In 11 studies, we found that participants typically did not enjoy spending 6 to 15 minutes in a room by themselves with nothing to do but think, that they enjoyed doing mundane external activities much more, and that many preferred to administer electric shocks to themselves instead of being left alone with their thoughts. Most people seem to prefer to be doing something rather than nothing, even if that something is negative. “The mind is its own place, and in it self/Can make a Heav'n of Hell, a Hell of Heav'n.”– John Milton, Paradise Lost
Affect and its object are separable, so that the same affective reaction can have different effects. Relevant principles from the affect-as-information approach include: (1) The impact of affect depends on implicit attributions -- what it appears to be about. (2) Affect is always taken to be about whatever is currently mentally accessible. Affective reactions can therefore serve as appraisals of objects of judgment or of initial thoughts and opinions about such objects, when they are more accessible. During problem solving, affect can serve as appraisals of thought style rather than thought content. Then, (3) positive and negative affect serve as go and stop signals for current inclinations. Affective influences on cognition are therefore not fixed, but malleable and context-dependent.
Over 35% of the world's population uses social media. Platforms like Facebook, Twitter, and Instagram have radically influenced the way individuals interact and communicate. These platforms facilitate both public and private communication with strangers and friends alike, providing rich insight into an individual's personality, health, and wellbeing. To date, many researchers have employed a variety of methods for extracting mental health-centric features from digital text communication (DTC) data, including natural language processing, social network analysis, and extraction of temporal discourse patterns. However, none have explored a hierarchical framework for extracting features from private messages with the goal of unifying approaches across methodological domains. Furthermore, while analyses of large, public corpora abound in existing literature, limited work has been done to explore the relationship between of private textual communications, personality traits, and symptoms of mental illness. We present a framework for constructing rich feature spaces from digital text communications. We then demonstrate the efficacy of our framework by applying it to a dataset of private Facebook messages in a college student population (N=103). Our results reveal key individual differences in temporal and relational behaviors, as well as language usage in relation to validated measures of trait-level anxiety, loneliness, and personality. This work represents a critical step forward in linking features of private social media messages to validated measures of mental health, wellbeing, and personality.
Emotions are experienced universally by people in every place, period, and culture. Where there are humans, there are emotions. They are a cornerstone of the human experience, playing a central role in how people communicate their experience ("I was upset when she left me") and explain their behavior ("I didn't answer him because I was angry"). Emotions take center stage in virtually every story ever told, regardless of whether the story is ancient or modern and whether it is a cartoon or a literary classic. Indeed, research indicates that the one key requirement for narratives to be considered stories is that they have emotional content-readers have to care about what happens (Brewer & Lichtenstein, 1982). Investigators have long sought the biological bases of emotions, but the fruits of that search have generally been disappointing (for a review, see Caccioppo, Berntson, Larsen, Poehlmann, & Ito, 2000). In 1962 Schachter and Singer attempted to reconcile the palpable nature of emotional experiences with the lack of evidence for distinct physiological underpinnings by separating affect, the felt, evaluative component of emotion from what the emotion is about. According to Schachter and Singer's two-factor theory of emotion, physiological arousal, the visceral component, is common to all emotions, which precludes differentiating them on physiological grounds (1962). But, emotions can be distinguished, they argued, by their cognitive content, or what they are about. In this view, variation among emotions lies in the different meaning and significance attached to similar experiences of physiological arousal. Integral to Schachter and Singer's (1962) two-factor theory of emotion are attributions, unconscious inferences about the causes of psychological states (Heider, 1958). Schachter and Singer proposed that arousal becomes an emotion when it is attributed to an event in the world. Attribution gained popularity in social psychological research in subsequent decades, where it was frequently applied to binary decisions, such as whether a behavior was attributed to a person or to the situation (e.g., Ross, 1977) or whether failure was attributed to lack of ability or lack of effort (e.g., Dweck, 1986). The subtlety, power, and beauty of Schachter and Singer's characterization of attributional processes were realized in these and many other applications. In this article, we revisit and expand upon their compelling account of how implicit attributional processes give structure and meaning to the flow of events. Overview Minds as Instruments of Navigation All organisms, including people, can be understood as means for genes to survive and replicate. Survival and reproduction
We present the first analysis of the connectome of the vertical lobe (VL) of Octopus vulgaris, a brain structure mediating the acquisition of long-term memory in this behaviorally advanced mollusk. Serial section electron microscopy revealed new types of interneurons, cellular components of extensive modulatory systems, and multiple synaptic motifs. The sensory input to the VL is conveyed via ≈1,800,000 axons that sparsely innervate two parallel and interconnected feedforward networks formed by the two AM types, simple AMs (SAMs) and complex AMS (CAMs). SAMs make up 89.3% of the 25,000,000 AMs, each receiving synaptic input from only a single input neuron on its non-bifurcating primary neurite, suggesting that each input neuron is represented in only ≈12 SAMs. The CAMs, a newly described amacrine type, comprise 1.6% of the amacrine population. Their bifurcating neurites integrate multiple inputs from the input axons and SAMs. While the SAM network appears to feedforward sparse memorizable sensory representations into the VL output layer, the CAMs appear to monitor global activity and feedforward a balancing inhibition for sharpening the stimulus-specific VL output. While sharing morphological and wiring features with circuits supporting associative learning in other animals, the VL has evolved a unique circuit that enables associative learning based strictly on feedforward information flow.
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