Identifying Emotions Using Topographic Conditioning Maps

The amygdala is the neural structure that acts as an evaluator of potentially threatening stimuli. We present a biologically plausible model of the visual fear conditioning pathways leading to the amygdala, using a topographic conditioning map (TCM). To evaluate the model, we first use abstract stimuli to understand its ability to form topographic representations, and subsequently to condition on arbitrary stimuli. We then present results on facial emotion recognition using the sub-cortical pathway of the model. Compared to other emotion classification approaches, our model performs well, but does not have the need to pre-specify features. This generic ability to organise visual stimuli is enhanced through conditioning, which also improves classification performance. Our approach demonstrates that a biologically motivated model can be applied to real-world tasks, while allowing us to explore biological hypotheses

EEG Microstates in Social and Affective Neuroscience.

Social interactions require both the rapid processing of multifaceted socio-affective signals (e.g., eye gaze, facial expressions, gestures) and their integration with evaluations, social knowledge, and expectations. Researchers interested in understanding complex social cognition and behavior face a "black box" problem: What are the underlying mental processes rapidly occurring between perception and action and why are there such vast individual differences? In this review, we promote electroencephalography (EEG) microstates as a powerful tool for both examining socio-affective states (e.g., processing whether someone is in need in a given situation) and identifying the sources of heterogeneity in socio-affective traits (e.g., general willingness to help others). EEG microstates are identified by analyzing scalp field maps (i.e., the distribution of the electrical field on the scalp) over time. This data-driven, reference-independent approach allows for identifying, timing, sequencing, and quantifying the activation of large-scale brain networks relevant to our socio-affective mind. In light of these benefits, EEG microstates should become an indispensable part of the methodological toolkit of laboratories working in the field of social and affective neuroscience

Platonic model of mind as an approximation to neurodynamics

Hierarchy of approximations involved in simplification of microscopic theories, from sub-cellural to the whole brain level, is presented. A new approximation to neural dynamics is described, leading to a Platonic-like model of mind based on psychological spaces. Objects and events in these spaces correspond to quasi-stable states of brain dynamics and may be interpreted from psychological point of view. Platonic model bridges the gap between neurosciences and psychological sciences. Static and dynamic versions of this model are outlined and Feature Space Mapping, a neurofuzzy realization of the static version of Platonic model, described. Categorization experiments with human subjects are analyzed from the neurodynamical and Platonic model points of view

A sensitive and specific neural signature for picture-induced negative affect

Neuroimaging has identified many correlates of emotion but has not yet yielded brain representations predictive of the intensity of emotional experiences in individuals. We used machine learning to identify a sensitive and specific signature of emotional responses to aversive images. This signature predicted the intensity of negative emotion in individual participants in cross validation (n =121) and test (n = 61) samples (high–low emotion = 93.5% accuracy). It was unresponsive to physical pain (emotion–pain = 92% discriminative accuracy), demonstrating that it is not a representation of generalized arousal or salience. The signature was comprised of mesoscale patterns spanning multiple cortical and subcortical systems, with no single system necessary or sufficient for predicting experience. Furthermore, it was not reducible to activity in traditional “emotion-related” regions (e.g., amygdala, insula) or resting-state networks (e.g., “salience,” “default mode”). Overall, this work identifies differentiable neural components of negative emotion and pain, providing a basis for new, brain-based taxonomies of affective processes

Mind the (computational) gap

Despite many advances in both computational intelligence and computational neuroscience, it is clear that we have yet to achieve the full potential of nature inspired solutions from studying the human brain. Models of brain function have reached the stage where large-scale models of the brain have become possible, yet these tantalising computational structures cannot yet be applied to real-world problems because they lack the ability to be connected to real-world inputs or outputs. This paper introduces the notion of creating a computational hub that has the potential to link real sensory stimuli to higher cortical models. This is achieved through modelling subcortical structures, such as the superior colliculus, which have desirable computational principles, including rapid, multisensory and discriminative processing. We demonstrate some of these subcortical principles in a system that performs real-time speaker localisation using live video and audio, showing how such models may help us bridge the computational gap

Neurocognitive evidence for mental imagery-driven hypoalgesic and hyperalgesic pain regulation

Mental imagery has the potential to influence perception by directly altering sensory, cognitive, and affective brain activity associated with imagined content. While it is well established that mental imagery can both exacerbate and alleviate acute and chronic pain, it is currently unknown how imagery mechanisms regulate pain perception. For example, studies to date have been unable to determine whether imagery effects depend upon a general redirection of attention away from pain or focused attentional mechanisms. To address these issues, we recorded subjective, behavioral and ERP responses using 64-channel EEG while healthy human participants applied a mental imagery strategy to decrease or increase pain sensations. When imagining a glove covering the forearm, participants reported decreased perceived intensity and unpleasantness, classified fewer high-intensity stimuli as painful, and showed a more conservative response bias. In contrast, when imagining a lesion on the forearm, participants reported increased pain intensity and unpleasantness, classified more lowintensity stimuli as painful, and displayed a more liberal response bias. Using a mass-univariate approach,we further showed differential modulation of the N2 potentials across conditions, with inhibition and facilitation respectively increasing and decreasing N2 amplitudes between 122 and 180 ms. Within this time window, source localization associated inhibiting vs. facilitating pain with neural activity in cortical regions involved in cognitive inhibitory control and in the retrieval of semantic information (i.e., right inferior frontal and temporal regions). In contrast, the main sources of neural activity associatedwith facilitating vs. inhibiting pain were identified in cortical regions typically implicated in salience processing and emotion regulation (i.e., left insular, inferior-middle frontal, supplementary motor and precentral regions). Overall, these findings suggest that the content of a mental image directly alters pain-related decision and evaluative processing to flexibly produce hypoalgesic and hyperalgesic outcomes

A Framework for Studying Emotions across Species

Since the 19th century, there has been disagreement over the fundamental question of whether “emotions” are cause or consequence of their associated behaviors. This question of causation is most directly addressable in genetically tractable model organisms, including invertebrates such as Drosophila. Yet there is ongoing debate about whether such species even have “emotions,” as emotions are typically defined with reference to human behavior and neuroanatomy. Here, we argue that emotional behaviors are a class of behaviors that express internal emotion states. These emotion states exhibit certain general functional and adaptive properties that apply across any specific human emotions like fear or anger, as well as across phylogeny. These general properties, which can be thought of as “emotion primitives,” can be modeled and studied in evolutionarily distant model organisms, allowing functional dissection of their mechanistic bases and tests of their causal relationships to behavior. More generally, our approach not only aims at better integration of such studies in model organisms with studies of emotion in humans, but also suggests a revision of how emotion should be operationalized within psychology and psychiatry