Refine
Has Fulltext
- yes (146)
Year of publication
- 2015 (146) (remove)
Document Type
- Doctoral Thesis (146) (remove)
Language
- English (146) (remove)
Keywords
- Topologischer Isolator (8)
- Taufliege (5)
- Krebs <Medizin> (4)
- Quecksilbertellurid (4)
- Drosophila (3)
- Elektronischer Transport (3)
- Ereigniskorreliertes Potenzial (3)
- Exziton (3)
- Femtosekundenspektroskopie (3)
- Festkörperphysik (3)
Institute
- Graduate School of Life Sciences (32)
- Theodor-Boveri-Institut für Biowissenschaften (26)
- Physikalisches Institut (14)
- Institut für Mathematik (13)
- Institut für Pharmazie und Lebensmittelchemie (10)
- Institut für Physikalische und Theoretische Chemie (10)
- Institut für Psychologie (10)
- Institut für Theoretische Physik und Astrophysik (8)
- Institut für Informatik (7)
- Institut für Organische Chemie (6)
- Fakultät für Chemie und Pharmazie (3)
- Graduate School of Science and Technology (3)
- Frauenklinik und Poliklinik (2)
- Institut für Geographie und Geologie (2)
- Institut für Musikforschung (2)
- Institut für Pharmakologie und Toxikologie (2)
- Medizinische Klinik und Poliklinik II (2)
- Pathologisches Institut (2)
- Rudolf-Virchow-Zentrum (2)
- Volkswirtschaftliches Institut (2)
- Abteilung für Forensische Psychiatrie (1)
- Abteilung für Funktionswerkstoffe der Medizin und der Zahnheilkunde (1)
- Fakultät für Physik und Astronomie (1)
- Graduate School of the Humanities (1)
- Institut für Anorganische Chemie (1)
- Institut für Hygiene und Mikrobiologie (1)
- Julius-von-Sachs-Institut für Biowissenschaften (1)
- Kinderklinik und Poliklinik (1)
- Klinik und Poliklinik für Allgemein-, Viszeral-, Gefäß- und Kinderchirurgie (Chirurgische Klinik I) (1)
- Klinik und Poliklinik für Anästhesiologie (ab 2004) (1)
- Klinik und Poliklinik für Psychiatrie, Psychosomatik und Psychotherapie (1)
- Klinik und Polikliniken für Zahn-, Mund- und Kieferkrankheiten (1)
- Lehrstuhl für Orthopädie (1)
- Medizinische Fakultät (1)
- Neurologische Klinik und Poliklinik (1)
- Philosophische Fakultät (Histor., philolog., Kultur- und geograph. Wissensch.) (1)
Sonstige beteiligte Institutionen
- ATLAS Collaboration (1)
- Adam Opel AG (1)
- CERN (1)
- Deutscher Akademischer Austauschdienst (DAAD) (1)
- Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Raumfahrtsysteme (1)
- Institut für Hygiene und Mikrobiologie (1)
- Instituto de Higiene, Universidad de la República, Montevideo, Uruguay (1)
- König-Ludwig-Haus, Orthopedic Clinic, Würzburg (1)
- Novartis AG (1)
- OTH Regensburg (1)
ResearcherID
- B-1911-2015 (1)
- C-2593-2016 (1)
- N-2030-2015 (1)
- N-3741-2015 (1)
Utility is perhaps the most central concept in modern economic theorizing. However, the behaviorist reduction to Revealed Preference not only removed the psychological content of utility but experimental investigations also exposed numerous anomalies in this theory.
This program of research focused on the psychological processes by which utility judgments are generated. For this purpose, the standard assumption of a homogeneous concept is substituted by the Utilitarian Duality Hypothesis.
In particular, judgments concerning categorical utility (uCat) infer an object's category based on its attributes which may subsequently allow the transfer of evaluative information like feelings or attitudes. In contrast, comparative utility (uCom) depends on the distance to a reference value on a specific dimension of comparison. Importantly, dimensions of comparison are manifold and context dependent.
In a series of experiments, we show that the resulting Dual Utility Model is able to explain several known anomalies in a parsimonious fashion. Moreover, we identify central factors determining the relative weight assigned to both utility components.
Finally, we discuss the implications of the Utilitarian Duality for both, the experimental practice in economics as well as the consequences for economic theorizing. In sum, we propose that the Dual Utility Model can serve as an integrative framework for both the rational model and its anomalies.
Microbial species (bacteria and archaea) in the gut are important for human health in various ways. Not only does the species composition vary considerably within the human population, but each individual also appears to have its own strains of a given species. While it is known from studies of bacterial pan-genomes, that genetic variation between strains can differ considerably, such as in Escherichia coli, the extent of genetic variation of strains for abundant gut species has not been surveyed in a natural habitat. This is mainly due to the fact that most of these species cannot be cultured in the laboratory. Genetic variation can range from microscale genomic rearrangements such as small nucleotide polymorphism (SNP) to macroscale large genomic rearrangements like structural variations. Metagenomics offers an alternative solution to study genetic variation in prokaryotes, as it involves DNA sequencing of the whole community directly from the environment. However, most metagenomic studies to date only focus on variation in gene abundance and hence are not able to characterize genetic variation (in terms of presence or absence of SNPs and genes) of gut microbial strains of individuals.
The aim of my doctorate studies was therefore to study the extent of genetic variation in the genomic sequence of gut prokaryotic species and its phenotypic effects based on: (1) the impact of SNP variation in gut bacterial species, by focusing on genes under selective pressure and (2) the gene content variation (as a proxy for structural variation) and their effect on microbial species and the phenotypic traits of their human host.
In the first part of my doctorate studies, I was involved in a project in which we created a catalogue of 10.3 million SNPs in gut prokaryotic species, based on metagenomes. I used this to perform the first SNP-based comparative study of prokaryotic species evolution in a natural habitat. Here, I found that strains of gut microbial species in different individuals evolve at more similar rates than the strains within an individual. In addition, I found that gene evolution can be uncoupled from the evolution of its originating species, and that this could be related to selective pressure such as diet, exemplified by galactokinase gene (galK). Despite the individuality (i.e. uniqueness of each individual within the studied metagenomic dataset) in the SNP profile of the gut microbiota that we found, for most cases it is not possible to link SNPs with phenotypic differences. For this reason I also used gene content as a proxy to study structural variation in metagenomes.
In the second part of my doctorate studies, I developed a methodology to characterize the variability of gene content in gut bacterial species, using metagenomes. My approach is based on gene deletions, and was applied to abundant species (demonstrated using a set of 11 species). The method is sufficiently robust as it captures a similar range of gene content variability as has been detected in completely sequenced genomes. Using this procedure I found individuals differ by an average of 13% in their gene content of gut bacterial strains within the same species. Interestingly no two individuals shared the same gene content across bacterial species. However, this variation corresponds to a lower limit, as it is only accounts for gene deletion and not insertions. This large variation in the gene content of gut strain was found to affect important functions, such as polysaccharide utilization loci (PULs) and capsular polysaccharide synthesis (CPS), which are related with digestion of dietary fibers.
In summary, I have shown that metagenomics based approaches can be robust in characterizing genetic variation in gut bacterial species. I also illustrated, using examples both for SNPs and gene content (galK, PULs and CPS), that this genetic variation can be used to predict the phenotypic characteristics of the microbial species, as well as predicting the phenotype of their human host (for example, their capacity to digest different food components). Overall, the results of my thesis highlight the importance of characterizing the strains in the gut microbiome analogous to the emerging variability and importance of human genomics.
The learned helplessness phenomenon is a specific animal behavior induced by prior exposure to uncontrollable aversive stimuli. It was first found by Seligman and Maier (1967) in dogs and then has been reported in many other species, e.g. in rats (Vollmayr and Henn, 2001), in goldfishes (Padilla, 1970), in cockroaches (Brown, 1988) and also in fruit flies (Brown, 1996; Bertolucci, 2008). However, the learned helplessness effect in fruit flies (Drosophila melanogaster) has not been studied in detail. Thus, in this doctoral study, we investigated systematically learned helplessness behavior of Drosophila for the first time.
Three groups of flies were tested in heatbox. Control group was in the chambers experiencing constant, mild temperature. Second group, master flies were punished in their chambers by being heated if they stopped walking for 0.9s. The heat pulses ended as soon as they resumed walking again. A third group, the yoked fly, was in their chambers at the same time. However, their behavior didn’t affect anything: yoked flies were heated whenever master flies did, with same timing and durations. After certain amount of heating events, yoked flies associated their own behavior with the uncontrollability of the environment. They suppressed their innate responses such as reducing their walking time and walking speed; making longer escape latencies and less turning around behavior under heat pulses. Even after the conditioning phase, yoked flies showed lower activity level than master and control flies. Interestingly, we have also observed sex dimorphisms in flies. Male flies expressed learned helplessness not like female flies. Differences between master and yoked flies were smaller in male than in female flies. Another interesting finding was that prolonged or even repetition of training phases didn’t enhance learned helplessness effect in flies.
Furthermore, we investigated serotonergic and dopaminergic nervous systems in learned helplessness. Using genetic and pharmacological manipulations, we altered the levels of serotonin and dopamine in flies’ central nervous system. Female flies with reduced serotonin concentration didn’t show helpless behavior, while the learned helplessness effect in male flies seems not to be affected by a reduction of serotonin. Flies with lower dopamine level do not display the learned helplessness effect in the test phase, suggesting that with low dopamine the motivational change in learned helplessness in Drosophila may decline faster than with a normal dopamine level.
This thesis deals with the hp-finite element method (FEM) for linear quadratic optimal control problems. Here, a tracking type functional with control costs as regularization shall be minimized subject to an elliptic partial differential equation. In the presence of control constraints, the first order necessary conditions, which are typically used to find optimal solutions numerically, can be formulated as a semi-smooth projection formula. Consequently, optimal solutions may be non-smooth as well. The hp-discretization technique considers this fact and approximates rough functions on fine meshes while using higher order finite elements on domains where the solution is smooth.
The first main achievement of this thesis is the successful application of hp-FEM to two related problem classes: Neumann boundary and interface control problems. They are solved with an a-priori refinement strategy called boundary concentrated (bc) FEM and interface concentrated (ic) FEM, respectively. These strategies generate grids that are heavily refined towards the boundary or interface. We construct an elementwise interpolant that allows to prove algebraic decay of the approximation error for both techniques. Additionally, a detailed analysis of global and local regularity of solutions, which is critical for the speed of convergence, is included. Since the bc- and ic-FEM retain small polynomial degrees for elements touching the boundary and interface, respectively, we are able to deduce novel error estimates in the L2- and L∞-norm. The latter allows an a-priori strategy for updating the regularization parameter in the objective functional to solve bang-bang problems.
Furthermore, we apply the traditional idea of the hp-FEM, i.e., grading the mesh geometrically towards vertices of the domain, for solving optimal control problems (vc-FEM). In doing so, we obtain exponential convergence with respect to the number of unknowns. This is proved with a regularity result in countably normed spaces for the variables of the coupled optimality system.
The second main achievement of this thesis is the development of a fully adaptive hp-interior point method that can solve problems with distributed or Neumann control. The underlying barrier problem yields a non-linear optimality system, which poses a numerical challenge: the numerically stable evaluation of integrals over possibly singular functions in higher order elements. We successfully overcome this difficulty by monitoring the control variable at the integration points and enforcing feasibility in an additional smoothing step. In this work, we prove convergence of an interior point method with smoothing step and derive a-posteriori error estimators. The adaptive mesh refinement is based on the expansion of the solution in a Legendre series. The decay of the coefficients serves as an indicator for smoothness that guides between h- and p-refinement.
The investigation of interacting multi-agent models is a new field of mathematical research with application to the study of behavior in groups of animals or community of people. One interesting feature of multi-agent systems is collective behavior. From the mathematical point of view, one of the challenging issues considering with these dynamical models is development of control mechanisms that are able to influence the time evolution of these systems.
In this thesis, we focus on the study of controllability, stabilization and optimal control problems for multi-agent systems considering three models as follows: The first one is the Hegselmann Krause opinion formation (HK) model. The HK dynamics describes how individuals' opinions are changed by the interaction with others taking place in a bounded domain of confidence. The study of this model focuses on determining feedback controls in order to drive the agents' opinions to reach a desired agreement. The second model is the Heider social balance (HB) model. The HB dynamics explains the evolution of relationships in a social network. One purpose of studying this system is the construction of control function in oder to steer the relationship to reach a friendship state. The third model that we discuss is a flocking model describing collective motion observed in biological systems. The flocking model under consideration includes self-propelling, friction, attraction, repulsion, and alignment features. We investigate a control for steering the flocking system to track a desired trajectory. Common to all these systems is our strategy to add a leader agent that interacts with all other members of the system and includes the control mechanism.
Our control through leadership approach is developed using classical theoretical control methods and a model predictive control (MPC) scheme. To apply the former method, for each model the stability of the corresponding linearized system near consensus is investigated. Further, local controllability is examined. However, only in the
Hegselmann-Krause opinion formation model, the feedback control is determined in order to steer agents' opinions to globally converge to a desired agreement. The MPC approach is an optimal control strategy based on numerical optimization. To apply the MPC scheme, optimal control problems for each model are formulated where the objective functions are different depending on the desired objective of the problem. The first-oder necessary optimality conditions for each problem are presented. Moreover for the numerical treatment, a sequence of open-loop discrete optimality systems is solved by accurate Runge-Kutta schemes, and in the optimization procedure, a nonlinear conjugate gradient solver is implemented. Finally, numerical experiments are performed to investigate the properties of the multi-agent models and demonstrate the ability of the proposed control strategies to drive multi-agent systems to attain a desired consensus and to track a given trajectory.
The recently discovered human DREAM complex (for DP, RB-like, E2F and MuvB complex) is a chromatin-associated pocket protein complex involved in cell cycle- dependent gene expression. DREAM consists of five core subunits and forms a complex either with the pocket protein p130 and the transcription factor E2F4 to repress gene expression or with the transcription factors B-MYB and FOXM1 to promote gene expression.
Gas2l3 was recently identified by our group as a novel DREAM target gene. Subsequent characterization in human cell lines revealed that GAS2L3 is a microtubule and F-actin cross-linking protein, expressed in G2/M, plays a role in cytokinesis, and is important for chromosomal stability.
The aim of the first part of the study was to analyze how expression of GAS2L3 is regulated by DREAM and to provide a better understanding of the function of GAS2L3 in mitosis and cytokinesis.
ChIP assays revealed that the repressive and the activating form of DREAM bind to the GAS2L3 promoter. RNA interference (RNAi) mediated GAS2L3 depletion demonstrated the requirement of GAS2L3 for proper cleavage furrow ingression in cytokinesis. Immunofluorescence-based localization studies showed a localization of GAS2L3 at the mitotic spindle in mitosis and at the midbody in cytokinesis. Additional experiments demonstrated that the GAS2L3 GAR domain, a putative microtubule- binding domain, is responsible for GAS2L3 localization to the constriction zones in cytokinesis suggesting a function for GAS2L3 in the abscission process.
DREAM is known to promote G2/M gene expression. DREAM target genes include several mitotic kinesins and mitotic microtubule-associated proteins (mitotic MAPs). However, it is not clear to what extent DREAM regulates mitotic kinesins and MAPs, so far. Furthermore, a comprehensive study of mitotic kinesin expression in cancer cell lines is still missing.
Therefore, the second major aim of the thesis was to characterize the regulation of mitotic kinesins and MAPs by DREAM, to investigate the expression of mitotic kinesins in cancer cell line panels and to evaluate them as possible anti-cancer targets.
ChIP assays together with RNAi mediated DREAM subunit depletion experiments demonstrated that DREAM is a master regulator of mitotic kinesins. Furthermore, expression analyses in a panel of breast and lung cancer cell lines revealed that mitotic kinesins are up-regulated in the majority of cancer cell lines in contrast to non-transformed controls. Finally, an inducible lentiviral-based shRNA system was developed to effectively deplete mitotic kinesins. Depletion of selected mitotic kinesins resulted in cytokinesis failures and strong anti-proliferative effects in several human cancer cell lines.
Thus, this system will provide a robust tool for future investigation of mitotic kinesin function in cancer cells.
In the course of the growth of the Internet and due to increasing availability of data, over the last two decades, the field of network science has established itself as an own area of research. With quantitative scientists from computer science, mathematics, and physics working on datasets from biology, economics, sociology, political sciences, and many others, network science serves as a paradigm for interdisciplinary research.
One of the major goals in network science is to unravel the relationship between topological graph structure and a network’s function. As evidence suggests, systems from the same fields, i.e. with similar function, tend to exhibit similar structure. However, it is still vague whether a similar graph structure automatically implies likewise function. This dissertation aims at helping to bridge this gap, while particularly focusing on the role of triadic structures.
After a general introduction to the main concepts of network science, existing work devoted to the relevance of triadic substructures is reviewed. A major challenge in modeling triadic structure is the fact that not all three-node subgraphs can be specified independently
of each other, as pairs of nodes may participate in multiple of those triadic subgraphs.
In order to overcome this obstacle, we suggest a novel class of generative network models based on so called Steiner triple systems. The latter are partitions of a graph’s vertices into pair-disjoint triples (Steiner triples). Thus, the configurations on Steiner triples can be specified independently of each other without overdetermining the network’s link
structure.
Subsequently, we investigate the most basic realization of this new class of models. We call it the triadic random graph model (TRGM). The TRGM is parametrized by a probability distribution over all possible triadic subgraph patterns. In order to generate a network instantiation of the model, for all Steiner triples in the system, a pattern is drawn from the distribution and adjusted randomly on the Steiner triple. We calculate the degree distribution of the TRGM analytically and find it to be similar to a Poissonian distribution. Furthermore, it is shown that TRGMs possess non-trivial triadic structure. We discover inevitable correlations in the abundance of certain triadic subgraph
patterns which should be taken into account when attributing functional relevance to particular motifs – patterns which occur significantly more frequently than expected at random. Beyond, the strong impact of the probability distributions on the Steiner triples on the occurrence of triadic subgraphs over the whole network is demonstrated. This interdependence allows us to design ensembles of networks with predefined triadic substructure. Hence, TRGMs help to overcome the lack of generative models needed for assessing the relevance of triadic structure.
We further investigate whether motifs occur homogeneously or heterogeneously distributed over a graph. Therefore, we study triadic subgraph structures in each node’s neighborhood individually. In order to quantitatively measure structure from an individual node’s perspective, we introduce an algorithm for node-specific pattern mining for both directed unsigned, and undirected signed networks. Analyzing real-world datasets, we find that there are networks in which motifs are distributed highly heterogeneously, bound to the proximity of only very few nodes. Moreover, we observe indication for the potential sensitivity of biological systems to a targeted removal of these critical vertices. In addition, we study whole graphs with respect to the homogeneity and homophily of their node-specific triadic structure. The former describes the similarity of subgraph distributions in the neighborhoods of individual vertices. The latter quantifies whether connected vertices
are structurally more similar than non-connected ones. We discover these features to be characteristic for the networks’ origins. Moreover, clustering the vertices of graphs regarding their triadic structure, we investigate structural groups in the neural network of C. elegans, the international airport-connection network, and the global network of diplomatic sentiments between countries. For the latter we find evidence for the instability of triangles considered socially unbalanced according to sociological theories.
Finally, we utilize our TRGM to explore ensembles of networks with similar triadic substructure in terms of the evolution of dynamical processes acting on their nodes. Focusing on oscillators, coupled along the graphs’ edges, we observe that certain triad motifs impose a clear signature on the systems’ dynamics, even when embedded in a larger
network structure.
Identification of human host cell factors involved in \(Staphylococcus\) \(aureus\) 6850 infection
(2015)
Staphylococcus aureus is both a human commensal and a pathogen. 20%-30% of all individuals are permanently or occasionally carriers of S. aureus without any symptoms. In contrast to this, S. aureus can cause life-threatening diseases e.g. endocarditis, osteomyelitis or sepsis. Here, the increase in antibiotic resistances makes it more and more difficult to treat these infections and hence the number of fatalities rises constantly. Since the pharmaceutical industry has no fundamentally new antibiotics in their pipeline, it is essential to better understand the interplay between S. aureus and the human host cell in order to find new, innovative treatment options.
In this study, a RNA interference based whole genome pool screen was performed to identify human proteins, which play a role during S. aureus infections. Since 1,600 invasion and 2,271 cell death linked factors were enriched at least 2 fold, the big challenge was to filter out the important ones. Here, a STRING pathway analysis proved to be the best option. Subsequently, the identified hits were validated with the help of inhibitors and a second, individualised small interfering RNA-based screen.
In the course of this work two important steps were identified, that are critical for host cell death: the first is bacterial invasion, the second phagosomal escape. The second step is obligatory for intracellular bacterial replication and subsequent host cell death. Invasion in turn is determining for all following events. Accordingly, the effect of the identified factors towards these two crucial steps was determined. Under screening conditions, escape was indirectly measured via intracellular replication. Three inhibitors (JNKII, Methyl-beta-cyclodeytrin, 9-Phenantrol) could be identified for the invasion process. In addition, siRNAs targeted against 16 different genes (including CAPN2, CAPN4 and PIK3CG), could significantly reduce bacterial invasion. Seven siRNAs (FPR2, CAPN4, JUN, LYN, HRAS, AKT1, ITGAM) were able to inhibit intracellular replication significantly. Further studies showed that the IP3 receptor inhibitor 2-APB, the calpain inhibitor calpeptin and the proteasome inhibitor MG-132 are able to prevent phagosomal escape and as a consequence intracellular replication and host cell death.
In this context the role of calpains, calcium, the proteasome and the mitochondrial membrane potential was further investigated in cell culture. Here, an antagonistic behaviour of calpain 1 and 2 during bacterial invasion was observed. Intracellular calcium signalling plays a major role, since its inhibition protects host cells from death. Beside this, the loss of mitochondrial membrane potential is characteristic for S. aureus infection but not responsible for host cell death. The reduction of membrane potential can be significantly diminished by the inhibition of the mitochondrial Na+/Ca2+ exchanger.
All together, this work shows that human host cells massively contribute to different steps in S. aureus infection rather than being simply killed by bacterial pore-forming toxins. Various individual host cell factors were identified, which contribute either to invasion or to phagosomal escape and therefore to S. aureus induced cytotoxicity. Finally, several inhibitors of S. aureus infection were identified. One of them, 2-APB, was already tested in a sepsis mouse model and reduced bacterial load of kidneys.
Thus, this study shows valuable evidence for novel treatment options against S. aureus infections, based on the manipulation of host cell signalling cascades.
Deregulated MYC expression contributes to cellular transformation as well as progression and
maintenance of human tumours. Interestingly, in the absence of additional genetic alterations,
potentially oncogenic levels of MYC sensitise cells to a variety of apoptotic stimuli. Hence, MYC-induced
apoptosis has long been recognised as a major barrier against cancer development.
However, it is largely unknown how cells discriminate physiological from supraphysiological levels
of MYC in order to execute an appropriate biological response.
The experiments described in this thesis demonstrate that induction of apoptosis in mammary
epithelial cells depends on the repressive actions of MYC/MIZ1 complexes. Analysis of gene
expression profiles and ChIP-sequencing experiments reveals that high levels of MYC are required
to invade low-affinity binding sites and repress target genes of the serum response factor SRF.
These genes are involved in cytoskeletal dynamics as well as cell adhesion processes and are likely
needed to transmit survival signals to the AKT kinase. Restoration of SRF activity rescues MIZ1-
dependent gene repression and increases AKT phosphorylation and downstream function.
Collectively, these results indicate that association with MIZ1 leads to an expansion of MYC’s
transcriptional response that allows sensing of oncogenic levels, which points towards a tumour-suppressive
role for the MYC/MIZ1 complex in epithelial cells.
Biased cognitive processes are very likely involved in the maintenance of fears and anxiety. One of such cognitive processes is the perceived relationship between fear-relevant stimuli and aversive consequences. If this relationship is perceived although objective contingencies have been random, it is called an (a posteriori) illusory correlation. If this relationship is overestimated before objective contingencies are experienced, it is called an (a priori) expectancy bias. Previous investigations showed that fear-relevant illusory correlations exist, but very few is known about how and why this cognitive bias develops. In the present dissertation thesis, a model is proposed based on a review of the literature on fear-relevant illusory correlations. This model describes how psychological factors might have an influence on fear and illusory correlations. Several critical implications of the model were tested in four experiments.
Experiment 1 tested the hypothesis that people do not only overestimate the proportion of aversive consequences (startle sounds) following emotionally negative stimuli (pictures of mutilations) relative to neutral stimuli (pictures of household objects), but also following highly arousing positive stimuli (pictures of erotic scenes), because arousal might be an important determinant of illusory correlations. The result was a significant expectancy bias for negative stimuli and a much smaller expectancy bias for positive stimuli. Unexpectedly, expectancy bias was restricted to women. An a posteriori illusory correlation was not found overall, but only in those participants who perceived the aversive consequences following negative stimuli as particularly aversive.
Experiment 2 tested the same hypothesis as experiment 1 using a paradigm that evoked distinct basic emotions (pictures inducing fear, anger, disgust or happiness). Only negative emotions resulted in illusory correlations with aversive outcomes (startle sounds), especially the emotions of fear and disgust. As in experiment 1, the extent of these illusory correlations was correlated with the perceived aversiveness of aversive outcomes. Moreover, only women overestimated the proportion of aversive outcomes during pictures that evoked fear, anger or disgust.
Experiment 3 used functional Magnetic Resonance Imaging (fMRI) to measure biased brain activity in female spider phobics during an illusory correlation paradigm. Both spider phobics and healthy controls expected more aversive outcomes (painful electrical shocks) following pictures of spiders than following neutral control stimuli (pictures of mushrooms). Spider phobics but not healthy controls overestimated the proportion of aversive outcomes following pictures of spiders in a trial-by-trial memory task. This a posteriori illusory correlation was correlated with enhanced shock aversiveness and activity in primary sensory-motor cortex in phobic participants. Moreover, spider phobics’ brain activity in the left dorsolateral prefrontal cortex was elevated in response to spider images. This activity also predicted the extent of the illusory correlation, which supports the theory that executive and attentional resources play an important role in the maintenance of illusory correlations.
Experiment 4 tested the hypothesis that the enhanced aversiveness of some outcomes would be sufficient to causally induce an illusory correlation. Neutral images (colored geometric figures) were paired with differently aversive outcomes (three startle sounds varying in intensity). Participants developed an illusory correlation between those images, which predicted the most aversive sound and this sound, which means that this association was overestimated relative to the other associations. The extent of the illusory correlation was positively correlated with participants’ self-reported anxiety. The results imply that the previously found relationship between illusory correlations and outcome aversiveness might reflect a causal impact of outcome aversiveness or salience on illusory correlations.
In sum, the conducted experiments indicate that illusory correlations between fear-relevant stimuli and aversive consequences might persist – among other factors - because of an enhanced aversiveness or salience of aversive consequences following feared stimuli. This assumption is based on correlational findings, a neural measure of outcome perception and a causal influence of outcome aversiveness on illusory correlations. Implications of these findings were integrated into a model of fear-relevant illusory correlations and potential implications are discussed. Future investigations should further elucidate the role of executive functions and gender effects. Moreover, the trial-by-trial assessment of illusory correlations is recommended to increase reliability of the concept. From a clinical perspective, the down-regulation of aversive experiences and the allocation of attention to non-aversive experiences might help to cure anxiety and cognitive bias.