Neuropsychological differential diagnosis of Alzheimer's disease and vascular dementia: a systematic review with meta-regressions.

Introduction: Diagnostic classification systems and guidelines posit distinguishing patterns of impairment in Alzheimer's (AD) and vascular dementia (VaD). In our study, we aim to identify which diagnostic instruments distinguish them. Methods: We searched PubMed and PsychInfo for empirical studies published until December 2020, which investigated differences in cognitive, behavioral, psychiatric, and functional measures in patients older than 64 years and reported information on VaD subtype, age, education, dementia severity, and proportion of women. We systematically reviewed these studies and conducted Bayesian hierarchical meta-regressions to quantify the evidence for differences using the Bayes factor (BF). The risk of bias was assessed using the Newcastle-Ottawa-Scale and funnel plots. Results: We identified 122 studies with 17,850 AD and 5,247 VaD patients. Methodological limitations of the included studies are low comparability of patient groups and an untransparent patient selection process. In the digit span backward task, AD patients were nine times more probable (BF = 9.38) to outperform VaD patients (ßg = 0.33, 95% ETI = 0.12, 0.52). In the phonemic fluency task, AD patients outperformed subcortical VaD (sVaD) patients (ßg = 0.51, 95% ETI = 0.22, 0.77, BF = 42.36). VaD patients, in contrast, outperformed AD patients in verbal (ßg = -0.61, 95% ETI = -0.97, -0.26, BF = 22.71) and visual (ßg = -0.85, 95% ETI = -1.29, -0.32, BF = 13.67) delayed recall. We found the greatest difference in verbal memory, showing that sVaD patients outperform AD patients (ßg = -0.64, 95% ETI = -0.88, -0.36, BF = 72.97). Finally, AD patients performed worse than sVaD patients in recognition memory tasks (ßg = -0.76, 95% ETI = -1.26, -0.26, BF = 11.50). Conclusion: Our findings show inferior performance of AD in episodic memory and superior performance in working memory. We found little support for other differences proposed by diagnostic systems and diagnostic guidelines. The utility of cognitive, behavioral, psychiatric, and functional measures in differential diagnosis is limited and should be complemented by other information. Finally, we identify research areas and avenues, which could significantly improve the diagnostic value of cognitive measures.

Genome diversity of Borrelia garinii in marine transmission cycles does not match host associations but reflects the strains evolutionary history.

Borrelia burgdorferi sensu lato is a species complex of spirochetal bacteria that occupy different ecological niches which is reflected in their reservoir host- and vector-associations. Borrelia genomes possess numerous linear and circular plasmids. Proteins encoded by plasmid genes play a major role in host- and vector-interaction and are important for Borrelia niche adaptation. However, the plasmid composition and therewith the gene repertoire may vary even in strains of a single species. Borrelia garinii, one of the six human pathogenic species, is common in Europe (vector Ixodes ricinus), Asia (vector Ixodes persulcatus) and in marine birds (vector Ixodes uriae). For the latter, only a single culture isolate (Far04) and its genome were previously available. The genome was rather small containing only one circular and six linear plasmids with a notable absence of cp32 plasmids. To further investigate B. garinii from marine transmission cycles and to explore i) whether the small number of plasmids found in isolate Far04 is a common feature in B. garinii from marine birds and presents an adaptation to this particular niche and ii) whether there may be a correlation between genome type and host species, we initiated in vitro cultures from live I. uriae collected in 2017 and 2018 from marine avian hosts and their nests. Hosts included common guillemots, Atlantic Puffin, razorbill, and kittiwake. We obtained 17 novel isolates of which 10 were sequenced using Illumina technology, one also with Pacific Bioscience technology. The 10 genomes segregated into five different genome types defined by plasmid types (based on PFam32 loci). We show that the genomes of seabird associated B. garinii contain fewer plasmids (6-9) than B. garinii from terrestrial avian species (generally ≥10), potentially suggesting niche adaptation. However, genome type did not match an association with the diverse avian seabird hosts investigated but matched the clonal complex they originated from, perhaps reflecting the isolates evolutionary history. Questions that should be addressed in future studies are (i) how is plasmid diversity related to host- and/or vector adaptation; (ii) do the different seabird species differ in reservoir host competence, and (iii) can the genome types found in seabirds use terrestrial birds as reservoir hosts.

Comparison of somatostatin receptor expression in patients with neuroendocrine tumours with and without somatostatin analogue treatment imaged with [18F]SiTATE.

Purpose: Somatostatin analogues (SSA) are frequently used in the treatment of neuroendocrine tumours. Recently, [18F]SiTATE entered the field of somatostatin receptor (SSR) positron emission tomography (PET)/computed tomography (CT) imaging. The purpose of this study was to compare the SSR-expression of differentiated gastroentero-pancreatic neuroendocrine tumours (GEP-NET) measured by [18F]SiTATE-PET/CT in patients with and without previous treatment with long-acting SSAs to evaluate if SSA treatment needs to be paused prior to [18F]SiTATE-PET/CT. Methods: 77 patients were examined with standardised [18F]SiTATE-PET/CT within clinical routine: 40 patients with long-acting SSAs up to 28 days prior to PET/CT examination and 37 patients without pre-treatment with SSAs. Maximum and mean standardized uptake values (SUVmax and SUVmean) of tumours and metastases (liver, lymphnode, mesenteric/peritoneal and bones) as well as representative background tissues (liver, spleen, adrenal gland, blood pool, small intestine, lung, bone) were measured, SUV ratios (SUVR) were calculated between tumours/metastases and liver, likewise between tumours/metastases and corresponding specific background, and compared between the two groups. Results: SUVmean of liver (5.4 ± 1.5 vs. 6.8 ± 1.8) and spleen (17.5 ± 6.8 vs. 36.7 ± 10.3) were significantly lower (p < 0.001) and SUVmean of blood pool (1.7 ± 0.6 vs. 1.3 ± 0.3) was significantly higher (p < 0.001) in patients with SSA pre-treatment compared to patients without. No significant differences between tumour-to-liver and specific tumour-to-background SUVRs were observed between both groups (all p > 0.05). Conclusion: In patients previously treated with SSAs, a significantly lower SSR expression ([18F]SiTATE uptake) in normal liver and spleen tissue was observed, as previously reported for 68Ga-labelled SSAs, without significant reduction of tumour-to-background contrast. Therefore, there is no evidence that SSA treatment needs to be paused prior to [18F]SiTATE-PET/CT.

Dependencies of the indoor climate on the course of the seasons and derivation of regressions from long-term measurements.

A building's indoor climate is an essential input variable for a variety of building physics computational models, simulations, and analyses. Precise knowledge of the indoor climate is necessary to minimize the risk of mold or moisture damage and is required to ensure minimum heat insulation standards in buildings. Detailed data are especially necessary for the progressive application of transient calculations, for example, concerning thermal comfort or energy consumption. While the properties of building materials and the (local) outdoor climate are known, only rudimentary information about the dynamic indoor climate is available. Most existing information in the literature about indoor climate is fairly general and forgoes a differentiation between climatic region, occupancy profile, and the utilization of rooms. In this paper, we report on indoor climate measurements in naturally ventilated apartments over a period of 1 year. The measurement results complement the existing data to provide accurate indoor climate data in buildings. The measured values of indoor temperature and relative humidity serve to derive the dew point temperature and moisture load whereby dynamic time-dependent regression functions are determined for these parameters. The evaluations are carried out separately according to room use. The comparison of living rooms and bedrooms indicates a great influence of room use on the indoor climate in residential buildings. The determined indoor climate model can be used for the planning of buildings and simulations. The classification into living rooms and bedrooms makes it possible to take user behavior into account more realistically in building physics simulations. The minimum thermal insulation in residential buildings can also be checked and designed based on realistic data. The prediction interval describes the limits in which residential rooms are free of damage with a high probability. In this way, the indoor climate model describes an approach to examine and evaluate simulation results regarding condensation risk and mold damage in naturally ventilated rooms.

Semantic feature activation takes time: longer SOA elicits earlier priming effects during reading.

While most previous studies of "semantic" priming confound associative and semantic relations, here we use a simple co-occurrence-based approach to examine "pure" semantic priming, while experimentally controlling for associative relations. We define associative relations by the co-occurrence of words in the sentences of a large text corpus. Contextual-semantic feature overlap, in contrast, is defined by the number of common associates that the prime shares with the target. Then we revisit the spreading activation theory and examine whether a long vs. short time available for semantic feature activation leads to early vs. late viewing time effects on the target words of a sentence reading experiment. We independently manipulate contextual-semantic feature overlap of two primes with one target word in sentences of the form pronoun, verb prime, article, adjective prime and target noun, e. g., "She rides the gray elephant." The results showed that long-SOA (verb-noun) overlap reduces early single and first fixation durations of the target noun, and short-SOA (adjective-noun) overlap reduces late go-past durations. This result pattern can be explained by the spreading activation theory: The semantic features of the prime words need some time to become sufficiently active before they can reliably affect target processing. Therefore, the verb can act on the target noun's early eye-movement measures presented three words later, while the adjective is presented immediately prior to the target-thus a difficult adjective-noun semantic integration leads to a late sentence re-examination of the preceding words.

Automated extraction of genes associated with antibiotic resistance from the biomedical literature.

The detection of bacterial antibiotic resistance phenotypes is important when carrying out clinical decisions for patient treatment. Conventional phenotypic testing involves culturing bacteria which requires a significant amount of time and work. Whole-genome sequencing is emerging as a fast alternative to resistance prediction, by considering the presence/absence of certain genes. A lot of research has focused on determining which bacterial genes cause antibiotic resistance and efforts are being made to consolidate these facts in knowledge bases (KBs). KBs are usually manually curated by domain experts to be of the highest quality. However, this limits the pace at which new facts are added. Automated relation extraction of gene-antibiotic resistance relations from the biomedical literature is one solution that can simplify the curation process. This paper reports on the development of a text mining pipeline that takes in English biomedical abstracts and outputs genes that are predicted to cause resistance to antibiotics. To test the generalisability of this pipeline it was then applied to predict genes associated with Helicobacter pylori antibiotic resistance, that are not present in common antibiotic resistance KBs or publications studying H. pylori. These genes would be candidates for further lab-based antibiotic research and inclusion in these KBs. For relation extraction, state-of-the-art deep learning models were used. These models were trained on a newly developed silver corpus which was generated by distant supervision of abstracts using the facts obtained from KBs. The top performing model was superior to a co-occurrence model, achieving a recall of 95%, a precision of 60% and F1-score of 74% on a manually annotated holdout dataset. To our knowledge, this project was the first attempt at developing a complete text mining pipeline that incorporates deep learning models to extract gene-antibiotic resistance relations from the literature. Additional related data can be found at https://github.com/AndreBrincat/Gene-Antibiotic-Resistance-Relation-Extraction.

Meditation affects word recognition of meditation novices.

This work represents one of the first attempts to examine the effects of meditation on the processing of written single words. In the present longitudinal study, participants conducted a lexical decision task and rated the affective valence of nouns before and after a 7-week class in mindfulness meditation, loving-kindness meditation, or a control intervention. Both meditation groups rated the emotional valence of nouns more neutral after the interventions, suggesting a general down-regulation of emotions. In the loving-kindness group, positive words were rated more positively after the intervention, suggesting a specific intensification of positive feelings. After both meditation interventions, response times in the lexical decision task accelerated significantly, with the largest facilitation occurring in the loving-kindness group. We assume that meditation might have led to increased attention, better visual discrimination, a broadened attentional focus, and reduced mind-wandering, which in turn enabled accelerated word recognition. These results extend findings from a previous study with expert Zen meditators, in which we found that one session of advanced meditation can affect word recognition in a very similar way.

Language Models Explain Word Reading Times Better Than Empirical Predictability.

Though there is a strong consensus that word length and frequency are the most important single-word features determining visual-orthographic access to the mental lexicon, there is less agreement as how to best capture syntactic and semantic factors. The traditional approach in cognitive reading research assumes that word predictability from sentence context is best captured by cloze completion probability (CCP) derived from human performance data. We review recent research suggesting that probabilistic language models provide deeper explanations for syntactic and semantic effects than CCP. Then we compare CCP with three probabilistic language models for predicting word viewing times in an English and a German eye tracking sample: (1) Symbolic n-gram models consolidate syntactic and semantic short-range relations by computing the probability of a word to occur, given two preceding words. (2) Topic models rely on subsymbolic representations to capture long-range semantic similarity by word co-occurrence counts in documents. (3) In recurrent neural networks (RNNs), the subsymbolic units are trained to predict the next word, given all preceding words in the sentences. To examine lexical retrieval, these models were used to predict single fixation durations and gaze durations to capture rapidly successful and standard lexical access, and total viewing time to capture late semantic integration. The linear item-level analyses showed greater correlations of all language models with all eye-movement measures than CCP. Then we examined non-linear relations between the different types of predictability and the reading times using generalized additive models. N-gram and RNN probabilities of the present word more consistently predicted reading performance compared with topic models or CCP. For the effects of last-word probability on current-word viewing times, we obtained the best results with n-gram models. Such count-based models seem to best capture short-range access that is still underway when the eyes move on to the subsequent word. The prediction-trained RNN models, in contrast, better predicted early preprocessing of the next word. In sum, our results demonstrate that the different language models account for differential cognitive processes during reading. We discuss these algorithmically concrete blueprints of lexical consolidation as theoretically deep explanations for human reading.

Predictive Modeling of Critical Temperatures in Superconducting Materials.

In this study, we have investigated quantitative relationships between critical temperatures of superconductive inorganic materials and the basic physicochemical attributes of these materials (also called quantitative structure-property relationships). We demonstrated that one of the most recent studies (titled "A data-driven statistical model for predicting the critical temperature of a superconductor" and published in Computational Materials Science by K. Hamidieh in 2018) reports on models that were based on the dataset that contains 27% of duplicate entries. We aimed to deliver stable models for a properly cleaned dataset using the same modeling techniques (multiple linear regression, MLR, and gradient boosting decision trees, XGBoost). The predictive ability of our best XGBoost model (R2 = 0.924, RMSE = 9.336 using 10-fold cross-validation) is comparable to the XGBoost model by the author of the initial dataset (R2 = 0.920 and RMSE = 9.5 K in ten-fold cross-validation). At the same time, our best model is based on less sophisticated parameters, which allows one to make more accurate interpretations while maintaining a generalizable model. In particular, we found that the highest relative influence is attributed to variables that represent the thermal conductivity of materials. In addition to MLR and XGBoost, we explored the potential of other machine learning techniques (NN, neural networks and RF, random forests).

Feigning memory impairment in a forced-choice task: Evidence from event-related potentials.

Strategies of malingering detection have brought about a wealth of neuropsychological studies in the last decades. However, the investigation of physiological measures to reliably differentiate between authentic and manipulated symptom presentations is still in its infancy. The present study examined event-related potentials (ERP) to identify feigned memory impairment. We tested instructed malingerers (n = 25) and control participants (n = 22) with a recognition task similar to the Test of Memory Malingering. No differences between groups were found for P1 (70-110 ms) but for N1 (120-170 ms) and P300 components, with lower amplitudes for instructed malingerers. Behavioral data showed a typical pattern of unrealistically high errors in a forced-choice recognition task and less overall recalled stimuli in instructed malingerers. We also found study-phase repetition and old/new effects in the P300, but no interactions with groups (control vs. malingering). Post-hoc analyses revealed that the P300 effect is greater when participants reported an attention-based faking strategy, as opposed to response-based malingerers and controls. The employment of physiological measures can yield additional information on the validity of test data without the need to perform additional tests.

Functional connectivity of the left inferior frontal gyrus during semantic priming.

Recent applications of computationally calculated word co-occurrences allowed the prediction of left inferior frontal gyrus (LIFG) activation during semantic word processing. Hence, an interactive activation model, the associative read-out model (AROM), utilizes co-occurrences in its semantic processing layer and proposes connectivity from the LIFG along the ventral visual stream. Direct empirical evidence for its connectivity assumptions is so far missing, however. In this study, we employed psychophysiological interaction analysis on the neuroimaging data of a semantic priming experiment, targeting the LIFG as main region to resolve semantic conflicts. We further manipulated the prime and target word by co-occurrence-based direct association and semantic similarity in a full-factorial design. At a low semantic similarity, we observed increased functional connectivity of the LIFG to the fusiform gyrus, the hippocampus, the anterior cingulate cortex and the orbitofrontal cortex, indicating a connective pattern analogous to the semantic layer of the AROM. Surprisingly, a low (compared to a high) direct association showed no difference in brain activation, which raises the question about the diverging cognitive processes of the two priming types.

Fixation-related NIRS indexes retinotopic occipital processing of parafoveal preview during natural reading.

While word frequency and predictability effects have been examined extensively, any evidence on interactive effects as well as parafoveal influences during whole sentence reading remains inconsistent and elusive. Novel neuroimaging methods utilize eye movement data to account for the hemodynamic responses of very short events such as fixations during natural reading. In this study, we used the rapid sampling frequency of near-infrared spectroscopy (NIRS) to investigate neural responses in the occipital and orbitofrontal cortex to word frequency and predictability. We observed increased activation in the right ventral occipital cortex when the fixated word N was of low frequency, which we attribute to an enhanced cost during saccade planning. Importantly, unpredictable (in contrast to predictable) low frequency words increased the activity in the left dorsal occipital cortex at the fixation of the preceding word N-1, presumably due to an upcoming breach of top-down modulated expectation. Opposite to studies that utilized a serial presentation of words (e.g. Hofmann et al., 2014), we did not find such an interaction in the orbitofrontal cortex, implying that top-down timing of cognitive subprocesses is not required during natural reading. We discuss the implications of an interactive parafoveal-on-foveal effect for current models of eye movements.

It is more than Interference: Examining the neurohemodynamic correlates of the flanker task with functional near-infrared spectroscopy.

Previous functional near-infrared spectroscopy studies using the Eriksen flanker task, in contrast to functional magnetic resonance imaging studies, revealed the quite puzzling finding of an inverted conflict effect, that is, greater middle and superior frontal activation in response compatible than in response incompatible trials. However, since neither prior functional near-infrared spectroscopy studies nor most previous functional magnetic resonance imaging studies separated between an identical and a compatible condition, it is hard to pinpoint whether this discrepancy occurs on the level of stimulus processing or response generation. By assigning two letters to both left (D, F) and right (J, K) hand reactions, we were able to separate identical (e.g., JJJ) and compatible (e.g., JKJ) conditions that solely differ in their stimulus congruency. Replicating prior functional magnetic resonance imaging findings, we found the standard conflict effect at the transition of superior and middle frontal gyrus, when comparing the activation in compatible trials to that in incompatible trials. Both changes in oxygenated and deoxygenated hemoglobin thus pointed to more effortful processing in incompatible trials. Interestingly, however, identical trials showed the highest activation in this region, according to both changes in oxygenated and deoxygenated blood. A finding that mirrors and extends prior functional near-infrared spectroscopy findings, which only regarded oxygenated blood. We argue that this pattern of results does not reflect the standard conflict effect. We rather assume that other processes like perceptual familiarity or strategic readjustment might be at play.

Zen meditation neutralizes emotional evaluation, but not implicit affective processing of words.

There is ample evidence that meditation can regulate emotions. It is questionable, however, whether meditation can down-regulate sensitivity to emotional experience in high-level cognitive representations such as words. The present study shows that adept Zen meditators rated the emotional valence of (low-arousal) positive and (high- and low-arousal) negative nouns significantly more neutral after a meditation session, while there was no change of valence ratings after a comparison intervention in the comparison group. Because the Zen group provided greater "openness to experience" and lower "need for achievement and performance" in the "Big Five" personality assessment, we used these scores as covariates for all analyses. We found no differential emotion effects of Zen meditation during lexical decision, but we replicated the slow-down of low-arousal negative words during lexical decision in both groups. Interestingly, Zen meditation elicited a global facilitation of all response times, which we discuss in terms of increased attentional resources after meditation.

Simple Co-Occurrence Statistics Reproducibly Predict Association Ratings.

What determines human ratings of association? We planned this paper as a test for association strength (AS) that is derived from the log likelihood that two words co-occur significantly more often together in sentences than is expected from their single word frequencies. We also investigated the moderately correlated interactions of word frequency, emotional valence, arousal, and imageability of both words (r's ≤ .3). In three studies, linear mixed effects models revealed that AS and valence reproducibly account for variance in the human ratings. To understand further correlated predictors, we conducted a hierarchical cluster analysis and examined the predictors of four clusters in competitive analyses: Only AS and word2vec skip-gram cosine distances reproducibly accounted for variance in all three studies. The other predictors of the first cluster (number of common associates, (positive) point-wise mutual information, and word2vec CBOW cosine) did not reproducibly explain further variance. The same was true for the second cluster (word frequency and arousal); the third cluster (emotional valence and imageability); and the fourth cluster (consisting of joint frequency only). Finally, we discuss emotional valence as an important dimension of semantic space. Our results suggest that a simple definition of syntagmatic word contiguity (AS) and a paradigmatic measure of semantic similarity (skip-gram cosine) provide the most general performance-independent explanation of association ratings.

A novel co-occurrence-based approach to predict pure associative and semantic priming.

The theoretical "difficulty in separating association strength from [semantic] feature overlap" has resulted in inconsistent findings of either the presence or absence of "pure" associative priming in recent literature (Hutchison, 2003, Psychonomic Bulletin & Review, 10(4), p. 787). The present study used co-occurrence statistics of words in sentences to provide a full factorial manipulation of direct association (strong/no) and the number of common associates (many/no) of the prime and target words. These common associates were proposed to serve as semantic features for a recent interactive activation model of semantic processing (i.e., the associative read-out model; Hofmann & Jacobs, 2014). With stimulus onset asynchrony (SOA) as an additional factor, our findings indicate that associative and semantic priming are indeed dissociable. Moreover, the effect of direct association was strongest at a long SOA (1,000 ms), while many common associates facilitated lexical decisions primarily at a short SOA (200 ms). This response pattern is consistent with previous performance-based accounts and suggests that associative and semantic priming can be evoked by computationally determined direct and common associations.

If You Don't Have Valence, Ask Your Neighbor: Evaluation of Neutral Words as a Function of Affective Semantic Associates.

How do humans perform difficult forced-choice evaluations, e.g., of words that have been previously rated as being neutral? Here we tested the hypothesis that in this case, the valence of semantic associates is of significant influence. From corpus based co-occurrence statistics as a measure of association strength we computed individual neighborhoods for single neutral words comprised of the 10 words with the largest association strength. We then selected neutral words according to the valence of the associated words included in the neighborhoods, which were either mostly positive, mostly negative, mostly neutral or mixed positive and negative, and tested them using a valence decision task (VDT). The data showed that the valence of semantic neighbors can predict valence judgments to neutral words. However, all but the positive neighborhood items revealed a high tendency to elicit negative responses. For the positive and negative neighborhood categories responses congruent with the neighborhood's valence were faster than incongruent responses. We interpret this effect as a semantic network process that supports the evaluation of neutral words by assessing the valence of the associative semantic neighborhood. In this perspective, valence is considered a semantic super-feature, at least partially represented in associative activation patterns of semantic networks.

Differences in the dynamics of affective and cognitive processing - An ERP study.

A controversy in emotion research concerns the question of whether affective or cognitive primacy are evident in processing affective stimuli and the factors contributing to each alternative. Using electrophysiological recordings in an adapted visual oddball paradigm allowed tracking the dynamics of affective and cognitive effects. Stimuli consisted of face pictures displaying affective expressions with rare oddballs differing from frequent stimuli in either affective expression, structure (while frequent stimuli were shown frontally these deviants were turned sideways) or they differed on both dimensions, i.e. in affective expression and structure. Results revealed a defined sequence of differences in ERP amplitudes: For stimuli deviating in their affective expression only, P1 modulations ~100ms were evident, while affective differences of structure deviants were not evident before the N170 time window. All three types of deviants differed in P300 amplitudes, indicating integration of affective and structural information. These results encompass evidence for both, cognitive and affective primacy depending on stimulus properties. Specifically affective primacy is only visible when the respective facial features can be extracted with ease. When structural differences make face processing harder, however, cognitive primacy is brought forward.

On Elementary Affective Decisions: To Like Or Not to Like, That Is the Question.

Perhaps the most ubiquitous and basic affective decision of daily life is deciding whether we like or dislike something/somebody, or, in terms of psychological emotion theories, whether the object/subject has positive or negative valence. Indeed, people constantly make such liking decisions within a glimpse and, importantly, often without expecting any obvious benefit or knowing the exact reasons for their judgment. In this paper, we review research on such elementary affective decisions (EADs) that entail no direct overt reward with a special focus on Neurocognitive Poetics and discuss methods and models for investigating the neuronal and cognitive-affective bases of EADs to verbal materials with differing degrees of complexity. In line with evolutionary and appraisal theories of (aesthetic) emotions and data from recent neurocognitive studies, the results of a decision tree modeling approach simulating EADs to single words suggest that a main driving force behind EADs is the extent to which such high-dimensional stimuli are associated with the "basic" emotions joy/happiness and disgust.

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.