The neural representation of an individualized relational affective space

Levine, Seth M.; Wackerle, Anja; Rupprecht, Rainer; Schwarzbach, Jens

doi:10.1016/j.neuropsychologia.2018.10.008

Cited by 19 publications

(20 citation statements)

References 77 publications

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“…Our findings also have implications for social and affective neuroscience studies. Recent functional magnetic resonance imaging investigations have employed multivariate pattern analysis (Haxby et al, 2001) in an effort to understand how brain regions or networks represent information related to emotion processing (Bush et al, 2018;Kryklywy et al, 2018;Levine et al, 2018c;Saarimäki et al, 2018). Findings like those we present here can inform such studies by underscoring how the heterogeneity of personality information can influence the distribution of information in affective spaces.…”

Section: Discussionsupporting

confidence: 51%

“…To this end, we employed the multi-arrangement method (Goldstone, 1994) and inverse multidimensional scaling (Kriegeskorte and Mur, 2012), which have recently been utilized in the domain of cognitive neuroscience (Mur et al, 2013;Charest et al, 2014;Bracci et al, 2016;Levine et al, 2018c), to emotionally charged stimuli in order to discern individualized structures (that reflect mental representations) of affective information. Participants freely arranged the stimuli according to their subjective emotional similarity in a continuous space, which resulted in a label-free, unique representation of an "affective space" for each participant.…”

Section: Introductionmentioning

confidence: 99%

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Linking Personality Traits to Individual Differences in Affective Spaces

et al. 2020

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Different individuals respond differently to emotional stimuli in their environment. Therefore, to understand how emotions are represented mentally will ultimately require investigations into individual-level information. Here we tasked participants with freely arranging emotionally charged images on a computer screen according to their subjective emotional similarity (yielding a unique affective space for each participant) and subsequently sought external validity of the layout of the individuals' affective spaces through the five-factor personality model (Neuroticism, Extraversion, Openness to Experience, Agreeableness, Conscientiousness) assessed via the NEO Five-Factor Inventory. Applying agglomerative hierarchical clustering to the group-level affective space revealed a set of underlying affective clusters whose within-cluster dissimilarity, per individual, was then correlated with individuals' personality scores. These cluster-based analyses predominantly revealed that the dispersion of the negative cluster showed a positive relationship with Neuroticism and a negative relationship with Conscientiousness, a finding that would be predicted by prior work. Such results demonstrate the non-spurious structure of individualized emotion information revealed by data-driven analyses of a behavioral task (and validated by incorporating psychological measures of personality) and corroborate prior knowledge of the interaction between affect and personality. Future investigations can similarly combine hypothesis-and data-driven methods to extend such findings, potentially yielding new perspectives on underlying cognitive processes, disease susceptibility, or even diagnostic/prognostic markers for mental disorders involving emotion dysregulation.

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Section: Discussionsupporting

confidence: 51%

Section: Introductionmentioning

confidence: 99%

Linking Personality Traits to Individual Differences in Affective Spaces

et al. 2020

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“…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).…”

Section: Introductionmentioning

confidence: 90%

“…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).…”

Section: Introductionmentioning

confidence: 99%

“…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).…”

Section: Introductionmentioning

confidence: 99%

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The Emotional Facet of Subjective and Neural Indices of Similarity

2019

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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.

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