Monday, January 25, 2021

A broad approach to understanding emotions - Semantic Space Theory

I recently offered a 14-installment series of posts covering the ideas in Lisa Feldman Barrett’s book “How Emotions are Made: The Secret Life of the Brain. It's content centered around the debate of essentialist versus constructivist views of how we generate emotional behaviors, with Barrett presenting overwhelming data supporting the constructivist view. Cowen and Keltner now offer a alternative perspective, "semantic space theory" that encompases and expands beyond the more rigid definitions of essentialist basic emotion theory (BET, that claims that emotional feelings associated with specific cognitive appraisals and behaviors are biologically prepared and modified by experience) and constructivism (which takes certain valence/arousal responses to be biologically prepared, while specific emotions involve valence and arousal but are artifacts of language). From Cowen and Keltner:
Although these perspectives diverge on what emotions are, they converge in assuming that emotions solve a biological dilemma: that our brains are adapted for survival and reproduction, but our daily decisions are often many steps removed from these goals. This makes the evolutionary calculus of daily life – risk-taking, courtship, and tribal politics – immensely complex. The cognitive priors that enable our brains to approximate this calculus are, in most any theory of emotion, at the root of emotional behavior.
Cowen and Keltner expand beyond the entrenched disagreements between essentialist and constructivist approaches to offer a more expansive and encyclopedic approach - semantic (def. meaning in language) space theory. Here is their description:
Our approach formalizes the study of emotion in the investigation of representational state spaces capturing systematic variation in emotion-related response (including experience and expression, as well as associated physiology, cognition, and motivation). We integrate computational studies of emotional experience, facial–bodily expression, and vocalization to visualize what one might think of as an emerging taxonomy of emotion. Next, we discuss how the brain represents these experiences in distinct configurations of activity across the default mode network and subcortical areas. Building upon these advances, we synthesize literatures on nonhuman emotion-like behavior and nervous system response, highlighting emerging evidence that emotional behaviors differentiated within a fine-grained taxonomy have animal homologies and evolved neural mechanisms. The implication of these developments is clear: moving beyond traditional models to a broad taxonomy of emotion (Figure 1) will provide for a richer, more comprehensive science of emotion.
The Figure 1 referenced is a real doozy. On request, I can send motivated readers a PDF of the whole article text. Here is the legend of Fig. 1 "Semantic Spaces of Experience and Expression" which contains links to many cloud based interactive maps showing an awesome amount of data. The actual six panel figure (A though F referred to in the legend) is too large to display in this post. Clicking the links below to go through the cloud based interactive graphics is interesting. One could spend a fair number of hours browsing the variety of emotional forms presented.
(A) The semantic space framework. A semantic space is described by (i) its dimensionality, or the number of distinct meanings of experiences or expressions within the space; (ii) the conceptualization of these meanings in terms of mental states, intentions, or appraisals; and (iii) the distribution of experiences or expressions within the space, capturing clusters or blends of states. (B) Semantic space of facial–bodily and vocal expression. A total of 3523 expressions are lettered, positioned, and colored according to 28 distinct emotions that people reliably attribute to them (28 in facial expression [42] and 24 in vocal expression [25]). Within the space are gradients in expression between emotions traditionally thought of as discrete, such as fear and surprise. To explore these expressions, see the interactive maps (face:, voice: (C) Semantic space of emotion evoked by 2185 brief videos. At least 27 distinct affective states are reliably captured in reports of emotional experience evoked by video, best conceptualized in terms of emotion concepts such as fear [26]. Again, gradients bridge emotion concepts traditionally thought of as discrete, such as fear and surprise. Interactive map: (D) Semantic space of emotional experience evoked by 1841 music samples in multiple cultures [36]. Music samples are positioned and colored according to 13 emotions with which they are reliably associated in both the USA and China. Within the space, we find gradients among these states. The similarities in affective response across cultures were most reliably revealed in the use of specific emotion concepts (e.g., desire and fear). Interactive map: (E) Semantic space of emotion conveyed by prosody in 2519 lexically identical speech samples. Across the USA and India, at least 12 kinds of emotion are preserved in the recognition of mental states from speech prosody, most reliably revealed in the use of emotion concepts [28]. Interactive map: (F) Emotional expression in Ancient American art [58]. Ancient American sculpture was found to portray at least five distinct kinds of facial expression that accord, in terms of the emotions they communicate to westerners, with western expectations for the emotions that might unfold in the eight contexts portrayed. Colors of individual faces (letters) are weighted averages of colors assigned to each kind of perceived facial expression. Eight example sculptures are shown. (To explore all 63 sculptures, see online map:
This post is already much too long, so I only mention section headings of the text following Fig. 1, with fragments of text:
Semantic Spaces of Emotion
Semantic spaces of emotion are defined by three properties (Figure 1A). The first is their dimensionality: how many different kinds of emotion are distinguished within the space? The second is the distribution of states within the space: are there discrete boundaries between emotion categories, or is there overlap? The third is the conceptualization of emotion: what concepts most precisely capture people’s implicit or explicit differentiation of subjective experiences and expressive behaviors?
Emotional experience and expression is high dimensional, categorical, and often blended
People reliably distinguish at least 27 distinct subjective experiences associated with video [26], 24 distinct emotions in nonverbal vocalizations [25,28], and 28 distinct emotions in the face and body (Figure 1B,C) [42]. These findings were observed using both traditional rating methods and open-ended free response. The specific numbers here matter less than the more general point that emotion is at least four times more complex than that represented in studies of six emotions. This finding, replicated across response systems of emotion, is not anticipated by BET, and stands in contrast to assumptions of low dimensionality – that emotion is largely reducible to valence and arousal – found in constructivist accounts
Extensions of an emergent taxonomy: patterns of brain response and mammalizn behavior...The primacy of specific emotions in neural response patterning.
This section discusses data on the brain representation of emotion.

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