnancy, partnership prob- tions are essential elements of higher-level social processing, they lems, diverse somatic and mental diseases and disorders, betrayal and are not directly related. The independence of empathy and ToM guilt, political violence, seeking refuge, rape, natural disaster, miscar- processing was demonstrated on the behavioral and the neural level riage, assault or burglary. These videos have been shown to elicit (Kanske, Böckler, Trautwein, Parianen Lesemann, & Singer, 2016). empathic responses on a subjective, peripheral physiological, and on a With three notable exceptions (Bruneau, Dufour, & Saxe, 2013; neural level. ToM reasoning is demanded in subsequent questions that Dodell-Feder, Koster-Hale, Bedny, & Saxe, 2011; Theriault, Waytz, either ask for the mental states of the narrator in the previous video Heiphetz, & Young, 2017), all previous empathy and ToM investigations or for factual reasoning about the events of the narration. The mental used conventional functional magnetic resonance imaging (fMRI) ana- state questions included first and second order, true and false lyses across participants. These analyses allow generalizing the observed beliefs, preferences and desires, irony, sarcasm, metaphors, (white) neural response patterns from the investigated participant sample to the lies, deception and faux pas. The empathy and ToM measures were human population they were sampled from, if they treat subjects as validated in several behavioral and fMRI studies through correlations random-effect (as has become standard since the late 1990s (Friston, and activation overlap with established empathy (Socio-affective Holmes, & Worsley, 1999)). However, the “fixed-effect fallacy” still Video Taks; Klimecki, Leiberg, Lamm, & Singer, 2013) and ToM tasks applies to the item-level (Clark, 1973), that is, it is unsubstantiated to (False Belief Task; Dodell-Feder et al., 2011, Imposing Memory Task; claim that activation patterns observed for a sample of stimuli would Kinderman, Dunbar, & Bentall, 1998) and additional overlap with generalize to the population of stimuli, for instance, that the activity meta-analytical findings (Bzdok et al., 2012; Dodell-Feder et al., 2011; observed in an experiment eliciting emotional responses would general- Kinderman et al., 1998; Klimecki et al., 2013). Conceptually, it is impor- ize to the population of emotion-eliciting stimuli. Furthermore, treating tant for social neuroscience to show that empathy related neural activ- items as fixed could give single items with extreme responses dispropor- ity generalizes beyond patterns only attributable to very specific tionate weight, thereby rendering a contrast of two conditions signifi- stimuli, and whether ToM tasks other than false-belief tasks (Dodell- cant, just because a (possibly small) subset of items in one condition Feder et al., 2011) also lead to generalizable brain activation. To illus- shows very strong activity, while the majority of items shows no effect. trate this form of generalization, as in psycholinguistics, where an item- To overcome these problems, item analyses that treat items as random analysis in an experiment on verb-processing allows generalizing the are common in many behavioral fields of study and have been shown results from the limited sample of verbs tested to the population of to be feasible for fMRI analyses as well (Andrews-Hanna, Reidler, verbs in that language (e.g., Bedny et al., 2007), replicating the subject- Sepulcre, Poulin, & Buckner, 2010; Bedny, Aguirre, & Thompson-Schill, analysis results in the EmpaToM with an item-analysis would allow gen- 2007; Dodell-Feder et al., 2011; Theriault et al., 2017; Troiani, Stigliani, eralizing to the population of empathy-inducing and ToM-demanding Smith, & Epstein, 2014; Yee, Drucker, & Thompson-Schill, 2010). Thus, conversational situations. Given the breadth of the sampled situations Theriault et al. (2017) demonstrated positive correlations between in the EmpaToM (240 distinct videos and questions), testing generaliz- regions in the ToM network and subjectivity ratings of metaethical judg- ability may be challenging, but could also have particular impact. ments. Dodell-Feder et al. (2011) replicated a subject-wise analysis with Furthermore, a principal problem in subject-analysis is that discrep- an item analysis showing generalizability for false-belief ToM stories. ancies between two experimental conditions beyond the intended dif- Bruneau et al. (2013) performed an item-analysis on brief stories of ference are uncontrollable confounds. Item-analysis, in contrast, allows 3 THOLEN ET AL. specifically testing whether activations observed in a contrast of two (ReSource Project; (Singer et al., 2016)).1 Participants were recruited conditions are actually due to unintended low-level differences from the general public through adverts. Recruitment of Sample 1 took between the conditions (e.g., more or less movement when telling an place in 2012–2013 and of Sample 2 in 2013–2014. Participants had emotionally negative compared to a neutral story) rather than the a very good language proficiency and were not included if they were intended difference (e.g., negative vs. neutral emotion). As item-specific below 20 or above 55 years of age, fulfilled the criteria for a mental or activation patterns are obtained, they can be associated to the specific neurological disorder (according to structured clinical interviews for features of each item. Given that it is impossible to completely match DSM-IV axis I and axis II disorders; Wittchen, Zaudig, & Fydrich, emotional and neutral stimuli without erasing the difference in emo- 1997) or had any contraindication for MRI scanning. Twenty-four par- tionality, ruling out the influence of such low-level features is a crucial ticipants had to be excluded due to study dropout (N = 5), dropout issue. With regard to ToM, because of the considerable overlap of ToM from MRI measurements (N = 1), or missing data due to technical, related activity with regions involved in language processing, particu- scheduling, or health issues (N = 18). larly in the temporal cortex and TPJ (Friederici, 2011; Schurz et al., For Sample 1, 13 participants were excluded yielding a final sam- 2014), it is critical to rule out the possibility that linguistic differences ple of 178 participants (age mean = 40.9 years, SD = 9.5, 106 female). account for the observed ToM effects. Dodell-Feder et al. (2011) con- For Sample 2, 11 participants were excluded yielding a final sample of vincingly demonstrated this for false-belief tasks, but it is important to 130 participants (age mean = 40.4 years, SD = 9.0, 72 female). The study was approved by the Research Ethics Committee of test whether this holds for other language-based ToM tasks as well. Because the EmpaToM was designed to be used in extensive longi- the University of Leipzig, number 376/12-ff and the Research Ethics tudinal designs, it includes five parallel sets of different videos and ques- Committee of the Humboldt University in Berlin, numbers 2013-02, tions that allow the repeated testing of the same participants across time. 2013-29, and 2014-10. The study was registered with the Protocol To enable usage of the EmpaToM in clinical and other settings, where Registration System of ClinicalTrials.gov under the title “Plasticity of only small participant samples are available or participants can be scanned the Compassionate Brain” with the ClinicalTrials.gov Identifier: for a very limited amount of time only, an item analysis on this large stim- NCT01833104. All participants signed informed consent prior to ulus set affords the chance to select the most effective items to create participation. stimulus sets that provide the most stable and reproducible results. Finally, a major criticism of fMRI studies has been the limited sample size that not only reduces the likelihood to detect true effects, but 2.2 | Stimuli and task also reduces the chance that a statistically significant result reflects a true effect (Button et al., 2013). Therefore, the present study made use For details of the EmpaToM task see (Kanske et al., 2015) (Figure 1). of a large sample of participants (N = 178) and checked for reproducibil- Each trial started with a fixation cross (1–3 s), followed by the name ity of the results in a second sample (N = 130). of a person (2 s), who would speak in the subsequent video (~15 s). In sum, applying item-analyses to an fMRI task probing empathy and Each participant was presented with videos of 12 persons, telling four ToM, the present study addresses several questions: (a) Will the item- different stories each that corresponded to four conditions (2 × 2 fac- analyses replicate the neural networks underlying empathy and ToM as torial design, negative vs. neutral emotion, ToM vs. no ToM demands). observed with subject-wise analyses? This would argue for generalizability After this, participants rated the valence of their current emotional of the observed brain activation patterns to the respective stimulus classes state (sliding scale from negative to neutral to positive; 4 s) and how (i.e., neutral and emotional autobiographical video narrations; factual rea- much compassion2 they felt for the person in the previous video (slid- soning and ToM questions, the latter involving a variety of ToM demands ing scale from none to very much; 4 s). A second fixation cross (1–3 s) such as irony, higher order mental state inference, false beliefs, etc.). was followed by a multiple choice question with three response (b) Can activity in the observed neural networks be predicted by low-level options (one correct). These questions demanded either the attribu- stimulus characteristics (i.e., number of sentences, words, syllables, charac- tion of mental states or factual reasoning (ToM vs. factual reasoning). ters, predicates, conjunctives, changes in tense, passive constructions, sub- Participants had to respond within 14 s. For example, stories and clauses, and the amount of motion)? (c) Does the item-analysis allow questions, see Data S1. After a third fixation cross (0–2 s), participants creating stimulus sets including the most effective items to provide the were asked to rate their confidence, that their decision was done cor- most stable and reproducible results? (d) Are all of the above described rect (4 s) to allow assessing metacognitive abilities (Molenberghs, results replicable in the second independent participant sample? Trautwein, Bockler, Singer, & Kanske, 2016; Valk et al., 2016). In the present study we focused on the main empathy and ToM measures, that is, comparing emotional with neutral videos and ToM with factual 2 METHODS | reasoning questions (see (Kanske et al., 2015) for a validation of these contrasts). 2.1 | Participants The total stimulus set of the EmpaToM task comprised 240 videos and questions showing 60 different narrators in 4 conditions (see Two samples of 191 and 141 German-speaking participants were Figure 2). Based on this set, five parallel versions were created that tested in the context of a large-scale longitudinal study at baseline each contained a different set of 12 narrators in 4 conditions (yielding 4 THOLEN ET AL. F I G U R E 1 EmpaToM trial sequence. Emotional and neutral videos with and without ToM demands (2 × 2 design) are followed by valence and compassion ratings, ToM and factual reasoning questions, and a confidence rating (adopted from Kanske et al. (2015)). This study investigated the effects of subject- and itemwise analyses on the empathy and theory of mind contrasts. Empathy was tested via emotionally negative versus neutral videos and theory of mind was tested via mental state versus factual reasoning questions. ToM, Theory of Mind 48 different videos and questions per set). The parallel sets were mat- after emotional and neutral videos as an indicator of empathic ched with regard to affect ratings, concern ratings, RTs, errors, confi- responding and analyzed performance (RTs and accuracies) after ToM dence ratings, video lengths and linguistic characteristics of the questions as an indicator of ToM capacity. Each subject contributed questions (number of words, characters, predicates, changes in tense, ratings and performance measures in these conditions, averaged complexity of the sentences [number of main and subordinate clau- across all items. Complementarily, each item (i.e., narrator, each of ses], number of passive sentence constructions, and number of con- which told four different stories) contributed measures in each condi- junctives), see (Kanske et al., 2015)). The five sets were randomly tion, averaged across all participants. assigned to the participants such that each set (of 48 videos and questions) was seen by a fifth of the participants in Samples 1 and 2. 2.5 2.3 | MRI data acquisition | fMRI data analysis Data preprocessing and statistical analyses were performed with SPM 8 (http://www.fil.ion.ucl.ac.uk/spm) running in a MATLAB 7.6 Data were acquired on a 3 T MRI scanner (Siemens Magnetom Verio, environment (Mathworks Inc., Sherbon MA). Functional images Siemens Medical Solutions, Erlangen, Germany) using a 32 channel were coregistered to the SPM single-subject canonical EPI image, head coil. Functional images were acquired with a T2*-weighted slice-time corrected and realigned to the mean image volume for echo-planar imaging (EPI) sequence (TR = 2,000 ms; TE = 27 ms, Flip motion correction. The high-resolution structural image was cor- Angle 90 , matrix = 70 × 70 mm, FOV = 210 mm). Within one TR, egistered to the SPM single-subject canonical T1 image and then to 37 axial slices of 3 mm were acquired. In addition, we collected a the average functional image. Normalization parameters of the high-resolution structural image (1 × 1 × 1 mm) with a T1-weighted structural image into the Montreal Neurological Institute (MNI) MPRAGE sequence. space were used for spatial normalization of the functional images. These images were resampled to isotropic 3 × 3 × 3 mm voxels and smoothed with an 8 mm FWHM Gaussian Kernel. 2.4 | Behavioral data analysis The statistical analyses were performed by using the general linear model. For the subject-wise analysis, onset and duration of the Repeated measures analyses of variance were calculated across sub- four video types and their corresponding questions were modeled. jects and across items. In particular, we contrasted valence ratings These regressors were convolved by a canonical hemodynamic 5 THOLEN ET AL. F I G U R E 2 EmpaToM stimulus material. The overall stimulus material of the EmpaToM task contains 240 videos and questions with 60 different narrators in 4 conditions (emotional vs. neutral, ToM vs. nonToM), allocated to one of five parallel subsets. Each subset contains 12 different narrators in 4 conditions. The subsets are matched with regard to affect ratings, concern ratings, RTs, errors, confidence ratings, video lengths, and linguistic characteristics of the questions (number of words, characters, predicates, changes in tense, complexity of the sentences, number of passive sentence constructions, and number of conjunctives) (see Kanske et al., 2015). Subjects were randomly assigned to one of the five subsets, so that each subset was seen by a fifth of the participants in Sample 1 (N = 178) and Sample 2 (N = 130). ToM, Theory of Mind response function. Six regressors accounting head movement effects contrasts between the condition differences (emotional vs. neutral were modeled as covariates of no interest. RobustWLS Toolbox videos, ToM vs. nonToM questions) together with the factor of sub- (Diedrichsen & Shadmehr, 2005) was used to reduce potential noise- groups as covariates of no interest in order to account for the depen- artifact. Contrast images for empathy (emotional vs. neutral videos) dencies between the 240 beta maps corresponding to the five and ToM (ToM vs. nonToM questions) were calculated by applying groups of participants. The main contrasts were tested with two sam- linear weights to the parameter estimates and entered into one- ple t-tests. The results for the subject-wise as well as the item-wise analyses sample t-tests for random effects analysis. The item analyses were performed for each contrast separately by modeling the emotional and neutral videos, and the ToM and fac- were thresholded at p < .001 at voxel-level together with an FWE (family-wise error) correction (p < .05) at the cluster level. tual reasoning questions on the individual subject level. Each analysis resulted in 48 beta maps per subject (12 narrators × 4 conditions). The beta maps were averaged across the subjects within the five par- 2.6 | Regression analysis allel versions (see Figure 2) to receive one single beta map per narrator and condition. For each of the five subgroups, this method yielded For both contrasts, regions of interest (ROI) (N = 46, 23 ToM, 48 beta maps at which each beta map comprised a mean beta value 23 empathy) were defined on the basis of the subject-wise random across subjects at every voxel, adding up to 240 beta maps in total. effects analyses of Sample 1 (see Table 1 for empathy, Table 2 for For the second-level random effects analyses, we modeled the main ToM). They were used to extract the beta values from Sample 2 for 6 THOLEN ET AL. T A B L E 1 Whole brain subject- and item-wise random effects results for Videos Emotional > Neutral. The results are reported at a voxel-level threshold of p < .001 uncorrected together with an FWE-corrected cluster threshold of p < .05 MNI coordinates T Z Cluster −9 10.68 >8.21 1,027 15 45 10.04 >8.21 21 −6 8.58 7.82 −3 33 51 10.53 >8.21 9 21 57 8.69 >8.21 H x y z Inferior frontal gyrus L −48 39 Middle frontal L −42 Anterior insula L −36 Superior medial frontal cortex L Superior medial frontal R Subject-wise Group#1 Inferior frontal gyrus R 51 30 −6 10.01 >8.21 Middle frontal R 42 21 39 6.96 6.53 Anterior insula R 30 24 −15 6.64 6.27 1,257 737 Ventral striatum R 9 3 0 6.29 5.97 Ventral striatum L −6 −3 0 6.16 5.86 Caudate L −12 6 12 6.12 5.82 Caudate R 12 6 12 6 5.82 0 −18 39 8.25 7.58 82 L −54 −30 −12 6.08 5.79 26 448 Middle cingulate Middle temporal cortex 153 TPJ-angular/supramarginal gyrus R 63 −48 33 9.71 >8.21 Middle temporal cortex R 60 −57 9 7.44 6.93 TPJ-angular/supramarginal gyrus L −54 −51 33 12.49 >8.21 599 0 −63 36 12.01 >8.21 614 L −6 −75 −3 8.07 7.43 162 Precuneus Lingual gyrus Middle occipital R 42 −84 18 5.98 5.7 30 Middle occipital L −39 −90 9 5.1 4.92 13 Cerebellum L −15 −78 −30 9.88 >8.21 186 Cerebellum R 18 −81 −33 10.04 >8.21 219 Anterior insula L −39 21 −9 9.83 >8.21 767 Inferior frontal gyrus L −45 42 −12 8.71 7.64 Middle frontal L −42 18 42 10.64 >8.21 248 0 42 45 10.89 >8.21 1,239 Item-wise Group#1 Superior medial frontal cortex Superior medial frontal R 12 18 54 7.67 6.90 Inferior frontal gyrus R 45 27 −12 9.33 >8.21 Anterior insula R 30 24 −12 8.19 7.27 Middle frontal R 39 24 39 6.68 6.14 148 59 Ventral striatum R 9 3 0 6.33 5.86 Caudate R 12 9 12 5.13 4.86 Caudate L −12 12 15 6.64 6.11 Ventral striatum L −6 0 −3 5.70 5.35 0 −18 39 7.00 6.39 Middle cingulate 492 54 45 Middle temporal cortex L −54 −30 −15 5.96 5.56 20 TPJ-angular/supramarginal gyrus R 60 −45 36 8.02 7.15 258 Middle temporal cortex R 48 −48 18 5.12 4.86 TPJ-angular/supramarginal gyrus L −54 −51 30 10.64 >8.21 412 Precuneus L −6 −60 33 8.87 7.74 413 Lingual gyrus L (Continues) 7 THOLEN ET AL. TABLE 1 (Continued) MNI coordinates H y z L −15 −78 −30 8.08 7.20 145 R 15 −78 −30 8.35 7.39 199 Inferior frontal gyrus L −45 36 −6 8.84 7.80 469 Anterior insula L −36 27 −3 7.27 6.64 Middle frontal L −39 15 39 7.18 6.57 106 Middle frontal L −36 60 −3 5.76 5.42 19 0 45 33 10.64 >8.21 758 R 15 21 63 5.18 4.93 R Middle occipital L Cerebellum Cerebellum T Cluster x Middle occipital Z Subject-wise Group#2 Superior medial frontal cortex Superior medial frontal Inferior frontal gyrus R 45 27 3 7.31 6.67 Anterior insula R 30 21 −15 6.50 6.03 290 Middle frontal R 42 18 36 5.36 5.08 17 Ventral striatum R 6 0 −3 6.00 5.62 32 Caudate R 12 9 9 5.35 5.07 Ventral striatum L −6 0 0 5.84 5.49 Caudate R −12 6 12 5.63 5.31 0 −18 39 6.38 5.93 16 63 −51 24 7.93 7.15 153 −57 −51 33 10.36 >8.21 242 Middle cingulate 32 Middle temporal cortex L TPJ-angular/supramarginal gyrus R Middle temporal cortex R TPJ-angular/supramarginal gyrus L Precuneus L −6 −51 33 7.78 7.03 261 Lingual gyrus L −9 −75 −3 6.09 5.70 19 Middle occipital R Middle occipital L Cerebellum L −18 −78 −33 7.85 7.08 115 Cerebellum R 24 −75 −33 7.90 7.12 96 Inferior frontal gyrus L −45 42 −6 8.52 7.50 588 Anterior insula L −27 24 −9 7.81 7.00 Middle frontal L −39 15 39 6.75 6.19 94 Superior medial frontal cortex L 0 39 42 9.61 >8.21 823 Superior medial frontal L −3 33 48 9.34 >8.21 Inferior frontal gyrus R 42 33 −3 7.90 7.06 Anterior insula R 33 21 −15 7.43 6.72 Itemwise Group#2 283 Middle frontal R 36 18 36 5.53 5.20 15 Caudate R 6 −6 −12 6.91 6.32 55 Ventral striatum L 9 0 −3 6.39 5.91 Ventral striatum L −6 0 0 6.03 5.62 Caudate R Middle cingulate Middle temporal cortex L (Continues) 8 THOLEN ET AL. TABLE 1 (Continued) MNI coordinates H TPJ-angular/supramarginal gyrus R Middle temporal cortex R TPJ-angular/supramarginal gyrus x y z T Cluster Z 63 −51 27 5.66 5.31 40 L −54 −51 33 9.92 >8.21 214 Precuneus L −6 −51 33 6.21 5.76 70 Lingual gyrus L Middle occipital R Middle occipital L Cerebellum L −15 −78 −30 7.94 7.09 99 Cerebellum R 15 −81 −30 7.42 6.71 108 Abbreviations: FWE, family-wise error; TPJ, temporoparietal junction/supramarginal gyrus. the respective contrasts and consisted each of a sphere of contiguous syntactic complexity influence activation patterns in the same cortical voxels, 5 mm in radius. This procedure has two advantages: First, by areas that are engaged during empathy and ToM processing. using the ROIs from the subject-wise analysis, we might be able to Besides to low-level features that are associated to spoken and explain differences between item- and subject-wise analyses that are written text, we additionally selected three general low-level features due to low-level features. Second, the data of the regression analysis that characterized the video material: duration of videos, motion and is based on independently defined ROIs. To test whether the activa- velocity of the narrator's movement. Emotionality may not only be tions can be additionally explained by linguistic factors each item was communicated by language and facial expression but is also facilitated coded by at least two researchers in 9 different features. They com- by spontaneous gestures and movements (Dick, Solodkin, & Small, prised the following set of variables and were coded for each of the 2010). Gesture comprehension is supported by a cortical network com- stories (spoken text, empathy contrast) and questions (written text, prising the bilateral temporo-parietal junction, bilateral superior parietal ToM contrast): number of words, characters, sentences, syllables lobe, left inferior and middle frontal gyrus, and the left superior and (as measures of the amount of spoken or written text), predicates, middle temporal gyrus (Yang, Andric, & Mathew, 2015). Because of the tenses, passives, conjunctives and complexity (as measures of syntac- considerable overlap with empathy related activity, we included these tic difficulty). Additionally, for the empathy contrast three general fea- factors into the regression analysis to rule out that differences in the tures were coded to characterize the video material: duration of the video material account for the observed empathy effects. We performed stepwise forward/backward regression analyses with video, motion and velocity of the narrator's movement. In the following three passages, we further illustrate the choice of these features. the item responses in the previously defined ROIs as dependent vari- The amount of spoken or written text, for example, measured by ables and condition and the selected features as independent variables. the number of words, has been used as a proxy for constituent size Stepwise regression is an iterative process of selecting and eliminating (Goucha & Friederici, 2015; Pallier, Devauchelle, & Dehaene, 2011). multiple variables depending on the model's best fit to the data. It is par- These studies showed that increasing constituent size is associated ticularly useful in cases where there are large numbers of predictors. In with an increase of neural activation in left hemispheric cortical areas each step, a predictor is added to the regression which most improves such as the inferior frontal gyrus, temporo-parietal junction, superior the fitting of the data (forward selection). To avoid overfitting, the pre- temporal sulcus and temporal pole, regions that are also engaged dur- dictors are excluded from the model if their contribution to predicting ing empathy and theory of mind processing. Therefore, we tested the outcome becomes non-significant (backward elimination). We used whether differences in the number of words, characters, sentences or rather strict entry and removal criteria that were based on the number syllables can account for the observed effects in the EmpaTom task. of predictors to account for multiple testing (theory of mind (10 predic- Five additional features measure aspects of syntactic complexity, tors): entry/removal: p = .005/p = .01; empathy (13 predictors): entry/ that is, number of predicates, tenses, passives, conjunctives and com- removal: p = .0038/p = .0077). The analyses were performed on IBM plexity (lexical diversity: type token ratio). Syntactic complexity is cor- SPSS Statistics for Windows, version 26.0 (IBM Corp., Armonk, NY). related with working memory load indicated by higher error rates and longer processing times in sentence comprehension. FMRI studies showed that this effect modulates the neural activity in the left infe- 2.7 | Optimized sets of stimuli rior frontal gyrus, middle frontal gyrus, and temporo-parietal junction (Meltzer, McArdle, Schafer, & Braun, 2010; Newman, Malaia, Seo, & The results of the item analyses were used to identify optimal sets of Cheng, 2013) suggesting the possibility that items with higher items which elicit the most prototypical response in both contrasts 9 THOLEN ET AL. T A B L E 2 Whole brain subject- and item-wise random effects results for Questions ToM > non ToM The results are reported at a voxel-level threshold of p < .001 uncorrected together with an FWE-corrected cluster threshold of p < .05 MNI coordinates H x y z T Cluster Z Subject-wise Group#1 Rectus R 3 57 −18 7.71 7.15 38 Superior medial frontal L −9 54 24 13.72 >8.21 1,185 Superior frontal L −9 54 33 12.34 >8.21 Superior medial frontal R 9 57 21 11.73 >8.21 Inferior frontal gyrus R 54 30 3 6.24 5.92 52 Inferior frontal gyrus L −51 24 6 10.32 >8.21 226 Inferior frontal gyrus L −45 27 −9 9.93 >8.21 Temporal pole R 51 9 −33 14.68 >8.21 121 Temporal pole L −51 3 −30 12 >8.21 79 Postcentral L −54 −6 48 6.05 5.76 13 0 −15 39 8.56 7.81 50 Supplementary motor area Middle cingulate R 6 −24 57 5.37 5.17 10 TPJ-middle temporal R 51 −30 −3 10.61 >8.21 640 TPJ-superior temporal R 48 −18 −9 9.81 >8.21 TPJ-angular gyrus R 63 −45 21 7.76 7.19 Posterior cingulate/precuneus L −6 −51 30 16.38 >8.21 328 TPJ-angular gyrus L −51 −57 24 15.81 >8.21 1,019 TPJ-middle temporal L −48 −30 −3 10.49 >8.21 TPJ-superior temporal L −60 −18 −6 9.53 >8.21 Cuneus L −9 −93 30 5.7 5.45 10 Cuneus R 15 −87 39 6.11 5.82 24 Cerebellum L −27 −81 −36 14.65 >8.21 101 Cerebellum R 27 −78 −33 15.82 >8.21 145 Superior medial frontal L −12 57 36 15.99 >8.21 1,241 Rectus R 3 57 −18 7.97 7.12 Item-wise Group#1 Superior frontal L −6 54 18 14.93 >8.21 Superior medial frontal L −9 30 57 10.34 >8.21 Inferior frontal gyrus R 48 30 −9 6.39 5.91 32 Inferior frontal gyrus L −54 24 6 11.74 >8.21 243 Inferior frontal gyrus L −45 30 −9 10.62 >8.21 Temporal pole R 51 9 −33 16.30 >8.21 125 Temporal pole L −54 24 6 11.74 >8.21 243 Postcentral L 0 −12 39 8.63 7.58 39 Middle cingulate Supplementary motor area R TPJ-middle temporal R 48 −30 −3 10.40 >8.21 332 TPJ-superior temporal R 63 −51 21 6.42 5.93 71 TPJ-angular gyrus R 66 −42 24 5.77 5.40 Posterior cingulate/precuneus L −9 −51 33 12.16 >8.21 253 TPJ-angular gyrus L −51 −54 24 15.64 >8.21 889 TPJ-superior temporal L −60 −15 −9 9.40 >8.21 TPJ-middle temporal L −48 −33 −6 8.82 7.71 (Continues) 10 TABLE 2 THOLEN ET AL. (Continued) MNI coordinates H x y Cluster z T Z 9 −33 16.30 >8.21 125 27 −78 −36 16.88 >8.21 135 −6 57 21 13.83 >8.21 1,008 Cuneus L Cuneus R Cerebellum L 51 Cerebellum R Subject-wise Group#2 Rectus R Superior medial frontal L Superior medial frontal R 6 57 15 12.56 >8.21 Supplementary motor area L −6 15 60 10.91 >8.21 Inferior frontal gyrus R 57 27 0 5.45 5.16 15 Inferior frontal gyrus L −48 27 0 10.69 >8.21 206 Temporal pole R 51 12 −27 14.12 >8.21 147 Temporal pole L −51 9 −30 11.66 >8.21 446 Postcentral L −51 −6 51 5.93 5.56 15 0 −15 39 8.55 7.60 68 446 Middle cingulate TPJ-middle temporal R 48 −27 −6 11.15 >8.21 TPJ-angular gyrus R 66 −45 18 6.52 6.05 TPJ-superior temporal R 66 −36 24 6.42 5.97 Posterior cingulate/precuneus L −6 −51 33 12.45 >8.21 251 TPJ-angular gyrus L −45 −54 24 13.41 >8.21 778 TPJ-middle temporal L −54 −27 −3 9.16 >8.21 TPJ-superior temporal L −63 −15 −15 6.43 5.98 Cuneus L −9 −93 30 5.69 5.36 17 Cuneus R Cerebellum L −27 −78 −36 13.97 >8.21 67 Cerebellum R 30 −78 −36 14.07 >8.21 79 0 51 −21 7.49 6.76 42 Superior medial frontal L −6 54 27 16.11 >8.21 1,213 Superior medial frontal R 6 60 15 12.49 >8.21 Superior medial frontal L −6 45 45 12.04 >8.21 Inferior frontal gyrus R 54 27 0 6.45 5.96 34 Inferior frontal gyrus L −45 30 −6 11.88 >8.21 264 Inferior frontal gyrus L −51 24 6 10.28 >8.21 Temporal pole R 51 12 −33 14.74 >8.21 164 Temporal pole L −48 12 −33 14.32 >8.21 97 Postcentral L −39 −21 21 5.68 5.33 14 Middle cingulate L −3 −12 39 7.18 6.52 45 Supplementary motor area R TPJ-middle temporal R 45 −27 −6 11.24 >8.21 368 TPJ-superior temporal R 60 −54 24 6.79 6.23 TPJ-angular gyrus R 66 −42 18 6.21 5.77 Posterior cingulate/precuneus L −9 −51 33 12.14 >8.21 TPJ-angular gyrus L −48 −57 27 13.33 >8.21 Item-wise Group#2 Rectus 261 653 (Continues) 11 THOLEN ET AL. (Continued) TABLE 2 MNI coordinates z T Cluster H x y TPJ-superior temporal L −48 −33 −3 9.77 >8.21 Z TPJ-middle temporal L −63 −18 −9 7.13 6.49 Cuneus L Cuneus R Cerebellum L −24 −78 −36 12.79 >8.21 76 Cerebellum R 27 −78 −36 13.29 >8.21 89 Abbreviations: FWE, family-wise error; TPJ, temporoparietal junction/supramarginal gyrus. (empathy and ToM), that is, those items that produce the greatest The pattern of results was the same in Sample 2. For emotion activation in the theory of mind and empathy network. More specifi- effects, the valence ratings after emotional (M = −1.00, SD = .70) and cally, we selected the items with the highest beta values in the experi- neutral videos (M = .48, SD = .42), yielded significant differences in mental conditions and the lowest beta values in the control conditions subject- (F[1,129] = 470.18, p < .001) and item-wise analyses for the regions of interest that were defined on the basis of the (F[1,59] = 937.13, p < .001). Performance in ToM (M = 8,471.71 ms, subject-wise random effects analyses of Sample 1. We identified two SD = 1,334.42; M = 67.14%, SD = 11.85, chance level = 33.33%) and sets, one with 48, the other with 40 videos and questions (see Data S2 nonToM questions (M = 8,563.97, SD = 1,329.51 ms; M = 57.43%, and S3). Additionally, to allow for use in longitudinal designs, we identi- SD = 15.30, chance level = 33.33%) resembled Sample 1. RTs did not fied two parallel sets of stimuli, that is, two sets with 48 and two sets differ in subject- (F[1,129] = 2.40, p > .10) and item-wise analyses with 40 videos and questions each (see Data S4 and S5). The sets with (F[1,59] < .81, p > .35), but accuracies were higher in the ToM than in a reduced number of trials still reliably produce activations in the theory the nonToM conditions in both subject- (F(1,129 = 53.96, p < .001) of mind and empathy network, and might therefore particularly be use- and item-wise analyses (F[1,59] = 16.08, p < .001). Again, the subject- ful in clinical studies. The parallel sets are matched regarding on the and item-wise analyses were perfectly in line with each other. extent to which they recruit the respective ROIs as well as to behavioral measures (affect, concern, confidence ratings and response time, accuracy in the questions), linguistic factors (number of words, charac- 3.2 Neuroimaging data | ters, sentences, syllables, predicates, tenses, passives, conjunctives, and complexity) and general characteristics of the stimulus material (gender 3.2.1 | Empathy and age of narrator, movement and velocity, duration of the video). We performed whole brain subject- and item-wise random effects analyses, first on the data set acquired in Sample 1 (see Figure 3a,b 3 RESULTS | and Table 1). The results show activity in the typical empathy related neural network for emotional versus neutral videos, both 3.1 | Behavioral data across subjects and across items. This network includes bilateral AI, ACC/DMPFC, IFG, and dorsal portions of TPJ/SMG. A few regions We first analyzed data from Sample 1. To test for emotion effects, we showed significant activity only in the subject-wise, but not the item- compared the valence ratings after emotional (M = −1.11, SD = .61) and wise analysis, including lingual and middle occipital gyrus, which neutral videos (M = .43, SD = .39), which yielded significant differences in would suggest that the activation is due to features of some specific subject- (F[1,177] = 794.29, p < .001) and item-wise analyses (F stimuli and that it is not generalizable. The pattern of results was the [1,59] = 1,243.56, p < .001). To test, whether performance in ToM and same in Sample 2 (see Figure 3c,d and Table 1). nonToM questions differed, we compared RTs and accuracies in ToM (M = 8,450.58 ms, SD = 1,346.38; M = 64.05%, SD = 12.31, chance level = 33.33%) and nonToM questions (M = 8,490.93, SD = 1,272.54 ms; 3.2.2 | Theory of mind M = 55.05%, SD = 16.11, chance level = 33.33%). In RTs we found no significant differences in subject- (F[1,177] = .63, p > .40) and item-wise ana- As for empathy, we first performed whole brain subject- and item- lyses (F[1,59] < .001, p > .99). Accuracies, in contrast, were higher in the wise random effects analyses on the data set acquired in Sample ToM than in the nonToM conditions in both subject- (F(1,177 = 69.20, 1 (see Figure 4a,b and Table 2). All of the brain regions typically p < .001) and item-wise analyses (F[1,59] = 14.92, p < .001), indicating that involved in ToM were activated for ToM questions compared to fac- the nonToM questions were slightly more difficult. Crucially, the subject- tual reasoning questions, both across subjects and across items. These and item-wise analyses were in line with each other for all measures. regions include bilateral ventral TPJ, STS, temporal poles, precuneus, 12 THOLEN ET AL. F I G U R E 3 Consistency of the empathy related activation patterns (video: emotional > neutral) across item-wise (a) and subject-wise (b) analyses in Sample 1 (N = 178) and in Sample 2 (N = 130) (c, d, respectively). The results show activity in the empathy related network for emotional versus neutral videos, both across subjects and across items. This network includes anterior insula, anterior cingulate cortex/ dorsomedial prefrontal cortex, inferior frontal gyrus and dorsal portions of the temporoparietal junction (supramarginal gyrus) and anterior MPFC. Brain regions active for subject-wise analysis and 3.2.3 | Regression analysis not item-wise analysis include parts of the supplementary motor area, postcentral gyrus, and cuneus. The pattern of results was the same in We performed stepwise forward/backward regression analyses on Sample 2 (see Figure 4c,d and Table 2). the data set acquired in Sample 2 for the empathy (23 ROIs) and ToM 13 THOLEN ET AL. F I G U R E 4 Consistency of the theory of mind related activation patterns (question: ToM > nonToM) across item-wise (a) and subject-wise (b) analyses in Sample 1 (N = 178) and in Sample 2 (N = 130) (c, d, respectively). The results show activity in the theory of mind related network for mental state vs. factual reasoning questions, both across subjects and across items. This network included bilateral ventral temporoparietal junction, superior temporal sulcus, temporal poles, precuneus and anterior medial prefrontal cortex. ToM, Theory of Mind (23 ROIs) contrast. All ROIs were independently defined by the questions, the activity in the right cuneus is positively associated with whole-brain subject-wise analysis of Sample 1. The results show that the number of words and the activity in the supplementary motor for both contrasts condition is almost the only predictor for all regions area could not be explained by either condition or by any other stimu- that were tested (see Table 3). For the ToM contrast, activity in the lus characteristic. For the empathy contrast, three ROIs (left middle left cuneus is positively associated with the number of syllables of the temporal cortex, bilateral middle occipital cortex) could not be 14 TABLE 3 THOLEN ET AL. Results of the regression analyses Coefficients ROI H F p R 2 Predictor β t p Question ToM > non ToM Rectus R 12.034 .001 .093 Condition .304 3.469 .001 Superior medial frontal L 247.114 <.001 .677 Condition .823 15.720 <.001 Superior frontal L 203.853 <.001 .633 Condition .796 14.278 <.001 Superior medial frontal R 147.355 <.001 .555 Condition .745 12.139 <.001 Inferior frontal gyrus R 35.606 <.001 .232 Condition .481 5.967 <.001 Inferior frontal gyrus L 120.307 <.001 .505 Condition .711 10.968 <.001 Inferior frontal gyrus L 147.889 <.001 .556 Condition .746 12.161 <.001 Temporal pole R 223.881 <.001 .655 Condition .809 14.963 <.001 Temporal pole L 159.097 <.001 .574 Condition .758 12.613 <.001 Postcentral L 12.593 .001 .096 Condition .311 3.549 .001 28.239 <.001 .193 Condition .439 5.314 <.001 Middle cingulate Supplementary motor area R None TPJ-middle temporal R 98.618 <.001 .455 Condition .675 9.931 <.001 TPJ-superior temporal R 71.325 <.001 .377 Condition .614 8.445 <.001 TPJ-angular gyrus R 27.034 <.001 .186 Condition .432 5.199 <.001 Posterior cingulate/precuneus L 107.008 <.001 .476 Condition .690 10.344 <.001 TPJ-angular gyrus L 148.454 <.001 .557 Condition .753 12.184 <.001 TPJ-middle temporal L 103.657 <.001 .468 Condition .684 10.181 <.001 TPJ-superior temporal L 59.992 <.001 .337 Condition .581 7.745 <.001 Cuneus L 15.237 <.001 .137 Syllables .354 4.294 <.001 .207 Condition .264 3.203 .002 Cuneus R 16.476 <.001 .123 Words .350 4.059 <.001 Cerebellum L 143.531 <.001 .549 Condition .741 11.980 <.001 Cerebellum R 165.993 <.001 .584 Condition .765 12.884 <.001 Inferior frontal gyrus L 58.877 <.001 .333 Condition .577 7.673 <.001 Middle frontal L 18.694 <.001 .137 Condition .370 4.324 <.001 Anterior insula L 36.633 <.001 .237 Condition .487 6.052 <.001 Superior medial frontal L 59.271 <.001 .334 Condition .578 7.699 <.001 Superior medial frontal R 19.299 <.001 .141 Condition .375 4.393 <.001 Inferior frontal gyrus R 53.658 <.001 .313 Condition .559 7.325 <.001 Middle frontal R 13.047 <.001 .100 Condition .316 3.612 <.001 Anterior insula R 37.890 <.001 .243 Condition .493 6.156 <.001 Ventral striatum R 17.745 <.001 .131 Condition .362 4.212 <.001 Ventral striatum L 33.096 <.001 .219 Condition .468 5.753 <.001 Caudate L 12.161 .001 .093 Condition .306 3.487 .001 Caudate R 10.419 .002 .081 Condition .285 3.228 .002 8.856 .004 .070 Condition .264 2.976 .004 Video emotional > non emotional Middle cingulate Middle temporal cortex L TPJ-angular/supramarginal R 17.682 <.001 .130 Condition .361 4.205 <.001 Middle temporal cortex R 15.180 <.001 .114 Condition .338 3.896 <.001 TPJ-angular/supramarginal L 78.740 <.001 .400 Condition .633 8.874 <.001 12.004 .001 .092 Condition .304 3.465 .001 Precuneus None (Continues) 15 THOLEN ET AL. TABLE 3 (Continued) Coefficients 2 Predictor β .069 Condition .264 2.968 .004 ROI H Lingual gyrus L Middle occipital R Middle occipital L Cerebellum L 45.261 <.001 .277 Condition .527 6.728 <.001 Cerebellum R 39.490 <.001 .251 Condition .501 6.284 <.001 F p 8.807 R .004 t p None None Abbreviations: ROI, regions of interest; ToM, Theory of Mind; TPJ, temporoparietal junction/supramarginal gyrus. associated with any of the predictors. Low-level factors such as lin- people express their emotions, such as gesture, body posture and guistic or general characteristics do not show a major influence movement or the direct observation of emotional situations, for regarding the activation across the empathy or ToM network. instance, of injury. Moreover, the emotional videos in the EmpaToM paradigm are negatively valenced which also precludes generalizing to positive empathy, that is, sharing, or joining others' positive emotions. 4 | DISCUSSION The theory of mind questions aim at an understanding of a person's mental states. Stimuli that target the prediction of a person's behavior The present study aimed to probe the generalizability and reproduc- are not included in this task. In comparison to other tasks in theory of ibility of the neural networks related to empathy and ToM. The results mind, the items cannot generalize to mental state attributions that are demonstrate replicability of subject- and item-wise analyses of both based on action observation as in social animations (e.g., Castelli et al., functions with AI, ACC/DMPFC, IFG, dorsal TPJ/SMG for empathy 2000), or to conceptual knowledge about persons as in trait judg- and ventral TPJ, STG/STS, temporal poles and anterior and posterior ments (e.g., Mitchell et al., 2002). Consequently, the stimuli of the midline regions for ToM, arguing for generalizability of the brain acti- EmpaToM task do not elicit all possible forms of empathic responses vation patterns to the respective stimulus classes. Importantly, the and theory of mind reasoning. A more comprehensive approach to observed activity was not predicted by low-level stimulus characteris- generate a random sample of items that is representative for theory tics such as the number of words or syllables, corroborating the valid- of mind and empathy might be realized by an ecological momentary ity of the activation patterns. Furthermore, we used the item assessment (EMA) (Shiffman, Stone, & Hufford, 2008). This approach information to construct stimulus sets that include the most effective involves repeated sampling of subject's social interactions in real time items to provide the most stable and reproducible results in future over periodic intervals, thereby enabling a high ecological validity. studies employing the EmpaToM paradigm. Lastly, demonstrating Future studies could therefore arrive at stronger conclusions about reproducibility of the findings, all of the above described results were the precise nature of the population of items. replicated in a second, independent participant sample. However, given the amount of videos and questions (240 in total The main result of the present study is the finding of consistent for each type) and the fact that no situation was repeated, there is activation patterns for empathy and ToM across subject- and item- considerable breadth within this conversation type situation. Comply- wise analyses. This consistency demonstrates that the observed net- ing with the call for “item-analyses with a larger and more variable set work activity is not due to idiosyncratic characteristics of (some of) of stimuli” (Dodell-Feder et al., 2011), the present results, thus, the utilized videos and questions, but is generalizable to the entire expand previous reports of consistent activity for reading false-belief populations of stimuli. One critical question here is what exactly (20 items; (Dodell-Feder et al., 2011)) and physical or emotional pain defines these stimulus populations. Just as the generalizability of stories (24 items each; (Bruneau et al., 2013)). subject-wise analyses is limited by how well the participant sample Another critical question pertains to possible confounds due to represents the population (e.g., the age range of 20–55 years in the the, in general, high error rates and the differences in behavioral per- present study precludes conclusions about empathy and ToM formance. The EmpaToM task was explicitly designed to be hard, processing in older adults), generalizing the results to empathy induc- which makes it unique among other theory of mind tasks in functional ing or ToM demanding situations needs to be done with care, consid- neuroscience in adults. In other tasks, for example, false belief or ering the breadth of situations covered in the applied stimuli. The social animations, healthy participants perform typically at 100% or shown videos were created to resemble brief episodes of a (putatively nearly 100% accuracy. The drawback of those measurements is that longer) complex conversation one might have with another person. they are not sensitive to pick up improvements in performance over The ToM questions ask for aspects of the mental state of this person time, whereas the EmpaToM task can (Böckler et al., 2017; Trautwein that were not overtly described. While this enables generalizing to the et al., 2020). Given that participants were less accurate in the nonToM empathic sharing of others' affect as conveyed in language, prosody condition than in the ToM condition, one might think that the differ- and facial expression, it precludes generalizing to other forms in which ential brain activation identified with the contrast (ToM > nonToM) 16 THOLEN ET AL. reflects the effect of general task difficulty. However, we think this is empathy regions. A different approach could also focus on high-level unlikely because of the following reasons: First, prior to the fMRI features, such as whether the ToM questions include true or false measurements, participants were sufficiently familiarized with the task belief, or first or second order reasoning. This approach might, there- and the different conditions. Second, a previous study that validated fore, be of particular importance for future research on social cogni- the EmpaToM task with other measures of empathy and theory of tion identifying areas with specific functions for ToM and empathy mind did not detect any differences in accuracy (Kanske et al., 2015; processing. Exp. 1). In line with these results, subjects' confidence ratings, indicat- Given the recent discussions about difficulties in replicating psy- ing their performance evaluation, were equal across all conditions, chological findings (Lindsay, 2015; Open Science Collaboration, 2015), meaning the participants did not evaluate the nonToM condition as we aimed at testing the stability of our findings in a within-study more difficult than the ToM condition. Finally, further results of this replication. Indeed, the results from a second independent sample cor- study also showed that the theory of mind performance does posi- roborated the conclusions of the first sample, that is, reproducible tively correlate with the activity of the default mode network, neuroimaging results in subject- and item-wise analyses that are inde- whereas areas in the default mode network typically tend to increase pendent in deactivation with increasing task difficulty (e.g., Buckner, 2008). addressing the critique of small sample sizes in many neuroimaging from low-level stimulus characteristics. Furthermore, Activity in a few regions observed in the subject-wise analyses studies (Button et al., 2013), the two samples we assessed were rela- was not present for the item-wise analyses. These include the supple- tively large in comparison to most fMRI investigations (which mostly mentary motor area, postcentral gyrus, and cuneus for ToM and lin- include <40 participants) (David et al., 2013). Thus, the present study gual and middle occipital gyrus for empathy. The results of the lends a high degree of trustworthiness to the observed neural activa- regression analyses could partly explain this difference by showing tion patterns for empathy and ToM. Future studies could of course fur- that activity in the bilateral cuneus was mainly due to the number of ther strengthen this conclusion, for instance by probing the test–retest- syllables and words of the theory of mind and factual reasoning ques- reliability of the results, which has been shown to be highly variable tions and not the condition difference itself. The lack of activation in across brain regions and experimental paradigms (Plichta et al., 2012). the other areas in the item-wise analyses suggests that their subject- The specific activation patterns observed for empathy and ToM wise activation is due to specifics of the videos and questions used, are not only consistent across subject- and item-wise analyses, but implying that they would not be activated by other empathy and ToM also correspond to the typical networks associated with the two func- stimuli. This is in line with the absence of these regions in empathy tions in large-scale meta-analyses (Bzdok et al., 2012; Lamm et al., and ToM meta-analyses (Bzdok et al., 2012; Lamm, Batson, & Decety, 2011; Molenberghs, Johnson, et al., 2016; Schurz et al., 2014). An 2007; Schurz et al., 2014). interesting aspect is that the meta-analyses suggest the existence of FMRI item-analyses allow an item-specific estimate for the neural core networks for empathy (AI, IFG, ACC) and ToM (TPJ, MPFC), acti- activity in a brain region which might serve as an indicator of the vated for all operationalizations of the respective functions, and regions function. As the items can be characterized not only regarding extended networks that include additional regions (for empathy: their experimental category but also regarding multiple other features DMPFC, dorsal TPJ/SMG; for ToM: STG/STS, temporal poles, (e.g., constituent size, or syntactic complexity), it is possible to deter- precuneus), when pooling across the different operationalizations. mine which features best predict the neural response in each brain Assuming that most experimental paradigms capture specific compo- region (see e.g., Bruneau et al., 2013; Dodell-Feder et al., 2011). This nent processes of full-fledged empathy or ToM (Schurz & Perner, allowed us to test whether low-level stimulus characteristics, which 2015), the finding of activation in the extended networks for the might confound the manipulation of empathy and ToM, have contrib- EmpaToM suggests that the task comprehensively captures the com- uted to some of the activations attributed to the experimental condi- plexity of these two social capacities (as is the case for other para- tion. The results of the regression analyses yielded the experimental digms aiming at ecological validity (Wolf, Dziobek, & Heekeren, condition as strongest predictor (by far) for all of the observed activa- 2010)). Furthermore, taking the independence of the neural bases of tion clusters, demonstrating convincingly that none of the low-level empathy and ToM into account (Kanske et al., 2015; Kanske et al., predictors exert major influence on the results. As it is impossible to 2016) and observing the two networks in both types of analyses here, completely match emotional and neutral videos without erasing the corroborates the assumption that empathy and ToM are distinct social difference in emotionality, this is an important, reassuring finding. Also functions, possibly serving specific purposes in social encounters, for with regard to ToM, ruling out the possibility that linguistic character- example, establishing the motivation for cooperation and enhancing istics account for the ToM effects is important, because of the consid- prosocial behavior (Kanske, Bockler, & Singer, 2017; Tusche, Bockler, erable overlap of ToM related activity with regions involved in Kanske, Trautwein, & Singer, 2016). language processing, particularly in the temporal cortex and TPJ The results of the item-analysis made it possible to select those (Friederici, 2011; Molenberghs, Johnson, Henry, & Mattingley, 2016; videos and questions that elicit the most prototypical responses in terms Schurz et al., 2014) and the discussion of the intricate relationship of of activation in the neural networks that meta-analyses have associated ToM and language processing (de Villiers & Pyers, 2002; Ferstl & von with empathy and ToM (Bzdok et al., 2012; Lamm et al., 2011; Schurz Cramon, 2002). The results of the regression analyses showed that et al., 2014) and in behavior. To avoid circularity, we selected the stimuli low-level features do not explain the neural response in the ToM or based on Sample 1 and tested them in the independent Sample 17 THOLEN ET AL. 2, showing strong and consistent activation patterns across the two sam- ENDNOTES ples. This way, we could form several optimized stimulus sets for future 1 Please note that sample 1 in the present study is based on the same participant sample as described in Kanske et al. (2015). Importantly however, the analyses and results described in the present study are novel and have not been described or shown elsewhere. 2 In contrast to empathy, compassion is defined as feelings of warmth and care, including the motivation to improve the other's wellbeing (Singer & Klimecki, 2014). usage in specific settings. In particular, the short versions of the task enable testing special populations with reduced attention spans, for instance, in psychopathology (Preckel, Kanske, Singer, Paulus, & Krach, 2016) or assessing multiple tasks, including the EmpaToM, within one session, for instance, to predict social behavior based on empathic and ToM capabilities (Tusche et al., 2016). The optimized parallel sets could be applied in longitudinal designs, including intervention research. To conclude, by replicating the empathy and ToM related neural networks across item- and subject-wise analyses and demonstrating their independence from low-level stimulus characteristics, the present results contribute methodologically to the social neuroscience literature and add to our understanding of these social capacities as distinct functions. ACKNOWLEDGMENTS This study forms part of the ReSource Project, headed by Tania Singer. Data for this project were collected between 2013 and 2016 at the former Department of Social Neuroscience at the Max Planck Institute for Human Cognitive and Brain Sciences Leipzig. Tania Singer (Principal Investigator) received funding for the ReSource Project from the European Research Council (ERC) under the European Community's Seventh Framework Program (FP7/2007–2013) ERC grant agreement number 205557. M.G.T. is supported by the Austrian Science Fund's Doctoral College “Imaging the Mind” (FWF-W1233). P.K. is supported by German Federal Ministry of Education and Research within the ASD-Net (BMBF FKZ 01EE1409A), the German Research Council (Heinz Maier-Leibnitz Prize KA 4412/1-1) and Die Junge Akademie at the Berlin-Brandenburg Academy of Sciences and Humanities and the German National Academy of Sciences Leopoldina. We wish to thank the entire ReSource support team for help in the organization of the study, in particular, we thank Nicole Pampus, Manuela Hoffmann and Sylvie Neubert for help with the data acquisition. The data of this study are available from the authors upon reasonable request. AUTHOR CONTRIBUTIONS Conceptualization, all authors; Data curation, F.M.T., A.B.R., P.K.; Formal analysis, M.G.T., P.K., F.M.T.; Funding Acquisition, T.S.; Investigation, F.M.T., A.B.R., P.K.; Methodology, F.M.T., A.B.R., P.K.; Project administration, T.S., F.M.T., A.B.R., P.K.; Resources, T.S.; Supervision, T.S., P.K.; Validation, M.G.T.; Visualization, M.G.T.; Writing—original draft preparation, P.K., M.G.T.; Writing—review and editing, all authors. DATA AVAI LAB ILITY S TATEMENT The data of this study are available from the authors upon reasonable request. ORCID Matthias G. 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