Distinct dimensions of emotion in the human brain and their representation on the cortical surface

Koide-Majima, Naoko; Nakai, Tomoya; Nishimoto, Shinji

doi:10.1016/j.neuroimage.2020.117258

Cited by 39 publications

(54 citation statements)

References 67 publications

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“…The significant positive correlation between the PG score and the CNN sequential layers indicates that the pathway for the vision-to-value transformation revealed by the IO-CNNs is aligned with the PG. Therefore, concrete, unimodal sensory information is likely to be sequentially transformed into abstract, transmodal cognitive representations through hierarchical processing along the PG, confirming the meta-analysis in ( 13 ) and studies on intracranial electric stimulation ( 22 ) and task-related fMRI scans based on encoding models ( 23, 24 ). These results also indicate that the hierarchical vision-to-value transformation is a global process involving areas on the whole brain.…”

Section: Resultssupporting

confidence: 56%

Vision-to-value transformations in artificial neural networks and human brain

Pham

Yoshimoto

Niwa

et al. 2021

Preprint

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Humans and now computers can derive subjective valuations from sensory events although such transformation process is largely a black box. In this study, we elucidate unknown neural mechanisms by comparing representations of humans and convolutional neural networks (CNNs). We optimized CNNs to predict aesthetic valuations of paintings and examined the relationship between the CNN representations and brain activity by using multivoxel pattern analysis. The activity in the primary visual cortex was similar to computations in shallow CNN layers, while that in the higher association cortex was similar to computations in deeper layers, being consistent with the principal gradient that connects unimodal to transmodal brain regions. As a result, representations of the hidden layers of CNNs can be understood and visualized by the correspondence with brain activity. These relations can provide parallels between artificial intelligence and neuroscience.

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Section: Resultssupporting

confidence: 56%

Vision-to-value transformations in artificial neural networks and human brain

Pham

Yoshimoto

Niwa

et al. 2021

Preprint

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“…Amusement Ratings, audience laughter Moran et al, 2004;Goldin et al, 2005;Sawahata et al, 2013;Jääskeläinen et al, 2016;Iidaka, 2017;Tu et al, 2019 Raz et al, 2012Raz et al, , 2014Raz et al, , 2016Schlochtermeier et al, 2017;Sachs et al, 2020 Suspense Ratings Naci et al, 2014;Lehne et al, 2015 Basic emotions Ratings Lettieri et al, 2019 Other emotion categories Ratings Horikawa et al, 2020;Koide-Majima et al, 2020;Chang et al, 2021 Emotional alignment…”

Section: Emotion Categoriesmentioning

confidence: 99%

“…Human ratings are currently the main method for accessing and modeling observer's emotional experiences. Most naturalistic studies in affective neuroimaging have collected self-report ratings of some experienced aspect of emotions, including affective dimensions (valence and arousal: Wallentin et al, 2011;Nummenmaa et al, 2012Nummenmaa et al, , 2014bYoung et al, 2017;Smirnov et al, 2019;Gruskin et al, 2020; multiple affective dimensions: Horikawa et al, 2020;Koide-Majima et al, 2020) or intensity of categorical emotion experiences (Goldin et al, 2005;Raz et al, 2012;Naci et al, 2014;Jacob et al, 2018;Lettieri et al, 2019;Horikawa et al, 2020;Hudson et al, 2020). Furthermore, also interoceptive observer features including moments of acute fear onset leading to startle reflex (Hudson et al, 2020) have been extracted with ratings.…”

Section: Emotion Categoriesmentioning

confidence: 99%

“…For instance, low-level visual feature models and semantic models could be compared to test whether the voxel represents low-level stimulus properties or more abstract semantic information. A few studies employing voxel-wise encoding models have proven useful, for instance, in comparing the suitability of categorical or dimensional models in explaining brain activity during short, emotional movie clips (Horikawa et al, 2020;Koide-Majima et al, 2020) or in modeling individual emotion rating time series across a full-length feature movie (Lettieri et al, 2019).…”

Section: Encoding Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Naturalistic Stimuli in Affective Neuroimaging: A Review

Saarimäki

2021

Front. Hum. Neurosci.

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Naturalistic stimuli such as movies, music, and spoken and written stories elicit strong emotions and allow brain imaging of emotions in close-to-real-life conditions. Emotions are multi-component phenomena: relevant stimuli lead to automatic changes in multiple functional components including perception, physiology, behavior, and conscious experiences. Brain activity during naturalistic stimuli reflects all these changes, suggesting that parsing emotion-related processing during such complex stimulation is not a straightforward task. Here, I review affective neuroimaging studies that have employed naturalistic stimuli to study emotional processing, focusing especially on experienced emotions. I argue that to investigate emotions with naturalistic stimuli, we need to define and extract emotion features from both the stimulus and the observer.

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“…Then, using dimension reduction techniques, they assess the representational similarities of the modelled dimensions, or the representational similarities of the brain activation patterns associated with each dimension. Such semantic brain representations have been mapped for visual objects and actions 15 , language 16 , and experienced emotions 17 . A recent study found that language based representations of social features converge with the visual representations on the border of occipital cortex and that language based representations are located anterior to the visual representations 18 .…”

Section: Introductionmentioning

confidence: 99%

Functional organization of social perception in the human brain

Santavirta

Karjalainen

Nazari-Farsani

et al. 2021

Preprint

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Humans can readily perceive a multitude of features from social interactions, but the phenomenological and neural basis of social perception has yet to be solved. Short film clips with rich social content were shown to 97 healthy participants while their haemodynamic brain activity was measured with fMRI. The stimulus clips were annotated for 112 social features yielding the initial stimulus model. Cluster analysis revealed that 13 dimensions were sufficient for describing the social perceptual space. Univariate GLM using these dimensions as predictors was used to map regional neural response profiles to different social features. Multivariate pattern analysis was then utilized to establish the regional specificity of the responses. The results revealed a posterior-anterior gradient in the processing of social information in the brain. Occipital and temporal regions responded to most social dimensions and the classifier revealed that these responses were dimension specific; in contrast Heschl gyri and parietal areas were also broadly tuned to different social signals yet the responses were domain-general and did not differentiate between dimensions. Altogether these results highlight the distributed nature of social processing in the brain as well as the specific contributions of feature-specific versus domain-general social perceptual processes.

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Distinct dimensions of emotion in the human brain and their representation on the cortical surface

Cited by 39 publications

References 67 publications

Vision-to-value transformations in artificial neural networks and human brain

Vision-to-value transformations in artificial neural networks and human brain

Naturalistic Stimuli in Affective Neuroimaging: A Review

Functional organization of social perception in the human brain

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