A Bipolar Continuum or Two Independent Dimensions?

In contrast to standard models of emotional valence, which assume a bipolar valence dimension ranging from negative to positive valence with a neutral midpoint, the evaluative space model (ESM) proposes two independent positivity and negativity dimensions. Previous imaging studies suggest higher predictive power of the ESM when investigating the neural correlates of verbal stimuli. The present study investigates further assumptions on the behavioral level. A rating experiment on more than 600 German words revealed 48 emotionally ambivalent stimuli (i.e., stimuli with high scores on both ESM dimensions), which were contrasted with neutral stimuli in two subsequent lexical decision experiments. Facilitative processing for emotionally ambivalent words was found in Experiment 2. In addition, controlling for emotional arousal and semantic ambiguity in the stimulus set, Experiment 3 still revealed a speed- accuracy trade-off for emotionally ambivalent words. Implications for future investigations of lexical processing and for the ESM are discussed

Affective-semantic integration of bivalent words

Single words have affective and aesthetic properties that influence their processing. Here we investigated the processing of a special case of word stimuli that are extremely difficult to evaluate, bivalent noun-noun-compounds (NNCs), i.e. novel words that mix a positive and negative noun, e.g. ‘Bombensex’ (bomb-sex). In a functional magnetic resonance imaging (fMRI) experiment we compared their processing with easier-to-evaluate non-bivalent NNCs in a valence decision task (VDT). Bivalent NNCs produced longer reaction times and elicited greater activation in the left inferior frontal gyrus (LIFG) than non-bivalent words, especially in contrast to words of negative valence. We attribute this effect to a LIFG-grounded process of semantic integration that requires greater effort for processing converse information, supporting the notion of a valence representation based on associations in semantic networks

10 years of BAWLing into affective and aesthetic processes in reading: what are the echoes?

Reading is not only “cold” information processing, but involves affective and aesthetic processes that go far beyond what current models of word recognition, sentence processing, or text comprehension can explain. To investigate such “hot” reading processes, standardized instruments that quantify both psycholinguistic and emotional variables at the sublexical, lexical, inter-, and supralexical levels (e.g., phonological iconicity, word valence, arousal-span, or passage suspense) are necessary. One such instrument, the Berlin Affective Word List (BAWL) has been used in over 50 published studies demonstrating effects of lexical emotional variables on all relevant processing levels (experiential, behavioral, neuronal). In this paper, we first present new data from several BAWL studies. Together, these studies examine various views on affective effects in reading arising from dimensional (e.g., valence) and discrete emotion features (e.g., happiness), or embodied cognition features like smelling. Second, we extend our investigation of the complex issue of affective word processing to words characterized by a mixture of affects. These words entail positive and negative valence, and/or features making them beautiful or ugly. Finally, we discuss tentative neurocognitive models of affective word processing in the light of the present results, raising new issues for future studies

Discrete Emotion Effects on Lexical Decision Response Times

Our knowledge about affective processes, especially concerning effects on cognitive demands like word processing, is increasing steadily. Several studies consistently document valence and arousal effects, and although there is some debate on possible interactions and different notions of valence, broad agreement on a two dimensional model of affective space has been achieved. Alternative models like the discrete emotion theory have received little interest in word recognition research so far. Using backward elimination and multiple regression analyses, we show that five discrete emotions (i.e., happiness, disgust, fear, anger and sadness) explain as much variance as two published dimensional models assuming continuous or categorical valence, with the variables happiness, disgust and fear significantly contributing to this account. Moreover, these effects even persist in an experiment with discrete emotion conditions when the stimuli are controlled for emotional valence and arousal levels. We interpret this result as evidence for discrete emotion effects in visual word recognition that cannot be explained by the two dimensional affective space account

Mean response times in ms (upper part) and summed error rates (lower part) for the lexical decision task.

<p>Error bars represent one standard deviation.</p

Comparison of three affective regression models.

<p><b>Note</b>: Log HAL  =  logarithm of HAL frequency, N = orthographical neighborhood size, Val (cat)  =  categorical valence, Val (con)/Val  =  continuous valence, arous  =  arousal</p

Backward elimination results.

<p><b>Note</b>: N = orthographic neighborhood size.</p

The relationship between the five discrete emotion variables happiness, anger, sadness, fear and disgust and the two affective space variables valence (left column) and arousal (right column).

<p>The relationship between the five discrete emotion variables happiness, anger, sadness, fear and disgust and the two affective space variables valence (left column) and arousal (right column).</p

Assessing how visual search entropy and engagement predict performance in a multiple-objects tracking air traffic control task

Behavioral performance metrics employed to assess the usability of visual displays are increasingly coupled with eye tracking measures to provide additional insights into the decision-making processes supported by visual displays. Eye tracking metrics can be coupled with users' neural data to investigate how human cognition interplays with emotions during visuo-spatial tasks. To contribute to these efforts, we present results of a study in a realistic air traffic control (ATC) setting with animated ATC displays, where ATC experts and novices were presented with an aircraft movement detection task. We find that higher stationary gaze entropy – which indicates a larger spatial distribution of visual gaze on the display – and expertise result in better response accuracy, and that stationary entropy positively predicts response time even after controlling for animation type and expertise. As a secondary contribution, we found that a single component comprised of engagement, measured by EEG and self-reported judgments, spatial abilities, and gaze entropy predicts task accuracy, but not completion time. We also provide MATLAB open source code for calculating the EEG measures utilized in the study. Our findings suggest designing spatial information displays that adapt their content according to users’ affective and cognitive states, especially for emotionally laden usage contexts