The study presented here provides researchers with a revised list of affective German words, the Berlin Affective Word List Reloaded (BAWL-R). This work is an extension of the previously published BAWL , which has enabled researchers to investigate affective word processing with highly controlled stimulus material. The lack of arousal ratings, however, necessitated a revised version of the BAWL. We therefore present the BAWL-R, which is the first list that not only contains a large set of psycholinguistic indexes known to influence word processing, but also features ratings regarding emotional arousal, in addition to emotional valence and imageability. The BAWL-R is intended to help researchers create stimulus material for a wide range of experiments dealing with the affective processing of German verbal material.

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Effects of frequency, predictability, and position of words on event-related potentials were assessed during word-by-word sentence reading in 48 subjects in an early and in a late time window corresponding to P200 and N400. Repeated-measures multiple regression analyses revealed a P200-effect in the high-frequency range; also the P200 was larger on words at the beginning and end of sentences than on words in the middle of sentences (i.e., a quadratic effect of word position). Predictability strongly affected the N400 component; the effect was stronger for low than for high-frequency words. The P200 frequency effect indicates that high-frequency words are lexically accessed very fast, independent of context information. Effects on the N400 suggest that predictability strongly moderates the late access especially of low-frequency words. Thus, contextual facilitation on the N400 appears to reflect both lexical and post-lexical stages of word recognition, questioning a strict classification into lexical and post-lexical processes.

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The study presented here investigated the effects of emotional valence on the memory for words by assessing both memory performance and pupillary responses during a recognition memory task. Participants had to make speeded judgments on whether a word presented in the test phase of the experiment had already been presented ("old") or not ("new"). An emotion-induced recognition bias was observed: Words with emotional content not only produced a higher amount of hits, but also elicited more false alarms than neutral words. Further, we found a distinct pupil old/new effect characterized as an elevated pupillary response to hits as opposed to correct rejections. Interestingly, this pupil old/new effect was clearly diminished for emotional words. We therefore argue that the pupil old/new effect is not only able to mirror memory retrieval processes, but also reflects modulation by an emotion-induced recognition bias.

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.

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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.