In our daily life, we often guess what other people are thinking or intending to do, based on their actions. This ability to ascribe thoughts, intentions or feelings to others is known as Theory of Mind.

While we often use our Theory of Mind to understand other humans and interpret social interactions, we can also apply our Theory of Mind to assign feelings and thoughts to animals and even inanimate objects. For example, people watching a movie where the characters are represented by simple shapes, such as triangles, can still see a story unfold, because they infer the triangles’ intentions based on what they see on the screen.

While it is clear that humans have a Theory of Mind, how the brain manages this capacity and whether other species have similar abilities remain open questions. Dureux et al. used animations showing abstract shapes engaging in social interactions and advanced brain imaging techniques to compare how humans and marmosets – a type of monkey that is very social and engages in shared childcare – interpret social cues. By comparing the eye movements and brain activity of marmosets to human responses, Dureux et al. wanted to uncover common strategies used by both species to understand social signals, and gain insight into how these strategies have evolved.

Dureux et al. found that, like humans, marmosets seem to perceive a difference between shapes interacting socially and moving randomly. Not only did their gaze linger longer on certain shapes in the social scenario, but their brain activity also mirrored that of humans viewing the same scenes. This suggests that, like humans, marmosets possess an inherent ability to interpret social scenarios, even when they are presented in an abstract form, providing a fresh perspective on primates’ abilities to interpret social cues.

The findings of Dureux et al. have broad implications for our understanding of human social behavior and could lead to the development of better communication strategies, especially for individuals social cognitive conditions, such as Autism Spectrum Disorder. However, further research will be needed to understand the neural processes underpinning the interpretation of social interactions. Dureux et al.’s research indicates that the marmoset monkey may be the ideal organism to perform this research on.

Theory of Mind (ToM) refers to the capacity to ascribe mental states to other subjects (Carruthers and Smith, 1996; Premack and Woodruff, 1978). Various experimental approaches have been devised to investigate the cognitive processes involved in ToM, including tasks involving text (Happé, 1994), non-verbal pictures (Sarfati et al., 1997), false belief (Wimmer and Perner, 1983), and silent animations featuring geometric shapes. The latter approach is based on Heider and Simmel’s observation that participants attribute intentional actions, human character traits, and even mental states to moving abstract shapes (Heider and Simmel, 1944). Subsequent studies used these animations to test the ability to ascribe mental states in autistic children (Bowler and Thommen, 2000; Klin, 2000).

In the Frith-Happé animations, a large red triangle and a small blue triangle move around the screen (Abell et al., 2000; Castelli et al., 2002; Castelli et al., 2000). In the Random condition, the two triangles do not interact and move purposelessly, in the Goal-Directed (GD) condition the triangles interact but in a purely physical manner (i.e. chase, dancing, fighting and leading) and in the ToM condition the two animated triangles move as if one triangle is reacting to the other’s mental state (i.e. coaxing, surprising, seducing and mocking). Functional imaging studies have demonstrated that the observation of ToM compared to Random animations activates brain regions typically associated with social cognition, including dorso-medial frontal, temporoparietal, inferior and superior temporal cortical regions (Barch et al., 2013; Bliksted et al., 2019; Castelli et al., 2000; Chen et al., 2023; Gobbini et al., 2007; Vandewouw et al., 2021; Weiss et al., 2021; Wheatley et al., 2007).

Although the spontaneous attribution of mental states to moving shapes has been well established in humans, it remains uncertain whether other primate species share this capacity. There is some evidence suggesting that monkeys can attribute goals to agents with varying levels of similarity and familiarity to conspecifics, including human agents, monkey robots, moving geometric boxes, animated shapes, and simple moving dots (Atsumi et al., 2017; Atsumi and Nagasaka, 2015; Krupenye and Hare, 2018; Kupferberg et al., 2013; Uller, 2004). However, the findings in this area are somewhat mixed, with some studies investigating the attribution of goals to inanimate moving objects yielding inconclusive results (Atsumi and Nagasaka, 2015; Kupferberg et al., 2013). Nonhuman primates' spontaneous attribution of mental states to Frith-Happé animations is even less certain. While human subjects exhibit longer eye fixations when viewing the ToM condition compared to the Random condition of the Frith-Happé animations (Klein et al., 2009), a recent eye tracking study in macaque monkeys did not observe similar differences (Schafroth et al., 2021). Similarly, a recent fMRI study conducted on macaques found no discernible differences in activations between ToM and random Frith-Happé animations (Roumazeilles et al., 2021).

In this study, we investigated the behaviour and brain activations of New World common marmoset monkeys (Callithrix jacchus) while they viewed Frith-Happé animations. Living in closely-knit family groups, marmosets exhibit significant social parallels with humans, including prosocial behavior, imitation, and cooperative breeding. These characteristics establish them as a promising nonhuman primate model for investigating social cognition (Burkart et al., 2009; Burkart and Finkenwirth, 2015; Miller et al., 2016). To directly compare humans and marmosets in their response to these animations, we employed high-speed video eye-tracking to record eye movements in eleven healthy humans and eleven marmoset monkeys. Additionally, we conducted ultra-high field fMRI scans on ten healthy humans at 7T and six common marmoset monkeys at 9.4T. These combined methods allowed us to examine the visual behavior and brain activations of both species while they observed the Frith-Happé animations.

To investigate whether marmoset monkeys, like humans, exhibit distinct processing patterns in response to the conditions in Frith-Happé animations (i.e. ToM, GD, and Random conditions), we compared gaze patterns and fMRI activations in both marmosets and human subjects as they watched shortened versions of the Frith-Happé animations (Figure 1).

Figure 1

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Gaze patterns for Frith-Happé’s ToM, GD and Random animations in humans and marmosets

We first investigated in both humans and marmosets whether fixation durations differed between the three conditions (Figure 2A). By conducting mixed analyses of variance (ANOVA), with factors of species (Human vs Marmoset) and condition (ToM vs GD vs Random animation videos), we found a significant interaction between species and condition (F_(2,40)=13.9, p=<0.001, η_p²p2.410). Here we observed longer fixation durations for ToM animation videos (M=432.6ms) as compared to GD videos (M=279.9ms, p=0.008) and Random videos (M=308.2ms, p=0.01) for humans (p=0.029) but not for marmosets (233.7ms for ToM videos, 219.6ms for GD videos and 235.6ms for Random videos, ToM vs GD: p=0.90 and ToM vs Random: p=1).This finding confirms that humans fixate longer in the ToM condition (Klein et al., 2009), whereas marmosets, like macaques (Schafroth et al., 2021), do not show this effect.

Figure 2

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To further analyze the gaze patterns of both humans and marmosets, we next measured the proportion of time subjects looked at each of the triangles in the videos (Figure 2B). We conducted mixed ANOVAs on the proportion of time the radial distance between the current gaze position and each triangle was within 4 visual degrees for each triangle separately.

Importantly, we observed a significant interaction between species and condition for the proportion of time spent looking at the large red triangle (F_(2,40)=9.83, p<0.001, η_p²p2.330). Specifically, both humans (Figure 2B left) and marmosets (Figure 2B right) spent a greater proportion of time looking at the red triangle in ToM compared to the GD and Random videos (for humans, ToM vs GD: Δ=.23, p<0.001 and ToM vs Random: Δ=0.31, p<0.001; for marmosets, ToM vs GD: Δ=0.13, p<0.01 and ToM vs Random: Δ=0.13, p<0.01). However, while humans also allocated a greater proportion of time to the red triangle in GD compared to Random animations (Δ=0.08, p=0.05), marmosets did not show any difference between these two conditions (Δ=0.0003, p=1).

For the small blue triangle, we also observed a significant interaction of species and condition (F_(2,40)=3.54, p=0.04, η_p²p2.151) but no significant pairwise differences were observed following Bonferroni correction. Therefore, humans and marmosets spent the same proportion of time looking at the blue triangle in the three different types of videos (for humans, ToM vs GD: Δ=-0.02, p=1, ToM vs Random: Δ=0.04, p=1 and GD vs Random: Δ=0.07, p=0.23; for marmosets, ToM vs GD: Δ=-0.05, p=0.89, ToM vs Random: Δ=0.07, p=0.66 and GD vs Random: Δ=-0.02, p=1; Figure 2B).

These results highlight the variation in gaze patterns observed in both humans and marmosets when their focus is directed towards the large red triangle during the viewing of ToM, GD, and Random videos. Notably, humans show a gradient of proportion of time spent looking at the red triangle across the three conditions, with the smallest proportion in Random videos and the greatest proportion in ToM videos. In contrast, marmosets exhibit a different pattern, spending more time looking at the red triangle in ToM videos, but allocating the same proportion of time to look at the red triangle in both Random and GD videos. This finding suggests that while humans demonstrate distinct attentional preferences for the red triangle across the three conditions, marmosets exhibit a similar attentional focus on the red triangle in the Random and GD conditions, but their pattern differs in the ToM condition. This suggests that marmosets process the Random and GD conditions in a similar manner, but their processing of the ToM condition is distinct, indicating a differential response to stimuli representing social interactions.

Functional brain activations while watching ToM and Random Frith-Happé’s animations in humans

Given that humans exhibited only minor differences, and marmosets showed no differences in eye movements between the Random and GD animations, coupled with task design constraints, we only used the Random and ToM animations for the fMRI studies in both humans and marmosets (see Materials and methods).

We first investigated ToM and Random animations processing in humans. Figure 3 shows group activation maps for ToM (A) and Random (B) conditions as well as the comparison between ToM and Random conditions (C) obtained for human participants.

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Both ToM (Figure 3A) and Random (Figure 3B) videos activated a large bilateral network. While the same larger areas were activated in both conditions, the specific voxels showing this activation within those areas were typically distinct. In some cases, both conditions activated the same voxels, but the degree of activation differed. This suggests a degree of both spatial and intensity variation in the activations for the two conditions within the same areas. The activated areas included visual areas (V1, V2, V3, V3CD, V3B, V4, V4T, V6A, V7, MT, MST), lateral occipital areas 1, 2, and 3 (LO1, LO2, LO3), temporal areas (FST, PH, PHT, TE2, posterior inferotemporal complex PIT and fusiform face complex FFC), temporo-parietal junction areas (TPOJ2 and TPOJ3), lateral posterior parietal areas also comprising the parietal operculum (supramarginal areas PF, PFt, angular areas PGp and PGi, superior temporal visual area STV, perisylvian language area PSL, medial intraparietal area MIP, ventral and dorsal lateral intraparietal areas LIPv and LIPd, anterior intraparietal area AIP, IPS1, IPS0, 7PC and 5 L), medial superior parietal areas (7am, PCV, 5 mv), secondary somatosensory cortex (S2), premotor areas (6, 55b, premotor eye field PEF, frontal eye field FEF), and frontal areas (8Av, 8 C, IFJp, IFIa).

The ToM condition (Figure 3A) also showed bilateral activations in posterior superior temporal sulcus (STSdp), in temporo-parietal junction area TPOJ1, in ventral visual complex (VVC), in parahippocampal area 3 (PHA3), in lateral posterior parietal areas Pfop and PFcm, in lateral prefrontal areas 8 C, 8Av, 44 and 45, in inferior frontal areas IFSp, IFSa, and in frontal opercular area 5 (FOP5).

To identify brain areas that are more active during the observation of ToM compared to Random videos, we directly compared the two conditions (i.e., ToM animations >Random animations contrast, Figure 3C and Figure 5A). This analysis reveals increased activations for the ToM condition compared to the Random condition in occipital, temporal, parietal and frontal areas. This includes notable differences in the bilateral visual areas V1, V2, V3, V3CD, V4, V4t, MT, MST, as well as the bilateral LO1, LO2 and LO3 regions. The increase extends into the lateral temporal lobe, as observed in the bilateral PH and FST areas and into the more inferior part of the temporal lobe in the bilateral TE2, FFC, and PIT areas. We also found greater activations in the bilateral temporo-parietal junction areas (TPOJ1, TPOJ2, TPOJ3), and along the right STS in STSdp and STSda areas. This extended to left and right parietal areas, especially in the inferior parietal lobule, including the right supramarginal and opercular supramarginal areas (PF, PFm, PFt, Pfop, and PFcm), left PFt, bilateral opercular areas PSL and STV, bilateral angular areas PGp and Pgi, right IPS1, and bilateral IP0. The activation also extended into the superior parietal lobule (right AIP). Moving anteriorly, we observed greater activations during ToM animations in the secondary somatosensory cortex, premotor areas (6 r and PEF), lateral prefrontal areas (8 C, 44, and 45), and the inferior (IFSa, IFSp, IFJa, and IFJp) as well as the opercular (FOP5) frontal areas in the right hemisphere. In contrast, the Random condition exhibited greater activations, than the ToM condition predominantly within the left and right visual areas (V1, V2, V3, V3A, V4) and in dorsolateral (10d and 10r bilateral), lateral (9m left) and medial frontal areas (d32 and a24 bilateral, p24 and s32 right).

At the subcortical level (see Figure 5—figure supplement 1, left panel), we observed enhanced bilateral activations in the cerebellum and in certain areas of the thalamus (namely, the right ventroposterior thalamus or THA-VP, and the left and right dorsoanterior thalamus or THA-DA) under both ToM (Figure 5—figure supplement 1A, left panel) and Random conditions (Figure 5—figure supplement 1B, left panel) when compared to the baseline. Additionally, a small section of the right amygdala was engaged in the ToM condition. We noted more pronounced activations in the posterior lobe of the cerebellum, the right amygdala and thalamus (right THA-VP, right ventroanterior thalamus or THA-VA, and left and right dorsoposterior thalamus or THA-DP) for the ToM condition compared to the Random condition (Figure 5—figure supplement 1C, left panel). No regions showed greater activations for Random condition compared to the ToM condition.

As we used shorter modified versions of the Frith-Happé animations (i.e. videos of 19.5 s instead of 40 s), we also validated our stimuli and our fMRI protocol by comparing the brain responses elicited by ToM animation videos – compared to Random animation videos – obtained in our group of 10 human subjects and those reported by the large group of humans (496) used in the social cognition task of the Human Connectome Project (HCP; Barch et al., 2013), which also used shortened versions of the Frith-Happé animations.

This comparison is shown in Figure 3—figure supplement 1. Overall, we observed similar distinct patterns of brain activations (Figure 3—figure supplement 1A and B), including a set of areas in occipital, temporal, parietal and frontal cortices, as described previously (Figure 3C). The main differences were stronger activations in the left hemisphere in the HCP dataset. Therefore, these results show that our stimuli and our protocol are appropriate to investigate mental state attribution to animated moving shapes.

Functional brain activations while watching ToM and Random Frith-Happé’s animations in marmosets

Having identified the brain regions activated during the processing of ToM or Random videos in human subjects and validated our protocol, we proceeded to use the same stimuli in marmosets. Figure 4 illustrates the brain network obtained for the ToM condition (A), Random condition (B), and the contrast between ToM and Random conditions (C) in six marmosets.

Figure 4

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Both the ToM (Figure 4A) and Random (Figure 4B) animations activated an extensive network involving a variety of areas in the occipito-temporal, parietal and frontal regions. As in human subjects, it should be noted that while both conditions elicited strong activation in some of the same larger areas, these activations might have either occurred in distinct voxels within those areas, or the same voxels were activated to varying degrees for both conditions. This suggests distinct yet overlapping patterns of neural processing for the ToM and Random conditions.

In the occipital and temporal cortex, the activations were located in the visual areas V1, V2, V3, V3A, V4, V4t, V5, V6, MST, the medial (19 M), and dorsointermediate parts (19DI) of area 19, ventral temporal area TH, enthorinal cortex, and lateral and inferior temporal areas TE3 and TEO. Activations were also observed in the posterior parietal cortex, specifically in bilateral regions surrounding the intraparietal sulcus (IPS), in areas LIP, MIP, PE, PG, PFG, PF, V6A, PEC, in the occipito-parietal transitional area (OPt) and in medial part of the parietal cortex (area PGM). More anteriorly, bilateral activations were present in areas 1/2, 3a, 3b of the somatosensory cortex, in primary motor area 4 parts a, b and c (area 4ab and 4c), in area 6 ventral part (6Va) of the premotor cortex and in frontal areas 45 and 8Av.

The ToM condition (Figure 4A) also recruited bilateral activations in areas V5, TE2, FST, Pga-IPa, temporoparietal transitional area (TPt), around the IPS in AIP and VIP, in the internal part (S2I), parietal rostral part (S2PR) and ventral part (S2PV) of the secondary somatosensory cortex, in agranular insular cortex (AI), granular and dysgranular insular areas (GI and DI), retroinsular area (ReI) and orbital periallocortex (OPAI), as well as in premotor cortex in area 8 caudal part (8C), in area 6 dorsocaudal and dorsorostral parts (6DC, 6DR). Additionally, we also observed activations in posterior cingulate areas 23a, 23b, 29d, 30, 24d, and 24b.

Next, we examined the difference between ToM and Random animations (i.e. ToM condition >Random condition contrast, Figure 4C and Figure 5B). We found enhanced bilateral activations for the ToM condition across a range of regions. These encompassed occipital areas V1, V2, V3, V3A, V4, V4t, V5, V6, 19DI, 19M, temporal areas TH, TE2, TE3, FST, MST, TPt, and parietal areas LIP, MIP, VIP, AIP, PE, PG, PFG, OPt, V6A, PEC. Moreover, these activations extended to the somatosensory cortex (areas 1/2, 3 a, 3b, S2I, S2PV), the primary motor cortex (areas 4ab and 4c), lateral frontal areas 6DC, 8C, 6Va, 8Av, 8Ad (left hemisphere), and insular areas (ReI, S2I, S2PV, DI, AI). Additional activations were observed in the OPAI area, medial frontal area 32 and posterior cingulate areas (23 a, 23b, 29d, 30). Contrarily, we did not find any regions exhibiting stronger activations for the Random condition compared to the ToM condition. This further emphasizes the distinctive neural recruitment and processing associated with ToM animations within the marmoset brain.

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At the subcortical level (see Figure 5—figure supplement 1A, right panel), the ToM condition showed involvement of several areas including the bilateral hippocampus, bilateral pulvinar (lateral, medial and inferior parts), bilateral amygdala, and left caudate. On the other hand, the Random condition recruited only the pulvinar (Figure 5—figure supplement 1B, right panel). Upon comparison of the ToM and Random conditions, the ToM animations showed stronger activations in the right superior colliculus (SC), right lateral geniculate nucleus (LGN), left caudate, left amygdala, left hippocampus and certain portions of the right and left pulvinar (lateral and inferior pulvinar; Figure 5—figure supplement 1C, right panel).

In the present study, we investigated whether New-World common marmoset monkeys, like humans, process videos of animated abstract shapes differently when these shapes appear to be reacting to each other (ToM condition) compared to when they interact in a purely physical manner (GD condition) or when they move purposelessly (Random condition). To facilitate a direct comparative analysis between the two primate species, we measured their gaze patterns and brain activations as they viewed the widely-used Frith-Happé’s animations (Abell et al., 2000; Castelli et al., 2000). In these animations, the ToM condition is characterized by one triangle reacting to the other’s mental state, exemplifying behaviors like coaxing, surprising, seducing, and mocking. In the GD condition, the two triangles appear to engage purely physically without any implied mental attribution, depicting behaviors such as chasing, dancing, fighting, and leading. In the Random condition, the two triangles move independently, depicting motions akin to a game of billiards, drifting movements, a star pattern, or a tennis game. In all these animations, the physical interaction of the triangles does appear to follow the laws of physics in a reasonably predictable manner. This is probably most evident in the random ‘billiard’ condition in which the two triangles bounce off the walls. However, the ToM animations also follow Newton’s third law, for example when the small triangle is trying to get inside the box and bounces against it in the ‘seducing’ condition, or when the large triangle pushes the small triangle in the ‘coaxing’ condition.

In our first experiment, we examined the gaze patterns of marmosets and humans during the viewing of these video animations. Klein et al., 2009 reported differing fixation durations for these animations, where the longest fixations were observed for ToM animations, followed by GD animations and the shortest fixations for Random animations. They further reported that the intentionality score - derived from verbal descriptions of the animations - followed a similar pattern: highest for ToM, lowest for Random, and intermediate for GD animations. This validated the degree of mental state attribution according to the categories and established that animations provoking mentalizing (ToM condition) were associated with long fixations. This, in turn, supports the use of fixation durations as a nonverbal metric for mentalizing capacity (Klein et al., 2009; Meijering et al., 2012). Our results with human subjects, which demonstrated longer fixation durations for the ToM animations compared to the GD and Random animations, paralleled those of Klein et al., 2009. However, unlike Klein et al.’s findings, we did not observe intermediate durations for GD animations in our study.

Interestingly, our marmoset data did not align with the human findings but instead resonated more with Schafroth et al., 2021’s observations in macaque monkeys, which did not show significant differences in fixation durations across the three animation types.

Our study went a step further than previous research in humans (Klein et al., 2009) and macaques (Schafroth et al., 2021) by investigating the proportion of time that subjects devoted to looking at the two central figures in the animations: the large red triangle and the small blue triangle. Our results indicate that both humans and marmosets spent significantly more time looking at the large red triangle during ToM, compared to GD and Random animations. Humans also exhibited a preference for the red triangle in the GD over the Random condition, a differentiation not evident in marmosets. This result suggests that marmosets process these conditions similarly, indicating that, unlike humans, they do not seem to discern a marked difference between purposeless and goal-directed motions. However, they did show a distinctive gaze pattern in the ToM condition, pointing to their capacity to potentially perceive or react to animated sequences with complex mental interactions. Our findings revealed no significant differences in gaze patterns towards the smaller blue triangle across the three conditions in both humans and marmosets, potentially due to the perception of the large red triangle as a more salient or socially relevant figure in the interactions.

Together, the observed gaze patterns do not support the idea that marmosets increase their cognitive processing during ToM animations in the same way as humans. However, the findings point to a certain level of sophistication in the marmosets' perception of abstract ToM animations.

Thus, in our second experiment, we investigated the brain networks involved when viewing the ToM and Random Frith-Happé’s animations in humans and marmosets. Previous fMRI studies in humans identified a specific network associated with ToM processing in tasks such as stories, humorous cartoons, false-belief tasks, and social gambling. This network typically includes areas such as the medial frontal gyrus, posterior cingulate cortex, inferior parietal cortex, and temporoparietal junction (Fletcher et al., 1995; Gallagher et al., 2000). Nevertheless, these studies used complex stimuli and yielded heterogeneous results, with varied activations in regions such as the medial and lateral prefrontal cortex, inferior parietal lobule, occipital cortex, and insula across different studies (Carrington and Bailey, 2009). This variability is likely due to the diverse experimental paradigms employed to study ToM. Studies employing Frith-Happé’s animations, which are less complex and more controlled, reported distinct patterns of brain activation when viewing ToM compared to Random animations, involving areas such as the dorso-medial prefrontal cortex, inferior parietal cortex, temporoparietal, inferior and superior temporal regions, and lateral superior occipital regions (Barch et al., 2013; Bliksted et al., 2019; Castelli et al., 2000; Chen et al., 2023; Gobbini et al., 2007; Vandewouw et al., 2021; Weiss et al., 2021; Wheatley et al., 2007).

Our slightly adapted versions of the Frith-Happé animations led to a similar distinct pattern of brain activations, with an exception for the lack of activations in the dorsal part of the medial prefrontal cortex. This discrepancy could be attributable to various factors, including differences in task design, methodological aspects such as statistical power, or variations in participants characteristics. Otherwise, our results and those of the HCP data from Barch et al., 2013, revealed stronger activations for ToM versus Random animations in areas in premotor, prefrontal, parietal, visual, inferior and superior temporal cortices including the STS and temporoparietal junction. Overall, the ToM network we identified, as well as that reported by Barch et al., 2013, appear to be more extensive than those described in studies employing more complex experimental paradigms to study ToM. This aligns with the recent meta-analysis conducted by Schurz et al., 2021, which demonstrated that the network activated by simpler, non-verbal stimuli like social animations differs from the traditional network, with involvement of both cognitive and affective networks (Schurz et al., 2021).

As in humans, the comparison of responses to ToM and Random animations in marmosets revealed activations in occipito-temporal, parietal, and frontal regions. Specifically, activations in the TE areas in marmosets could be equivalent to those observed along the STS in humans (Yovel and Freiwald, 2013). We also observed in both our human subjects and marmosets, activations in the inferior parietal cortex, previously reported in human literature (Carrington and Bailey, 2009; Fletcher et al., 1995; Gallagher et al., 2000). We also found the similar activations in the superior parietal cortex in both our human and marmoset subjects, specifically in the area surrounding the IPS, but this have not been predominantly described in previous work (Carrington and Bailey, 2009; Chen et al., 2023; Fletcher et al., 1995; Gallagher et al., 2000; Gobbini et al., 2007). However, there are also noteworthy differences between our results and those of our human data. Firstly, while we did not observe activations in the medial prefrontal cortex in humans, they were present in marmosets, aligning with previous human fMRI studies (Bliksted et al., 2019; Castelli et al., 2000; Weiss et al., 2021; Wheatley et al., 2007). The marmoset network also included the posterior cingulate cortex and the insula, areas known to be involved in mentalizing and affective processing respectively in human ToM studies that employed more complex stimuli (Fletcher et al., 1995; Gallagher et al., 2000; Wheatley et al., 2007). Finally, a prominent difference between humans and marmosets is the strong activation in the marmoset motor cortex for ToM animations, which was absent in humans, in addition to the differences observed at the subcortical level. Interestingly, we have also recently reported activations in marmoset primary motor cortex during the observation of social interactions (Cléry et al., 2021), suggesting a potential role for the marmoset motor cortex in interaction observation. Regarding the distinct subcortical activations observed in humans and marmosets, it’s important to consider the specific social cognitive demands that might be unique to each species. The involvement of the dorsal thalamus, cerebellum, and a small portion of the amygdala in humans may reflect the complexities of information processing, social cognition, and emotional involvement required to interpret the ToM animations (e.g. Halassa and Sherman, 2019; Janak and Tye, 2015; Van Overwalle et al., 2014). Conversely, the activation of the amygdala and hippocampus in marmosets could suggest a more emotion- and memory-based processing of the social stimuli (e.g. Eichenbaum, 2017; Van Overwalle et al., 2014). However, it’s critical to consider that these interpretations are speculative and would require further study for confirmation.

Together, these findings demonstrate that marmosets, while observing interacting animated shapes as opposed to randomly moving shapes, exhibit enhanced activation in several brain regions previously associated with ToM processing in humans.

Interestingly, our results differed from those obtained by Roumazeilles et al., 2021 in their fMRI study conducted in macaques using the same animations. Roumazeilles and colleagues reported no differences in activation between ToM and Random animations, suggesting that rhesus macaques may not respond to the social cues presented by the ToM Frith-Happé animations. This disparity between our marmoset findings and those of macaques raises intriguing questions about potential differences in the evolutionary development of ToM processing within non-human primates. Marmosets, as New World monkeys, are part of an evolutionary lineage that diverged earlier than the lineage of Old-World monkeys such as macaques. This difference in lineage might lead to distinct evolutionary trajectories in cognitive processing, which could include varying sensitivity to abstract social cues in animations.

In summary, our study reveals novel insights into how New World marmosets, akin to humans, differentially process abstract animations that depict complex social interactions and animations that display purely physical or random movements. Our findings, supported by both specific gaze behaviors (i.e. the proportion of time spent on the red triangle, despite the inconclusiveness of overall fixation) and distinct neural activation patterns, shed light on the marmosets' capacity to interpret social cues embedded in these animations.

The differences observed between humans, marmosets, and macaques underscore the diverse cognitive strategies that primate species have evolved to decipher social information. This diversity may be influenced by unique evolutionary pressures that arise from varying social structures and lifestyles. Like macaque monkeys, humans often live in large, hierarchically organized social groups where status influences access to resources. However, both humans and marmosets share a common trait: a high degree of cooperative care for offspring within the group, with individuals other than the biological parents participating in child-rearing. These distinctive social dynamics of marmosets and humans may have driven the development of unique social cognitive abilities. This could explain their enhanced sensitivity to abstract social cues in the Frith-Happé animations.

Nonetheless, it is crucial to emphasize that even though marmosets respond to the social cues in the Frith-Happé animations, this does not automatically imply that they possess mental-state attributions comparable to humans. As such, future research including a range of tasks, from sensory-affective components to more abstract and decoupled representations of others' mental states (Schurz et al., 2021), will be fundamental in further unravelling the complexities of the evolution and functioning of the Theory of Mind across the primate lineage.

All fMRI and eye tracking data generated and analysed as well as the scripts used have been deposited in Github and the link has been provided in the manuscript. Here the link: https://github.com/audreydureux/Theory-of-mind_Human_Marmosets_Paper (copy archived at Dureux, 2023).

1. 1. Abell F
   2. Happé F
   3. Frith U

   (2000) Do triangles play tricks? Attribution of mental states to animated shapes in normal and abnormal development

   Cognitive Development 15:1–16.

   https://doi.org/10.1016/S0885-2014(00)00014-9

   • Google Scholar
2. 1. Atsumi T
   2. Nagasaka Y

   (2015) Perception of chasing in squirrel monkeys (Saimiri sciureus)

   Animal Cognition 18:1243–1253.

   https://doi.org/10.1007/s10071-015-0893-x

   • PubMed
   • Google Scholar
3. 1. Atsumi T
   2. Koda H
   3. Masataka N

   (2017) Goal attribution to inanimate moving objects by Japanese macaques (Macaca fuscata)

   Scientific Reports 7:40033.

   https://doi.org/10.1038/srep40033

   • PubMed
   • Google Scholar
4. 1. Barch DM
   2. Burgess GC
   3. Harms MP
   4. Petersen SE
   5. Schlaggar BL
   6. Corbetta M
   7. Glasser MF
   8. Curtiss S
   9. Dixit S
   10. Feldt C
   11. Nolan D
   12. Bryant E
   13. Hartley T
   14. Footer O
   15. Bjork JM
   16. Poldrack R
   17. Smith S
   18. Johansen-Berg H
   19. Snyder AZ
   20. Van Essen DC
   21. WU-Minn HCP Consortium

   (2013) Function in the human connectome: task-fMRI and individual differences in behavior

   NeuroImage 80:169–189.

   https://doi.org/10.1016/j.neuroimage.2013.05.033

   • PubMed
   • Google Scholar
5. 1. Bliksted V
   2. Frith C
   3. Videbech P
   4. Fagerlund B
   5. Emborg C
   6. Simonsen A
   7. Roepstorff A
   8. Campbell-Meiklejohn D

   (2019) Hyper- and hypomentalizing in patients with first-episode schizophrenia: fMRI and behavioral studies

   Schizophrenia Bulletin 45:377–385.

   https://doi.org/10.1093/schbul/sby027

   • PubMed
   • Google Scholar
6. 1. Bowler DM
   2. Thommen E

   (2000) Attribution of mechanical and social causality to animated displays by children with autism

   Autism 4:147–171.

   https://doi.org/10.1177/1362361300004002004

   • Google Scholar
7. 1. Burkart JM
   2. Hrdy SB
   3. Van Schaik CP

   (2009) Cooperative breeding and human cognitive evolution

   Evolutionary Anthropology 18:175–186.

   https://doi.org/10.1002/evan.20222

   • Google Scholar
8. 1. Burkart JM
   2. Finkenwirth C

   (2015) Marmosets as model species in neuroscience and evolutionary anthropology

   Neuroscience Research 93:8–19.

   https://doi.org/10.1016/j.neures.2014.09.003

   • PubMed
   • Google Scholar
9. 1. Carrington SJ
   2. Bailey AJ

   (2009) Are there theory of mind regions in the brain? A review of the neuroimaging literature

   Human Brain Mapping 30:2313–2335.

   https://doi.org/10.1002/hbm.20671

   • PubMed
   • Google Scholar
10. Book

    1. Carruthers P
    2. Smith PK

    (1996) Theories of Theories of Mind

    Cambridge University Press.

    https://doi.org/10.1017/CBO9780511597985

    • Google Scholar
11. 1. Castelli F
    2. Happé F
    3. Frith U
    4. Frith C

    (2000) Movement and mind: a functional imaging study of perception and interpretation of complex intentional movement patterns

    NeuroImage 12:314–325.

    https://doi.org/10.1006/nimg.2000.0612

    • PubMed
    • Google Scholar
12. 1. Castelli F
    2. Frith C
    3. Happé F
    4. Frith U

    (2002) Autism, Asperger syndrome and brain mechanisms for the attribution of mental states to animated shapes

    Brain 125:1839–1849.

    https://doi.org/10.1093/brain/awf189

    • PubMed
    • Google Scholar
13. 1. Chen T
    2. Gau SSF
    3. Wu YY
    4. Chou TL

    (2023) Neural substrates of theory of mind in adults with autism spectrum disorder: An fMRI study of the social animation task

    Journal of the Formosan Medical Association = Taiwan Yi Zhi 122:621–628.

    https://doi.org/10.1016/j.jfma.2022.10.009

    • PubMed
    • Google Scholar
14. 1. Cléry JC
    2. Hori Y
    3. Schaeffer DJ
    4. Menon RS
    5. Everling S

    (2021) Neural network of social interaction observation in marmosets

    eLife 10:e65012.

    https://doi.org/10.7554/eLife.65012

    • PubMed
    • Google Scholar
15. 1. Cox RW

    (1996) AFNI: software for analysis and visualization of functional magnetic resonance neuroimages

    Computers and Biomedical Research, an International Journal 29:162–173.

    https://doi.org/10.1006/cbmr.1996.0014

    • PubMed
    • Google Scholar
16. Software

    1. Dureux A

    (2023) Theory-of-Mind_Human_Marmosets_Paper, version swh:1:rev:0453c93c481cd3a1f4bfe21bea21a538c5fc53e7

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:2503b3212158bb8b1bf22db6948d699976310e5a;origin=https://github.com/audreydureux/Theory-of-mind_Human_Marmosets_Paper;visit=swh:1:snp:6a4f9fd13d4d1b955ebe320dce9aa4ad0fe5448f;anchor=swh:1:rev:0453c93c481cd3a1f4bfe21bea21a538c5fc53e7
17. 1. Eichenbaum H

    (2017) Prefrontal-hippocampal interactions in episodic memory

    Nature Reviews. Neuroscience 18:547–558.

    https://doi.org/10.1038/nrn.2017.74

    • PubMed
    • Google Scholar
18. 1. Fletcher PC
    2. Happé F
    3. Frith U
    4. Baker SC
    5. Dolan RJ
    6. Frackowiak RSJ
    7. Frith CD

    (1995) Other minds in the brain: a functional imaging study of “theory of mind” in story comprehension

    Cognition 57:109–128.

    https://doi.org/10.1016/0010-0277(95)00692-r

    • PubMed
    • Google Scholar
19. 1. Gallagher HL
    2. Happé F
    3. Brunswick N
    4. Fletcher PC
    5. Frith U
    6. Frith CD

    (2000) Reading the mind in cartoons and stories: an fMRI study of “theory of mind” in verbal and nonverbal tasks

    Neuropsychologia 38:11–21.

    https://doi.org/10.1016/s0028-3932(99)00053-6

    • PubMed
    • Google Scholar
20. 1. Gilbert KM
    2. Klassen LM
    3. Mashkovtsev A
    4. Zeman P
    5. Menon RS
    6. Gati JS

    (2021) Radiofrequency coil for routine ultra-high-field imaging with an unobstructed visual field

    NMR in Biomedicine 34:e4457.

    https://doi.org/10.1002/nbm.4457

    • PubMed
    • Google Scholar
21. 1. Gilbert KM
    2. Dureux A
    3. Jafari A
    4. Zanini A
    5. Zeman P
    6. Menon RS
    7. Everling S

    (2023) A radiofrequency coil to facilitate task-based fMRI of awake marmosets

    Journal of Neuroscience Methods 383:109737.

    https://doi.org/10.1016/j.jneumeth.2022.109737

    • PubMed
    • Google Scholar
22. 1. Glasser MF
    2. Smith SM
    3. Marcus DS
    4. Andersson JLR
    5. Auerbach EJ
    6. Behrens TEJ
    7. Coalson TS
    8. Harms MP
    9. Jenkinson M
    10. Moeller S
    11. Robinson EC
    12. Sotiropoulos SN
    13. Xu J
    14. Yacoub E
    15. Ugurbil K
    16. Van Essen DC

    (2016) The human connectome project’s neuroimaging approach

    Nature Neuroscience 19:1175–1187.

    https://doi.org/10.1038/nn.4361

    • PubMed
    • Google Scholar
23. 1. Gobbini MI
    2. Koralek AC
    3. Bryan RE
    4. Montgomery KJ
    5. Haxby JV

    (2007) Two takes on the social brain: a comparison of theory of mind tasks

    Journal of Cognitive Neuroscience 19:1803–1814.

    https://doi.org/10.1162/jocn.2007.19.11.1803

    • PubMed
    • Google Scholar
24. 1. Halassa MM
    2. Sherman SM

    (2019) Thalamocortical circuit motifs: A general framework

    Neuron 103:762–770.

    https://doi.org/10.1016/j.neuron.2019.06.005

    • PubMed
    • Google Scholar
25. 1. Happé FGE

    (1994) An advanced test of theory of mind: understanding of story characters’ thoughts and feelings by able autistic, mentally handicapped, and normal children and adults

    Journal of Autism and Developmental Disorders 24:129–154.

    https://doi.org/10.1007/BF02172093

    • PubMed
    • Google Scholar
26. 1. Heider F
    2. Simmel M

    (1944) An experimental study of apparent behavior

    The American Journal of Psychology 57:243.

    https://doi.org/10.2307/1416950

    • Google Scholar
27. 1. Hwang J
    2. Mitz AR
    3. Murray EA

    (2019) NIMH MonkeyLogic: Behavioral control and data acquisition in MATLAB

    Journal of Neuroscience Methods 323:13–21.

    https://doi.org/10.1016/j.jneumeth.2019.05.002

    • PubMed
    • Google Scholar
28. 1. Janak PH
    2. Tye KM

    (2015) From circuits to behaviour in the amygdala

    Nature 517:284–292.

    https://doi.org/10.1038/nature14188

    • PubMed
    • Google Scholar
29. 1. Johnston KD
    2. Barker K
    3. Schaeffer L
    4. Schaeffer D
    5. Everling S

    (2018) Methods for chair restraint and training of the common marmoset on oculomotor tasks

    Journal of Neurophysiology 119:1636–1646.

    https://doi.org/10.1152/jn.00866.2017

    • PubMed
    • Google Scholar
30. 1. Klein AM
    2. Zwickel J
    3. Prinz W
    4. Frith U

    (2009) Animated triangles: an eye tracking investigation

    Quarterly Journal of Experimental Psychology 62:1189–1197.

    https://doi.org/10.1080/17470210802384214

    • PubMed
    • Google Scholar
31. 1. Klin A

    (2000)

    Attributing social meaning to ambiguous visual stimuli in higher-functioning autism and Asperger syndrome: The Social Attribution Task

    Journal of Child Psychology and Psychiatry, and Allied Disciplines 41:831–846.

    • PubMed
    • Google Scholar
32. 1. Krupenye C
    2. Hare B

    (2018) Bonobos prefer individuals that hinder others over those that help

    Current Biology 28:280–286.

    https://doi.org/10.1016/j.cub.2017.11.061

    • PubMed
    • Google Scholar
33. 1. Kupferberg A
    2. Glasauer S
    3. Burkart JM

    (2013) Do robots have goals? How agent cues influence action understanding in non-human primates

    Behavioural Brain Research 246:47–54.

    https://doi.org/10.1016/j.bbr.2013.01.047

    • PubMed
    • Google Scholar
34. 1. Liu C
    2. Ye FQ
    3. Yen CCC
    4. Newman JD
    5. Glen D
    6. Leopold DA
    7. Silva AC

    (2018) A digital 3D atlas of the marmoset brain based on multi-modal MRI

    NeuroImage 169:106–116.

    https://doi.org/10.1016/j.neuroimage.2017.12.004

    • PubMed
    • Google Scholar
35. 1. Marcus DS
    2. Harwell J
    3. Olsen T
    4. Hodge M
    5. Glasser MF
    6. Prior F
    7. Jenkinson M
    8. Laumann T
    9. Curtiss SW
    10. Van Essen DC

    (2011) Informatics and data mining tools and strategies for the human connectome project

    Frontiers in Neuroinformatics 5:4.

    https://doi.org/10.3389/fninf.2011.00004

    • PubMed
    • Google Scholar
36. 1. Meijering B
    2. van Rijn H
    3. Taatgen NA
    4. Verbrugge R

    (2012) What eye movements can tell about theory of mind in a strategic game

    PLOS ONE 7:e45961.

    https://doi.org/10.1371/journal.pone.0045961

    • PubMed
    • Google Scholar
37. 1. Miller CT
    2. Freiwald WA
    3. Leopold DA
    4. Mitchell JF
    5. Silva AC
    6. Wang X

    (2016) Marmosets: A neuroscientific model of human social behavior

    Neuron 90:219–233.

    https://doi.org/10.1016/j.neuron.2016.03.018

    • PubMed
    • Google Scholar
38. 1. Premack D
    2. Woodruff G

    (1978) Does the chimpanzee have a theory of mind?

    Behavioral and Brain Sciences 1:515–526.

    https://doi.org/10.1017/S0140525X00076512

    • Google Scholar
39. 1. Roumazeilles L
    2. Schurz M
    3. Lojkiewiez M
    4. Verhagen L
    5. Schüffelgen U
    6. Marche K
    7. Mahmoodi A
    8. Emberton A
    9. Simpson K
    10. Joly O
    11. Khamassi M
    12. Rushworth MFS
    13. Mars RB
    14. Sallet J

    (2021) Social prediction modulates activity of macaque superior temporal cortex

    Science Advances 7:eabh2392.

    https://doi.org/10.1126/sciadv.abh2392

    • PubMed
    • Google Scholar
40. 1. Sarfati Y
    2. Hardy-Baylé MC
    3. Besche C
    4. Widlöcher D

    (1997) Attribution of intentions to others in people with schizophrenia: a non-verbal exploration with comic strips

    Schizophrenia Research 25:199–209.

    https://doi.org/10.1016/s0920-9964(97)00025-x

    • PubMed
    • Google Scholar
41. 1. Schafroth JL
    2. Basile BM
    3. Martin A
    4. Murray EA

    (2021) No evidence that monkeys attribute mental states to animated shapes in the Heider-Simmel videos

    Scientific Reports 11:3050.

    https://doi.org/10.1038/s41598-021-82702-6

    • PubMed
    • Google Scholar
42. 1. Schurz M
    2. Radua J
    3. Tholen MG
    4. Maliske L
    5. Margulies DS
    6. Mars RB
    7. Sallet J
    8. Kanske P

    (2021) Toward A hierarchical model of social cognition: A neuroimaging meta-analysis and integrative review of empathy and theory of mind

    Psychological Bulletin 147:293–327.

    https://doi.org/10.1037/bul0000303

    • PubMed
    • Google Scholar
43. 1. Smith SM
    2. Jenkinson M
    3. Woolrich MW
    4. Beckmann CF
    5. Behrens TEJ
    6. Johansen-Berg H
    7. Bannister PR
    8. De Luca M
    9. Drobnjak I
    10. Flitney DE
    11. Niazy RK
    12. Saunders J
    13. Vickers J
    14. Zhang Y
    15. De Stefano N
    16. Brady JM
    17. Matthews PM

    (2004) Advances in functional and structural MR image analysis and implementation as FSL

    NeuroImage 23 Suppl 1:S208–S219.

    https://doi.org/10.1016/j.neuroimage.2004.07.051

    • PubMed
    • Google Scholar
44. 1. Uller C

    (2004) Disposition to recognize goals in infant chimpanzees

    Animal Cognition 7:154–161.

    https://doi.org/10.1007/s10071-003-0204-9

    • PubMed
    • Google Scholar
45. 1. Vandewouw MM
    2. Safar K
    3. Mossad SI
    4. Lu J
    5. Lerch JP
    6. Anagnostou E
    7. Taylor MJ

    (2021) Do shapes have feelings? Social attribution in children with autism spectrum disorder and attention-deficit/hyperactivity disorder

    Translational Psychiatry 11:493.

    https://doi.org/10.1038/s41398-021-01625-y

    • PubMed
    • Google Scholar
46. 1. Van Overwalle F
    2. Baetens K
    3. Mariën P
    4. Vandekerckhove M

    (2014) Social cognition and the cerebellum: A meta-analysis of over 350 fMRI studies

    NeuroImage 86:554–572.

    https://doi.org/10.1016/j.neuroimage.2013.09.033

    • Google Scholar
47. 1. Weiss B
    2. Jahn A
    3. Hyatt CS
    4. Owens MM
    5. Carter NT
    6. Sweet LH
    7. Miller JD
    8. Haas BW

    (2021) Investigating the neural substrates of Antagonistic Externalizing and social-cognitive Theory of Mind: an fMRI examination of functional activity and synchrony

    Personality Neuroscience 4:e1.

    https://doi.org/10.1017/pen.2020.12

    • PubMed
    • Google Scholar
48. 1. Wheatley T
    2. Milleville SC
    3. Martin A

    (2007) Understanding animate agents: distinct roles for the social network and mirror system

    Psychological Science 18:469–474.

    https://doi.org/10.1111/j.1467-9280.2007.01923.x

    • PubMed
    • Google Scholar
49. 1. Wimmer H
    2. Perner J

    (1983) Beliefs about beliefs: representation and constraining function of wrong beliefs in young children’s understanding of deception

    Cognition 13:103–128.

    https://doi.org/10.1016/0010-0277(83)90004-5

    • PubMed
    • Google Scholar
50. 1. Yovel G
    2. Freiwald WA

    (2013) Face recognition systems in monkey and human: are they the same thing?

    F1000prime Reports 5:10.

    https://doi.org/10.12703/P5-10

    • PubMed
    • Google Scholar

Author details

1. Audrey Dureux

   Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Canada

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing - original draft, Writing - review and editing

   For correspondence

   audrey.dureux@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1687-8347

2. Alessandro Zanini

   Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Canada

   Contribution

   Conceptualization, Formal analysis, Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

3. Janahan Selvanayagam

   Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Canada

   Contribution

   Formal analysis, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3708-8742

4. Ravi S Menon

   Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Canada

   Contribution

   Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

5. Stefan Everling

   1. Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Canada
   2. Department of Physiology and Pharmacology, University of Western Ontario, London, Canada

   Contribution

   Conceptualization, Supervision, Funding acquisition, Investigation, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9714-9757

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