Emotion and language: valence and arousal affect word recognition

Emotion influences most aspects of cognition and behavior, but emotional factors are conspicuously absent from current models of word recognition. The influence of emotion on word recognition has mostly been reported in prior studies on the automatic vigilance for negative stimuli, but the precise nature of this relationship is unclear. Various models of automatic vigilance have claimed that the effect of valence on response times is categorical, an inverted U, or interactive with arousal. In the present study, we used a sample of 12,658 words and included many lexical and semantic control factors to determine the precise nature of the effects of arousal and valence on word recognition. Converging empirical patterns observed in word-level and trial-level data from lexical decision and naming indicate that valence and arousal exert independent monotonic effects: Negative words are recognized more slowly than positive words, and arousing words are recognized more slowly than calming words. Valence explained about 2% of the variance in word recognition latencies, whereas the effect of arousal was smaller. Valence and arousal do not interact, but both interact with word frequency, such that valence and arousal exert larger effects among low-frequency words than among high-frequency words. These results necessitate a new model of affective word processing whereby the degree of negativity monotonically and independently predicts the speed of responding. This research also demonstrates that incorporating emotional factors, especially valence, improves the performance of models of word recognition

Concreteness ratings for 40 thousand generally known English word lemmas

Concreteness ratings are presented for 37,058 English words and 2,896 two-word expressions (such as zebra crossing and zoom in), obtained from over 4,000 participants by means of a norming study using Internet crowdsourcing for data collection. Although the instructions stressed that the assessment of word concreteness would be based on experiences involving all senses and motor responses, a comparison with the existing concreteness norms indicates that participants, as before, largely focused on visual and haptic experiences. The reported data set is a subset of a comprehensive list of English lemmas and contains all lemmas known by at least 85 % of the raters. It can be used in future research as a reference list of generally known English lemmas

A sense of embodiment is reflected in people's signature size

BACKGROUND: The size of a person's signature may reveal implicit information about how the self is perceived although this has not been closely examined. METHODS/RESULTS: We conducted three experiments to test whether increases in signature size can be induced. Specifically, the aim of these experiments was to test whether changes in signature size reflect a person's current implicit sense of embodiment. Experiment 1 showed that an implicit affect task (positive subliminal evaluative conditioning) led to increases in signature size relative to an affectively neutral task, showing that implicit affective cues alter signature size. Experiments 2 and 3 demonstrated increases in signature size following experiential self-focus on sensory and affective stimuli relative to both conceptual self-focus and external (non-self-focus) in both healthy participants and patients with anorexia nervosa, a disorder associated with self-evaluation and a sense of disembodiment. In all three experiments, increases in signature size were unrelated to changes in self-reported mood and larger than manipulation unrelated variations. CONCLUSIONS: Together, these findings suggest that a person's sense of embodiment is reflected in their signature size

SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods

In the last few years thousands of scientific papers have investigated sentiment analysis, several startups that measure opinions on real data have emerged and a number of innovative products related to this theme have been developed. There are multiple methods for measuring sentiments, including lexical-based and supervised machine learning methods. Despite the vast interest on the theme and wide popularity of some methods, it is unclear which one is better for identifying the polarity (i.e., positive or negative) of a message. Accordingly, there is a strong need to conduct a thorough apple-to-apple comparison of sentiment analysis methods, \textit{as they are used in practice}, across multiple datasets originated from different data sources. Such a comparison is key for understanding the potential limitations, advantages, and disadvantages of popular methods. This article aims at filling this gap by presenting a benchmark comparison of twenty-four popular sentiment analysis methods (which we call the state-of-the-practice methods). Our evaluation is based on a benchmark of eighteen labeled datasets, covering messages posted on social networks, movie and product reviews, as well as opinions and comments in news articles. Our results highlight the extent to which the prediction performance of these methods varies considerably across datasets. Aiming at boosting the development of this research area, we open the methods' codes and datasets used in this article, deploying them in a benchmark system, which provides an open API for accessing and comparing sentence-level sentiment analysis methods

The Glasgow Norms:Ratings of 5,500 words on nine scales

The Glasgow Norms are a set of normative ratings for 5,553 English words on nine psycholinguistic dimensions: arousal, valence, dominance, concreteness, imageability, familiarity, age of acquisition, semantic size, and gender association. The Glasgow Norms are unique in several respects. First, the corpus itself is relatively large, while simultaneously providing norms across a substantial number of lexical dimensions. Second, for any given subset of words, the same participants provided ratings across all nine dimensions (33 participants/word, on average). Third, two novel dimensions—semantic size and gender association—are included. Finally, the corpus contains a set of 379 ambiguous words that are presented either alone (e.g., toast) or with information that selects an alternative sense (e.g., toast (bread), toast (speech)). The relationships between the dimensions of the Glasgow Norms were initially investigated by assessing their correlations. In addition, a principal component analysis revealed four main factors, accounting for 82% of the variance (Visualization, Emotion, Salience, and Exposure). The validity of the Glasgow Norms was established via comparisons of our ratings to 18 different sets of current psycholinguistic norms. The dimension of size was tested with megastudy data, confirming findings from past studies that have explicitly examined this variable. Alternative senses of ambiguous words (i.e., disambiguated forms), when discordant on a given dimension, seemingly led to appropriately distinct ratings. Informal comparisons between the ratings of ambiguous words and of their alternative senses showed different patterns that likely depended on several factors (the number of senses, their relative strengths, and the rating scales themselves). Overall, the Glasgow Norms provide a valuable resource—in particular, for researchers investigating the role of word recognition in language comprehension

Pathogenesis of HIV in the Central Nervous System

HIV can infect the brain and impair central nervous system (CNS) function. Combination antiretroviral therapy (cART) has not eradicated CNS complications. HIV-associated neurocognitive disorders (HAND) remain common despite cART, although attenuated in severity. This may result from a combination of factors including inadequate treatment of HIV reservoirs such as circulating monocytes and glia, decreased effectiveness of cART in CNS, concurrent illnesses, stimulant use, and factors associated with prescribed drugs, including antiretrovirals. This review highlights recent investigations of HIV-related CNS injury with emphasis on cART-era neuropathological mechanisms in the context of both US and international settings

Group mindfulness based cognitive therapy vs group support for self-injury among young people: Study protocol for a randomised controlled trial

Background: Non-suicidal self-injury (NSSI) is a transdiagnostic behaviour that can be difficult to treat; to date no evidence based treatment for NSSI exists. Mindfulness Based Cognitive Therapy (MBCT) specifically targets the mechanisms thought to initiate and maintain NSSI, and thus appears a viable treatment option. The aims of the current study are to test the ability of MBCT to reduce the frequency and medical severity of NSSI, and explore the mechanisms by which MBCT exerts its effect. Methods/Design: We will conduct a parallel group randomised controlled trial of Mindfulness Based Cognitive Therapy (MBCT) versus Supportive Therapy (ST) in young people aged 18-25 years. Computerised block randomisation will be used to allocate participants to groups. All participants will meet the proposed DSM-5 criteria for NSSI (i.e. five episodes in the last twelve months). Participants will be excluded if they: 1) are currently receiving psychological treatment, 2) have attempted suicide in the previous 12 months, 3) exhibit acute psychosis, 4) have a diagnosis of borderline personality disorder, or 5) have prior experience of MBCT. Our primary outcome is the frequency and medical severity of NSSI. As secondary outcomes we will assess changes in rumination, mindfulness, emotion regulation, distress tolerance, stress, and attentional bias, and test these as mechanisms of change. Discussion: This is the first randomised controlled trial to test the efficacy of MBCT in reducing NSSI. Evidence of the efficacy of MBCT for self-injury will allow provision of a brief intervention for self-injury that can be implemented as a stand-alone treatment or integrated with existing treatments for psychiatric disorders

Can the Crowd Tell How I Feel? Trait Empathy and Ethnic Background in a Visual Pain Judgment Task.

Many advocate for artificial agents to be empathic. Crowdsourc- ing could help, by facilitating human-in-the-loop approaches and dataset crea- tion for visual emotion recognition algorithms. Although crowdsourcing has been employed successfully for a range of tasks, it is not clear how effective crowdsourcing is when the task involves subjective rating of emotions. We ex- amined relationships between demographics, empathy and ethnic identity in pain emotion recognition tasks. Amazon MTurkers viewed images of strangers in painful settings, and tagged subjects’ emotions. They rated their level of pain arousal and confidence in their responses, and completed tests to gauge trait empathy and ethnic identity. We found that Caucasian participants were less confident than others, even when viewing other Caucasians in pain. Gender cor- related to word choices for describing images, though not to pain arousal or confidence. The results underscore the need for verified information on crowdworkers, to harness diversity effectively for metadata generation tasks