Mark A. Thornton

Neural evidence that three dimensions organize mental state representation: Rationality, social impact, and valence

Significance This study uses advanced functional neuroimaging analyses to test both existing and novel psychological theories about how we understand others’ minds. Analyses show that three dimensions—rationality, social impact, and valence—account for almost half of the variation in the neural representation of mental states, the most comprehensive theory to date regarding our ability to think about others’ minds. These findings both inform long-standing debates within social psychology about theory of mind and generate testable predictions about how our neural hardware supports our ability to mentalize. How do people understand the minds of others? Existing psychological theories have suggested a number of dimensions that perceivers could use to make sense of others’ internal mental states. However, it remains unclear which of these dimensions, if any, the brain spontaneously uses when we think about others. The present study used multivoxel pattern analysis (MVPA) of neuroimaging data to identify the primary organizing principles of social cognition. We derived four unique dimensions of mental state representation from existing psychological theories and used functional magnetic resonance imaging to test whether these dimensions organize the neural encoding of others’ mental states. MVPA revealed that three such dimensions could predict neural patterns within the medial prefrontal and parietal cortices, temporoparietal junction, and anterior temporal lobes during social thought: rationality, social impact, and valence. These results suggest that these dimensions serve as organizing principles for our understanding of other people.

Working memory for social information: chunking or domain-specific buffer?

Humans possess unique social abilities that set us apart from other species. These abilities may be partially supported by a large capacity for maintaining and manipulating social information. Efficient social working memory might arise from two different sources: chunking of social information or a domain-specific buffer. We test these hypotheses with functional magnetic resonance imaging (fMRI) by manipulating sociality and working memory load in an n-back paradigm. We observe (i) an effect of load in the frontoparietal control network, (ii) an effect of sociality in regions associated with social cognition and face processing, and (iii) an interaction within the frontoparietal network such that social load has a smaller effect than nonsocial load. These results support the hypothesis that working memory is more efficient for social information than for nonsocial information, and suggest that chunking, rather than a domain-specific buffer, is the mechanism of this greater efficiency.

Raman Spectroscopy and Chemometrics for Identification and Strain Discrimination of the Wine Spoilage Yeasts Saccharomyces cerevisiae, Zygosaccharomyces bailii, and Brettanomyces bruxellensis

ABSTRACT The yeasts Zygosaccharomyces bailii, Dekkera bruxellensis (anamorph, Brettanomyces bruxellensis), and Saccharomyces cerevisiae are the major spoilage agents of finished wine. A novel method using Raman spectroscopy in combination with a chemometric classification tool has been developed for the identification of these yeast species and for strain discrimination of these yeasts. Raman spectra were collected for six strains of each of the yeasts Z. bailii, B. bruxellensis, and S. cerevisiae. The yeasts were classified with high sensitivity at the species level: 93.8% for Z. bailii, 92.3% for B. bruxellensis, and 98.6% for S. cerevisiae. Furthermore, we have demonstrated that it is possible to discriminate between strains of these species. These yeasts were classified at the strain level with an overall accuracy of 81.8%.

Modeling the Predictive Social Mind

The social mind is tailored to the problem of predicting the mental states and actions of other people. However, social cognition researchers have only scratched the surface of the predictive social mind. We discuss here a new framework for explaining how people organize social knowledge and use it for social prediction. Specifically, we propose a multilayered framework of social cognition in which two hidden layers - the mental states and traits of others - support predictions about the observable layer - the actions of others. A parsimonious set of psychological dimensions structures each layer, and proximity within and across layers guides social prediction. This simple framework formalizes longstanding intuitions from social cognition, and in doing so offers a generative model for deriving new hypotheses about predictive social cognition.

Theories of person perception predict patterns of neural activity during mentalizing

Abstract Social life requires making inferences about other people. What information do perceivers spontaneously draw upon to make such inferences? Here, we test 4 major theories of person perception, and 1 synthetic theory that combines their features, to determine whether the dimensions of such theories can serve as bases for describing patterns of neural activity during mentalizing. While undergoing functional magnetic resonance imaging, participants made social judgments about well‐known public figures. Patterns of brain activity were then predicted using feature encoding models that represented target peoples positions on theoretical dimensions such as warmth and competence. All 5 theories of person perception proved highly accurate at reconstructing activity patterns, indicating that each could describe the informational basis of mentalizing. Cross‐validation indicated that the theories robustly generalized across both targets and participants. The synthetic theory consistently attained the best performance—approximately two‐thirds of noise ceiling accuracy‐‐indicating that, in combination, the theories considered here can account for much of the neural representation of other people. Moreover, encoding models trained on the present data could reconstruct patterns of activity associated with mental state representations in independent data, suggesting the use of a common neural code to represent others’ traits and states.

Consistent Neural Activity Patterns Represent Personally Familiar People

How does the brain encode and organize our understanding of the people we know? In this study, participants imagined personally familiar others in a variety of contexts while undergoing fMRI. Using multivoxel pattern analysis, we demonstrated that thinking about familiar others elicits consistent fine-grained patterns of neural activity. Person-specific patterns were distributed across many regions previously associated with social cognition, including medial prefrontal, medial parietal, and lateral temporoparietal cortices, as well as other regions including the anterior and mid-cingulate, insula, and precentral gyrus. Analogous context-specific patterns were observed in medial parietal and superior occipital regions. These results suggest that medial parietal cortex may play a particularly central role in simulating familiar others, as this is the only region to simultaneously represent both person and context information. Moreover, within portions of medial parietal cortex, the degree to which person-specific patterns were typically instated on a given trial predicted subsequent judgments of accuracy and vividness in the mental simulation. This suggests that people may access neural representations in this region to form metacognitive judgments of confidence in their mental simulations. In addition to fine-grained patterns within brain regions, we also observed encoding of both familiar people and contexts in coarse-grained patterns spread across the independently defined social brain network. Finally, we found tentative evidence that several established theories of person perception might explain the relative similarity between person-specific patterns within the social brain network.

Mental models accurately predict emotion transitions

Significance People naturally understand that emotions predict actions: angry people aggress, tired people rest, and so forth. Emotions also predict future emotions: for example, tired people become frustrated and guilty people become ashamed. Here we examined whether people understand these regularities in emotion transitions. Comparing participants’ ratings of transition likelihood to others’ experienced transitions, we found that raters’ have accurate mental models of emotion transitions. These models could allow perceivers to predict others’ emotions up to two transitions into the future with above-chance accuracy. We also identified factors that inform—but do not fully determine—these mental models: egocentric bias, the conceptual properties of valence, social impact, and rationality, and the similarity and co-occurrence between different emotions. Successful social interactions depend on people’s ability to predict others’ future actions and emotions. People possess many mechanisms for perceiving others’ current emotional states, but how might they use this information to predict others’ future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others’ emotional dynamics. People could then use these mental models of emotion transitions to predict others’ future emotions from currently observable emotions. To test this hypothesis, studies 1–3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants’ ratings of emotion transitions predicted others’ experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation—valence, social impact, rationality, and human mind—inform participants’ mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants’ accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone.

Discrimination of wine lactic acid bacteria by Raman spectroscopy

Species of Lactobacillus, Pediococcus, Oenococcus, and Leuconostoc play an important role in winemaking, as either inoculants or contaminants. The metabolic products of these lactic acid bacteria have considerable effects on the flavor, aroma, and texture of a wine. However, analysis of a wine’s microflora, especially the bacteria, is rarely done unless spoilage becomes evident, and identification at the species or strain level is uncommon as the methods required are technically difficult and expensive. In this work, we used Raman spectral fingerprints to discriminate 19 strains of Lactobacillus, Pediococcus, and Oenococcus. Species of Lactobacillus and Pediococcus and strains of O. oeni and P. damnosus were classified with high sensitivity: 86–90 and 84–85%, respectively. Our results demonstrate that a simple, inexpensive method utilizing Raman spectroscopy can be used to accurately identify lactic acid bacteria isolated from wine.