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subdivisions and mapping into a spatial reference system. *Neuroimage* 13, 684–701.

biological-motion cues. *Curr. Dir. Psychol. Sci.* 15, 136–140.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 15 December 2012; accepted: 08 March 2013; published online: 27 March 2013.*

*Citation: Juan E, Frum C, Bianchi-Demicheli F, Wang Y-W, Lewis JW and Cacioppo S (2013) Beyond human intentions and emotions. Front. Hum. Neurosci. 7:99. doi: 10.3389/fnhum. 2013.00099*

*Copyright © 2013 Juan, Frum, Bianchi-Demicheli, Wang, Lewis and Cacioppo. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.*

# **APPENDIX**

**Table A1 | List of fMRI studies on intention understanding.**

#### **Table A2 | List of fMRI studies on passionate love.**

#### **Table A3 | Intention understanding activation peaks.**

#### **Table A4 | Passionate love activations peaks.**

*(Continued)*

#### **Table A4 | Continued**

*(Continued)*

#### **Table A4 | Continued**

#### **Table A5 | Common activation peaks between intention understanding and passionate love studies.**

*(Continued)*

#### **Table A5 | Continued**

*Note: Activations of 10 voxels or above are reported here. All brain areas reported are significant at p* < *0.001, corrected.

# Your emotion or mine: labeling feelings alters emotional face perception—an ERP study on automatic and intentional affect labeling

#### *Cornelia Herbert 1 \*†, Anca Sfärlea1 and Terry Blumenthal 2*

*1 Department of Psychology, University of Würzburg, Würzburg, Germany*

*2 Department of Psychology, Wake Forest University, Winston-Salem, NC, USA*

#### *Edited by:*

*Maria Ruz, Universidad de Granada, Spain*

#### *Reviewed by:*

*Stephan Moratti, Universidad Complutense de Madrid, Spain Mareike Bayer, Georg-August-Universität Göttingen, Germany*

#### *\*Correspondence:*

*Cornelia Herbert, Department of Psychology, University of Würzburg, 97070 Würzburg, Germany e-mail<EMAIL_ADDRESS>
#### *†Present address:*

*Cornelia Herbert, Institute of Psychology, German Sport University Cologne, 50933 Cologne, Germany*

Empirical evidence suggests that words are powerful regulators of emotion processing. Although a number of studies have used words as contextual cues for emotion processing, the role of *what* is being labeled by the words (i.e., one's own emotion as compared to the emotion expressed by the sender) is poorly understood. The present study reports results from two experiments which used ERP methodology to evaluate the impact of emotional faces and self- vs. sender-related emotional pronoun-noun pairs (e.g., my fear vs. his fear) as cues for emotional face processing. The influence of self- and sender-related cues on the processing of fearful, angry and happy faces was investigated in two contexts: an automatic (experiment 1) and intentional affect labeling task (experiment 2), along with control conditions of passive face processing. ERP patterns varied as a function of the label's reference (self vs. sender) and the intentionality of the labeling task (experiment 1 vs. experiment 2). In experiment 1, self-related labels increased the motivational relevance of the emotional faces in the time-window of the EPN component. Processing of sender-related labels improved emotion recognition specifically for fearful faces in the N170 time-window. Spontaneous processing of affective labels modulated later stages of face processing as well. Amplitudes of the late positive potential (LPP) were reduced for fearful, happy, and angry faces relative to the control condition of passive viewing. During intentional regulation (experiment 2) amplitudes of the LPP were enhanced for emotional faces when subjects used the self-related emotion labels to label their own emotion during face processing, and they rated the faces as higher in arousal than the emotional faces that had been presented in the "label sender's emotion" condition or the passive viewing condition. The present results argue in favor of a differentiated view of language-as-context for emotion processing.

**Keywords: emotion regulation, language-as-context, affect labeling, face processing, event-related brain potentials, social context, social cognition, perspective taking**

# **INTRODUCTION**

Emotion perception in oneself and others is an important aspect of successful social interaction. It is important for emotional selfregulation, and is often compromised in affective and mental disorders such as autism, sociopathy, schizophrenia, and depression, as well as in disorders associated with emotional blindness (alexithymia).

Narrative writing has been shown to have positive effects on emotional self-regulation (Hayes and Feldman, 2004) and individual well-being, possibly by increasing self-referential processing and reappraisal of emotionally challenging events from different perspectives (Seih et al., 2011; for an overview: Pennebaker and Chung, 2011). There is strong evidence from cognitive emotion regulation research supporting reappraisal as one of the most effective cognitive strategies for intentional downregulation of negative feelings experienced in real life situations, or in the laboratory during viewing of emotion inducing stimuli including pictures, faces, or films (Gross, 2002; John and Gross, 2004; Blechert et al., 2012). Similarly, several studies investigating the neural correlates of self-referential processing suggest that appraising emotional stimuli in terms of their personal relevance can lead to adaptive emotion processing (e.g., Ochsner et al., 2004; Northoff et al., 2006; Moran et al., 2009).

An important key component of narrative writing is affect labeling. During narrative writing people learn to "put their feelings into words" and to express or reframe them verbally. The success of narrative writing suggests that words can be powerful regulators of emotions. Empirical support for this suggestion comes from neurophysiological research investigating brain responses, in addition to subjective indicators of emotion processing in participants exposed to affective labels or verbal descriptions while viewing emotional stimuli. For example, Foti and Hajcak (2008), and Macnamara et al. (2009) recorded event-related brain potentials (ERPs) from the electroencephalogram (EEG). Participants viewed unpleasant pictures which were preceded by neutral or negative verbal sentences. Processing of neutrally framed unpleasant pictures decreased ratings of picture emotionality, self-reported negative affect, and amplitudes of the late positive potential (LPP). The LPP often shows larger amplitudes during processing of emotional stimuli than during the processing of neutral stimuli (Olofsson et al., 2008). Attenuated LPP amplitudes to verbally framed unpleasant pictures therefore suggest a decrease in the depth of emotional stimulus encoding.

Verbal reframing effects are not restricted to neutral cues or sentences. Hariri et al. (2000) and Lieberman et al. (2007) scanned brain activity patterns by means of functional imaging while individuals viewed faces and affective labels. Affective labels consisted of simple words which were presented together with unpleasant faces or socially stereotyped faces, including black and white people. The task was to indicate which of the words fit best with the emotion depicted in the face. Control conditions included passive stimulus viewing (without labels), non-verbal affect matching (i.e., verbal labels were replaced by faces), and shape matching of simple geometric figures. Affect labeling with words as cues was the only condition that decreased amygdala activation significantly. It also enhanced activity in the right ventrolateral cortex (Lieberman et al., 2007), an important prefrontal control area involved in a variety of tasks requiring executive control of attention, response-inhibition, and intentional emotion regulation (Cohen et al., 2013). Consistent with these neurophysiological observations affect labeling with words as cues decreased peripheral-physiologic responses of emotional arousal and, in another study, reduced self-reported negative affect and distress in response to unpleasant emotional pictures to a similar extent as did intentional emotion regulation by means of reappraisal (Lieberman et al., 2011). Along these lines, developmental research has demonstrated a relationship between language impairments in childhood and diminished self-control (Izard, 2001), and poor emotional competence and emotion regulation abilities during adulthood (Fujiki et al., 2004). Furthermore, reducing the accessibility of emotion words experimentally (via a semantic satiation procedure) has been shown to decrease emotion recognition accuracy during face processing in healthy subjects (Lindquist et al., 2006; Gendron et al., 2012), a finding that matches with clinical observations of decreased face recognition abilities in patients with aphasia, who experience extreme difficulties in naming words (Katz, 1980).

Together, all these findings support the theoretical view that language provides a conceptual context for emotion processing (Barrett et al., 2007, 2011). Specifically, they suggest that words as affective labels can improve emotion recognition and at the same time regulate emotion processing much like intentional emotion regulation strategies, lending support for the idea of an incidental emotion regulation process underlying language processing in emotional contexts (Lieberman et al., 2011). Therefore, affect labeling has been suggested as an additional technique for emotion regulation in clinical and therapeutic settings (Tabibnia et al., 2008; Lieberman, 2011; Kircanski et al., 2012), especially in patients who, due to the severity of their symptoms, are less sensitive to more complex cognitive behavioral interventions that often require that people are able and willing to reflect in detail upon their feelings and the logic of their maladaptive appraisals.

Two questions have not yet been answered: (1) are words (affective labels) equally effective in modulating emotion recognition and emotion processing when those labels directly relate to the participants' own emotion as compared to the emotion expressed in the sender's face? (2) would these effects be the same during intentional regulation as compared to automatic or uninstructed regulation? In other words, does it matter "what" is going to be labeled by the words (own emotion vs. emotion conveyed by the sender) and "how" (automatically or intentionally) this is done? One way to answer these questions would be to expose individuals to faces expressing discrete emotions, such as fear, anger, or happiness, and to instruct them to find words which best describe either their own emotions, or the emotion expressed by the sender's face. This would involve participants correctly identifying their own emotions and finding words to express them, and these abilities vary across individuals (Barrett et al., 2011). The appraisal of cortical and peripheral physiological changes associated with emotion processing are often limited to more basal emotional dimensions of perceived pleasantness (good-bad, likedislike) or arousal intensity (calming-arousing). However, ERP methodology may be useful in identifying cue-driven emotion processing and intentional emotion regulation, using as affective cues pronoun-noun pairs that are either self-or sender-related.

Research on the processing of pronouns has shown that readers adopt a first person perspective (1PP) during reading of selfrelated pronouns and a third-person perspective (3PP) during reading of other-related pronouns (Borghi et al., 2004; Ruby and Decety, 2004; Brunye et al., 2009). Moreover, EEG studies have reported emotional pronoun-noun pairs describing the reader's own emotion (e.g., my fear, my fun) to be processed more deeply than pronoun-noun pairs making a reference to the emotion of others (e.g., his fear, his fun) or emotion words that contain no reference at all (e.g., the fun, the fear) (Herbert et al., 2011a,b). Further, in an imaging study, reading of self-related emotional pronoun-noun pairs selectively enhanced activity in medial prefrontal brain structures involved in the processing of one's own feelings (Herbert et al., 2011c), providing neurophysiological evidence that people spontaneously discriminate between the self and the other during reading (see also Walla et al., 2008; Zhou et al., 2010; Shi et al., 2011).

Self-related emotion labels (e.g., my fear, my happiness) provide a window to one's own emotions, linking one's own sensations with the emotion expressed by the sender's face. Sender-related labels (e.g., his/her fear, happiness) make a direct reference to the emotion expressed by the sender. Therefore, processing of self- and sender-related labels should have differential effects on how emotional information conveyed by a stimulus such as a face is decoded, and also which emotional reactions and feelings are experienced in return. While labels depicting one's own emotions should make facial expression more relevant to the self, thereby increasing attention capture by emotional faces, sender-related labels might specifically improve decoding of structural information from the face. As explained in more detail below, this should be accompanied by different modulation patterns of early brain potentials in the EEG, such as the face specific N170 and the early posterior negativity (EPN). In addition, self- and sender-related labels both contribute information that goes beyond the affective information available from the face. Both labels contain information required for appraising the meaning of the emotion expressed in the face. Making this information available to the subject could reduce processing resources required for appraising the emotional meaning of the faces. In the EEG, this should be reflected by modulation of late ERP components such as the LPP.

The aim of the present EEG-ERP study was to shed light on these questions by investigating how processing of self- and sender-related affective labels modulates emotional face processing across different stages of processing, i.e., from initial processing of emotional stimulus features to the more fine grained, in depth analysis of the emotional content of the presented faces. Two separate experiments were conducted. The first experiment investigated emotional face processing during an "unintentional labeling" task. This was done to investigate spontaneous effects of self- and sender-related affective labels on emotional face processing. The second experiment used an active emotion regulation context in which self- and sender-related affective labels served as cues for emotion regulation and faces as targets of emotion regulation. The active emotion regulation context was chosen to separate unintentional from intentional affect labeling processes, and to explore whether emotion regulation with affective labels and "unintentional" processing of affective labels would differentially modulate event-related brain potentials (ERPs) to emotional faces. Moreover, the active emotion regulation context will allow us to investigate whether emotion regulation with selfand sender-related affective labels will facilitate self-referential processing and cognitive reappraisal of emotional faces from different perspectives (1PP vs. 3PP). Specifically the active process of labeling and the intention to use these labels for emotion regulation should allow a person to get in touch with his/her feelings when self-related affective cues are being presented and to distance him- herself from the own feelings when faces are cued with sender-related affective cues.

By comparing ERPs elicited by the faces during the affective label conditions and during a control condition of passive face viewing it is possible to precisely determine the particular stages at which processing of self- and sender-related emotion labels impact emotional face processing, in which direction these effects will occur (up- vs. down-regulation), and if effects vary across the two processing conditions of spontaneous, unintentional processing (experiment 1) and active, intentional regulation (experiment 2). ERPs of interest included early and late ERP components, the P1, the face specific N170, the early posterior negativity potential (EPN), and the late positive potential (LPP) or slow wave (SW). These cortical components are thought to indicate stimulusdriven as well as sustained processing of emotional stimuli. The P1 reflects very early stimulus feature processing while the N170 reflects increased structural encoding and the EPN facilitated capture of attentional resources by stimuli of emotional relevance (Bentin and Deouell, 2000; Junghöfer et al., 2001; Schupp et al., 2004; Blau et al., 2007). Amplitude modulations of the LPP are thought to index sustained processing and encoding of emotional stimuli in functionally coupled, fronto-parietal brain networks (Moratti et al., 2011).

Furthermore, participants' subjective appraisals of the presented stimuli, their mood state, and their emotion perception and empathic abilities were assessed via self-report. As additional exploratory outcome measures, subjective measures could provide information about potential variables that mediate affect labeling and face processing.

# **METHODS: EXPERIMENT 1 AND EXPERIMENT 2 SUBJECTS**

Twenty-one right-handed adults (16 females, 5 males), all native speakers of German, with a mean age of 22 years (*SD* = 3.1 years) participated in experiment 1. Seventeen right-handed adults (12 females, 5 males), all native speakers of German, with a mean age of 22 years (*SD* = 2.2 years) participated in experiment 2. Participants were recruited via the posting board of the University of Würzburg and received course credit or financial reimbursement of 15C for participation. Exclusion criteria for participation were current or previous psychiatric, neurological, or somatic diseases, as well as medication for any of these. Participants reported normal audition, and normal or corrected to normal vision. Both experiments were conducted in accordance with the Declaration of Helsinki and methods were approved by the ethical committee of the German Psychological Society (http://www.dgps.de/en/).

Participants of experiment 1 and of experiment 2 had comparable scores on the Beck Depression Inventory (Hautzinger et al., 1994) (experiment 1: *M* = 5.3, *SD* = 3.1; experiment 2: *M* = 3.4, *SD* = 2.6). Both groups scored normally on the trait (experiment 1: *M* = 44.7, *SD* = 5.1; experiment 2: *M* = 45.5, *SD* = 4.3) and state (experiment 1: *M* = 39.6, *SD* = 4.5; experiment 2: *M* = 42.3, *SD* = 5.7) scales of the Spielberger State-Trait Anxiety Inventory (STAI, Laux et al., 1981). They reported more positive affect (experiment 1: *M* = 37.0, *SD* = 6.1; experiment 2: *M* = 38.8, *SD* = 5.9) than negative affect (experiment 1: *M* = 19.4, *SD* = 6.2; experiment 2: *M* = 17.8, *SD* = 4.7) on the PANAS mood assessment scales (Watson et al., 1988) and they did not differ in empathy (experiment 1: *M* = 14.19, *SD* = 2.2; experiment 2: *M* = 15.1, *SD* = 1.1), perspective taking (experiment 1: *M* = 14.3, *SD* = 2.3; experiment 2: *M* = 15, *SD* = 2.2), emotional intelligence (experiment 1: *M* = 117.9, *SD* = 7.1; experiment 2: *M* = 120.1, *SD* = 7.5), or emotional blindness (experiment 1: *M* = 50.6, *SD* = 6.7; experiment 2: *M* = 47.9, *SD* = 7.3). They did also not differ in self-esteem (experiment 1: *M* = 39.4, *SD* = 2.8; experiment 2: *M* = 39.2, *SD* = 3.1). Empathy and perspective taking were measured with the Saarbrückener Personality Questionnaire (Paulus, 2009), the German Version of the Trait Emotional Intelligence Questionnaire (TEIQue) was used for emotional intelligence (Freudenthaler et al., 2008) and emotional blindness was assessed with the German Version of the Toronto Alexithymia Scale (TAS-20, Bagby et al., 1994). Selfesteem was measured via the Frankfurter Self Concept Scale (FSSW, Deusinger, 1986). Habitual emotion regulation strategies including reappraisal or suppression were also assessed with a German translation of the emotion regulation questionnaire (ERQ, Gross and John, 2003). Again both groups reported comparable scores on the ERQ for using either reappraisal (experiment 1: *M* = 5.0, *SD* = 0.7; experiment 2: *M* = 4.7, *SD* = 1.2) or suppression (experiment 1: *M* = 3.6, *SD* = 1.0; experiment 2: *M* = 3.4, *SD* = 0.7) as an emotion regulation strategy in daily life.

#### **STIMULI (EXPERIMENT 1 AND EXPERIMENT 2)**

Faces (fearful, angry, happy, and neutral) were taken from the Karolinska Directed Emotional Face database (KDEF, Lundqvist et al., 1998). Affective labels were sixty pronoun-noun pairs. Twenty of these pairs were related to fear, 20 to anger, and 20 to happiness. Stimuli were presented in six randomized blocks, three self-related blocks and three sender-related blocks. Each block contained twenty faces (half male/half female characters) from one emotion category (fear, anger, or happiness) and twenty labels. Labels matched the emotion depicted in the face and were related to the participants' own emotions (e.g., my fear, my pleasure, my anger) in the self-related blocks and to the emotion of the sender's face (e.g., his/her fear, his/her pleasure, his/her anger) in the sender-related blocks. Thus, each block consisted of 20 emotion congruent trials. Labels and faces were presented for 1.5 s each, and separated by a fixation cross of 500 ms duration. Trials were separated by an inter-trial interval lasting about 1 s and blocks were separated by a fixation cross indicating the beginning of a new block.

Blocks of passive viewing, in which 20 faces (half male/half female characters) of each emotion category as well as 20 neutral faces were preceded by random letter strings instead of affective labels, served as control condition (see **Figure 1**). Akin to the experimental condition, each trial consisted of 1.5 s letter presentation, a 500 ms fixation-cross period, and a picture viewing period of 1.5 s. Trials were separated by inter-trial intervals of about 1 s and blocks by inter-block intervals. Block order was randomized. Emotional faces were randomly assigned to the blocks, such that none of the faces was repeated across blocks (self vs. sender) and conditions (control vs. affective label conditions). In line with previous studies, the passive viewing conditions were always presented first to guarantee a neutral baseline of emotional face processing. Also in line with previous studies, neutral faces were presented in the control condition, only (e.g., Hajcak and Nieuwenhuis, 2006; Hajcak et al., 2006; Blechert et al., 2012).

Affective labels and letter strings were presented in black letters (font "Times"; size = 40) centered on a white background of a 19 inch computer monitor. Faces were presented in color, centered on the computer screen. Stimuli were presented at a visual angle of 4◦. Experimental runs were controlled by Presentation software (Neurobehavioral Systems Inc.). An overview of the experimental design is shown in **Figure 1**.

# **PROCEDURE**

After arrival at the laboratory, participants were informed in detail about the EEG procedure; they were questioned about their handedness and health, and electrodes for EEG recording were attached before they received the following instructions.

# **EXPERIMENT 1**

In the control condition, participants were told to view the faces attentively without paying specific attention to the preceding letter strings. In the following "affective label" condition, they were told that again a series of faces would be presented, each of which would be preceded by a verbal cue describing either their own emotion to the face or the emotion of the person presented in the face. Participants were asked to attend to the stimulus pairs (cue and face), but received no instruction to appraise the stimuli in a specific way nor to intentionally regulate their emotions during face processing. Prior to the start of the experimental recording,

participants were given practice trials to familiarize them with the task. After the experimental recording, participants were asked to rate the stimuli for valence and arousal on a nine-point paperpencil version of the Self Assessment Manikin (Bradley and Lang, 1994), they were questioned about their experience during picture viewing, and they filled in the additional questionnaires for perspective taking, emphatic concerns, emotion perception abilities, and habitual emotion regulation strategies. Finally, they were debriefed in detail about the purpose of the experiment.

#### **EXPERIMENT 2**

Experiment 2 used the same stimuli and experimental set-up as experiment 1. However, in experiment 2, participants were asked to control their emotions during face processing by means of the cues presented prior to each face. When cues were self-related to their own emotion they should try to use the cues to get in touch with their feelings and label them when looking at the faces ("label own emotion" blocks). When cues were related to the emotion of the sender's face ("label sender's emotion" blocks) they should try to use the cues to distance themselves from their own emotions by labeling the emotion of the sender's face when looking at the faces. Participants received practice trials prior to the start of the experimental recording sessions to ensure that they understood the task and to familiarize them with cue-driven intentional regulation. In addition, they were asked to indicate their regulation success as well as their present feelings immediately after each regulation block on nine-point Likert scales. After the experiment, they were questioned in more detail about their regulation experiences. They rated the stimuli for valence and arousal on the Self-Assessment Manikin (Bradley and Lang, 1994). Afterwards, they filled in the questionnaires on perspective taking, emphatic concerns, emotion perception and habitual emotion regulation strategies, and were debriefed in detail about the purpose of the experiment.

# **ELECTROPHYSIOLOGICAL RECORDINGS (EXPERIMENT 1 AND EXPERIMENT 2)**

Electrophysiological data was recorded from 32 active electrodes using the actiCap system (Brain Products GmBH). For all electrodes impedance was kept below 10 kOhm. Raw EEG data were sampled at a rate of 500 Hz with FCz as reference. Off-line, EEG signals were digitally re-referenced to an average reference passing from 0.01 to 30 Hz, and corrected for eye-movement artifacts using the traditional algorithm by Gratton et al. (1983), implemented in the Analyzer2 software package (BrainProducts GMBH). Further artifacts due to head- or body-movements were rejected via a semi-automated artifact rejection algorithm. In total, this resulted in a loss of about 2–3 trails per block, leaving about 17 trials per block for averaging. Although signal to noise ratio increases with the number of averaged trials, recent research (Moran et al., 2013) has shown that differences in ERPs between experimental conditions can be reliably detected after a few averaged trials. This has been shown for late ERP components, which are more susceptible to background noise than early ERP components. Artifact-free EEG data were segmented from 500 ms before until 1500 ms after onset of the target faces using the 100 ms interval before target face onset for baseline correction. Baselines corrected epochs were then averaged for each experimental condition (affective label, control) and valence category (fear, anger, happy, neutral). Electrodes and time-windows for amplitude scoring of early (P1, N170, EPN) and late event-related brain potentials (LPP/slow wave) were determined in line with the previous literature on emotional face processing and by visual inspection of the grand mean average waveforms.

Amplitudes of early ERPs (P1, N170, and EPN) were analyzed at left and right posterior electrodes (O1, O2, PO10, PO9, P8, P7, TP10, TP9) from 80 to 120 ms (P1), from 140 to 180 ms (N170), and from 200 to 400 ms (EPN) post target face onset. Amplitudes of the late positive potential (LPP) were analyzed at parietal electrodes (P3, P4, Pz, P7, P8) in a time-window from 400 to 600 ms post target face onset. In experiment 2, intentional emotion regulation elicited a more pronounced cortical positivity (slow wave) over parietal electrodes compared to unintentional affect labeling. Akin to the LPP, amplitudes of this slow wave were analyzed at parietal electrodes (P3, P4, Pz, P7, P8), starting in a time-window from 400 to 800 ms from post-target face onset. ERP amplitudes were scored at each electrode as the averaged amplitude (in µV) in the respective time-window.

In addition, latencies of ERP amplitudes were defined via a semi-automatic peak detection algorithm of the Analyzer2 software package (BrainProducts GMBH). Latencies were analyzed in both experiments to determine if processing of affective labels had an influence on the speed of face processing.

Event-related potentials (P1, N1/EPN, and LPP) elicited during the processing of self- and sender-related affective labels were also analyzed from the epochs from 500 ms before until 1000 ms after word onset. The 100 ms interval before word onset was used for baseline correction. Amplitudes of early ERPs (P1, N1, and EPN) were analyzed at left and right posterior electrodes (O1, O2, PO10, PO9, P8, P7, TP10, TP9) from 80 to 120 ms (P1), 120 to 180 (N1), and from 200 to 400 ms (EPN) post word onset; amplitudes of the LPP were analyzed from 400 to 600 ms post word onset at the parietal electrodes P3, P4, and Pz.

#### **DATA REDUCTION AND STATISTICAL ANALYSIS**

# **ELECTROPHYSIOLOGICAL DATA—EXPERIMENT 1 AND EXPERIMENT 2** *Affective labels*

ERPs (P1, N1, EPN, LPP) elicited during the presentation of affective labels were analyzed with repeated measures analyses of variance (ANOVAs), which contained the factors *emotion (fearful, happy, angry)*, *label (self vs. sender)*, and *electrode location* as within-subject factors.

#### *Faces*

For faces the ANOVAs contained the factors *emotion (fearful, happy, angry)*, *condition (self vs. sender vs. passive viewing)*, and *electrode location* as within-subject factors. We also evaluated whether processing of emotional faces elicited larger ERP amplitudes than processing of neutral faces during passive viewing. The ANOVAs for the passive viewing comparisons included the factors *valence* (*fearful, happy, angry and neutral*) and *electrode location* as within-subject factors and were conducted for each ERP component of interest (P1, N170, EPN, and LPP/slow wave).

Where appropriate, p-values were adjusted according to Greenhouse and Geisser (1959). Significant main effects were decomposed by simple contrast test and results from these comparisons are reported uncorrected at *p* < 0.05. Interactions were followed up with planned comparisons within a row or column of the design matrix, to decrease the total number of comparisons by avoiding those that would not make theoretical sense. For example, an interaction between *condition* and *emotion* might involve contrasting fearfulself vs. fearfulother, but would not involve a contrast between fearfulself vs. angryother, since this would involve a confound across levels of both variables. Again, results are reported *p* < 0.05, uncorrected.

#### **BEHAVIORAL DATA – EXPERIMENT 1 AND EXPERIMENT 2 SUBJECTIVE RATINGS**

Ratings were analyzed with repeated measures analyses of variance (ANOVAs). For faces, the ANOVAs contained the factors *emotion* (*fearful, happy, angry*) and *condition (passive viewing vs. self vs. sender)* as within-subject factors. Similar to the analysis of the ERPs, separate ANOVAs were calculated to consider differences in ratings between emotional and neutral faces. Ratings of the affective labels were analyzed with ANOVAs containing the factors *emotion* (*fearful, happy, and angry*) and *label (self vs. sender)* as within-subject factors. Self-report data including reports about changes in mood and regulation success given after each regulation block were also analyzed in separate ANOVAs, each containing the factors *emotion* (*fearful, happy, and angry*) and *label (self vs. sender)* as within-subject factors.

#### **CORRELATIONAL ANALYSIS: ERPs AND SELF-REPORT MEASURES**

In both experiments, ERPs (P1, N170, EPN, and LPP/slow wave) were correlated with participants' self-report measures. Self-report measures of interest included positive and negative affect, depression, state and trait anxiety, and empathic concerns, perspective taking, self-esteem, and the ability or inability to describe and identify feelings as measured with the subscales of the Toronto Alexithymia Scale and the TEIQue emotional intelligence questionnaire. Although these analyses are exploratory in the present study, a mediating role of these variables could theoretically be expected based on clinical findings and the literature on individual differences (e.g., Herbert et al., 2011a,d; Moratti et al., 2011).

#### **RESULTS**

#### **EXPERIMENT 1—LABELING WITHOUT INTENTIONAL REGULATION INSTRUCTION**

#### *Electrophysiological data*

*Affective Labels.* Processing of self- and sender-related labels did not differ in the P1, N1, and EPN time-windows. In the timewindow of the LPP, amplitudes were more pronounced for selfrelated than for sender-related affective labels. The main factor *label* was significant, *F*(1, 20) = 7.08, *p* = 0.02.

*Faces. Emotional faces: self vs. sender vs. control (passive viewing).* Emotional face processing differed significantly when preceded by affective labels as compared to during passive viewing. A first difference was observed in the P1 time-window and is indicated by the main factor *condition*, *F*(2, 40) = 4.0, *p* = 0.027. P1 amplitudes were significantly more positive for fearful, angry, and happy faces during the "affective label" conditions compared to during passive viewing. During passive viewing, P1 amplitudes did not differ significantly between fearful, angry, happy, and neutral faces.

N170 amplitudes showed a main effect of *emotion*, *F*(2, 40) = 6.4, *p* = 0.003: Fearful faces elicited significantly larger N170 amplitudes than angry faces, *F*(1, 20) = 14.1, *p* = 0.001. In addition, the interaction of the factors *emotion* and *condition* was significant, *F*(4, 80) = 3.0, *p* = 0.05: fearful faces elicited significantly larger N170 amplitudes when preceded by labels describing the sender's emotion as compared to when preceded by labels describing the viewer's own emotion, *F*(1, 20) = 4.1, *p* = 0.05, as well as compared to when presented without any labels (control condition), *F*(1, 20) = 12.8, *p* = 0.002. For emotional faces preceded by labels describing the viewer's own emotion, N170 amplitudes did not differ from passive viewing of emotional faces. During passive viewing, the factor *valence* was also significant, *F*(3, 60) = 4.64, *p* = 0.02: fearful faces elicited significantly larger N170 effects in comparison to neutral faces, *F*(1, 20) = 7.03, *p* = 0.02.

EPN amplitudes showed a significant main effect of *condition*, *F*(2, 40) = 3.6, *p* = 0.05. EPN amplitudes were significantly more pronounced for fearful, angry, and happy faces when they were preceded by self-related labels than when they were presented without any labels in the control condition, *F*(1, 20) = 4.48, *p* = 0.046. Cueing fearful, angry, and happy faces with emotion words describing the emotion of the sender's face did not change EPN amplitudes relative to when presented without any labels (control condition). EPN amplitudes did also not differ significantly between emotional and neutral faces during passive viewing. The factor *valence* was not significant, *F*(3, 60) = 1.2, *p* = 0.32.

The amplitude of the LPP was significantly modulated by the factor *condition*, *F*(2, 40) = 3.8, *p* = 0.05. LPP amplitudes were attenuated for emotional faces regardless of their valence (fearful, angry, and happy) when preceded by self-related and senderrelated affective labels as compared to when preceded by letter strings (control condition). During the control condition, fearful, *F*(1, 20) = 14.3, *p* = 0.001, as well as happy faces, *F*(1, 20) = 11.5, *p* = 0.002, elicited significantly larger LPP amplitudes compared to neutral faces. The factor *valence* was significant, *F*(3, 60) = 4.86, *p* = 0.004.

In contrast to amplitude measures, cueing faces with affective labels had no significant effects on ERP latencies.

ERP results are summarized in **Figure 2**.

#### *Behavioral data*

*Ratings.* Fearful and angry faces were rated as significantly more negative in valence compared to happy faces, regardless of whether faces were cued with affective labels or not, *F*(2, 40) = 201.9, *p* < 0.01. Ratings of emotional faces did not differ in terms of arousal, *F*(2, 40) = 2.7, *p* = 0.08, but emotional faces were rated as higher in arousal than neutral faces. This was true for faces shown in the control condition, *F*(3, 60) = 26.7, *p* < 0.01, and for faces shown in the "affective label" conditions,

*self* : *F*(3, 60) = 26.2, *p* < 0.01, and *sender*: *F*(3, 60) = 29.9, *p* < 0.01.

N170, and EPN, respectively). Upper right panel **(B)** displays amplitudes in the

Self-related labels were rated as higher in arousal and as more relevant to the self compared to sender-related labels, *arousal: F*(1, 20) = 11.1, *p* < 0.01, *self-relevance*: *F*(1, 20) = 37.0, *p* < 0.01. This self-relevance effect was most pronounced for positive pronoun-noun pairs, *F*(2, 40) = 10.2, *p* < 0.01. Valence ratings confirmed a self positivity bias, *F*(2, 40) = 5.1, *p* = 0.03. Positive pronoun-noun pairs were rated more positive when related to the self.

Rating data are summarized in **Table 1** and **Table 2**.

*Manipulation check.* None of the participants reported consistently using any strategy throughout the experiment. Some participants (*N* = 14) reported that they repeated the labels during face processing, but retrospectively none of them had the impression that this had reduced the emotionality of the faces or their own feeling state. Subjects reported no difficulties in understanding the intention of the labels, relating them spontaneously to either the self or the sender's face.

# **EXPERIMENT 2—LABELING WITH INTENTIONAL REGULATION INSTRUCTIONS**

#### *Electrophysiological data*

*Affective Labels.* P1, N1, and EPN amplitude modulations did not differ between self- and sender-related labels. However, akin to experiment 1, LPP amplitudes were more pronounced for **Table 1 | Experiment 1: rating data of emotional faces which were preceded by either self-related pronoun-noun pairs, sender-related pronoun-noun pairs, or no pronoun-noun pairs (control condition).**

category (fearful, angry, and happy).

*Scales ranged from 1 (extremely negative valence, extremely low arousal) to 9 (extremely positive valence, extremely high arousal). Standard deviations are in parentheses.*

self-related than for sender-related affective labels, *condition: F*(1, 16) = 7.08, *p* = 0.02.

*Faces.* In the P1 time-window a main effect of *condition* was observed, *F*(2, 32) = 3.7, *p* = 0.041. Amplitudes of P1 were significantly enhanced for emotional faces during intentional regulation as compared to passive face processing, especially when **Table 2 | Experiment1: mean valence, arousal, and self-relevance ratings of self-related and sender-related pronoun-noun pairs obtained after the experimental recordings.**

*Scales ranged from 1 (extremely negative valence, extremely low arousal, extremely low self-relevance) to 9 (extremely positive valence, extremely high arousal, extremely high self-relevance). Standard deviations are in parentheses.*

using self-related labels for emotion regulation, *self* : *F*(1, 16) = 6.23, *p* = 0.023, *sender*: *F*(1, 16) = 3.68, *p* = 0.073. During passive viewing, P1 amplitudes did not differ between emotional and neutral faces, *F*(3, 48) = 1.8, *p* = 1.7.

In the N170 time-window, the factor *condition* showed only a trend toward significance, *F*(2, 32) = 2.8, *p* = 0.079, which indicated a slight reduction in N170 amplitudes to emotional faces during emotion regulation trials relative to passive viewing of emotional faces. During passive viewing, N170 amplitudes were more pronounced for emotional than for neutral faces, especially for fearful faces, *F*(1, 16) = 6.02, *p* = 0.026. The factor *valence* was significant, *F*(3, 48) = 4.68, *p* = 0.046.

In the EPN time-window a main effect of *emotion* was observed, *F*(2, 32) = 3.9, *p* = 0.037: EPN amplitudes were more pronounced for fearful and angry faces than for happy faces, *F*(1, 16) = 4.94, *p* = 0.041; *F*(1, 16) = 5.0, *p* = 0.04. There was no significant interaction of the factors *emotion x condition*, *F*(4, 64) = 0.67, *p* = 0.61. During passive viewing, EPN amplitudes did not differ significantly between emotional and neutral faces. The factor *valence* was not significant, *F*(3, 48) = 3.48, *p* = 0.18.

In the LPP/slow wave time-window amplitudes were modulated by the main factor *condition*, *F*(2, 32) = 5.1, *p* = 0.021. Amplitudes were significantly greater for emotional faces during the "label own emotion" condition compared to the passive viewing condition, *F*(1, 16) = 10.58, *p* = 0.005, and also compared to the "label sender's emotion" condition, *F*(1, 16) = 7.0, *p* = 0.018. During "label sender's emotion", amplitudes were attenuated compared to passive viewing of emotional faces. However, this attenuation for emotional faces during "label sender's emotion" was only significant when LPP amplitudes elicited during "label sender's emotion" were compared to passive viewing of neutral faces.

Akin to experiment 1, no significant differences were observed for ERP latencies.

ERP results of experiment 2 are displayed in **Figure 3**. Results obtained from both experiments are summarized in **Figure 4**.

#### *Behavioral data*

*Ratings.* Fearful and angry faces were rated as significantly more negative in valence compared to happy faces, *F*(2, 32) = 129.6, *p* < 0.01. This was true regardless of whether faces were cued with affective labels or presented during the control condition. Valence ratings also differed between emotional faces and neutral faces (see **Table 3**). Again, this was true regardless of the condition in which the faces had been presented during the experiment. Arousal ratings were significantly modulated by the factor *condition*, *F*(2, 32) = 7.07, *p* < 0.01. Emotional faces presented in the "label own emotion" condition were retrospectively rated higher in arousal than faces being presented in the control condition of passive viewing and the "label sender's emotion" condition.

Self-related pronouns were rated higher in arousal and in selfrelevance than pronouns related to the sender, *arousal: F*(1, 16) = 14.2, *p* < 0.01, *self-relevance*: *F*(1, 16) = 37.0, *p* < 0.01. Positive pronoun-noun pairs were rated significantly higher in valence compared to pronoun-noun pairs describing fear and anger, as was indicated by a significant main effect of *emotion*, *F*(2, 32) = 129.1, *p* < 0.01. A significant interaction of *emotion x condition*, *F*(2, 32) = 12.1, *p* < 0.01 revealed a self-positivity bias: akin to experiment 1, valence ratings were higher for positive pronounnoun pairs when related to the self than when related to the sender.

Rating data are summarized in **Table 3** and **Table 4**.

*Manipulation check.* All participants were familiar with the task prior to the start of the experiment. All but 2 of them reported using a particular strategy to control their feelings when labeling their own or the sender's emotion. As did the participants in experiment 1, they reported rehearsing the labels during face processing. However, in contrast to the participants in experiment 1, they reported rehearsing the labels to experience the faces more intensively, for instance by linking faces with an autobiographical event in the "label own emotion" condition. During the "label sender's emotion" condition most subjects reported rehearsing the cues to increase the emotional distance between the self and the sender's face (*N* = 10 subjects). Some (*N* = 6) additionally tried to not show any feelings at all (emotion suppression) or to not empathize with the sender. Participants reported no difficulties in relating the cues to their own emotion or the emotion of the face. Furthermore, they reported that their feelings had increased during the "label own emotion" blocks and had the opposite impression in the "label sender's emotion" blocks. Ratings obtained after each block also indicated that subjects found it somewhat harder to regulate their emotions during the "label sender's emotion" blocks compared to during the "label own emotion" blocks. ANOVAs revealed a trend for the main factor *condition*, *F*(1, 16) = 3.5, *p* = 0.07. In addition, a significant main effect of *emotion*, *F*(2, 32) = 3.6, *p* = 0.038, indicated that amongst the to be regulated emotions (fear, anger, happiness), participants had the impression of putting more effort on the regulation of fear. Regarding the direction of their success, they reported that feelings became more positive, particularly when viewing happy faces and during the "label own emotion" blocks. This was indicated by a significant main effect of the factor

**FIGURE 3 | ERPs obtained during emotional face processing in experiment 2 (affect labeling with intentional regulation instruction).** Passive viewing of emotional faces (black line). Upper left panel **(A)** displays ERP modulation patterns in the early time-windows (P1, N170, and EPN, respectively). Upper right panel **(B)** displays amplitude modulation of the

LPP. Maps display the topographic distribution of the ERPs in µV. Lower panel **(C)** overview of P1, N170, EPN and LPP modulation (means and SEMs) during emotional face processing in experiment 2. Color shadings highlight ERP modulation for each emotional category (fearful, angry, and happy).

**Table 3 | Experiment 2: rating data of emotional faces for the faces that were preceded by self-related pronoun-noun pairs, sender-related pronoun-noun pairs, or no pronoun-noun pairs (control condition).**

*Neutral faces were presented during the control condition only.*

*Scales ranged from 1 (extremely negative valence, extremely low arousal) to 9 (extremely positive valence, extremely high arousal). Standard deviations are in parentheses.*

time-windows about 1 s a significant relationship between ERPs and any of the selected self-report indices was found. In experiment 2, amplitudes of the slow wave showed a positive correlation with self-esteem (Pearson's *r* = 0.4, *p* = 0.035) during the "label own emotion" regulation blocks, and a significant negative correlation with empathy (Pearson's *r* = −0.6, *p* = 0.004) during

**FIGURE 4 | Modulation of event-related brain potentials obtained during experiment 1 and experiment 2.** Arrows indicate the direction of the modulation (up vs. down) compared to passive face processing. Only significant results are considered, trends are reported in the text.

*emotion*, *F*(2, 32) = 27.76, *p* < 0.01, and a significant interaction of the factors *emotion* x *condition*, *F*(2, 32) = 8.56, *p* = 0.001.

#### **CORRELATION ANALYSIS OF ERPs AND SELF-REPORT MEASURES**

In experiment 1, correlation analyses revealed no significant results. Neither in the early nor in the late face processing

**Table 4 | Experiment1: mean valence, arousal, and self-relevance ratings of self-related and sender related pronoun-noun pairs obtained after the experimental recordings.**

*Scales ranged from 1 (extremely negative valence, extremely low arousal, extremely low self-relevance) to 9 (extremely positive valence, extremely high arousal, extremely high self-relevance). Standard deviations are in parentheses.*

the "label sender's emotion" regulation blocks. Emotional intelligence was also negatively correlated with the amplitude of the slow wave during the "label sender's emotion" regulation (Pearson's *r* = −0.43, *p* = 0.04). However, when *p*-values were Bonferroni corrected for multiple comparisons (*p* = 0.008), only for empathy was the correlation still significant.

# **DISCUSSION**

Two separate experiments were conducted to investigate the impact of affective labels on face processing during spontaneous, automatic emotion processing or in an intentional emotion regulation context. EEG-ERP methodology was used to examine how decoding emotions from facial expressions changes when faces are preceded by verbal labels that vary in the extent to which they describe one's own emotion or the emotion expressed by the sender.

Processing of self- and sender-related affective labels increased perceptual processing of emotional faces as early as in the P1 time-window. The P1 component is assumed to reflect a global and coarse processing of facial stimulus features in the primary visual cortex. This stage temporally precedes a more fine grained, configural analysis of the structural features of the face, this later stage being reflected in amplitude modulations of the face specific N170 component (e.g., Bentin et al., 1996). Modulation of the P1 component by emotions and context has been reported in some but not all face processing studies (for an overview Righart and de Gelder, 2006). However, agreement exists that very early facial feature processing as reflected by the P1 can be modulated by task-related and context-dependent top-down processes (Heinze et al., 1994; Rauss et al., 2012). Processing of affective labels could influence early facial feature processing via top-down cognitive mechanisms of anticipation, or by activating conceptual processing of emotional faces in anticipation of their encounter. During passive viewing, faces were cued by letter strings instead of affective labels, but P1 amplitudes did not differ between emotional and neutral face conditions. Due to their semantic content, words as labels possess a greater anticipatory signal character than meaningless letter strings. P1 modulation by self- and sender-related affective labels occurred independently of the emotional valence of the faces (happy, fearful, or angry) and across experiments (automatic processing in experiment 1 vs. intentional emotion regulation in experiment 2), supporting the robustness of this observation.

The perceptual processing stages that followed the P1 and which were indexed by the N170 and the EPN component were influenced differently by self- and sender-related labels. As predicted, structural encoding of fearful faces (N170) increased significantly when verbal labels described the emotion of the sender's face, whereas cueing the faces with self-related labels describing the reader's own emotion facilitated attention capture by emotional faces in the time-window of the EPN component, after the N170. In contrast to the N170, the EPN is considered to reflect early conceptual and semantic analysis of a stimulus in the ventral visual processing stream (Schupp et al., 2006; Kissler et al., 2007). The N170 and the EPN effects were significant in experiment 1 and not observed in experiment 2, supporting their unintentional and implicit nature.

This corroborates the idea that "what" is to be labeled during affect labeling and "how" this is done (automatically or intentionally) influences both the direction and the intensity of our perceptual experiences. Decoding emotions from facial expressions is indeed not fully determined by the sensory information derived from the face, nor is it completely insensitive to contextual factors or independent from the experience on the perceiver's side. Also, the relationship between language and perception is stronger than traditionally assumed (Lindquist and Gendron, 2013). Not only does reading emotion words activate our sensory and motor systems, activation in these systems is also temporarily reduced when access to emotion concepts is blocked experimentally (Lindquist et al., 2006; Gendron et al., 2012) or when concept activation during face processing is changed by verbal negation (Herbert et al., 2012). The present observations, including P1, N170, and EPN modulations by affective labels, further emphasize an embodied view of language. They demonstrate that even minor linguistic variations that change the personal reference or ownership of a particular emotion concept can be powerful mediators of emotion perception. While spontaneous processing of affective labels describing one's own emotions increases the motivational relevance of emotional faces, be they happy, angry, or fearful (see EPN results), sender-related labels seem to improve emotion recognition more specifically by facilitating decoding of structural information from the face, especially from fearful faces (see N170 results).

Processing of affective labels modulated later stages of face processing as well. Again, results differed between the two experiments, supporting the notion of psychologically and physiologically different mechanisms underlying automatic and intentional affect labeling. Spontaneous processing of affective labels reduced the amplitude of the late positive potential (LPP) to fearful, happy, and angry faces relative to the control condition of passive viewing. Moreover, this was observed for emotional faces cued by selfand sender-related affective labels. The LPP and the slow wave are cortical correlates of sustained attention and depth of stimulus encoding (Kok, 1997; Schupp et al., 2000; Moratti et al., 2011). Lower LPP amplitudes thus imply a reduction in depth of stimulus processing during automatic and hence unintentional affect labeling. This was not restricted to the processing of fearful or angry faces, applying to all faces expressing negative emotions. To the contrary, processing of self- and sender-related labels seemed to reduce processing costs for both, negative and positive emotional facial expressions.

A reduction in cortical processing depth, however, does not necessarily imply a dampening of affect on a subjective experiential level. When asked post-experimentally, none of the participants in experiment 1 had the impression that processing of affective labels had transiently dampened the own feeling state during the experiment. Ratings obtained for a subset of the faces after the experiment also indicated no changes in perceived valence or arousal. This underscores findings from previous studies suggesting that processing of affective labels has incidental effects on a bio-physiological level, but not necessarily on a subjective experiential level (Kircanski et al., 2012).

During active emotion regulation a different pattern emerged, distinguishing between automatic and intentional affect labeling processes. Unlike in experiment 1, amplitudes of the LPP/cortical slow wave in experiment 2 were enhanced for emotional faces when subjects used the self-related affective labels for emotion regulation during face processing. Likewise, they reported the feeling that the emotionality of the faces increased during the "label own emotion" regulation blocks and rated the faces afterwards as higher in arousal than the emotional faces that had been presented in the "label sender's emotion" or the passive viewing conditions. Increased processing in the "label own emotion" regulation condition contrasts with the view that making oneself aware of one's own emotions would transiently dampen the feeling state itself. It also runs counter to the assumption that affect labeling would always have a down-regulatory effect on emotional stimulus processing (Lieberman, 2007, 2011), independent of the personal reference properties of the label and the participants' intentions.

The results of experiment 2 suggest that intentional affect labeling can intensify encoding of emotional faces and make their content more intense and self-relevant. This is in line with observations from imaging studies on self-referential processing of emotional stimuli. Self referential processing of emotional stimuli activates medial prefrontal cortex regions, which are part of the salience network (Schmitz and Johnson, 2007). Parts of this network have been found to be active during reading of selfrelated emotional pronoun-noun pairs as well (Herbert et al., 2011c). In line with these observations, self-related emotional pronoun-noun pairs elicited larger LPP amplitudes relative to sender-related emotional-pronoun-noun pairs, which corroborates findings from recent studies showing similar effects (e.g., Walla et al., 2008; Zhou et al., 2010; Herbert et al., 2011a,b; Shi et al., 2011) and supports the notion that participants discriminated between the self and the other during reading of self- and sender-related labels.

Experiencing a strong sense of ownership can diminish selfother boundaries, such as when watching one's own face and the face of a sender being touched simultaneously (Maister et al., 2013). Labeling one's own emotion seems to provide another way to get in touch with one's own emotion while viewing someone else's face, particularly when there is the intention to do so, as seen in experiment 2. This could have effects similar to the resonance of being touched in real time as it might help synchronizing one's own feelings with the expressions of the sender's face.

Resonance between sender and observer might also play a role when attempting to regulate one's own emotion by labeling the emotion of the sender's face. In the present study, empathy and emotional intelligence were inversely related with amplitudes of the slow wave during the "label sender's emotion" conditions. In addition, self-esteem, which reflects the most fundamental appraisals about the self, was positively correlated with depth of face processing during the "label own emotion" blocks, corroborating theoretical conceptions that link self esteem with improved self consciousness or an improved self-awareness (Branden, 1969). Positive correlations between the personality measures found here might reverse when, instead of healthy subjects, clinically relevant samples with poor self-esteem, low empathy, and high emotional blindness are investigated. Gender likewise could play a role because more females than males took part in the present experiments. In any event, inter-individual differences should be taken into account in future research. In the present study, results on inter-individual differences can only be considered tentative due to their exploratory nature and the small sample sizes studied.

It has been debated to what extent interventions in which individuals are asked to focus on their own emotion and to become aware of them are helpful for emotion regulation. Looking at clinical disorders, internal self focus of attention can give rise to negative feelings of distress and heightened physiological arousal (Ingram, 1990). Similar observations of an increase in symptomatology (e.g., increase in distress, negative mood, and physical symptoms) have been reported immediately after expressive writing (Pennebaker and Beall, 1986; Pennebaker and Chung, 2007, 2011). In the present study, participants reported an increase in feelings during intentional affect labeling as well. However, this increase in feelings when labeling one's own emotion did not push subject's mood in a negative direction. A major difference between self monitoring in clinical disorders, expressive writing, and intentional affect labeling is that, during intentional affect labeling, labels provide a concrete context for appraising one's feelings and concomitant bodily changes, the latter often being verbally accessible only along simple physical dimensions of valence (good-bad) and arousal (calm-arousing). In this sense, using self- and sender-related labels actively and intentionally for emotion regulation seems to have comparable effects on emotion processing and cognitive reappraisal. A more speculative possibility is that using self- and sender-related labels actively and intentionally for emotion regulation might have facilitated verbal self-guidance and self-regulation by means of inner speech (Morin, 2005). Future studies could test this assumption.

# **CONCLUSION**

Language has long been considered as being somewhat independent from emotions, both with regard to its capacity to induce emotions and with regard to its potential to up- and downregulate emotion processing in accord with situational demands. The present study sheds light on the mechanisms underlying the emotional regulatory capacity of language. The results are the first to show that "what" is being labeled verbally (e.g., one's own emotion or the emotion of the sender's face) during face processing and how intentionally this is done matters. The present observations therefore pave the way for a differentiated view of language as a context for emotion processing. Some of our observations are specifically suggestive for future research, particularly the observation, that, with few exceptions, processing of affective labels with or without regulatory intent did not interact with the emotional valence of the faces, i.e., regardless of whether facial expressions were fearful, angry, or happy. That is, ERP modulations reflected the different experimental conditions of self vs. sender labeling, rather than the emotional content itself. Previous studies can provide limited information on this issue because stimulus material was limited to either fearful or angry or stereotypic material, whereas other researchers used emotional pictures instead of faces as stimuli. Similarly, much of the previous research outlined in this paper

# **REFERENCES**

used functional imaging methods while the present studies delineate the influence of affective labels on face processing in real time with a resolution of milliseconds, by means of EEG methodology. Future studies using larger numbers of stimuli or different sets of stimuli, including emotion pictures or voices, are needed to test the valence specificity assumption. Inclusion of different stimulus materials besides faces will also show if effects occur across sensory modalities, which could improve our understanding of language-emotion-cognition interactions in real life social situations.

#### **ACKNOWLEDGMENTS**

We thank Eunkyung Ho for help with data collection and analysis.

#### **FUNDING**

Research was supported by the German Research Foundation (DFG, HE5880/3-1). This publication was funded by the German Research Foundation (DFG) and the University of Wuerzburg in the funding programme Open Access Publishing.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 23 March 2013; accepted: 01 July 2013; published online: 23 July 2013. Citation: Herbert C, Sfärlea A and Blumenthal T (2013) Your emotion* *or mine: labeling feelings alters emotional face perception—an ERP study on automatic and intentional affect labeling. Front. Hum. Neurosci. 7:378. doi: 10.3389/fnhum.2013.00378*

*Copyright © 2013 Herbert, Sfärlea and Blumenthal. This is an openaccess article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.*

# Age and gender dependent development of Theory of Mind in 6- to 8-years old children

# *Cecilia I. Calero1,2\*†, Alejo Salles 1,2†, Mariano Semelman1,2 and Mariano Sigman1,2,3*

*1 Laboratorio de Neurociencia Integrativa, Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires,*

*Buenos Aires, Argentina*

*2 IFIBA, CONICET, Buenos Aires, Argentina*

*3 Universidad Torcuato Di Tella, Buenos Aires, Argentina*

#### *Edited by:*

*Agustin Ibanez, Institute of Cognitive Neurology, Argentina*

#### *Reviewed by:*

*David Huepe, Universidad Diego Portales, Chile Marina L. Puglisi, University of Oxford, UK*

#### *\*Correspondence:*

*Cecilia I. Calero, Laboratorio de Neurociencia Integrativa, Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Intendente Güiraldes 2160, Pabellón I, Ciudad Universitaria, C1428EGA Buenos Aires, Argentina e-mail<EMAIL_ADDRESS>
*†These authors have contributed equally to this work.*

The ability to attribute different mental states to distinct individuals, or Theory of Mind (ToM), is widely believed to be developed mostly during preschool years. How different factors such as gender, number of siblings, or coarse personality traits affect this development is not entirely agreed upon. Here, we introduce a computerized version of the scaled ToM suite of tasks introduced by Wellman and Liu (2004), which allows us to meaningfully test ToM development on children 6 to 8-years old. We find that kids this age are still not entirely proficient in all ToM tasks, and continue to show a progression of performance with age. By testing this new age range, too, we are able to observe a significant advantage of girls over boys in ToM performance. Other factors such as number of siblings, birth order, and coarse personality traits show no significant relation with the ToM task results. Finally, we introduce a novel way to quantify the scaling property of the suite involving a sequence of set inclusions on one hand and a comparison between specially tailored sets of logistic models on the other. These measures confirm the validity of the scale in the 6- to 8-years old range.

**Keywords: Theory of Mind, scaling, mental states, development, gender differences**

# **INTRODUCTION**

Theory of Mind (ToM) is an important cognitive skill that refers broadly to our capacity to understand others' mental states including beliefs, desires, and knowledge, and the ability to comprehend that these may differ from our own (Premack and Woodruff, 1978). A paradigmatic example of a task requiring a well-developed ToM is that of false belief, which involves understanding that an agent might have a wrong representation of external reality, and act according to that representation (Baron-Cohen et al., 1985). ToM is deeply integrated with other cognitive domains and abilities. Among other factors, ToM development has been related to the success on executive function tests, and various assessments of language and social experience (Dunn et al., 1991; Dunn, 1995; Astington and Jenkins, 1999; Carlson and Moses, 2001; Apperly, 2012).

Over the past two decades, developmental changes in children's understanding of others' minds have been the focus of intense research (Wellman and Woolley, 1990; Dunn et al., 1991; Gopnik and Slaughter, 1991; Flavell, 1999; Astington and Jenkins, 1999; Meltzoff, 1999; Wellman et al., 2001). Most of the work performed to date was done in preschool children (3 to 5-years old) and coarsely agrees in that the basic aspects of ToM are mostly developed within this age range [Bartsch and Wellman, 1995; see Flavell (1999), for a review]. However, some studies argue that ToM continues to develop and change throughout life (Bosacki and Astington, 1999; Apperly, 2012; Devine and Hughes, 2012; Moran, 2013). Rai and Mitchell's (2004) study has shown that there is still considerable instability in understanding false beliefs in 5-years old, especially when the false belief scenario is framed in relation to a person's conscious choice or decision, rather than a physical object. Furthermore, Dumontheil et al.' (2010) results suggest that ToM improves between late adolescence and adulthood and even if ToM tasks are passed by age 4; their data indicate that the interaction between understanding others' mind and executive functions continues to develop in late adolescence (Dumontheil et al., 2010). Bosacki and Astington (1999) used ambiguous social vignettes followed by questions to assess the understanding of particular aspects of other's mental states in a study with preadolescent children. Their study was conceived from a ToM perspective in order to quantify preadolescents' mentalizing abilities and their results partially support that, individual differences in preadolescents' ability to understand the thoughts and emotions of others would be related to their social competence (Bosacki and Astington, 1999).

Most studies on ToM have not addressed issues of gender, family environment, and measures of temperament. A slight advantage of preschool girls on emotion understanding and false belief task performance has been observed before (Banerjee, 1997; Charman and Clements, 2002; Walker, 2005), nevertheless, most previous studies have found no significant gender differences on ToM development (Hughes and Dunn, 1998; Charman and Clements, 2002; Walker, 2005; Mathieson and Banerjee, 2011; Devine and Hughes, 2012). On the other hand, in studies carried out in preadolescence, girls performed significantly higher on the social understanding task (ToM) than boys independent of vocabulary ability (Bosacki and Astington, 1999). These results support Hatcher et al.' (1990) findings, in which girls scored higher than boys on social understanding tasks across grades 4 through 12. Further, the recent study in adolescents by Ibanez et al. (2013) presents a model that shows the direct effect of empathy, sex, and fluid intelligence on ToM. Only recent studies have found some relations between scores on false belief tasks and preschool children's family environments (Perner et al., 1994; Farhadian et al., 2010). However, there is no consensus on whether the amount of siblings or the birth order influences the development of ToM (Azmitia and Hesser, 1993; Lewis et al., 1996; Ruffman et al., 1998; Cutting and Dunn, 1999; Farhadian et al., 2010).

A complementary aim of most developmental studies of ToM consists in understanding the sequential unfolding of abilities underlying a full ToM. To date there is consensus in the notion that a child will correctly judge a person's desires before she can correctly judge her beliefs, and that she will be able to grasp that an agent might have a belief different from her own first if she doesn't know the true state of affairs, and only later if she does know what reality really is like (false belief). Cutting and Dunn (1999), Wellman and Liu (2004), Wellman et al. (2001) and, more recently, Wellman et al. (2011) have investigated this progression of abilities. In particular, Wellman and Liu (2004) proposed a suite of ToM tasks, based upon a meta-analysis of the literature of ToM developmental studies, and tested it in children from 3 to 5-years of age. Their results suggest that the abilities underlying ToM are attained progressively, and can thus be tested individually by an ordered suite such that a child capable of correctly performing a certain task in the suite should also be able to correctly perform all preceding tasks. Wellman and Liu's scaled suite of tasks has been subsequently employed to pinpoint cultural differences in the development of ToM (Wellman et al., 2006, 2011; Shahaeian et al., 2011).

In this work, we implement and test a computer version of Wellman and Liu's (2004) ToM suite in children in the 6 to 8-years old range. The aim of this work is threefold: first, we test whether it is possible to use the suite to test ToM development in older kids and check if the scaling seen in preschoolers is still valid in our version of the test. The age range chosen in this work is sometimes overlooked in the literature, even if it has been shown that children do not understand metaphor or irony before the age of six to seven (Ackerman, 1981)—-two behaviors that entail the capacity to go beyond the literal meaning of a statement—- and that they cannot reliably distinguish jokes from lies before age 6 to 7-years (Sullivan et al., 1995). In accordance, our first hypothesis is that ToM progression of Wellman and Liu's suite will still be present in children 6 to 8-years old.

Second, given that gender differences might be expected in ToM, and in line with the gender intensification hypothesis (Hill and Lynch, 1983), which establishes that gender differences increase in time because of growing pressure to conform to traditional gender-role stereotypes, we hypothesized that in a slightly older group of children—-in relation to the usually age range explore in the literature (3 to 5-years old)—- gender may have an appreciable effect on ToM performance.

The third and final aim of this work is to develop a novel analysis to quantify the validity of the scaling in the suite. The method we use has two parts: one uses set inclusions to quantify the extent to which the data differ from a perfect scaling, while the other involves the comparison between specially tailored sets of logistic models. The difference in prediction power among these sets of models gives another measure of the scaling quality.

# **MATERIALS AND METHODS PARTICIPANTS**

Seventy-six first (36) and second (40) graders [mean age: 7-years and 3 months (86.5 months); range from 6-years and 1 month to 8-years and 7 months] participated in the study. There were 42 boys and 34 girls in the sample, all of a high socioeconomic status and attending a well reputed private bilingual school in Buenos Aires. The school in which the study was performed approved the research and all children's parents or legal guardians gave signed voluntary consent. The consent form, presented to the caregivers supplemented with a note which explained the procedure, was previously authorized by the *Centro de Educación Médica e Investigaciones Clínicas "Norberto Quirno*" (CEMIC)'s Ethical Committee.

#### **ToM SUITE**

Wellman and Liu's ToM suite of tasks is thoroughly described in the appendix of the original paper (Wellman and Liu, 2004). Briefly, the tasks involved in our version are: (1, DD) Diverse Desires: the child judges that two persons (her vs. someone else) have different desires about the same objects; (2, DB) Diverse Beliefs: the child judges that two persons (her vs. someone else) have different beliefs about the same object, while she does not know which belief is the right one; (3, KA) Knowledge Access: the child sees what is in a box and judges the knowledge of another person who does not see what is in it; (4, FB) Contents False Belief: the child judges another person's false belief about what is in a distinctive container while she (the child) knows what actually is inside the container; (5, EFB) Explicit False Belief: the child judges how someone will search, given that person's mistaken belief, and (6, BE) Belief vs. Emotion: the child judges how a person will feel given a belief that is mistaken. All tasks involve a control question which is used to make sure that the child understood the task, and a target question, which evaluates their performance. Although all six tasks were used for studying the influence of diverse factors in ToM development, we note that only the first four (DD, DB, KA, and FB) are involved in the progressive suite, and thus all scaling tests were performed only on these. Finally, we note that Wellman and Liu's original version of the suite included also a hidden emotion task. As this task involves two target questions instead of one target and one control, we chose to leave it out in order to facilitate direct comparison (the random choice performance baseline for this task is 33% instead of the rest of the tasks' two choice 50%). The implementation of the suite is depicted in **Figure 1**.

In Wellman and Liu's version of the suite, some tasks were presented using toy figurines while others involved drawings. This makes it difficult both to preserve the parallels among the different tasks and to compare the results with those involving other

wrong answers, such that, independently of the answer, a "CORRECT" screen appeared next. Three of the tasks are shown in the figure.

methods found in the literature. Further, following our aim of carrying the scaled ToM suite to an older age range, we formulated it as an engaging computer game, which not only unifies the presentation format across all tasks, but also reduces the experimenter's involvement.

#### **PROCEDURE**

Children were tested in a quiet room in the school by one of two adult experimenters. The six tasks were presented in the increasing difficulty order proposed by Wellman and Liu (2004). All the children that participated in the study correctly answered the control question in each task.

Teachers completed the short form of the Child Behavioral Questionnaire (CBQ) for all their students. This allowed us to measure child's temperament along three broad dimensions: (1) Extraversion/Surgency, (2) Negative Affectivity, and (3) Effortful Control (Rothbart et al., 2001; Putnam and Rothbart, 2006). They also filled a second form that included the family background data (birth order and number of siblings), age, and gender of each child.

#### **DATA ANALYSIS**

In order to summarize the children's performance in the ToM suite we compute for each child her *z*-score, defined as the amount of correct target answers. Alternative measures were also tested, in which the contribution of each task to the total score was weighed either progressively with the task number or with a factor equal to one minus the observed mean performance for that task, and then all contributions summed together in a final score. The results obtained are insensitive to the scoring scheme chosen; we hence stick to the *z*-score above.

Throughout the analysis, we used non-parametric permutation tests in order to assess the significance of results. In each case, we randomly shuffled the assignments between predictor and dependent variables, to produce a surrogate version of the data. The relevant quantity (for instance correlation) was then evaluated for this surrogate data. By iterating this procedure many times (typically a thousand), we obtained the significance level of the result.

Apart from studying the correlations in the data, we built a logistic model to assess kids' responses for all tasks. With this model, we can study the effect of the different factors in the whole set of responses, without limiting the analysis to a particular definition of score. Nevertheless, a certain amount of independence among the factors entering the model is required in order to correctly interpret the results, and hence we restrict the model to age, gender, and number of siblings and birth order (considering these last two as a single factor in order to account for their interdependence). CBQ scores, on the other hand, were left out, since they correlate mildly with gender (ex. 0.4 linear correlation between surgency and gender).

Apart from gender, age, number of siblings, and birth order as predictors, the model includes dummy variables for discerning among the six tasks in the suite. Since each kid responds to all six tasks, we also need to index the subjects. As it turns out, age, sex, sibling amount, and sibling order taken together are almost enough to identify all subjects. There are however, five cases in which these repeat, so we resolve them by adding an extra indicator variable (i.e., order in which they took the experiment).

To measure the importance of each factor in predicting the results, we compare the full model with that with the factor in question removed. The difference between the log likelihoods of both models follows a χ2 distribution [apart from a factor of 2, see Stevenson (2004)], and we can hence evaluate the corresponding *p*-value. The *p*-values thus obtained are in agreement with those computed with the full model under the assumption of normally distributed errors.

# **RESULTS**

# **ToM PERFORMANCE IN 6 TO 8-YEARS OLD CHILDREN**

Despite the fact that some studies indicate that the development of a full ToM continues all throughout life (Devine and Hughes, 2012; Moran, 2013), almost all research has focused in 3 to 5-years old children. We took a step further to contribute to elucidate these notions and we examined age, gender, and family background influence on ToM development in the age range of 6 to 8 years old. **Figure 2** shows the average performance for the four tasks involved in the scaling for all children (thick black bars).

The average performance for our first and second graders is in the same range as that of the preschoolers studied by Wellman and Liu, suggesting that the smaller intervention of the experimenter enabled by the computer platform allows for the testing of older kids without saturating the suite. Average performance for tasks EFB and BE was 0.64 and 0.6, respectively, also similar to that of Wellman and Liu's preschoolers. These tasks were not included in the graph to emphasize the progression effect in the first four.

In accordance with our first hypothesis that ToM progression of Wellman and Liu's suite will still be present in 6 to 8-years old, we observed an increase in ToM proficiency with age, with *z*scores significantly correlated with age (*r* = 0.334, *p* = 0.003 permutation test), This result stresses even further the fact that the test is effective in the new age range, and shows that the general performance drop is not merely due to a statistical fluctuation.

Average performance divided by gender is also shown in **Figure 2** (boys in left blue thin bars and girls in right red thin bars). For task EFB we have average performances of 0.7 for girls and 0.6 for boys, while for the BE task we obtain 0.76 for girls and 0.48 for boys. We observe a clear effect of gender in ToM performance, with girls performing significantly better than boys in all tasks (*p* = 0.037 permutation test, grouped tasks). This gender effect is task-independent, as we show using the logistic model described in the following section.

We also studied the effect of family background and child temperament, factors whose potential influence in ToM performance has been previously discussed in the literature (Dunn et al., 1991; Perner et al., 1994; Dunn, 1995; Farhadian et al., 2010). Contrary to age and gender findings and in agreement with previous data (Lewis et al., 1996; Cutting and Dunn, 1999) we find no effect of sibling amount (*r* = 0.034, *p* = 0.766, permutation test) or birth order (*r* = −0.043, *p* = 0.729, permutation test) in ToM performance.

Similarly, coarse personality traits as evaluated by the CBQ do not correlate significantly with the *z*-score (Surgency: *r* = −0.169, *p* = 0.149; Negative Affect: *r* = 0.197, *p* = 0.093; Effortful Control: *r* = 0.118, *p* = 0.315; permutation tests).

The logistic model analysis fully supports the results discussed above. By taking one factor at a time out of the model, we can see how relevant each factor is in explaining the kids' responses (see Materials and Methods). The computed *p*-values are: age, *p* = 0.002; sex, *p* = 0.015; siblings (amount and order), *p* = 0.978. By testing an expanded model including a joint sex-task factor, we see that these two variables do not interact (*p* = 0.47). Hence, in accordance with our second hypothesis we observed a gender effect in ToM which was not observed for the same task in preschoolers. The other demographic or individual variables had no effect in performance.

#### **ToM SUITE PROGRESSION**

We now turn to quantify the extent to which the ToM suite embodies a progressive test, that is, one in which in order to correctly resolve a given step, one must have the abilities required to solve all previous steps. As we mentioned before, we only consider tasks 1–4 (DD, DB, KA, and FB) for this part of the analysis, since it is only these that are involved in the hierarchy.

The progression in difficulty of ToM performance is evident from **Figure 2**, which shows a diminishing performance with increasing task number. An average decrease in performance, however, does not necessarily imply a sequential process. It is still possible that a group of children is capable of successfully performing Task 1 (*G*1) and a smaller group is capable of successfully performing Task 2 (*G*2) but that these groups have no intrinsic relation other than their difference in size, i.e., |*G*2| < |*G*1|, where |·| denotes set cardinality. The condition of strict sequential dependence in performance implies that all kids in *G*2 are also in *G*1(*G*2 ⊂ *G*1), in other words, that children succeeding in Task 2 also succeed in Task 1. This logic extends for all tasks from 1 to 4, such that *Gj* ⊂ *Gi* for all *i* < *j* in 1 to 4.

**Figure 3** depicts this sequence of inclusions for two extreme cases, **(A)** one in which there is no progression at all, and **(B)** one in which the hierarchy is perfect, along with our actual result **(C)**. In this figure, each task is represented by a square whose area encodes number of kids who successfully passed the task and area overlap indicates kids passing both corresponding tasks. For clarity, we only represent overlaps of successive tasks. Out of 76 kids, 62 passed DD, 56 passed DB, 48 passed KA, and 36 passed FB. Of these, 6 passed DB but not DD (11% of those that passed DB), 11 passed KA but not DB (23% of those that passed KA), and 10 passed FB but not KA (28% of those that passed KA). In a perfect hierarchy, there would be no such cases. These cases, however, constitute an expected fluctuation. In order to test this statistically, we take as a figure of merit the sum of cases in which a kid passed a task without passing the previous one, which is 27 in our case (lower values of this number represent better scalings). We then perform a bootstrap procedure in which we shuffle both kids and tasks, and obtain a surrogate value for this figure of merit. We find a better (lower) value in less than 1% of the cases, with an actual *p-*value of 0.059.

We refine this analysis by examining three sets of logistic models for the kids' responses, taken one task of the ToM suite at a time. The first is a set of Pointwise models, one for each task,

which include as predictors age, gender and sibling amount and order. The second is a set of Markov models, again one for each task, which comprise besides the factors in the Pointwise models the response for the previous task in the scale. For the first task, these two models are identical. Finally, the third set is one of Cumulative models, also one for each task, incorporating the same predictors as the Pointwise models plus the whole history of previous responses for each kid up to the current task. We point out that for the second task of the scale the Markov and Cumulative models coincide.

We compare pairwise the model sets described before, getting a *p*-value for the extra variables in a model to be explanatory. The summarized results are presented in **Table 1**. We find that, comparing the Pointwise and Markov models, the response to the first task is a good predictor of the response to the second. This is consistent with the scaling property: a child that did not pass the first task is very likely to also not pass the second, hence making the Markov model more powerful than the Pointwise. We note however, that even in a perfect scaling, we would have some amount of unpredictability, since we do not know at which point the kids will start failing the tasks, so that even having the previous task information will not help in predicting the outcome of the next. This **Table 1 | Model sets comparison.**

is indeed what we see when comparing the Markov and Pointwise models for the subsequent tasks, where more kids begin to fail.

We can address this issue by further comparing the Markov and the Cumulative model sets (different only for task 3 and up). This comparison shows that the extent to which one can predict the outcome in a certain task by knowing the result for the previous task is not improved by knowing further previous results, as should be the case in a perfect hierarchy. In other words, knowing the result for the first task does not add information to knowing the result for the second task, if we are to predict the response to the third one, and similarly for task 4. This further stresses the scaling property of the suite.

# **DISCUSSION**

While many studies argue that by the age of four most normally developing children have already acquired an understanding of the mind; others instead have shown that ToM continues to mature at older ages Bosacki and Astington, 1999; Dumontheil et al., 2010; Devine and Hughes, 2012; Moran, 2013. In the present study we implement the ToM suite of tasks by means of a game in a computer platform, thus diminishing the experimenter's involvement. Corroborating Wellman and Liu (2004) previous results with preschool kids, we also found that the progressive and sequential effect of the suite could remain a major factor in older kids, revealing a hierarchy of nested processes of ToM in the 6 to 8-years range. However, because this ToM suite of tasks was never tested with the original age range and procedures in Argentina more studies need to be done.

We successfully apply the suite in older kids and to reach this conclusion we introduced a novel method to quantify the scaling property of the suite. This new proposal involves on one hand an intuitive quantification through set inclusions, and, on the other, a thorough comparison of a variety of logistic models including a varying amount of previous results as predictors for the outcome of a certain task. Both methods provide strong support for the scaling, and validate its use in the new age range tested.

Most studies on ToM have not addressed issues of gender. By testing older kids, we could examine the hypothesis that gender differences in ToM proficiency may develop late. This would be in accordance to the gender intensification hypothesis (Hill and Lynch, 1983), which predicts that gender differences increase in time because of increased pressure to conform to traditional gender-role stereotypes. Here, we conclusively showed a strong effect indicating that girls perform significantly better than boys for all ToM tasks in the age range tested.

Although some studies have proposed facilitative effects of (older) siblings that may operate via shared experiences of pretend play and deception, and talk about feelings and internal mental states (Perner et al., 1994; Ruffman et al., 1998; Peterson and McAlister, 2006), other work has found no relation between ToM performance an number of siblings or birth order (Cutting and Dunn, 1999; Cole and Mitchell, 2000; Hughes and Ensor, 2005). In our study, the family background included in the analysis did not correlate with ToM performance, contributing to the idea that birth order and number of siblings would not be related to the development of ToM. However, we need to take into consideration that our results come from children in a new age range (6 to 8-years old), while previous evidence related to this particular topic was concentrated on preschoolers.

Similarly, there is some evidence supporting a relation between understanding of false belief and emotion and peer-related social competence (Dunn et al., 1991; Dunn, 1995), although the individual influence of child temperament on ToM has not yet been studied. The relation between ToM performance and emotion and temperament is thus surprisingly unclear, despite the importance of both domains to social interactions (Cutting and Dunn, 1999). Only recent studies have found some relations between a direct relation of gender and ToM mediated by empathy (Ibanez et al., 2013). Here, we evaluated the relation between three personality traits as quantified by the CBQ and ToM performance. None of these traits appears to have an impact in the understanding of other's minds. We should point out, however, that given the lack of memory or general

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intelligence measure, the use of non-standardized measures and the ethnical homogeneity of our sample, among other factors, interpretations of the present findings are to be made with caution.

Finally, we introduced a novel way in which to quantify the scaling property of the suite. This new proposal involves on one hand an intuitive quantification through set inclusions, and, on the other, a thorough comparison of a variety of logistic models including a varying amount of previous results as predictors for the outcome of a certain task. Both methods provide strong support for the scaling, and validate its use in the new age range tested.

#### **ACKNOWLEDGMENTS**

This research was supported by a grant UBACyT 2010- 2012 (N◦<PHONE_NUMBER>0286), by a grant UBACyT joven (N◦<PHONE_NUMBER>0047) and received support from the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). Mariano Sigman is sponsored by the James McDonnell Foundation. We gratefully acknowledge the helpful efforts of the school and, especially, the children who participated with the generous consent of their parents. Authors also thank Joaquín Ais and Ariel Zylberberg for fruitful discussions and useful comments on the manuscript.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 29 March 2013; accepted: 29 May 2013; published online: 17 June 2013.*

*Citation: Calero CI, Salles A, Semelman M and Sigman M (2013) Age and gender dependent development of Theory of Mind in 6- to 8-years old children. Front. Hum. Neurosci. 7:281. doi: 10.3389/ fnhum.2013.00281*

*Copyright © 2013 Calero, Salles, Semelman and Sigman. This is an openaccess article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.*

#### *Ruud van den Bos 1 \*, Jolle W. Jolles 2 and Judith R. Homberg3*

*1 Department of Organismal Animal Physiology, Faculty of Science, Radboud University Nijmegen, Nijmegen, Netherlands*

*2 Department of Zoology, University of Cambridge, Cambridge, UK*

*3 Department of Cognitive Neuroscience, Centre for Neuroscience, Donders Institute for Brain, Cognition, and Behaviour, UMC St. Radboud, Nijmegen, Netherlands*

#### *Edited by:*

*Agustin Ibanez, Institute of Cognitive Neurology, Argentina*

#### *Reviewed by:*

*René San Martín, Duke University, USA Koji Jimura, Tokyo Institute of*

#### *Technology, Japan \*Correspondence:*

*Ruud van den Bos, Department of Organismal Animal Physiology, Radboud University Nijmegen, Heyendaalseweg 135, NL-6524 AJ Nijmegen, Netherlands e-mail<EMAIL_ADDRESS>
Taking decisions plays a pivotal role in daily life and comprises a complex process of assessing and weighing short-term and long-term costs and benefits of competing actions. Decision-making has been shown to be affected by factors such as sex, age, genotype, and personality. Importantly, also the social environment affects decisions, both via social interactions (e.g., social learning, cooperation and competition) and social stress effects. Although everyone is aware of this social modulating role on daily life decisions, this has thus far only scarcely been investigated in human and animal studies. Furthermore, neuroscientific studies rarely discuss social influence on decision-making from a functional perspective such as done in behavioral ecology studies. Therefore, the first aim of this article is to review the available data of the influence of the social context on decision-making both from a causal and functional perspective, drawing on animal and human studies. Also, there is currently still a gap between decision-making in real life where influences of the social environment are extensive, and decision-making as measured in the laboratory, which is often done without any (deliberate) social influences. However, methods are being developed to bridge this gap. Therefore, the second aim of this review is to discuss these methods and ways in which this gap can be increasingly narrowed. We end this review by formulating future research questions.

#### **Keywords: decision-making, translational research, social environment, stress, psychological, humans, animals**

# **INTRODUCTION**

Decision-making plays a pivotal role in daily life and comprises a complex process of assessing and weighing short-term and longterm costs and benefits of competing actions. The output of the decision-making process, i.e., which action is to be taken, is determined by an interaction between impulsive or emotionally based systems, responding to immediate (potential) rewards and losses or threats, and reflective or cognitive control systems controlling long-term goals (Bechara, 2005; de Visser et al., 2011). Decisionmaking is influenced by many factors. However, whereas factors such as sex, age, genotype, and personality have been extensively investigated and discussed (reviews; Crone and van der Molen, 2004; Overman, 2004; Overman et al., 2004; de Visser et al., 2011; Homberg, 2012; van den Bos et al., 2013a), relatively little attention has been paid to the crucial moderating effect of social context on decision-making. This is all the more surprising given that decisions in real life are often strongly influenced by the social environment and involve direct and indirect social interactions.

The social environment may affect decision-making in different ways. For instance, decisions may directly involve social partners such as when deciding to share knowledge or goods with others or to provide support (review; Rilling and Sanfey, 2011). Furthermore, subjects may adjust their decisions depending on who is with them or who they consider as their reference-point at the time of the decision. For instance, in the case of so called "conformity behavior," subjects change their behavior to match that of the rest of the group (Morgan and Laland, 2012). Finally, the social environment may influence decisions globally by "setting the atmosphere." For instance, the social environment may breathe a tense or relaxed atmosphere, which influences the individual's emotional state and thereby its decisions (review; Starcke and Brand, 2012). While studies in the field of behavioral ecology have provided elaborate understanding of functional aspects of the social context of decision-making behavior, studies in the field of neuroscience have begun to provide information on the causal aspects and the neural substrate underlying decision-making behavior in a social context. Still, crosstalk between these fields rarely occurs. Researchers in both fields may benefit from insights from both domains that will enable progress toward a common understanding of the social modulating role on decision-making. Therefore, the first aim of our review is to discuss the influence of the social context on decision-making both from a causal and functional perspective, drawing on animal and human studies.

Currently, there is still a gap between decision-making in real life where influences of the social environment are extensive, and decision-making as measured in the laboratory, which is often done without any (deliberate) social influences. Subjects may for instance be less disturbed by stressful conditions when in company of friends or relatives with thereby little effect on their decisions in real life, while showing high levels of stress and concomitant effects on decision-making in the laboratory when tested singly. While these laboratory findings may be important for studying basic mechanisms of e.g., the effects of stress on decision-making (Preston et al., 2007; Lighthall et al., 2009; van den Bos et al., 2009), they hamper for instance assessing the value and general applicability of laboratory findings to the functioning of people, such as patients, in daily life. Furthermore, they miss out the important impact the social environment may normally play on individual and group decision-making. However, a major obstacle to assess the role of the social environment in decision-making under laboratory conditions in humans is that it is difficult to create ecologically valid conditions. Therefore, monitoring real life effects of the social environment on decisionmaking would be a significant step forward. In rodents, homecage experimental set-ups, which allow for careful manipulation of brain-behavior relationships in social settings, have been developed as means of bridging precisely this gap. Therefore, a second aim of our review is to discuss these developments in methodology to address the question of the effect of the social environment on decision-making.

Given the foregoing, in the following sections we will discuss how the social environment may modulate decision-making and how this can be incorporated in experimental studies. In section Decision-making in a social context, we will discuss direct and indirect social influences on decision-making, while in section Social stress and decision-making the effects of social stress on decision-making are addressed. Where possible we link a causal and functional perspective and discuss underlying neural substrates. In section Laboratory studies and real-life studies we will (briefly) discuss ways to incorporate the social environment into studies of decision-making. We end this review (section Concluding remarks) with a brief summary of the main issues addressed and define (some) future questions.

#### **DECISION-MAKING IN A SOCIAL CONTEXT**

Humans are an exceptionally successful species, both in the number of individuals and in our flexibility to expand to the range of environments and situations in which we live. A major factor underlying this success boils down to our complex social life as we have the ability to acquire valuable knowledge and skills from others through social learning and teaching and build upon this generation after generation (Boyd and Richerson, 1985; Laland et al., 2011). In our daily life we constantly make decisions based on our personal information and experience as well as that of others, i.e., social learning. Our behavior may be restricted through social conformity (Asch, 1956), or promoted or enhanced through facilitation (Zajonc, 1965). Furthermore, often the decisions of multiple individuals may result in collective behavior, such as the synchronization of applause (Néda et al., 2000), or have to be made jointly to reach a consensus (Conradt and Roper, 2005; Dyer et al., 2008). Living with others comes with the potential benefit of cooperation (Fehr and Fischbacher, 2004a) as well as costs of competition when resources are limited (Davies et al., 2012). Finally, an individual's decisions may be indirectly influenced by the social environment, by affecting an individual's emotional state. Importantly, the modulating role of the social environment is strongly affected by an individual's characteristics and personality as well as that of its group mates (Webster and Ward, 2011).

To fully understand the role of social modulation on decisionmaking, it is important to consider it from both a causal and functional perspective (Tinbergen, 1963; see e.g., Morgan and Laland, 2012). In neuropsychology, functional explanations are rarely taken into account while this behavioral ecological perspective may help to understand how the behavior of individuals is adapted to the social environment in which they live (Davies et al., 2012). A growing list of behaviors once described as uniquely human have now been described in a range of animals, such as teaching (Franks and Richardson, 2006; Thornton and McAuliffe, 2006), culture (see Laland, 2008; Laland et al., 2011), and conformity (Whiten et al., 2005; Galef and Whiskin, 2008; Jolles et al., 2011), which provide us with new insights into our own behavior. Therefore, the next few sections are focused on a behavioral ecological perspective with links to relevant human and animal laboratory studies. However, as the human literature on social decision-making has been reviewed elsewhere, we limit ourselves to the most relevant human experimental studies (see e.g., Fehr and Fischbacher, 2004a,b; Lieberman, 2007; Frith and Singer, 2008; Behrens et al., 2009; Rilling and Sanfey, 2011).

# **SOCIAL LEARNING**

For social species, like humans, the social environment plays a critical role in day-to-day decision-making, such as where to live, what to eat and with whom to mate, and may affect their emotional state (see section Observational fear learning). Decisions can be based on either personal experience and/or information gathered by others (Danchin et al., 2004) and through "social learning," individuals may for example learn how (observational learning) to deal with a resource or where it is located (local enhancement; Thorpe, 1956; Webster and Laland, 2012). Although social learning may involve several different learning mechanisms (Laland, 2008) only some rely on advanced cognitive abilities (Galef, 1988; Heyes, 1994) and most cases appear to result from very simple processes (Galef, 1988). Indeed, although social learning may seem particular to humans, animals from a broad range of species gather and exploit information generated by others (review; Galef, 1988; Heyes, 1994; Heyes and Galef, 1996).

A considerable part of the social learning literature has been performed with rats (review; Galef and Giraldeau, 2001; Galef, 2007) and has shown that rats use information from others to learn where, what, how and even when to eat (Galef and Giraldeau, 2001). Both the social information provided by visual and olfactory cues from conspecifics provide a strong basis for individual foraging decisions. Just by observing conspecifics, rats quickly locate food and join to feed with them (see Galef and Giraldeau, 2001). This is further intensified by deposited olfactory cues on both the food and the location of the food (Galef, 2007), which may for example enable young rats to learn what foods are best to eat as they may not be able to figure this out by themselves (see Galef, 2007). In particular the olfactory cues via the breath of conspecifics may result in these socially induced food preferences that may overrule personal preferences (Galef and Whiskin, 2008; Jolles et al., 2011) and even reverse learned aversions to foods (Galef, 1986).

To accurately make decisions, individuals need to constantly weigh the costs and benefits of private and social information and need to be selective when and whom to copy (Galef, 1995; Laland, 2004). Social learning may be beneficial as it allows individuals to acquire relevant information without having the risk or costs associated with individual learning. However, social information may be outdated, for example when the environment is highly variable, or less valuable, when the environment is very stable (Boyd and Richerson, 1985). Thus, relative reliance on social and individual learning can be viewed as involving a trade-off between accuracy and cost (Boyd and Richerson, 1985; Laland, 2004; Kendal et al., 2005). For example, Dally and colleagues (2008) showed that rooks selectively consumed the same food as a demonstrator when the foods were novel, but not when the foods were familiar. Likewise, Galef and Whiskin showed that the greater the discrepancy between private and social information, the less likely the subject is to behave in accord with the socially acquired information (Galef and Whiskin, 1998). Moreover, Brown and colleagues (2008) showed that personal and social information about spatial choices are combined in a rat's working memory and both the quality of the food available and the memory of a familiar conspecific's behavior affect an individual's tendency to visit spatial locations in a radial-arm maze.

The trade-off between accuracy and costs is nicely illustrated by the difference in public information use of two closely related species of sticklebacks. Coolen and colleagues (Coolen et al., 2003) showed that while nine-spined sticklebacks exploited public information and foraged at the areas they observed others to have better feeding rates, three-spined sticklebacks ignored this information and relied in their decisions on their own experience. This difference in social information use may be explained by the relative difference in costs of self-acquired information between the two stickleback species. The robust defenses that three-spined but not nine-spined sticklebacks have, such as large spines and armored body plates, allows them to sample alternative food patches directly in relatively better safety, as reflected by the increased time nine-spines spent hidden amongst vegetation (Laland, 2008).

When the presence of group mates affects the behavior of an individual, allowing or causing them to engage in certain behaviors at a different rate, or to perform behaviors that they would not perform at all if they were alone, this is called social facilitation (Zajonc, 1965). For example, in animals it has been shown that the presence of others may result in higher activity (Griffiths and Foster, 1998; Webster et al., 2007), increased foraging (Webster et al., 2007; Dally et al., 2008) and provide scrounging opportunities (review; Giraldeau and Caraco, 2000). For example, conform to human work, studies on rats have shown that the greater the number of models and the greater their uniformity in behavior, the more likely a naive subject will act in accord with the information that conspecifics provide (Galef and Whiskin, 1995). These changes in behavior can probably be ascribed to proximate mechanisms such as greater anti-predator benefits of larger groups (review; Krause and Ruxton, 2002), investment in vigilance and/or increased competition (review; Beauchamp, 2003). This is nicely illustrated by two studies in ravens (Stöwe et al., 2006a,b) which showed that when individuals were alone compared to in a group, they approached a novel object faster but spent less time close to and manipulating it. Although the social group enabled individuals to decrease time investment in vigilance, they may have a higher approach latency because individuals might wait for the other to take the risk and lead.

#### **CONFORMITY BEHAVIOUR**

Social learning theory suggest that in most circumstances where natural selection favors reliance on social learning, conformity is favored and individuals, both humans and other animals, should adopt the behavior of the majority (Boyd and Richerson, 1985; Laland, 2004). This particular form of social modulation on decision-making is especially important as it has been argued to be a major mechanism in human cultural evolution (Boyd and Richerson, 1985; Efferson et al., 2008). One of the earliest described studies on human conformity was performed by Asch (1955, 1956). In a very influential paper, Asch (1955) described how adults would be willing to abandon their own perceptual judgment in a simple visual task and go with the overtly false alternative as a result of group normative behavior. Since then a huge number of studies has replicated these kinds of findings (see Bond and Smith, 1996; Morgan and Laland, 2012). Interestingly, the extent of conformity behavior seems to be strongly dependent on the situation. Namely, if a participant has to make a public response and is face-to-face with the majority, there is a strong normative influence of conformity, whereas it is weaker when participants make a private response and indirectly communicate with the majority (Bond, 2005). Furthermore, conformity behavior may be dependent on task difficulty and its importance (Baron, 1996), group size (Asch, 1955; Bond, 2005) and culture (Bond and Smith, 1996) among others.

Recently, several studies have addressed the neurobiological basis of conformity (see also Morgan and Laland, 2012). For instance, studies using mental rotation and auditory tasks (Berns et al., 2005, 2010) showed that social information may affect brain regions classically associated with perception as well as the processing areas associated with each task, suggestion that social information was affecting the subjects' perception as well as decision-making (see Morgan and Laland, 2012). Moreover, it has been shown that while cingulate areas are involved in monitoring the difference between private and public information (Klucharev et al., 2009), the ventral striatum is involved in the tendency to adjust one's behavior to the social information (Burke et al., 2010; Campbell-Meiklejohn et al., 2010), which may be related to rewarding aspects of being in line with the behavior of others (Klucharev et al., 2009; Burke et al., 2010; Campbell-Meiklejohn et al., 2010).

Conformity has been described in a wide range of animal species including fish (Laland and Williams, 1998; Day et al., 2001; Pike and Laland, 2010), rats (Galef and Whiskin, 2008; Jolles et al., 2011) and primates (Whiten et al., 2005; Dindo et al., 2009) (see Webster and Ward, 2011 for a review). For example, Laland and Williams (1997) showed that guppies preferentially chose a foraging route they had previously observed demonstrators use despite an equally valid available alternative. Individuals may base these kind of conformity decisions on heuristic rules of social attraction (Webster and Laland, 2012) such as to approach others (e.g., Laland and Williams, 1997), to approach larger over smaller groups (e.g., Lachlan et al., 1998; Day et al., 2001) and to approach groups that produce cues indicative of higher foraging success (e.g., Coolen et al., 2003, 2005). These tendencies are likely to benefit animals in most cases as it allows them to detect food without having to pay the costs of sampling the environment directly (see e.g., Pitcher et al., 1982; Day et al., 2001). However, sometimes this conforming to the behavior of others may come with "opportunity costs." For example, individual fish may discover a visually isolated food patch faster and exploit it for longer than when a group of conspecifics is present (Webster and Laland, 2012), and smaller groups may discover a hidden food patch more quickly than larger ones (Day et al., 2001). The reliance on social information may sometimes even result in individuals to base their decisions on maladaptive information, such as rats consuming less palatable and sodium-deficient diets based on the breath of conspecifics (Galef, 1986), and even after the source of information is removed, such as guppies that kept on using energetically costly routes to food patches despite shorter alternatives available (Laland and Williams, 1998).

Although conformity of the basic "follow the majority" kind has been demonstrated in a variety of species of which Pike and Laland's (2010) study on public information use in sticklebacks provides compelling evidence, only a few animal studies (Whiten et al., 2005; Galef and Whiskin, 2008; Jolles et al., 2011) have investigated the situation where conformity overrides the discovery of valid alternative means (cf. Asch, 1955, 1956). In a two-action diffusion study in chimpanzees, Whiten and colleagues (2005) showed that although some individuals discovered an alternative technique to free trapped food items to the one seeded in their group, they later re-converged on the norm of their group, demonstrating conformity in the face of discovering a functional alternative. Two recent studies also suggest the existence of this type of conformity in rats (Galef and Whiskin, 2008; Jolles et al., 2011). Rats were given the opportunity to learn that two diets differed in palatability. They were subsequently exposed to a demonstrator that had eaten the less palatable food and were thereafter exposed to the same diets again. By simply being exposed to the odors in the breath of a conspecific for 30 min, individuals considerably decreased their preference for the more palatable food. Interestingly, despite similar initial preferences and similar social information, some rats were more resistant to changing their preference in relation to private and social information than others (Jolles et al., 2011), suggesting a different sensitivity to conflicting information (cf. Klucharev et al., 2009).

#### **COLLECTIVE BEHAVIOUR AND GROUP DECISION-MAKING**

Both humans and many group living animals exhibit complex, coordinated, group patterns, such as lanes of traffic flow in human crowds (Helbing and Molnar, 1995) and the threedimensional movements of fish shoals (Couzin and Krause, 2003). Through collective action, individuals can enhance their capacity to detect and respond to salient features of the environment, resulting in more accurate decision-making (Couzin, 2009) without the need of explicit signals or complex communication (Couzin et al., 2005; Dyer et al., 2008). The common property of these phenomena is self-organization, suggesting that much of complex group behavior may be coordinated by relatively simple interactions among the members of the group (review; Couzin and Krause, 2003). Indeed, recently studies have begun to reveal that collective decision-making mechanisms across animal species, from insects to birds and even humans, seem to share similar functional characteristics (Couzin and Krause, 2003; Conradt and Roper, 2005; Sumpter, 2006). For example, Helbing and colleagues (Helbing and Molnar, 1995; Helbing et al., 2000) have shown that simple rules such as "try to minimize travel time," "avoid collisions" and "move in the direction of other people" may help explain pedestrian movements on busy streets and in life-threatening situations. Similar patterns have been described for non-human animals including the spectacular trails of ants on foraging trips (Couzin and Franks, 2003), the collective movements of starlings (Ballerini et al., 2008), and social interactions in shoaling fish (Herbert-Read et al., 2011).

In some cases group decisions are the result of a consensus reached by the individuals in the group (Conradt and Roper, 2005). Humans make these kinds of decisions all the time, from agreements in groups of a few people, to large-scale international conventions and political elections. However, also amongst nonhuman animals consensus decision-making is very common, such as travel routes in navigating birds use and the timing of activities (review; Conradt and Roper, 2003, 2005). In many situations conflicts may exist between the preferences of different individuals (Couzin et al., 2005). However, all individuals in the group have to decide on the same action because the group will fall apart unless a consensus is reached (Conradt and Roper, 2005), resulting in a loss of many of the advantages of group living (review; Krause and Ruxton, 2002). In line with theoretical predictions (Couzin et al., 2005), it has now been demonstrated that only a small proportion of knowledgeable individuals is needed to influence the direction of movement of the whole group, such as has been shown for nest site choice in social insect colonies (Franks et al., 2003; Seeley, 2003), the foraging movements in golden shiner fish (Reebs, 2000), and humans moving to a target without the use of verbal communications or obvious signaling (Dyer et al., 2008).

#### **COOPERATION AND COMPETITION**

An important way to understand social decision-making in humans and other social animals is to look at it in terms of costs and benefits, not only to the actor as indicated above, but also to the recipient in the social context (Hamilton, 1964; West et al., 2007; Davies et al., 2012). For this it is important to keep in mind that via natural selection those genes are favored that increase an organism's ability to survive and reproduce (fitness). Therefore, individuals will often attempt to act in such a way as to receive immediate, selfish benefits, which may often result in competition or mutualistic cooperation. This is nicely illustrated by the Prisoner's Dilemma (PD; Axelrod and Hamilton, 1981) in which individuals can either cooperate or defect. Both individuals would benefit from mutual cooperation but both are also tempted to cheat, as it would be more rewarding to the individual. Therefore, irrespective of the other player's choice, it pays to defect. This raises the problem why cooperation is so common among human and animal societies (see West et al., 2007) and why individuals not act selfishly all the time and exploit the cooperative behavior of others (see Davies et al., 2012). In many cases, the cooperating individual simply acts selfishly and gains an immediate benefit, but thereby provides by-product benefits to its group mates, such as the benefits of an increased group size, i.e., reduced chance of predation, due to helping behavior in meerkats (Clutton-Brock, 2002). When on the other hand cooperation is altruistic—costly to the cooperator and beneficial to the recipient—cooperating individuals may still gain selfish benefits in the long term by using conditional strategies (Stevens and Hauser, 2004), such as cooperating only with relatives (kin selection; Hamilton, 1964), interacting only with those that have cooperated previously (reciprocity; Trivers, 1971; see Clutton-Brock, 2009), or under enforcement (Frank, 2003).

Individuals may help relatives as this may increase their genetic representation in future generations, and thus their fitness, as relatives share genes by common descent (see further Hamilton, 1964; West et al., 2007; Davies et al., 2012). If individuals preferentially help those that have helped them or those that help others, also known as reciprocity, the short-term cost of being cooperative is outweighed by the long-term benefit of receiving cooperation (Trivers, 1971). Although the PD has shown that when individuals meet only once it is better for individuals to defect than to cooperate, some form of cooperation may be stable if there is a chance both players will meet again because the longterm benefits of cooperation may outweigh the short-term benefit of defecting (Axelrod and Hamilton, 1981). Indeed, experimental work on both humans (Fehr and Fischbacher, 2003) and rodents (Rutte and Taborsky, 2007, 2008; Viana et al., 2010) has shown that individuals cooperate at higher levels in repeated interactions. For example, Rutte and Taborsky showed that rats that were trained to pull a stick in order to produce food for a partner pulled more often for an unknown partner after they were helped than if they had not received help before (generalized reciprocity; Rutte and Taborsky, 2007) and more often from a partner they received help from (direct reciprocity; Rutte and Taborsky, 2008). Furthermore, Schneeberger and colleagues (2012) showed that, similar to human PD studies, rats provided more food to cooperative partners than to defectors and that furthermore, this was dependent on costs: when rats experienced experimentally increased resistance to pull the stick of the apparatus and deliver food to the social partner, they reduced their help. It remains unclear, however, to what extent these behaviors may potentially be ascribed to simpler processes such as conditioned place preference. For example, rats have been shown to prefer a social partner over an empty space (Trezza et al., 2009) and to cooperate 80% of the time if they have the choice to act either alone or in cooperation with a social partner to obtain food pellets (Tsoory et al., 2012). Indeed, although reciprocity has attracted a huge amount of attention, it is thought to be generally unimportant outside humans (Hammerstein, 2003; Stevens and Hauser, 2004) as in most cases cooperation can be explained by more simple mechanisms such as by-product-benefits (Hammerstein, 2003; Clutton-Brock, 2009). Nevertheless, it shows that (lab) rodents may provide a good model system to investigate the mechanisms and development of cooperation (Łopuch and Popik, 2011).