Run screaming or slow retreat?
Trinity College Dublin
The research, led by Trinity College Dublin
neuroscientist Prof. Sonia Bishop and Google researcher Samy Abdel-Ghaffar
while he was a PhD student in Prof. Bishop's lab at UC Berkeley, has identified
how the brain represents different categories of emotional stimuli in a way
that allows for more than a simple 'approach avoid' dichotomy when guiding
behavioral responses. The research was funded by the National Institutes of
Health, USA.
Sonia Bishop, now Chair of Psychology, in Trinity's
School of Psychology and senior author of the paper explains: "It is
hugely important for all species to be able to recognize and respond
appropriately to emotionally salient stimuli, whether that means not eating
rotten food, running from a bear, approaching an attractive person in a bar or
comforting a tearful child.
"How the brain enables us to respond in a nuanced
way to emotionally-charged situations and stimuli has long been of interest.
But, little is known about the how the brain stores schemas or neural
representations to support the nuanced behavioral choices we make in response
to emotional natural stimuli.
"Neuroscience studies of motivated behavior often
focus on simple approach or avoidance behaviors -- such as lever pressing for
food or changing locations to avoid a shock. However, when faced with natural
emotional stimuli, humans don't simply choose between 'approach' or 'avoid'.
Rather they select from a complex range of suitable responses. So, for example,
our 'avoid' response to a large bear (leave the area ASAP) is different to our
'avoid' response to a weak, diseased, animal (don't get too close). Similarly
our 'approach' response to the positive stimuli of a potential mate differs to
our 'approach' reaction to a cute baby.
"Our research reveals that the occipital temporal cortex is tuned not only to different categories of stimuli but it also breaks down these categories based on their emotional characteristics in a way that is well suited to guide selection between alternate behaviors."
The research team from Trinity College Dublin, University
of California Berkeley, University of Texas at Austin, Google and University of
Nevada Reno, analyzed the brain activity of a small group of volunteers when
viewing over 1,500 images depicting natural emotional scenes such as a couple
hugging, an injured person in a hospital bed, a luxurious home, and an
aggressive dog.
Participants were asked to categories the images as
positive, negative or neutral and to also rate the emotional intensity of the
images. A second group of participants picked the behavioural responses that
best matched each scene.
Using cutting-edge modelling of brain activity divided
into tiny cubes (of under 3mm3) the study discovered that the
occipital temporal cortex (OTC), a region at the back of the brain, is tuned to
represent both the type of stimulus (single human, couple, crowd, reptile,
mammal, food, object, building, landscape etc.) and the emotional characteristics
of the stimulus -- whether it's negative, positive or neutral and also whether
it's high or low in emotional intensity.
Machine learning showed that these stable tuning patterns
were more efficient in predicting the behaviors matched to the images by the
second group of participants than could be achieved by applying machine
learning directly to image features -- suggesting that the OTC efficiently
extracts and represents the information needed to guide behavior.
Samy Abdel-Ghaffar, Google, commented: "For this
project we used Voxel-Wise Modeling, which combines machine learning methods,
large datasets and encoding models, to give us a much more fine-grained
understanding of what each part of the OTC represents than traditional
neuroimaging methods. This approach let us explore the intertwined
representation of categorical and emotional scene features, and opened the door
to novel understanding of how OTC representations predict behaviour."
Prof. Bishop added: "These findings expand our
knowledge of how the human brain represents emotional natural stimuli. In
addition, the paradigm used does not involve a complex task making this
approach suitable in the future, for example, to further understanding of how
individuals with a range of neurological and psychiatric conditions differ in
processing emotional natural stimuli."
More about the study method:
The team used a novel large dataset of 1,620 emotional
natural images and conducted functional magnetic resonance imaging with adult
human volunteers, acquiring over 3,800 3D pictures of brain activity while
participants viewed these images. Participants judged these images on valence
(positive, negative or neutral) and arousal (or emotional intensity).
Modelling this data using small 2.4x2.4x3mm chunks or
'voxels' of brain activity, the researchers found that regions of occipital
temporal cortex, in the back of the brain, showed differential representation
of both stimulus semantic category and affective value. For example, positive
high arousal faces were represented in slightly different regions to negative
high arousal faces and neutral low arousal faces.
Furthermore, when a completely new set of participants were asked to select behaviors that went with each image, the top dimensions of this neural coding representational 'space' better predicted the behaviors selected than the top dimensions based directly on image features (for example is the stimulus animate-positive?).
This suggests that the brain chooses which information is important or not important to represent and hold stable representations of sub-categories of animate and inanimate stimuli that integrate affective information and are optimally organized to support the selection of behaviors to different types of emotional natural stimuli.
