Brain Topography

, Volume 32, Issue 6, pp 956–964 | Cite as

The Emotional Facet of Subjective and Neural Indices of Similarity

  • Martina Riberto
  • Gorana PobricEmail author
  • Deborah TalmiEmail author
Open Access


Emotional similarity refers to the tendency to group stimuli together because they evoke the same feelings in us. The majority of research on similarity perception that has been conducted to date has focused on non-emotional stimuli. Different models have been proposed to explain how we represent semantic concepts, and judge the similarity among them. They are supported from behavioural and neural evidence, often combined by using Multivariate Pattern Analyses. By contrast, less is known about the cognitive and neural mechanisms underlying the judgement of similarity between real-life emotional experiences. This review summarizes the major findings, debates and limitations in the semantic similarity literature. They will serve as background to the emotional facet of similarity that will be the focus of this review. A multi-modal and overarching approach, which relates different levels of neuroscientific explanation (i.e., computational, algorithmic and implementation), would be the key to further unveil what makes emotional experiences similar to each other.


Emotions Semantic memory Similarity Multivariate pattern analysis 


Emotional similarity refers to the similarity between the feelings that stimuli evoke in us. Poets and storytellers routinely use the power of emotional similarity to convey the emotional tone of a situation by analogy, for example, when the sadness that follows the breakup of a relationship is likened to that we feel when the weather is bad. As the famous song goes, it is ‘stormy weather, since my man and I ain’t together, keeps raining all the time…’. According to Bruner, stimuli that are very different visually and semantically may nevertheless be perceived as similar to each other because of the feelings they evoke in us (Bruner 2017). For example, we may judge an image of a homeless person begging for food and an image of a businesswoman talking on the phone as different, even if the pictures are taken at the same street corner, because one evokes a negative feeling and one a neutral feeling. On the contrary, the same image of a beggar and an image of a person injured in a car accident may be evaluated as more similar if both evoke negative feelings, even if the pictures are taken in different places around the world. In Bruner’s discussion, emotional similarity is considered an orthogonal dimension to the visual and semantic dimensions of a stimulus. Alternatively, the emotional facet of our experience of a stimulus may be considered part of its semantic meaning; in that case, emotional similarity may be reduced to a specific form of semantic similarity. This may be more appropriate when a person groups together neutral stimuli that they have experienced while the person is in the same mood. In this review, we define emotional similarity as the similarity between the emotional dimension of stimuli in the representational space. This space is in part objective and shared among individuals, and in part subjective and in continuous interaction with our experience.

The majority of research on similarity perception that has been conducted to date has focused on non-emotional stimuli, such as words, object, shapes, faces and scenes. In these studies (Goldstone et al. 1997; Golonka and Estes 2009; Greene et al. 2014; Iordan et al. 2015; King et al. 2019), participants were involved in explicit similarity judgement tasks. In others (Haxby et al. 2001; Kriegeskorte et al. 2008a, b, Haxby et al. 2011; Bruffaerts et al. 2013; Clarke and Tyler 2014; Guntupalli et al. 2016; Neyens et al. 2017), the main interest was the neural similarity, namely the similarity among neural representations associated with non-emotional stimuli during tasks not related to the similarity judgements. By contrast, less is known about what makes people perceive richer, life-like events as similar, and even less when these are emotional. Understanding the cognitive and neural mechanisms underlying emotional similarity may have implications for research on categorisation (Barrett 2004, 2017; Barsalou 2017), memory of emotional experiences (Talmi and McGarry 2012; Leal et al. 2014; Leal et al. 2018), and generalisation (Schechtman et al. 2010; Laufer and Paz 2012; Dunsmoor et al. 2013). From a clinical perspective, the study of emotional similarity could help in understanding why patients with anxiety disorders overgeneralise and judge a variety of subsequent experiences to be similar to the original fearful one (Lissek et al. 2009; Laufer et al. 2016).

Below, we review the major findings and debates in the literature on similarity, with the goal of placing the concept of ‘emotional similarity’ within the context of relevant research. With this aim, we will summarise two lines of research, one focused on explicit similarity judgements and the other on neural similarity. This is because both of them provide interesting information about what makes two stimuli similar, in terms of both cognitive dimensions and neural mechanisms. First, we will focus on semantic similarity, namely the similarity among non-emotional stimuli. We will use this literature as background for the emotional facet of the similarity, and to ask how the emotional similarity could be incorporated. Is emotional similarity a facet of semantic similarity or is a further dimension in a complex semantic space? We will end by proposing future directions in this research field.

Semantic Similarity

We may judge two stimuli, such as a blue circle and a blue ellipse, as similar, because they share some features (the rounded shape and blue colour). Because of the number of properties that they share, we will consider them more similar than a blue ellipse and a pink square. This is line with the ‘contrast model’, which posited that similarity between two items is a function of their common features weighed against their distinctive features (Tversky 1977). The ‘contrast model is limited in that it fails to consider the relationships among features (Markman and Gentner 1993, 1994, 1997). These include thematic and taxonomic relationships, which widely contribute to semantic memory and similarity judgements (Lin and Murphy 2001; Ralph et al. 2010; Schwartz et al. 2011; Hoffman et al. 2013).

Milk paired with jam is an example of thematic relationship. Thematic relationships are defined as any temporal, spatial, causal, or functional relationships between objects, which perform complementary roles in the same scenario or event (e.g., breakfast) (Estes et al. 2011). It is widely known in the semantic memory literature that people judge thematically related stimuli to be more similar to each other than other stimuli (Simmons and Estes 2008; Golonka and Estes 2009; Estes et al. 2011; Chen et al. 2013). The paradigmatic stimuli are natural, complex pictures (Lang et al. 2008; Marchewka et al. 2014). For these stimuli, thematic relationships can arise from affordances (Maguire 2010), namely the possible actions that a person can perform in a specific situation. As shown by Greene et al. (2014), affordances may even be the most salient dimension in the categorisation of natural scenes. In that study, participants categorised natural complex pictures mainly according to affordance, rather than visual or taxonomic similarity (Greene et al. 2014).

Labrador and Chihuahua are taxonomically similar. While visually these animals are different (different colour, size, etc.), they are also similar, because they share some features (both bark and are four-legged), which once related bring out the category dogs. Thus, we group these items in the same category, dogs, and judge them as more similar than items from different categories (Wisniewski and Bassok 1999; Chen et al. 2013; Xiao et al. 2016; Xu et al. 2018). People also generalise these properties to new items with similar features (e.g., German Shepherd), and attribute to these items extra features that define the category, even if those were never directly experienced (Jackson et al. 2015). Features-based categories are organised hierarchically in semantic memory (Rosch et al. 1976). Within this hierarchy, it is often possible to distinguish between different levels: the broadest level is the superordinate (e.g., animals), then the basic (e.g., dogs) and then the subordinate (e.g., Labrador). Although some examples do not fit this neat classification (e.g., screwdriver or lawnmower) and there are a number of contradictory findings (Rogers and Patterson 2007; Taylor et al. 2012), many studies showed that participants are more accurate and faster in categorising objects at the basic level than at the superordinate and the subordinate level (Anglin 1977, Horton et al. 1980; Murphy and Brownell 1985; Mack et al. 2009; Iordan et al. 2015). Many of the stimuli in the emotional cognition literature have taxonomic relationships. In the IAPS set, for example, a picture of a man pointing a gun and a man wielding a knife are subordinates of the basic level ‘aggravated assault’. Emotional events are the core of our life stories and their categorisation, as well as the similarity among them, are fundamental to make them meaningful. However, most of the studies focused on the neural mechanisms underlying the perception of similarity among neutral stimuli and on the neural representations of non-emotional stimuli during cognitive and perceptual tasks.

Neuroimaging Studies

It is possible to map in the brain the similarity structure observed at behavioural level, by using innovative Multivariate Pattern Analysis (MVPA) methods. Among them, Representational Similarity Analysis (RSA) gained popularity in neuroscience in the last decade to investigate the cognitive and neural mechanisms of perceived similarity. This technique allows combining neural evidence with behavioural and computational data by calculating their correlation. In this way, it is possible to test whether and where the similarity structure observed at behavioural level is represented in the brain. In addition, this correlational-based technique examines the correlation between the neural representations of stimuli, as it is measured through the BOLD signal during cognitive tasks in fMRI, to draw conclusions about their similarity (Kriegeskorte et al. 2008a, b, 2012; Nili et al. 2014). In a recent MVPA study, Iordan et al. (2015) explored how the different levels of semantic categories are represented across the occipitotemporal cortex. They hypothesised that categorisation may be an emergent property of the human ventral visual system. In order to test this hypothesis, they calculated the category boundary effect as the difference between cohesiveness (within-category neural similarity) and distinctiveness (between-categories neural similarity). This quantity provides a measure of how well categories are separated at each taxonomic level. For example, at the basic level, cohesion for ‘dogs’ is defined as the average correlation between voxel activations associated with the presentation of a ‘dog’ and any other type of ‘dog’. On the other side, at the basic level, distinctiveness for ‘dogs’ is defined as average correlation between voxel activations associated with the presentation of a ‘dog’ and, for example, a ‘flower’. They found high cohesiveness in V1, such that patterns elicited by subordinates are not distinguishable. As we move up in the ventral visual stream (i.e., lateral occipital cortex, posterior middle temporal gyrus, inferior temporal gyrus), the categories become more sharply distinguishable at basic level (Iordan et al. 2015). This is in line with other studies, which showed that inferior temporal regions are involved in semantic categorisation and perceived similarity of objects (Malach et al. 1995; Martin et al. 1996; Epstein and Kanwisher 1998; Grill-Spector et al. 1998; Kriegeskorte et al. 2008a, b; Charest et al. 2014) and faces (Haxby et al. 2001, 2011; Guntupalli et al. 2016). Thus, according to these studies, semantic knowledge is not ‘located in’ one brain area, but it arises from distinct patterns of response that are distributed across brain regions (Haxby et al. 2001).

A similar perspective is reflected in the ‘hub and spoke’ model, an influential model of semantic memory. According to this model, semantic categorisation is the result of an interaction between different modality-specific cortices (i.e., the ‘spokes’) distributed across the brain, and a transmodal ‘hub’, located in the ventral part of the anterior temporal lobe (vATL) (Rogers et al. 2004; Patterson et al. 2007; Ralph et al. 2010; Lambon Ralph 2014). In particular, the ‘hub’ integrates sensory, motor and verbal information that together define a concept, and which are encoded in the different ‘spokes’. It also extracts inter-stimulus relationships that go beyond visual similarities, such as taxonomic and thematic relationships, and generalise these relationships to new stimuli with similar features. Many neuropsychological and neuroimaging findings, both in patients with semantic dementia (Bozeat et al. 2000; Nestor et al. 2006; Ralph et al. 2007; Jefferies et al. 2009; Guo et al. 2013) and in healthy controls (Pobric et al. 2007; Visser et al. 2012) support this model. The vATL interacts also with other brain regions, which are part of the semantic control (SC) network, to generate context-dependent semantic representations. This network include the posterior middle temporal gyrus, the prefrontal cortex, the intraparietal sulcus, the pre-supplementary motor area and the anterior cingulate cortex (for a review on this topic, see (Ralph et al. 2017)). Finally, as reviewed by Rice et al. (2018), the ATL is also involved in processing socially relevant semantic concepts, including person face knowledge and emotional concepts (Zahn et al. 2007, 2009; Olson et al. 2013; Collins and Olson 2014; Wang et al. 2017), because of its connection with the amygdala and orbitofrontal regions through the uncinated fasciculus (Highley et al. 2002; Von Der Heide et al. 2013). These regions might be thought as ‘emotional spokes’, which interact with the ATL to generate emotional concepts. Future studies are needed to test this hypothesis.

To summarise, semantic similarity supports core cognitive functions, such as semantic categorisation and semantic memory. Recent neuroimaging findings showed that conceptual knowledge is a widely distributed neural network, which include occipitotemporal and prefrontal regions. Different model have been proposed to explain the cognitive and neural mechanisms of semantic knowledge and similarity judgments (Riddoch et al. 1988; Damasio 1989; Caramazza et al. 1990). However, to our knowledge, these perspectives are limited to non-emotional stimuli, and have never been tested in the context of emotional similarity and categorisation.

Emotional Similarity

While the majority of the studies about similarity judgements focused on non-emotional stimuli, a vast literature in emotion research asks what makes two emotional stimuli similar. To answer this question, participants are often asked to sort simple stimuli, such as words or faces, according to their similarity, or to rate the similarity among them on a Likert scale (Osgood 1952; Schlosberg 1952; Russell and Pratt 1980; Russell and Bullock 1985; Roberts and Wedell 1994; Halberstadt et al. 1995, 1997; Calvo and Nummenmaa 2008; Said et al. 2010; Koch et al. 2016; van Tilburg and Igou 2017). The paradigmatic finding is that participants judge the similarity according to two dimensions, the valence and the arousal of the stimuli. These dimensions are not explicitly used during the similarity judgements, but rather they represent implicit components of the cognitive structure underlying these stimuli (Barrett 2004). We can map this cognitive structure by using Multidimensional Scaling (MDS) procedure. When represented in a geometric space, defined by valence and arousal as orthogonal axes, emotional stimuli are placed along the perimeter of a circle. This is the core idea of Russell’s ‘circumplex model of emotion’ (Russell and Pratt 1980) and other dimensional theories of emotion (Mehrabian 1980; Watson and Tellegen 1985; Bradley et al. 1992; Plutchik 2001), which have been widely used in emotion research (Zevon and Tellegen 1982; Barrett and Russell 1999; Damasio 2003; Lang et al. 2008; Kuppens et al. 2013; Marchewka et al. 2014; Mäntylä et al. 2016; Yu et al. 2016). In this representational space, the distance among stimuli reflects their similarity, with short distances representing high similarity. The multi-arrangement method, a direct way to measure similarity, is based on this principle (Kriegeskorte and Mur 2012). This quick and efficient task is used for experiments with a relatively large set of stimuli, because participants simultaneously judge the similarity among many stimuli displayed together (Chikazoe et al. 2014; Chavez and Heatherton 2015), as opposed to a pairwise presentation.

Emotional similarity can be also quantified indirectly. Asking participants to rate the semantic relatedness between words (Talmi and Moscovitch 2004) or pictures (Sison and Mather 2007; Talmi et al. 2007; Gallo et al. 2009; Talmi and McGarry 2012) is an example of an indirect measure of similarity. This is because the higher the relatedness between concepts in semantic memory, the higher the similarity between them. These studies suggest that emotions increase the semantic relatedness, resulting in higher ratings among negative emotional stimuli compared to neutral ones. This might lead to a better organisation of emotional stimuli, and might explain the advantage they have in immediate memory tests (Talmi and McGarry 2012, 2013).

The findings above indicate that emotion increases perceived similarity between stimuli. Greater perceived similarity among emotional stimuli might be related to the effect of arousal on hippocampal pattern separation, the ability to store similar experiences in distinct and non-overlapping representations. This might explain why participants find it harder to discriminate between targets and similar lures when those are emotional (Segal et al. 2012; Leal et al. 2014, 2018; Mattar and Talmi 2019; Zheng et al. 2019). Other studies suggested that the arousal might also increase the generalisation among neutral stimuli during fear condition paradigms, both in healthy controls (Schechtman et al. 2010; Laufer and Paz 2012; Dunsmoor et al. 2013) and in patients with anxiety disorders (Lissek et al. 2009; Laufer et al. 2016). The generalisation is another example of indirect measure of similarity, because the higher the similarity between stimuli, the wider the generalisation between these stimuli.

Neuroimaging Studies

The number of neuroimaging studies in emotional similarity research is limited. To our knowledge, no neuroimaging studies have investigated neural differences in explicit judgments of similarity among the prevalent stimuli in research of emotional cognition, namely, natural, complex neutral and emotional picture scenes. Only a handful of studies have combined behavioural measures of similarity with neural data by using RSA. The results of these studies might help in understand the brain regions, which code the similarity among emotional stimuli. In these studies, during the fMRI scan participants were asked to attend to pictures while performing non-emotional rating tasks (e.g., ratings of indoor versus outdoor scenes). This was combined with behavioural judgements of similarity among the experimental stimuli. They found that brain activity patterns in regions involved in emotional processing, such as the insula and the ventromedial prefrontal cortex (VMPFC), represent the similarity structure between emotional and neutral stimuli observed at behavioural level (Chavez and Heatherton 2015; Levine et al. 2018).

Additional, indirect evidence about what make two emotional stimuli similar to each other at neural level is gleaned from neuroimaging investigations of emotional processing and categorisation. These mainly aimed at investigating how the brain codes the relationship between specific emotions, supporting either a categorical (Ekman and Friesen 1976), a dimensional (Russell and Pratt 1980), or a constructionist view (Barrett 2017). In these studies, participants were asked either to passively look at images, to attend to the feelings they evoke, to rate the valence and the arousal of these feelings, or to rate the valence and arousal of the picture and categorise it according to emotional labels (Costa et al. 2014; Ohira et al. 2006; Machajdik et al. 2010; Baucom et al. 2012; Sakaki et al. 2012; Yuen et al. 2012; Edmiston et al. 2013; den Stock et al. 2014; Motzkin et al. 2015; Hrybouski et al. 2016). The results of these studies were discrepant, probably because of the different perspectives of emotions adopted and methods used to elicit the emotions (Wager et al. 2015). In particular, locationist studies attempted to discover the unique brain feature associated with each emotional category, by adopting a one (brain region)-to-one (emotion) approach. For example, fear has been consistently localised in the amygdala (LaBar et al. 1998; LeDoux 2007; Öhman 2009), disgust in the anterior insula (Calder 2003; Wicker et al. 2003; Jabbi et al. 2008), sadness in the anterior cingulate cortex (Phan et al. 2002; Murphy et al. 2003), anger in the orbitofrontal cortex (Murphy et al. 2003; Vytal and Hamann 2010), and happiness in the dorsomedial prefrontal cortex (DMPFC) (Lindquist et al. 2012). As highlighted by Lindquist et al. (2012), supports for a locationist account would be found if instances of an emotion category (e.g., fear) are consistently and specifically associated with increased activity in a brain region (or in a set of regions within a network) across multiple published studies. However, first, many studies showed that the aforementioned regions are associated with multiple categories of emotions (Lindquist et al. 2012), and during many other sensory, perceptual and cognitive functions (Yarkoni et al. 2011; LeDoux 2012). Second, it is not clear whether the findings from the locationist literature are reliable enough or consistent across studies (Wager et al. 2015). For these reasons, a psychological constructionist approach to emotion is preferable. According to this perspective, emotions are ‘situated conceptualisations’, that is, subjective interpretations of what is happening around us. Emotions arise from the interaction among many brain regions, interconnected in large-scale networks, according to a many-to-one approach. These brain regions are implicated not only in emotional processing, but also in more ‘cognitive’ functions, such as conceptualization (simulation of previous experiences), language (representation and retrieval of semantic concepts), and executive attention (attention and working memory).

However, this represents only indirect evidence of the neurobiological underpinnings of emotional similarity. The neural mechanism that allows emotion to influence overall perceptions of similarity is still unknown, as are putative neural differences during explicit judgements of similarity between natural, complex neutral and emotional events.

Limitations in Emotional Similarity Literature

Although the emotional similarity literature provided interesting and relevant results, it is also limited in several important ways. First, most studies used decontextualized, simple stimuli, such as emotional faces, or words, a choice that yields more experimental control at the cost of ecological validity. This is particularly important because the known influence of context on emotional categorisation (Barrett 2017). For example, Avierez et al. (2008) observed this effect in a study about emotional categorisation, where participants were asked to indicate the category that best describes the facial expressions. They were less accurate in categorising sad faces embedded in a fearful than in a sad context: they were more likely to categorise sad faces as fearful when the faces appeared in a fearful context than when they appeared in a sad context. The same effect was observed in the categorisation of disgusted faces embedded in a pride context (Aviezer et al. 2008). Future studies in emotional similarity should adopt complex stimuli, which depict both emotional and neutral real-world scenes, such as those provided in well-validated datasets, the International Affective Picture System (IAPS) (Lang et al. 2008) and the Nencki Affective Picture System (NAPS) (Marchewka et al. 2014). So far, these more complex stimuli have seldom been used to study emotional similarity (Gallo et al. 2009; Talmi and McGarry 2012; Chikazoe et al. 2014; Chavez and Heatherton 2015; Levine et al. 2018).

As hinted above, one of the reasons that research on semantic memory and emotional similarity shied away from these more life-like picture scenes might be because there are many factors to control for during the stimuli selection. To mention some of them: the low-level visual measures (e.g., luminance, contrast, and color), the visual complexity of the pictures, the different degrees of similarity among taxonomic levels, the action(s) that the situation can afford, and the thematic similarity within emotional stimuli. In particular, as explained by Talmi and McGarry (2012), emotional stimuli are more thematically inter-related than the neutral stimuli found in validated datasets. For example, the term car accident may be related to hospital, and then to death in a common scenario, while neutral stimuli, such as architecture, telephone and laundry, are less inter-related thematically. In addition, the range of themes within the set of negative and arousing pictures (e.g. death, violence, car accidents, hospital scenes, and assaults) is reduced compared to those within the neutral images. This is also in line with higher ratings of content overlap among arousing (both positive and negative) than neutral IAPS stimuli, observed by Gallo et al. (2009).

To our knowledge, there are no studies, which controlled for all these factors, and this represent a further limitation in emotional similarity literature. For example, few recent studies have controlled complex pictures (positive, negative, neutral) for visual properties, as well as for some elements of semantic similarity—animacy (Chikazoe et al. 2014) and social/non-social (Chavez and Heatherton 2015). However, like other studies (Yuen et al. 2012; Levine et al. 2018), they did not control the stimuli for thematic similarity. For example, in the study by Chavez et al. (2015), the negative categories (i.e., social: ‘depiction of pain’ and ‘people crying’; non-social: ‘polluted water’ and ‘dirty toilet’) look more thematically related compared to neutral (i.e., social: ‘person at a computer’ and ‘person on the phone’; non-social: ‘a stack of book’ and ‘a spoon’) (Chavez and Heatherton 2015). It is relevant to control for these factors to be able to decouple the effect of emotions and of other factors (e.g., thematic similarity) on the overall perception of similarity, both at behavioural and at neural level. For example, in an unpublished pilot study, we hypothesised higher similarity ratings within 10 negative versus 10 neutral complex pictures, randomly selected from the NAPS database. The results supported our hypothesis. However, we could not conclude whether this effect was related to the emotional nature of the pictures or to a bias in the stimulus selection. This is because we did not control for the higher thematic similarity within the emotional pictures: the range of emotional themes was reduced compared to that in the neutral set. The same reasoning would be valid at neural level, if we observe higher similarity within the activity patterns in occipitotemporal regions associated with emotional than neutral stimuli. Indeed, without a method to select natural scenes in a way that is representative of their frequency in the environment it is difficult to conclude that emotional stimuli are represented as more similar at neural level than neutral stimuli. To our knowledge, no studies investigated behavioural or neural differences between neutral and emotional complex stimuli during direct similarity judgements.

Conclusion and Future Directions

Emotional similarity is a core construct in neuroscience, because it supports many cognitive functions, including categorization, memory, and learning. It is also involved in mechanisms underlying psychiatric conditions, such as anxiety disorders. However, very little is known about what makes us perceive real-life emotional experiences as similar. At behavioral, or computational, level, most of the studies showed that we implicitly consider the valence and the arousal as relevant dimensions during similarity judgements. Although these studies were very successful in relating behavioral and neural data using innovative MVPA, they mainly used very simple and ‘non-naturalistic’ emotional stimuli.

At neural, or implementation level, we gleaned indirect evidence about brain regions involved in emotional similarity from research on the structure of the emotional representation of complex stimuli. However, they do not explain which mechanisms lead to the activity associated with those stimuli. As suggested by Barsalou (2017), this is a common mistake in neuroscience: most of the studies related the computational and the implementation levels, ignoring the algorithmic level, namely the latent mechanisms within the ‘system’ brain ‘that performs the task’ (Barsalou 2017). Future studies should relate all these levels of explanation in MVPA emotional similarity studies, which will benefit of new and well-controlled set of stimuli. This may help in unveiling the influence of emotional similarity on the overall perception of similarity. Finally, we might discover any neural and behavioral differences in this perception between emotional and neutral real-life events, to understand whether emotional similarity is a facet of semantic similarity or a further dimension in a complex semantic space.



  1. Anglin JM (1977) Word, object, and conceptual development. WW Norton, New YorkGoogle Scholar
  2. Aviezer H, Hassin RR, Ryan J, Grady C, Susskind J, Anderson A, Moscovitch M, Bentin S (2008) Angry, disgusted, or afraid? studies on the malleability of emotion perception. Psychol Sci 19(7):724–732PubMedGoogle Scholar
  3. Barrett LF (2004) Feelings or words? understanding the content in self-report ratings of experienced emotion. J Personal Soc Psychol 87(2):266Google Scholar
  4. Barrett LF (2017) The theory of constructed emotion: an active inference account of interoception and categorization. Soc Cogn Affect Neurosci 12(1):1–23PubMedGoogle Scholar
  5. Barrett LF, Russell JA (1999) The structure of current affect: controversies and emerging consensus. Curr Dir Psychol Sci 8(1):10–14Google Scholar
  6. Barsalou LW (2017) What does semantic tiling of the cortex tell us about semantics? Neuropsychologia 105:18–38PubMedGoogle Scholar
  7. Baucom LB, Wedell DH, Wang J, Blitzer DN, Shinkareva SV (2012) Decoding the neural representation of affective states. Neuroimage 59(1):718–727PubMedGoogle Scholar
  8. Bozeat S, Ralph MAL, Patterson K, Garrard P, Hodges JR (2000) Non-verbal semantic impairment in semantic dementia. Neuropsychologia 38(9):1207–1215PubMedGoogle Scholar
  9. Bradley MM, Greenwald MK, Petry MC, Lang PJ (1992) Remembering pictures: pleasure and arousal in memory. J Exp Psychol 18(2):379Google Scholar
  10. Bruffaerts R, Dupont P, Peeters R, De Deyne S, Storms G, Vandenberghe R (2013) Similarity of fMRI activity patterns in left perirhinal cortex reflects semantic similarity between words. J Neurosci 33(47):18597–18607PubMedPubMedCentralGoogle Scholar
  11. Bruner J (2017) A study of thinking. Routledge, AbingdonGoogle Scholar
  12. Calder AJ (2003) Disgust discussed. Ann Neurol 53(4):428Google Scholar
  13. Calvo MG, Nummenmaa L (2008) Detection of emotional faces: salient physical features guide effective visual search. J Exp Psychol 137(3):471Google Scholar
  14. Caramazza A, Hillis AE, Rapp BC, Romani C (1990) The multiple semantics hypothesis: multiple confusions? Cogn Neuropsychol 7(3):161–189Google Scholar
  15. Charest I, Kievit RA, Schmitz TW, Deca D, Kriegeskorte N (2014) Unique semantic space in the brain of each beholder predicts perceived similarity. Proc Natl Acad Sci USA 111(40):14565–14570PubMedGoogle Scholar
  16. Chavez RS, Heatherton TF (2015) Representational similarity of social and valence information in the medial pFC. J Cogn Neurosci 27(1):73–82PubMedPubMedCentralGoogle Scholar
  17. Chen Q, Li P, Xi L, Li F, Lei Y, Li H (2013) How do taxonomic versus thematic relations impact similarity and difference judgments? an ERP study. Int J Psychophysiol 90(2):135–142PubMedGoogle Scholar
  18. Chikazoe J, Lee DH, Kriegeskorte N, Anderson AK (2014) Population coding of affect across stimuli, modalities and individuals. Nat Neurosci 17(8):1114PubMedPubMedCentralGoogle Scholar
  19. Clarke A, Tyler LK (2014) Object-specific semantic coding in human perirhinal cortex. J Neurosci 34(14):4766–4775PubMedPubMedCentralGoogle Scholar
  20. Collins JA, Olson IR (2014) Beyond the FFA: the role of the ventral anterior temporal lobes in face processing. Neuropsychologia 61:65–79PubMedGoogle Scholar
  21. Costa T, Cauda F, Crini M, Tatu MK, Celeghin A, de Gelder B, Tamietto M (2014) Temporal and spatial neural dynamics in the perception of basic emotions from complex scenes. Soc Cogn Affect Neurosci 9(11):1690–1703PubMedGoogle Scholar
  22. Damasio AR (1989) The brain binds entities and events by multiregional activation from convergence zones. Neural Comput 1(1):123–132Google Scholar
  23. Damasio A (2003) Feelings of emotion and the self. Ann N Y Acad Sci 1001(1):253–261PubMedGoogle Scholar
  24. den Stock JV, Vandenbulcke M, Sinke CB, de Gelder B (2014) Affective scenes influence fear perception of individual body expressions. Hum Brain Mapp 35(2):492–502PubMedGoogle Scholar
  25. Dunsmoor JE, Kragel PA, Martin A, LaBar KS (2013) Aversive learning modulates cortical representations of object categories. Cereb Cortex 24(11):2859–2872PubMedPubMedCentralGoogle Scholar
  26. Edmiston EK, McHugo M, Dukic MS, Smith SD, Abou-Khalil B, Eggers E, Zald DH (2013) Enhanced visual cortical activation for emotional stimuli is preserved in patients with unilateral amygdala resection. J Neurosci 33(27):11023–11031PubMedPubMedCentralGoogle Scholar
  27. Ekman P, Friesen WV (1976) Measuring facial movement. Environ Psychol Nonverbal Behav 1(1):56–75Google Scholar
  28. Epstein R, Kanwisher N (1998) A cortical representation of the local visual environment. Nature 392(6676):598PubMedGoogle Scholar
  29. Estes Z, Golonka S, Jones LL (2011) Thematic thinking: the apprehension and consequences of thematic relations. Psychol Learn Mot 54:249–294Google Scholar
  30. Gallo DA, Foster KT, Johnson EL (2009) Elevated false recollection of emotional pictures in young and older adults. Psychol Aging 24(4):981PubMedPubMedCentralGoogle Scholar
  31. Gentner D, Markman AB (1994) Structural alignment in comparison: no difference without similarity. Psychol Sci 5(3):152–158Google Scholar
  32. Gentner D, Markman AB (1997) Structure mapping in analogy and similarity. Am Psychol 52(1):45Google Scholar
  33. Goldstone RL, Medin DL, Halberstadt J (1997) Similarity in context. Mem Cogn 25(2):237–255Google Scholar
  34. Golonka S, Estes Z (2009) Thematic relations affect similarity via commonalities. J Exp Psychol Learn Mem Cogn 35(6):1454PubMedGoogle Scholar
  35. Greene MR, Baldassano C, Esteva A, Beck DM Fei-Fei L (2014) Affordances provide a fundamental categorization principle for visual scenes. arXiv preprint arXiv:1411.5340Google Scholar
  36. Grill-Spector K, Kushnir T, Edelman S, Itzchak Y, Malach R (1998) Cue-invariant activation in object-related areas of the human occipital lobe. Neuron 21(1):191–202PubMedGoogle Scholar
  37. Guntupalli JS, Wheeler KG, Gobbini MI (2016) Disentangling the representation of identity from head view along the human face processing pathway. Cereb Cortex 27(1):46–53PubMedCentralGoogle Scholar
  38. Guo CC, Gorno-Tempini ML, Gesierich B, Henry M, Trujillo A, Shany-Ur T, Jovicich J, Robinson SD, Kramer JH, Rankin KP (2013) Anterior temporal lobe degeneration produces widespread network-driven dysfunction. Brain 136(10):2979–2991PubMedPubMedCentralGoogle Scholar
  39. Halberstadt JB, Niedenthal PM (1997) Emotional state and the use of stimulus dimensions in judgment. J Pers Soc Psychol 72(5):1017PubMedGoogle Scholar
  40. Halberstadt JB, Niedenthal PM, Kushner J (1995) Resolution of lexical ambiguity by emotional state. Psychol Sci 6(5):278–282Google Scholar
  41. Haxby JV, Gobbini MI, Furey ML, Ishai A, Schouten JL, Pietrini P (2001) Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293(5539):2425–2430PubMedGoogle Scholar
  42. Haxby JV, Guntupalli JS, Connolly AC, Halchenko YO, Conroy BR, Gobbini MI, Hanke M, Ramadge PJ (2011) A common, high-dimensional model of the representational space in human ventral temporal cortex. Neuron 72(2):404–416PubMedPubMedCentralGoogle Scholar
  43. Highley JR, Walker MA, Esiri MM, Crow TJ, Harrison PJ (2002) Asymmetry of the uncinate fasciculus: a post-mortem study of normal subjects and patients with schizophrenia. Cereb Cortex 12(11):1218–1224PubMedGoogle Scholar
  44. Hoffman P, Ralph MAL, Rogers TT (2013) Semantic diversity: a measure of semantic ambiguity based on variability in the contextual usage of words. Behav Res Methods 45(3):718–730PubMedGoogle Scholar
  45. Horton MS, Markman EM (1980) Developmental differences in the acquisition of basic and superordinate categories. Child Dev 51(3):708–719Google Scholar
  46. Hrybouski S, Aghamohammadi-Sereshki A, Madan CR, Shafer AT, Baron CA, Seres P, Beaulieu C, Olsen F, Malykhin NV (2016) Amygdala subnuclei response and connectivity during emotional processing. Neuroimage 133:98–110PubMedGoogle Scholar
  47. Iordan MC, Greene MR, Beck DM, Fei-Fei L (2015) Basic level category structure emerges gradually across human ventral visual cortex. J Cogn Neurosci 27(7):1427–1446PubMedGoogle Scholar
  48. Jabbi M, Bastiaansen J, Keysers C (2008) A common anterior insula representation of disgust observation, experience and imagination shows divergent functional connectivity pathways. PLoS ONE 3(8):e2939PubMedPubMedCentralGoogle Scholar
  49. Jackson RL, Hoffman P, Pobric G, Lambon Ralph MA (2015) The nature and neural correlates of semantic association versus conceptual similarity. Cereb Cortex 25(11):4319–4333PubMedPubMedCentralGoogle Scholar
  50. Jefferies E, Patterson K, Jones RW, Ralph L, Matthew A (2009) Comprehension of concrete and abstract words in semantic dementia. Neuropsychology 23(4):492PubMedPubMedCentralGoogle Scholar
  51. King ML, Groen II, Steel A, Kravitz DJ, Baker CI (2019) Similarity judgments and cortical visual responses reflect different properties of object and scene categories in naturalistic images. NeuroImage 197:368–382PubMedGoogle Scholar
  52. Koch A, Alves H, Krüger T, Unkelbach C (2016) A general valence asymmetry in similarity: good is more alike than bad. J Exp Psychol Learn Mem Cogn 42(8):1171PubMedGoogle Scholar
  53. Kriegeskorte N, Mur M (2012) Inverse MDS: inferring dissimilarity structure from multiple item arrangements. Front Psychol 3:245PubMedPubMedCentralGoogle Scholar
  54. Kriegeskorte N, Mur M, Bandettini PA (2008a) Representational similarity analysis-connecting the branches of systems neuroscience. Front Syst Neurosci 2:4PubMedPubMedCentralGoogle Scholar
  55. Kriegeskorte N, Mur M, Ruff DA, Kiani R, Bodurka J, Esteky H, Tanaka K, Bandettini PA (2008b) Matching categorical object representations in inferior temporal cortex of man and monkey. Neuron 60(6):1126–1141PubMedPubMedCentralGoogle Scholar
  56. Kuppens P, Tuerlinckx F, Russell JA, Barrett LF (2013) The relation between valence and arousal in subjective experience. Psychol Bull 139(4):917PubMedGoogle Scholar
  57. LaBar KS, Gatenby JC, Gore JC, LeDoux JE, Phelps EA (1998) Human amygdala activation during conditioned fear acquisition and extinction: a mixed-trial fMRI study. Neuron 20(5):937–945PubMedGoogle Scholar
  58. Lambon Ralph MA (2014) Neurocognitive insights on conceptual knowledge and its breakdown. Philos Trans R Soc B 369(1634):20120392Google Scholar
  59. Lang PJ, Bradley MM, Cuthbert BN (2008) International affective picture system (IAPS): affective ratings of pictures and instruction manual. University of Florida, GainesvilleGoogle Scholar
  60. Laufer O, Paz R (2012) Monetary loss alters perceptual thresholds and compromises future decisions via amygdala and prefrontal networks. J Neurosci 32(18):6304–6311PubMedPubMedCentralGoogle Scholar
  61. Laufer O, Israeli D, Paz R (2016) Behavioral and neural mechanisms of overgeneralization in anxiety. Curr Biol 26(6):713–722PubMedGoogle Scholar
  62. Leal SL, Yassa MA (2018) Integrating new findings and examining clinical applications of pattern separation. Nat Neurosci 21(2):163PubMedPubMedCentralGoogle Scholar
  63. Leal SL, Tighe SK, Jones CK, Yassa MA (2014) Pattern separation of emotional information in hippocampal dentate and CA3. Hippocampus 24(9):1146–1155PubMedPubMedCentralGoogle Scholar
  64. LeDoux J (2007) The amygdala. Curr Biol 17(20):R868–R874PubMedGoogle Scholar
  65. LeDoux J (2012) Rethinking the emotional brain. Neuron 73(4):653–676PubMedPubMedCentralGoogle Scholar
  66. Levine SM, Wackerle A, Rupprecht R, Schwarzbach JV (2018) The neural representation of an individualized relational affective space. Neuropsychologia 120:35–42PubMedGoogle Scholar
  67. Lin EL, Murphy GL (2001) Thematic relations in adults’ concepts. J Exp Psychol Gen 130(1):3PubMedGoogle Scholar
  68. Lindquist KA, Wager TD, Kober H, Bliss-Moreau E, Barrett LF (2012) The brain basis of emotion: a meta-analytic review. Behav Brain Sci 35(3):121–143PubMedPubMedCentralGoogle Scholar
  69. Lissek S, Rabin S, Heller RE, Lukenbaugh D, Geraci M, Pine DS, Grillon C (2009) Overgeneralization of conditioned fear as a pathogenic marker of panic disorder. Am J Psychiatry 167(1):47–55PubMedPubMedCentralGoogle Scholar
  70. Machajdik J, Hanbury A (2010) Affective image classification using features inspired by psychology and art theory. Proceedings of the 18th ACM International Conference on Multimedia, ACMGoogle Scholar
  71. Mack ML, Wong AC-N, Gauthier I, Tanaka JW, Palmeri TJ (2009) Time course of visual object categorization: fastest does not necessarily mean first. Vision Res 49(15):1961–1968PubMedGoogle Scholar
  72. Maguire P, Maguire R, Cater AW (2010) The influence of interactional semantic patterns on the interpretation of noun–noun compounds. J Exp Psychol Learn Mem Cogn 36(2):288PubMedGoogle Scholar
  73. Malach R, Reppas J, Benson R, Kwong K, Jiang H, Kennedy W, Ledden P, Brady T, Rosen B, Tootell R (1995) Object-related activity revealed by functional magnetic resonance imaging in human occipital cortex. Proc Natl Acad Sci USA 92(18):8135–8139PubMedGoogle Scholar
  74. Mäntylä M, Adams B, Destefanis G, Graziotin D, Ortu M (2016) Mining valence, arousal, and dominance: possibilities for detecting burnout and productivity? Proceedings of the 13th International Conference on Mining Software Repositories, ACMGoogle Scholar
  75. Marchewka A, Żurawski Ł, Jednoróg K, Grabowska A (2014) The Nencki Affective Picture System (NAPS): introduction to a novel, standardized, wide-range, high-quality, realistic picture database. Behav Res Methods 46(2):596–610PubMedGoogle Scholar
  76. Markman AB, Gentner D (1993) Splitting the differences: a structural alignment view of similarity. J Mem Lang 32(4):517–535Google Scholar
  77. Martin A, Wiggs CL, Ungerleider LG, Haxby JV (1996) Neural correlates of category-specific knowledge. Nature 379(6566):649PubMedGoogle Scholar
  78. Mattar MG, Talmi D (2019) Patterns of neural oscillations in emotional memory discrimination. Neuron 102(4):715–717PubMedGoogle Scholar
  79. Mehrabian A (1980) Basic dimensions for a general psychological theory: implications for personality, social, environmental, and developmental studies, Oelgeschlager. Gunn & Hain Cambridge, MAGoogle Scholar
  80. Motzkin JC, Philippi CL, Wolf RC, Baskaya MK, Koenigs M (2015) Ventromedial prefrontal cortex is critical for the regulation of amygdala activity in humans. Biol Psychiat 77(3):276–284PubMedGoogle Scholar
  81. Murphy GL, Brownell HH (1985) Category differentiation in object recognition: typicality constraints on the basic category advantage. J Exp Psychol Learn Mem Cogn 11(1):70PubMedGoogle Scholar
  82. Murphy FC, Nimmo-Smith I, Lawrence AD (2003) Functional neuroanatomy of emotions: a meta-analysis. Cogn Affect Behav Neurosci 3(3):207–233PubMedGoogle Scholar
  83. Nestor PJ, Fryer TD, Hodges JR (2006) Declarative memory impairments in Alzheimer’s disease and semantic dementia. Neuroimage 30(3):1010–1020PubMedGoogle Scholar
  84. Neyens V, Bruffaerts R, Liuzzi AG, Kalfas I, Peeters R, Keuleers E, Vogels R, De Deyne S, Storms G, Dupont P (2017) Representation of semantic similarity in the left intraparietal sulcus: functional magnetic resonance imaging evidence. Front Hum Neurosci 11:402PubMedPubMedCentralGoogle Scholar
  85. Nili H, Wingfield C, Walther A, Su L, Marslen-Wilson W, Kriegeskorte N (2014) A toolbox for representational similarity analysis. PLoS Comput Biol 10(4):e1003553PubMedPubMedCentralGoogle Scholar
  86. Ohira H, Nomura M, Ichikawa N, Isowa T, Iidaka T, Sato A, Fukuyama S, Nakajima T, Yamada J (2006) Association of neural and physiological responses during voluntary emotion suppression. Neuroimage 29(3):721–733PubMedGoogle Scholar
  87. Öhman A (2009) Of snakes and faces: an evolutionary perspective on the psychology of fear. Scand J Psychol 50(6):543–552PubMedGoogle Scholar
  88. Olson IR, McCoy D, Klobusicky E, Ross LA (2013) Social cognition and the anterior temporal lobes: a review and theoretical framework. Soc Cogn Affect Neurosci 8(2):123–133PubMedPubMedCentralGoogle Scholar
  89. Osgood CE (1952) The nature and measurement of meaning. Psychol Bull 49(3):197PubMedGoogle Scholar
  90. Patterson K, Nestor PJ, Rogers TT (2007) Where do you know what you know? the representation of semantic knowledge in the human brain. Nat Rev Neurosci 8(12):976PubMedGoogle Scholar
  91. Phan KL, Wager T, Taylor SF, Liberzon I (2002) Functional neuroanatomy of emotion: a meta-analysis of emotion activation studies in PET and fMRI. Neuroimage 16(2):331–348PubMedGoogle Scholar
  92. Plutchik R (2001) The nature of emotions: human emotions have deep evolutionary roots, a fact that may explain their complexity and provide tools for clinical practice. Am Sci 89(4):344–350Google Scholar
  93. Pobric G, Jefferies E, Ralph MAL (2007) Anterior temporal lobes mediate semantic representation: mimicking semantic dementia by using rTMS in normal participants. Proc Natl Acad Sci USA 104(50):20137–20141PubMedGoogle Scholar
  94. Ralph ML, Lowe C, Rogers TT (2007) Neural basis of category-specific semantic deficits for living things: evidence from semantic dementia, HSVE and a neural network model. Brain 130(4):1127–1137Google Scholar
  95. Ralph MAL, Sage K, Jones RW, Mayberry EJ (2010) Coherent concepts are computed in the anterior temporal lobes. Proc Natl Acad Sci USA 107(6):2717–2722Google Scholar
  96. Ralph MAL, Jefferies E, Patterson K, Rogers TT (2017) The neural and computational bases of semantic cognition. Nat Rev Neurosci 18(1):42PubMedPubMedCentralGoogle Scholar
  97. Rice GE, Hoffman P, Binney RJ, Lambon Ralph MA (2018) Concrete versus abstract forms of social concept: an fMRI comparison of knowledge about people versus social terms. Philos Trans R Soc B 373(1752):20170136Google Scholar
  98. Riddoch MJ, Humphreys GW, Coltheart M, Funnell E (1988) Semantic systems or system? neuropsychological evidence re-examined. Cogn Neuropsychol 5(1):3–25Google Scholar
  99. Roberts JS, Wedell DH (1994) Context effects on similarity judgments of multidimensional stimuli: inferring the structure of the emotion space. J Exp Soc Psychol 30(1):1–38Google Scholar
  100. Rogers TT, Patterson K (2007) Object categorization: reversals and explanations of the basic-level advantage. J Exp Psychol Gen 136(3):451PubMedGoogle Scholar
  101. Rogers TT, Ralph L, Matthew A, Garrard P, Bozeat S, McClelland JL, Hodges JR, Patterson K (2004) Structure and deterioration of semantic memory: a neuropsychological and computational investigation. Psychol Rev 111(1):205PubMedGoogle Scholar
  102. Rosch E, Mervis CB, Gray WD, Johnson DM, Boyes-Braem P (1976) Basic objects in natural categories. Cogn Psychol 8(3):382–439Google Scholar
  103. Russell JA, Bullock M (1985) Multidimensional scaling of emotional facial expressions: similarity from preschoolers to adults. J Pers Soc Psychol 48(5):1290Google Scholar
  104. Russell JA, Pratt G (1980) A description of the affective quality attributed to environments. J Pers Soc Psychol 38(2):311Google Scholar
  105. Said CP, Moore CD, Engell AD, Todorov A, Haxby JV (2010) Distributed representations of dynamic facial expressions in the superior temporal sulcus. J Vision 10(5):11Google Scholar
  106. Sakaki M, Niki K, Mather M (2012) Beyond arousal and valence: the importance of the biological versus social relevance of emotional stimuli. Cogn Affect Behav Neurosci 12(1):115–139PubMedPubMedCentralGoogle Scholar
  107. Schechtman E, Laufer O, Paz R (2010) Negative valence widens generalization of learning. J Neurosci 30(31):10460–10464PubMedPubMedCentralGoogle Scholar
  108. Schlosberg H (1952) The description of facial expressions in terms of two dimensions. J Exp Psychol 44(4):229PubMedGoogle Scholar
  109. Schwartz MF, Kimberg DY, Walker GM, Brecher A, Faseyitan OK, Dell GS, Mirman D, Coslett HB (2011) Neuroanatomical dissociation for taxonomic and thematic knowledge in the human brain. Proc Natl Acad Sci USA 108(20):8520–8524PubMedGoogle Scholar
  110. Segal SK, Stark SM, Kattan D, Stark CE, Yassa MA (2012) Norepinephrine-mediated emotional arousal facilitates subsequent pattern separation. Neurobiol Learn Mem 97(4):465–469PubMedPubMedCentralGoogle Scholar
  111. Simmons S, Estes Z (2008) Individual differences in the perception of similarity and difference. Cognition 108(3):781–795PubMedGoogle Scholar
  112. Sison JAG, Mather M (2007) Does remembering emotional items impair recall of same-emotion items? Psychon Bull Rev 14(2):282–287PubMedGoogle Scholar
  113. Talmi D (2013) Enhanced emotional memory: cognitive and neural mechanisms. Curr Dir Psychol Sci 22(6):430–436Google Scholar
  114. Talmi D, McGarry LM (2012) Accounting for immediate emotional memory enhancement. J Mem Lang 66(1):93–108Google Scholar
  115. Talmi D, Moscovitch M (2004) Can semantic relatedness explain the enhancement of memory for emotional words? Mem Cogn 32(5):742–751Google Scholar
  116. Talmi D, Luk BT, McGarry LM, Moscovitch M (2007) The contribution of relatedness and distinctiveness to emotionally-enhanced memory. J Mem Lang 56(4):555–574Google Scholar
  117. Taylor KI, Devereux BJ, Acres K, Randall B, Tyler LK (2012) Contrasting effects of feature-based statistics on the categorisation and basic-level identification of visual objects. Cognition 122(3):363–374PubMedGoogle Scholar
  118. Tversky A (1977) Features of similarity. Psychol Rev 84(4):327Google Scholar
  119. van Tilburg WA, Igou ER (2017) Boredom begs to differ: differentiation from other negative emotions. Emotion 17(2):309PubMedGoogle Scholar
  120. Visser M, Jefferies E, Embleton KV, Lambon Ralph MA (2012) Both the middle temporal gyrus and the ventral anterior temporal area are crucial for multimodal semantic processing: distortion-corrected fMRI evidence for a double gradient of information convergence in the temporal lobes. J Cogn Neurosci 24(8):1766–1778PubMedGoogle Scholar
  121. Von Der Heide RJ, Skipper LM, Klobusicky E, Olson IR (2013) Dissecting the uncinate fasciculus: disorders, controversies and a hypothesis. Brain 136(6):1692–1707Google Scholar
  122. Vytal K, Hamann S (2010) Neuroimaging support for discrete neural correlates of basic emotions: a voxel-based meta-analysis. J Cogn Neurosci 22(12):2864–2885PubMedGoogle Scholar
  123. Wager TD, Kang J, Johnson TD, Nichols TE, Satpute AB, Barrett LF (2015) A Bayesian model of category-specific emotional brain responses. PLoS Comput Biol 11(4):e1004066PubMedPubMedCentralGoogle Scholar
  124. Wang Y, Collins JA, Koski J, Nugiel T, Metoki A, Olson IR (2017) Dynamic neural architecture for social knowledge retrieval. Proc Natl Acad Sci USA 114(16):E3305–E3314PubMedGoogle Scholar
  125. Watson D, Tellegen A (1985) Toward a consensual structure of mood. Psychol Bull 98(2):219PubMedGoogle Scholar
  126. Wicker B, Keysers C, Plailly J, Royet J-P, Gallese V, Rizzolatti G (2003) Both of us disgusted in My insula: the common neural basis of seeing and feeling disgust. Neuron 40(3):655–664Google Scholar
  127. Wisniewski EJ, Bassok M (1999) What makes a man similar to a tie? Stimulus compatibility with comparison and integration. Cogn Psychol 39(3–4):208–238PubMedGoogle Scholar
  128. Xiao X, Dong Q, Chen C, Xue G (2016) Neural pattern similarity underlies the mnemonic advantages for living words. Cortex 79:99–111PubMedGoogle Scholar
  129. Xu Y, Wang X, Wang X, Men W, Gao J-H, Bi Y (2018) Doctor, teacher, and stethoscope: neural representation of different types of semantic relations. J Neurosci 38(13):3303–3317PubMedPubMedCentralGoogle Scholar
  130. Yarkoni T, Poldrack RA, Nichols TE, Van Essen DC, Wager TD (2011) Large-scale automated synthesis of human functional neuroimaging data. Nat Methods 8(8):665PubMedPubMedCentralGoogle Scholar
  131. Yu LC, Lee LH, Hao S, Wang J, He Y, Hu J, Lai KR, Zhang X (2016) Building Chinese affective resources in valence-arousal dimensions. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language TechnologiesGoogle Scholar
  132. Yuen K, Johnston S, Martino F, Sorger B, Formisano E, Linden D, Goebel R (2012) Pattern classification predicts individuals’ responses to affective stimuli. Trans Neurosci 3(3):278–287Google Scholar
  133. Zahn R, Moll J, Krueger F, Huey ED, Garrido G, Grafman J (2007) Social concepts are represented in the superior anterior temporal cortex. Proc Natl Acad Sci USA 104(15):6430–6435PubMedGoogle Scholar
  134. Zahn R, Moll J, Iyengar V, Huey ED, Tierney M, Krueger F, Grafman J (2009) Social conceptual impairments in frontotemporal lobar degeneration with right anterior temporal hypometabolism. Brain 132(3):604–616PubMedPubMedCentralGoogle Scholar
  135. Zevon MA, Tellegen A (1982) The structure of mood change: an idiographic/nomothetic analysis. J Pers Soc Psychol 43(1):111Google Scholar
  136. Zheng J, Stevenson RF, Mander BA, Mnatsakanyan L, Hsu FP, Vadera S, Knight RT, Yassa MA, Lin JJ (2019) Multiplexing of theta and alpha rhythms in the amygdala-hippocampal circuit supports pattern separation of emotional information. Neuron 102(4):887–898PubMedPubMedCentralGoogle Scholar

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Authors and Affiliations

  1. 1.Division of Neuroscience and Experimental PsychologyUniversity of ManchesterManchesterUK
  2. 2.Department of NeurobiologyWeizmann Institute of ScienceRehovotIsrael

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