Cognitive dimensions of predator responses to imperfect mimicry?

Many palatable insects, for example hoverflies, deter predators by mimicking well-defended insects such as wasps. However, for human observers, these flies often seem to be little better than caricatures of wasps – their visual appearance and behaviour are easily distinguishable. This imperfect mimicry baffles evolutionary biologists, because one might expect natural selection to do a more thorough job. Here we discuss two types of cognitive processes that might explain why mimics distinguishable mimics might enjoy increased protection from predation. Speed accuracy tradeoffs in predator decision making might give imperfect mimics sufficient time to escape, and predators under time constraint might avoid time-consuming discriminations between well-defended models and inaccurate edible mimics, and instead adopt a “safety first” policy of avoiding insects with similar appearance. Categorization of prey types by predators could mean that wholly dissimilar mimics may be protected, provided they share some common property with noxious prey

Patients with Parkinsons Disease Show Impaired Use of Priors in Conditions of Sensory Uncertainty.

Perceptual decisions arise after considering the available sensory evidence [1]. When sensory information is unreliable, a good strategy is to rely on previous experience in similar situations to guide decisions [2-6]. It is well known that patients with Parkinsons disease (PD) are impaired at value-based decision-making [7-11]. How patients combine past experience and sensory information to make perceptual decisions is unknown. We developed a novel, perceptual decision-making task and manipulated the statistics of the sensory stimuli presented to patients with PD and healthy participants to determine the influence of past experience on decision-making. We show that patients with PD are impaired at combining previously learned information with current sensory information to guide decisions. We modeled the results using the drift-diffusion model (DDM) and found that the impairment corresponds to a failure in adjusting the amount of sensory evidence needed to make a decision. Our modeling results also show that two complementary mechanisms operate to implement a bias when two sets of priors are learned concurrently. Asymmetric decision threshold adjustments, as reflected by changes in the starting point of evidence accumulation, are responsible for a general choice bias, whereas the adjustment of a dynamic bias that develops over the course of a trial, as reflected by a drift-rate offset, provides the stimulus-specific component of the prior. A proper interplay between these two processes is required to implement a bias based on concurrent, stimulus-specific priors in decision-making. We show here that patients with PD are impaired in these across-trial decision threshold adjustments

Microbial community structure and function is shaped by microhabitat characteristics in soil

Soil microorganisms play a key role in degradation processes in soil, such as organic matter decomposition and degradation of xenobiotics. Microbial growth and activity and therefore degradation processes are influenced by different ecological factors, such as substrate availability, pH and temperature. During soil development different microhabitats are formed which differ in their physiochemical properties. There is some evidence that mineral composition is a driver for specific microbial colonization. Thereby, the heterogeneity of soils with differences in mineral composition and substrate availability can lead to a spatial distribution of soil microorganisms. At the soil-litter interface, a biogeochemical hot spot in soil, the abundance and activity of soil microorganisms increases due to high substrate availability, and degradation processes such as pesticide degradation are enhanced. This thesis aimed to clarify the influence of habitat properties on the structure and function of the microbial community in soil. In particular, focus was on mineral-microbe interactions that result from the mineral composition and substrate availability in an artificial soils system. Furthermore this thesis was designed to increase our understanding of the bacterial and fungal roles in pesticide degradation at the soil-litter interface using 4-chloro-2-methylphenoxyacetic acid (MCPA) as a model xenobiotic. These two aspects of the thesis were examined in three studies. The first study focused on the succession of microbial communities and enzyme activities in an artificial soils system with varying mineral composition and substrate availability over a period of 18 months. In the second study a microcosm experiment was used to study the bacterial pathway of MCPA degradation at the soil-litter interface. Over a period of 27 days the succession of bacterial degraders was followed. The third study focused on the degradation of MCPA in soil by nonspecific fungal enzymes, through the addition of fungal laccases as well as litter during 42 days of incubation. Both studies indicated the involvement of fungi in MCPA degradation and the importance of the ecological behavior of different degraders as a function of substrate availability. Results of the first study indicated that the microbial community was affected by mineral properties under high substrate availability and by the availability of beneficial nutrients at the end of incubation when substrate had become limited. The measured enzyme activities provided clear evidence that microbial community structure was driven by nutrient limitation during incubation. In the presence of easily available organic substrates at the beginning of the experiment, the soil microbial community was dominated by copiotrophic bacteria (e.g. Betaproteobacteria), whereas under substrate limitation at the end of incubation, more recalcitrant compounds became important to oligotrophic bacteria (e.g. Acidobacteria), which then became dominant. The results of the second study indicated that the contribution of the potential degraders to degradation of MCPA differed, and this was also seen in the succession of specific bacterial MCPA degraders. Added litter stimulated MCPA degradation due to the availability of litter-derived carbon and induced a two-phase response of fungi. This was seen in the development of pioneer and late stage fungal communities. Both fungal communities were probably involved in MCPA degradation. Therefore, the third study focused on the fungal pathway. These results indicated that the fungal laccases used had no direct influence on degradation and were as efficient as litter in providing additional nutrient sources, increasing MCPA degradation by bacteria and fungi. The observed differences between litter and enzyme addition underscored the observation that the enzyme effect was short-lived and that substrate quality is an important factor in degradation processes. In conclusion, this thesis demonstrated that soil microbial communities and therefore degradation processes are driven by mineral composition as well as substrate availability and quality. In addition, this thesis extends our understanding of degradation processes such as the degradation of xenobiotics, with MCPA as model compound, in soil. The combined insights from all three studies suggest that the use of a simple system such as the artificial soil system can increase our understanding of complex mechanisms such as degradation of pesticides.Bodenmikroorganismen spielen im Boden eine Schlüsselrolle bei Abbauprozessen, wie Zersetzung von organischem Material und Abbau von Xenobiotika. Wachstum und Aktivität von Mikroorganismen und somit auch Abbauprozesse werden durch verschiedene ökologische Faktoren, wie Substratverfügbarkeit, pH und Temperatur beeinflusst. Während der Bodenentwicklung werden verschiedene Mikrohabitate mit unterschiedlichen physiochemischen Eingenschaften geformt. Es gibt einige Hinweise, dass die Mineralzusammensetzung ein Einflussfaktor für die spezifische mikrobielle Kolonisation ist. Dabei kann die Heterogenität von Böden mit unterschiedlicher mineralischer Zusammensetzung und Substratverfügbarkeit zu einer räumlichen und zeitlichen Verteilung von Bodenmikroorganismen führen. An der Boden-Streu Grenzfläche, einem biogeochemischen Hotspot, ist die Menge und Akivität von Bodenmikroorganismen auf Grund von hoher Substratverfügbarkeit erhöht und Abbauprozesse, wie Pestizidabbau verbessert. Ziel dieser Arbeit war es, den Einfluss von Habitateigenschaften auf Struktur und Funktion von mikrobiellen Gemeinschaften in Böden zu klären. Insbesoders lag der Fokus dieser Arbeit auf Mineral-Mikroorganismen Interaktionen im Bezug auf Mineralzusammensetzung und Substratverfügbarkeit in künstlichen Böden. Darüber hinaus wollte diese Arbeit unser Wissen über die bakterielle und pilzliche Rolle im Pestizidabbau an der Boden-Streu Grenzfläche, mit 4-Chlor-2-Methylphenoxyessigsäure (MCPA) als Modell-Xenobiotika, erweitern. Die zwei Aspekte dieser These wurden in drei Studien untersucht. Die erste Studie konzentrierte sich auf die zeitliche Abfolge der mikrobiellen Gemeinschaften und Enzymaktivitäten in künstlichen Böden, mit unterschiedlicher mineralischer Zusammensetzung und Substratverfügbarkeit über einen Zeitraum von 18 Monaten. In der zweiten Studie wurde ein Mikrokosmenexperiment verwendet um den bakteriellen Weg des MCPA-Abbaus an der Boden-Streu Grenzfläche zu untersuchen. Über einen Zeitraum von 27 Tagen wurde die Sukzession der bakteriellen Abbauer verfolgt. Die dritte Studie konzentrierte sich auf den Abbau von MCPA im Boden mittels unspezifischer pilzlicher Enzyme, durch Zugabe von pilzlichen Laccasen sowie Streu über einen Zeitraum von 42 Tagen. Beide Studien deuten die Beteiligung von Pilzen am MCPA-Abbau und die Bedeutung von ökologischem Verhalten verschiedener Abbauer in Abhänigkeit von Substratverfügbarkeit an. Die Ergebnisse aus der ersten Studie deuteten an, dass die mikrobielle Gemeinschaft durch Mineraleigenschaften unter hoher Substratverfügbarkeit und durch die Verfügbarkeit von förderlichen Nährstoffen am Ende der Inkubation unter Substratlimitierung beeinflusst wurden. Die gemessenen Enzymaktivitäten liefern einen klaren Hinweis, dass die mikrobielle Gemeinschaft durch Nährstofflimitierung während der Inkubation gelenkt wurde. In Anwesenheit von einfach verfügbaren organischen Substanzen wurde die mikrobielle Bodengemeinschaft von copiotrophen Bakterien (z.B. Betaproteobakterien) dominiert, wohingegen am Ende der Inkubation unter Substratlimitierung schwer abbaubare Komponenten für oligotrophe Bakterien (z.B. Acidobakterien) entscheidend wurden, welche daraufhin dominiereten. Die Ergebnisse der zweiten Studie deuteten darauf hin, dass der Beitrag der potentiellen Abbauer MCPA abzubauen unterschiedlich war, was auch in der Sukzession der spezifischen bakteriellen MCPA Abbauer zu sehen war. Zugabe von Streu stimuliert den MCPA Abbau in Abhängigkeit von verfügbarem streubürtigen Kohlenstoff und induziert eine zwei-Phasen Antwort der Pilze. Dies war in der Entwicklung von frühen und späten pilzlichen Gemeinschaften zu sehen. Beide Pilzgemeinschaften waren vermutlich am MCPA Abbau beteiligt. Aus diesem Grund konzentrierte sich die dritte Studie auf den pilzlichen Abbauweg. Die Ergebnisse deuten an, dass die verwendeten pilzlichen Laccasen keinen direkten Einfluss auf den Abbau hatten und dass sie genauso effektiv waren zusätzliche Nährstoffquellen bereitzustellen wie Streu, um den MCPA Abbau durch Bakterien und Pilze zu fördern. Die beobachteten Unterschiede zwischen Streu- und Enzymzugabe unterstreicht die Beobachtung, dass der Enzymeffekt kurzlebig war und dass Substratqualität ein wichtiger Faktor in Abbauprozessen ist. Zusammenfassend zeigte diese Arbeit, dass die mikrobielle Gemeinschaft durch die Mineralzusammensetzung sowie Substratverfügbarkeit und Qualität angetrieben wird. Darüber hinaus erweitert diese Arbeit unser Wissen über Abbauprozesse in Böden, wie den Abbau von Xenobiotika, mit MCPA als Modelkomponente. Die Einblicke von allen drei Studien deuten an, dass ein einfaches System, wie das System der künstlichen Böden hilfreich sein kann, um unser Wissen über komplexe Mechanismen, wie Pestizidabbau zu erhöhen

Second Guessing in Perceptual Decision-Making.

Human subjects of both sexes were asked to make a perceptual decision between multiple directions of visual motion. In addition to reporting a primary choice, they also had to report a second guess, indicating which of the remaining options they would rather bet on, assuming that they got their primary choice wrong. The second guess was clearly informed by the amounts of sensory evidence that were provided for the different options. A single computational integration-to-threshold model, based on the assumption that the second guess is determined by the rank ordering of accumulated evidence at or shortly after the time of the decision, was able to explain the distribution of primary choices, associated response times, and the distribution of second guesses. This suggests that the decision-maker has access to how well supported unchosen options are by the sensory evidence.SIGNIFICANCE STATEMENT Perceptual decisions require conversion of sensory evidence into a discrete choice. Computational models based on the accumulation of evidence to a decision threshold can explain the distribution of choices and associated decision times. Subjects are also able to report the level of confidence in their decision. Here we show that, when making decisions between more than two alternatives, the decision-maker can even report a second guess that is clearly informed by the sensory evidence. These second guesses show a distribution that is consistent with subjects having access to how much sensory evidence was accumulated for the unchosen options. The decision-maker therefore has knowledge about the outcome of the decision process that goes beyond just the choice and an associated confidence

Exploring the suitability of piecewise-linear dynamical system models for cognitive neural dynamics

Dynamical system models have proven useful for decoding the current brain state from neural activity. So far, neuroscience has largely relied on either linear models or non-linear models based on artificial neural networks (ANNs). Piecewise linear approximations of non-linear dynamics have proven useful in other technical applications. Moreover, such explicit models provide a clear advantage over ANN-based models when the dynamical system is not only supposed to be observed, but also controlled, in particular when a controller with guarantees is needed. Here we explore whether piecewise-linear dynamical system models (recurrent Switching Linear Dynamical System or rSLDS models) could be useful for modeling brain dynamics, in particular in the context of cognitive tasks. These models have the advantage that they can be estimated not only from continuous observations like field potentials or smoothed firing rates, but also from sparser single-unit spiking data. We first generate artificial neural data based on a non-linear computational model of perceptual decision-making and demonstrate that piecewise-linear dynamics can be successfully recovered from these observations. We then demonstrate that the piecewise-linear model outperforms a linear model in terms of predicting future states of the system and associated neural activity. Finally, we apply our approach to a publicly available dataset recorded from monkeys performing perceptual decisions. Much to our surprise, the piecewise-linear model did not provide a significant advantage over a linear model for these particular data, although linear models that were estimated from different trial epochs showed qualitatively different dynamics. In summary, we present a dynamical system modeling approach that could prove useful in situations, where the brain state needs to be controlled in a closed-loop fashion, for example, in new neuromodulation applications for treating cognitive deficits. Future work will have to show under what conditions the brain dynamics are sufficiently non-linear to warrant the use of a piecewise-linear model over a linear one

Untersuchungen zum altrussischen Akzent

In der Reihe Slavistische Beiträge werden vor allem slavistische Dissertationen des deutschsprachigen Raums sowie vereinzelt auch amerikanische, englische und russische publiziert. Darüber hinaus stellt die Reihe ein Forum für Sammelbände und Monographien etablierter Wissenschafter/innen dar

Differentiating between integration and non-integration strategies in perceptual decision making.

Many tasks used to study decision-making encourage subjects to integrate evidence over time. Such tasks are useful to understand how the brain operates on multiple samples of information over prolonged timescales, but only if subjects actually integrate evidence to form their decisions. We explored the behavioral observations that corroborate evidence-integration in a number of task-designs. Several commonly accepted signs of integration were also predicted by non-integration strategies. Furthermore, an integration model could fit data generated by non-integration models. We identified the features of non-integration models that allowed them to mimic integration and used these insights to design a motion discrimination task that disentangled the models. In human subjects performing the task, we falsified a non-integration strategy in each and confirmed prolonged integration in all but one subject. The findings illustrate the difficulty of identifying a decision-maker's strategy and support solutions to achieve this goal

Closed-form approximations of first-passage distributions for a stochastic decision making model

In free response choice tasks, decision making is often modeled as a first-passage problem for a stochastic differential equation. In particular, drift-diffusion processes with constant or time-varying drift rates and noise can reproduce behavioral data (accuracy and response-time distributions) and neuronal firing rates. However, no exact solutions are known for the first-passage problem with time-varying data. Recognizing the importance of simple closed-form expressions for modeling and inference, we show that an interrogation or cued-response protocol, appropriately interpreted, can yield approximate first-passage (response time) distributions for a specific class of time-varying processes used to model evidence accumulation. We test these against exact expressions for the constant drift case and compare them with data from a class of sigmoidal functions. We find that both the direct interrogation approximation and an error-minimizing interrogation approximation can capture a variety of distribution shapes and mode numbers but that the direct approximation, in particular, is systematically biased away from the correct free response distribution