Benny B. Briesemeister

Discrete emotion norms for nouns: Berlin affective word list (DENN–BAWL)

The Berlin Affective Word List (BAWL, Võ, Jacobs, & Conrad, Behavior Research Methods, 35, 606-609, 2006) and the BAWL-R (Võ et al. in Behavior Research Methods 38, 606-609, 2009) are two commonly used lists to investigate affective properties of German words. The two-dimensional valence and arousal model of affect underlying the BAWL is traditionally contrasted with models describing affect in discrete emotional categories, which, however, are not currently incorporated in the BAWL. In order to allow future studies to investigate affective processing from both perspectives—or to directly compare them—in the present study, we collected data by assigning nouns taken from the BAWL-R to discrete emotion intensities, which in turn allowed the assignment to discrete emotion categories. In the study, we present Discrete Emotion Norms for Nouns–Berlin Affective Word List (DENN–BAWL). Using these ratings and the psycholinguistic indexes from the BAWL-R, the DENN–BAWL allows researchers to design experiments using highly controlled and reliable word material. Data have been archived at www.fu-berlin.de/allgpsy/DENN-BAWL

Emotion word recognition: discrete information effects first, continuous later?

Manipulations of either discrete emotions (e.g. happiness) or affective dimensions (e.g. positivity) have a long tradition in emotion research, but interactive effects have never been studied, based on the assumption that the two underlying theories are incompatible. Recent theorizing suggests, however, that the human brain relies on two affective processing systems, one working on the basis of discrete emotion categories, and the other working along affective dimensions. Presenting participants with an orthogonal manipulation of happiness and positivity in a lexical decision task, the present study meant to test the appropriateness of this assumption in emotion word recognition. Behavioral and electroencephalographic data revealed independent effects for both variables, with happiness affecting the early visual N1 component, while positivity affected an N400-like component and the late positive complex. These results are interpreted as evidence for a sequential processing of affective information, with discrete emotions being the basis for later dimensional appraisal processes.

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.

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.

Avoid violence, rioting, and outrage; approach celebration, delight, and strength: Using large text corpora to compute valence, arousal, and the basic emotions

Ever since Aristotle discussed the issue in Book II of his Rhetoric, humans have attempted to identify a set of “basic emotion labels”. In this paper we propose an algorithmic method for evaluating sets of basic emotion labels that relies upon computed co-occurrence distances between words in a 12.7-billion-word corpus of unselected text from USENET discussion groups. Our method uses the relationship between human arousal and valence ratings collected for a large list of words, and the co-occurrence similarity between each word and emotion labels. We assess how well the words in each of 12 emotion label sets—proposed by various researchers over the past 118 years—predict the arousal and valence ratings on a test and validation dataset, each consisting of over 5970 items. We also assess how well these emotion labels predict lexical decision residuals (LDRTs), after co-varying out the effects attributable to basic lexical predictors. We then demonstrate a generalization of our method to determine the most predictive “basic” emotion labels from among all of the putative models of basic emotion that we considered. As well as contributing empirical data towards the development of a more rigorous definition of basic emotions, our method makes it possible to derive principled computational estimates of emotionality—specifically, of arousal and valence—for all words in the language.

The pseudohomophone effect: evidence for an orthography-phonology-conflict.

The standard pseudohomophone effect in the lexical decision task, i.e. longer response times and higher error rates for pseudohomophones compared with spelling controls, is commonly explained by an orthography-phonology-conflict. This study tested this conflict account, using a multi-method approach including participants behavioral responses, confidence ratings, pupillary responses and event-related potentials (ERPs). The classic pseudohomophone effect was replicated using relatively long, multi-syllabic stimuli. Pseudohomophones were rated less confidently as being nonwords than spelling controls, and they affected the pupillary response by increasing the peak pupil diameter. Both findings are interpreted in terms of increased conflict and higher cognitive demands leading to uncertainty while solving the task. The ERP revealed an N400 component for spelling controls, showing a graded effect: word<pseudohomophone<spelling control. This can be seen as evidence for (partial) semantic activation through pseudohomophones. Taken together, the results provide strong multi-method evidence for the conflict account of the pseudohomophone effect.

Emotions in reading: Dissociation of happiness and positivity

The hierarchical emotion model proposed by Panksepp (1998) predicts that affective processing will rely on three functionally and neuroanatomically distinct levels, engaging subcortical networks (primary level), the limbic system (secondary level), and the neocortex (tertiary level). In the present fMRI study, we manipulated happiness and positivity, which are assumed to rely on secondary- and tertiary-level processes, respectively, to test these assumptions in a word recognition task. In accordance with the model predictions, evidence for a double dissociation was found in the brain activation patterns: Secondary-level processes engaged parts of the limbic system—specifically, the right hemispheric amygdala. Tertiary-level processes, in contrast, relied predominantly on frontal neocortical structures such as the left inferior frontal and medial frontal gyri. These results are interpreted as support for Panksepp’s (1998) model and as an indicator of a semantic foundation of affective dimensions.

Now you see it, now you don't: on emotion, context, and the algorithmic prediction of human imageability judgments.

Many studies have shown that behavioral measures are affected by manipulating the imageability of words. Though imageability is usually measured by human judgment, little is known about what factors underlie those judgments. We demonstrate that imageability judgments can be largely or entirely accounted for by two computable measures that have previously been associated with imageability, the size and density of a words context and the emotional associations of the word. We outline an algorithmic method for predicting imageability judgments using co-occurrence distances in a large corpus. Our computed judgments account for 58% of the variance in a set of nearly two thousand imageability judgments, for words that span the entire range of imageability. The two factors account for 43% of the variance in lexical decision reaction times (LDRTs) that is attributable to imageability in a large database of 3697 LDRTs spanning the range of imageability. We document variances in the distribution of our measures across the range of imageability that suggest that they will account for more variance at the extremes, from which most imageability-manipulating stimulus sets are drawn. The two predictors account for 100% of the variance that is attributable to imageability in newly-collected LDRTs using a previously-published stimulus set of 100 items. We argue that our model of imageability is neurobiologically plausible by showing it is consistent with brain imaging data. The evidence we present suggests that behavioral effects in the lexical decision task that are usually attributed to the abstract/concrete distinction between words can be wholly explained by objective characteristics of the word that are not directly related to the semantic distinction. We provide computed imageability estimates for over 29,000 words.

Emotional Valence: 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.

Mixing positive and negative valence: 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.