Normatively irrelevant disgust interferes with decision under uncertainty: Insights from the Iowa gambling task.

This study investigates whether a not informative, irrelevant emotional reaction of disgust interferes with decision-making under uncertainty. We manipulate the Iowa Gambling Task (IGT) by associating a disgust-eliciting image with selections from Disadvantageous/Bad decks (Congruent condition) or Advantageous/Good decks (Incongruent condition). A Control condition without manipulations is also included. Results indicate an increased probability of selecting from a Good deck as the task unfolds in all conditions. However, this effect is modulated by the experimental manipulation. Specifically, we detect a detrimental effect (i.e., a significant decrease in the intercept) of the disgust-eliciting image in Incongruent condition (vs. Control), but this effect is limited to the early stages of the task (i.e., first twenty trials). No differences in performance trends are detected between Congruent and Control conditions. Anticipatory Skin Conductance Response, heart rate, and pupil dilation are also assessed as indexes of anticipatory autonomic activation following the Somatic Marker Hypothesis, but no effects are shown for the first two indexes in any of the conditions. Only a decreasing trend is detected for pupil dilation as the task unfolds in Control and Incongruent conditions. Results are discussed in line with the "risk as feelings" framework, the Somatic Marker Hypothesis, and IGT literature.

Risky decisions are influenced by individual attributes as a function of risk preference.

It has long been assumed in economic theory that multi-attribute decisions involving several attributes or dimensions - such as probabilities and amounts of money to be earned during risky choices - are resolved by first combining the attributes of each option to form an overall expected value and then comparing the expected values of the alternative options, using a unique evidence accumulation process. A plausible alternative would be performing independent comparisons between the individual attributes and then integrating the results of the comparisons afterwards. Here, we devise a novel method to disambiguate between these types of models, by orthogonally manipulating the expected value of choice options and the relative salience of their attributes. Our results, based on behavioral measures and drift-diffusion models, provide evidence in favor of the framework where information about individual attributes independently impacts deliberation. This suggests that risky decisions are resolved by running in parallel multiple comparisons between the separate attributes - possibly alongside an additional comparison of expected value. This result stands in contrast with the assumption of standard economic theory that choices require a unique comparison of expected values and suggests that at the cognitive level, decision processes might be more distributed than commonly assumed. Beyond our planned analyses, we also discovered that attribute salience affects people of different risk preference type in different ways: risk-averse participants seem to focus more on probability, except when monetary amount is particularly high; risk-neutral/seeking participants, in contrast, seem to focus more on monetary amount, except when probability is particularly low.

Humans account for cognitive costs when finding shortcuts: An information-theoretic analysis of navigation.

When faced with navigating back somewhere we have been before we might either retrace our steps or seek a shorter path. Both choices have costs. Here, we ask whether it is possible to characterize formally the choice of navigational plans as a bounded rational process that trades off the quality of the plan (e.g., its length) and the cognitive cost required to find and implement it. We analyze the navigation strategies of two groups of people that are firstly trained to follow a "default policy" taking a route in a virtual maze and then asked to navigate to various known goal destinations, either in the way they want ("Go To Goal") or by taking novel shortcuts ("Take Shortcut"). We address these wayfinding problems using InfoRL: an information-theoretic approach that formalizes the cognitive cost of devising a navigational plan, as the informational cost to deviate from a well-learned route (the "default policy"). In InfoRL, optimality refers to finding the best trade-off between route length and the amount of control information required to find it. We report five main findings. First, the navigational strategies automatically identified by InfoRL correspond closely to different routes (optimal or suboptimal) in the virtual reality map, which were annotated by hand in previous research. Second, people deliberate more in places where the value of investing cognitive resources (i.e., relevant goal information) is greater. Third, compared to the group of people who receive the "Go To Goal" instruction, those who receive the "Take Shortcut" instruction find shorter but less optimal solutions, reflecting the intrinsic difficulty of finding optimal shortcuts. Fourth, those who receive the "Go To Goal" instruction modulate flexibly their cognitive resources, depending on the benefits of finding the shortcut. Finally, we found a surprising amount of variability in the choice of navigational strategies and resource investment across participants. Taken together, these results illustrate the benefits of using InfoRL to address navigational planning problems from a bounded rational perspective.

Amortized Bayesian Model Comparison With Evidential Deep Learning.

Comparing competing mathematical models of complex processes is a shared goal among many branches of science. The Bayesian probabilistic framework offers a principled way to perform model comparison and extract useful metrics for guiding decisions. However, many interesting models are intractable with standard Bayesian methods, as they lack a closed-form likelihood function or the likelihood is computationally too expensive to evaluate. In this work, we propose a novel method for performing Bayesian model comparison using specialized deep learning architectures. Our method is purely simulation-based and circumvents the step of explicitly fitting all alternative models under consideration to each observed dataset. Moreover, it requires no hand-crafted summary statistics of the data and is designed to amortize the cost of simulation over multiple models, datasets, and dataset sizes. This makes the method especially effective in scenarios where model fit needs to be assessed for a large number of datasets, so that case-based inference is practically infeasible. Finally, we propose a novel way to measure epistemic uncertainty in model comparison problems. We demonstrate the utility of our method on toy examples and simulated data from nontrivial models from cognitive science and single-cell neuroscience. We show that our method achieves excellent results in terms of accuracy, calibration, and efficiency across the examples considered in this work. We argue that our framework can enhance and enrich model-based analysis and inference in many fields dealing with computational models of natural processes. We further argue that the proposed measure of epistemic uncertainty provides a unique proxy to quantify absolute evidence even in a framework which assumes that the true data-generating model is within a finite set of candidate models.

Increased Entropic Brain Dynamics during DeepDream-Induced Altered Perceptual Phenomenology.

In recent years, the use of psychedelic drugs to study brain dynamics has flourished due to the unique opportunity they offer to investigate the neural mechanisms of conscious perception. Unfortunately, there are many difficulties to conduct experiments on pharmacologically-induced hallucinations, especially regarding ethical and legal issues. In addition, it is difficult to isolate the neural effects of psychedelic states from other physiological effects elicited by the drug ingestion. Here, we used the DeepDream algorithm to create visual stimuli that mimic the perception of hallucinatory states. Participants were first exposed to a regular video, followed by its modified version, while recording electroencephalography (EEG). Results showed that the frontal region's activity was characterized by a higher entropy and lower complexity during the modified video, with respect to the regular one, at different time scales. Moreover, we found an increased undirected connectivity and a greater level of entropy in functional connectivity networks elicited by the modified video. These findings suggest that DeepDream and psychedelic drugs induced similar altered brain patterns and demonstrate the potential of adopting this method to study altered perceptual phenomenology in neuroimaging research.

Normatively Irrelevant Affective Cues Affect Risk-Taking under Uncertainty: Insights from the Iowa Gambling Task (IGT), Skin Conductance Response, and Heart Rate Variability.

Being able to distinguish between safe and risky options is paramount in making functional choices. However, deliberate manipulation of decision-makers emotions can lead to risky behaviors. This study aims at understanding how affective reactions driven by normatively irrelevant affective cues can interfere with risk-taking. Good and Bad decks of the Iowa Gambling Task have been manipulated to make them unpleasant through a negative auditory manipulation. Anticipatory skin conductance response (SCR) and heart rate variability (HRV) have been investigated in line with the somatic marker hypothesis. Results showed fewer selections from Good decks when they were negatively manipulated (i.e., Incongruent condition). No effect of the manipulation was detected when Bad decks were negatively manipulated (i.e., Congruent condition). Higher anticipatory SCR was associated with Bad decks in Congruent condition. Slower heart rate was found before selections from Good decks in Control and Congruent condition and from Bad decks in Incongruent condition. Differences in heart rate between Bad and Good decks were also detected in Congruent condition. Results shed light on how normatively irrelevant affective cues can interfere with risk-taking.

A Bayesian brain model of adaptive behavior: an application to the Wisconsin Card Sorting Task.

Adaptive behavior emerges through a dynamic interaction between cognitive agents and changing environmental demands. The investigation of information processing underlying adaptive behavior relies on controlled experimental settings in which individuals are asked to accomplish demanding tasks whereby a hidden regularity or an abstract rule has to be learned dynamically. Although performance in such tasks is considered as a proxy for measuring high-level cognitive processes, the standard approach consists in summarizing observed response patterns by simple heuristic scoring measures. With this work, we propose and validate a new computational Bayesian model accounting for individual performance in the Wisconsin Card Sorting Test (WCST), a renowned clinical tool to measure set-shifting and deficient inhibitory processes on the basis of environmental feedback. We formalize the interaction between the task's structure, the received feedback, and the agent's behavior by building a model of the information processing mechanisms used to infer the hidden rules of the task environment. Furthermore, we embed the new model within the mathematical framework of the Bayesian Brain Theory (BBT), according to which beliefs about hidden environmental states are dynamically updated following the logic of Bayesian inference. Our computational model maps distinct cognitive processes into separable, neurobiologically plausible, information-theoretic constructs underlying observed response patterns. We assess model identification and expressiveness in accounting for meaningful human performance through extensive simulation studies. We then validate the model on real behavioral data in order to highlight the utility of the proposed model in recovering cognitive dynamics at an individual level. We highlight the potentials of our model in decomposing adaptive behavior in the WCST into several information-theoretic metrics revealing the trial-by-trial unfolding of information processing by focusing on two exemplary individuals whose behavior is examined in depth. Finally, we focus on the theoretical implications of our computational model by discussing the mapping between BBT constructs and functional neuroanatomical correlates of task performance. We further discuss the empirical benefit of recovering the assumed dynamics of information processing for both clinical and research practices, such as neurological assessment and model-based neuroscience.

Static Polarizabilities at the Basis Set Limit: A Benchmark of 124 Species.

Benchmarking molecular properties with Gaussian-type orbital (GTO) basis sets can be challenging, because one has to assume that the computed property is at the complete basis set (CBS) limit, without a robust measure of the error. Multiwavelet (MW) bases can be systematically improved with a controllable error, which eliminates the need for such assumptions. In this work, we have used MWs within Kohn-Sham density functional theory to compute static polarizabilities for a set of 92 closed-shell and 32 open-shell species. The results are compared to recent benchmark calculations employing the GTO-type aug-pc4 basis set. We observe discrepancies between GTO and MW results for several species, with open-shell systems showing the largest deviations. Based on linear response calculations, we show that these discrepancies originate from artifacts caused by the field strength and that several polarizabilies from a previous study were contaminated by higher order responses (hyperpolarizabilities). Based on our MW benchmark results, we can affirm that aug-pc4 is able to provide results close to the CBS limit, as long as finite difference effects can be controlled. However, we suggest that a better approach is to use MWs, which are able to yield precise finite difference polarizabilities even with small field strengths.

A Joint Modelling Approach to Analyze Risky Decisions by Means of Diffusion Tensor Imaging and Behavioural Data.

Understanding dependencies between brain functioning and cognition is a challenging task which might require more than applying standard statistical models to neural and behavioural measures to be accomplished. Recent developments in computational modelling have demonstrated the advantage to formally account for reciprocal relations between mathematical models of cognition and brain functional, or structural, characteristics to relate neural and cognitive parameters on a model-based perspective. This would allow to account for both neural and behavioural data simultaneously by providing a joint probabilistic model for the two sources of information. In the present work we proposed an architecture for jointly modelling the reciprocal relation between behavioural and neural information in the context of risky decision-making. More precisely, we offered a way to relate Diffusion Tensor Imaging data to cognitive parameters of a computational model accounting for behavioural outcomes in the popular Balloon Analogue Risk Task (BART). Results show that the proposed architecture has the potential to account for individual differences in task performances and brain structural features by letting individual-level parameters to be modelled by a joint distribution connecting both sources of information. Such a joint modelling framework can offer interesting insights in the development of computational models able to investigate correspondence between decision-making and brain structural connectivity.

Flexibilities of wavelets as a computational basis set for large-scale electronic structure calculations.

The BigDFT project was started in 2005 with the aim of testing the advantages of using a Daubechies wavelet basis set for Kohn-Sham (KS) density functional theory (DFT) with pseudopotentials. This project led to the creation of the BigDFT code, which employs a computational approach with optimal features of flexibility, performance, and precision of the results. In particular, the employed formalism has enabled the implementation of an algorithm able to tackle DFT calculations of large systems, up to many thousands of atoms, with a computational effort that scales linearly with the number of atoms. In this work, we recall some of the features that have been made possible by the peculiar properties of Daubechies wavelets. In particular, we focus our attention on the usage of DFT for large-scale systems. We show how the localized description of the KS problem, emerging from the features of the basis set, is helpful in providing a simplified description of large-scale electronic structure calculations. We provide some examples on how such a simplified description can be employed, and we consider, among the case-studies, the SARS-CoV-2 main protease.

A Dynamic Framework for Modelling Set-Shifting Performances.

Higher-order cognitive functions can be seen as a class of cognitive processes which are crucial in situations requiring a flexible adjustment of behaviour in response to changing demands of the environment. The cognitive assessment of these functions often relies on tasks which admit a dynamic, or longitudinal, component requiring participants to flexibly adapt their behaviour during the unfolding of the task. An intriguing feature of such experimental protocols is that they allow the performance of an individual to change as the task unfolds. In this work, we propose a Latent Markov Model approach to capture some dynamic aspects of observed response patterns of both healthy and substance dependent individuals in a set-shifting task. In particular, data from a Wisconsin Card Sorting Test were analysed in order to represent performance trends in terms of latent cognitive states dynamics. The results highlighted how a dynamic modelling approach can considerably improve the amount of information a researcher, or a clinician, can obtain from the analysis of a set-shifting task.

A new nonparametric test for the race model inequality.

The race model inequality (RMI), as first introduced by Miller (Cognitive Psychology, 14, 247-279, 1982), entails an upper bound on the amount of statistical facilitation for reaction times (RTs) attainable by a race model within the redundant-signals paradigm. A violation of RMI may be considered as empirical evidence for a coactivation model rather than a race model. Here, we introduce a novel nonparametric procedure for evaluating the RMI for single participant analysis. The statistical procedure is based on a new probabilistic representation that highlights some neglected, but important distributional features of the RMI. In particular, we show how the reconstructed distribution function under maximal statistical facilitation for a race model is characterized by a specific truncated-type property. The results of two Monte Carlo simulation studies suggest that our procedure efficiently controls for type I error with reasonable power. Finally, unlike previous proposals for single participant analysis (e.g., Maris and Maris (Journal of Mathematical Psychology 47, 507-514, 2003)), our approach is also more consistent with the typical way to collect RT data in experimental works. R script functions for running the statistical analysis on single participant data are made freely available to the readers on a dedicated web server.

A State Space Approach to Dynamic Modeling of Mouse-Tracking Data.

Mouse-tracking recording techniques are becoming very attractive in experimental psychology. They provide an effective means of enhancing the measurement of some real-time cognitive processes involved in categorization, decision-making, and lexical decision tasks. Mouse-tracking data are commonly analyzed using a two-step procedure which first summarizes individuals' hand trajectories with independent measures, and then applies standard statistical models on them. However, this approach can be problematic in many cases. In particular, it does not provide a direct way to capitalize the richness of hand movement variability within a consistent and unified representation. In this article we present a novel, unified framework for mouse-tracking data. Unlike standard approaches to mouse-tracking, our proposal uses stochastic state-space modeling to represent the observed trajectories in terms of both individual movement dynamics and experimental variables. The model is estimated via a Metropolis-Hastings algorithm coupled with a non-linear recursive filter. The characteristics and potentials of the proposed approach are illustrated using a lexical decision case study. The results highlighted how dynamic modeling of mouse-tracking data can considerably improve the analysis of mouse-tracking tasks and the conclusions researchers can draw from them.

Label-free and non-invasive discrimination of HaCaT and melanoma cells in a co-culture model by hyperspectral confocal reflectance microscopy.

A novel hyperspectral confocal microscopy method to separate different cell populations in a co-culture model is presented here. The described methodological and instrumental approach allows discrimination of different cell types using a non-invasive, label free method with good accuracy with a single cell resolution. In particular, melanoma cells are discriminated from HaCaT cells by hyperspectral confocal imaging, principal component analysis and optical frequencies signing, as confirmed by fluorescence labelling cross check. The identification seems to be quite robust to be insensitive to the cellular shape within the studied samples, enabling to separate cells according to their cytotype down to a single cell sensitivity. Set of hyperspectral images of melanoma-keratinocytes co-culture model (left), score plot of principal component analysis and spectral analysis of principal components coefficients (center), label-free spectral identification of cell populations (right).

Volatiles produced by soil-borne endophytic bacteria increase plant pathogen resistance and affect tritrophic interactions.

Volatile organic compounds (VOCs) released by soil microorganisms influence plant growth and pathogen resistance. Yet, very little is known about their influence on herbivores and higher trophic levels. We studied the origin and role of a major bacterial VOC, 2,3-butanediol (2,3-BD), on plant growth, pathogen and herbivore resistance, and the attraction of natural enemies in maize. One of the major contributors to 2,3-BD in the headspace of soil-grown maize seedlings was identified as Enterobacter aerogenes, an endophytic bacterium that colonizes the plants. The production of 2,3-BD by E. aerogenes rendered maize plants more resistant against the Northern corn leaf blight fungus Setosphaeria turcica. On the contrary, E. aerogenes-inoculated plants were less resistant against the caterpillar Spodoptera littoralis. The effect of 2,3-BD on the attraction of the parasitoid Cotesia marginiventris was more variable: 2,3-BD application to the headspace of the plants had no effect on the parasitoids, but application to the soil increased parasitoid attraction. Furthermore, inoculation of seeds with E. aerogenes decreased plant attractiveness, whereas inoculation of soil with a total extract of soil microbes increased parasitoid attraction, suggesting that the effect of 2,3-BD on the parasitoid is indirect and depends on the composition of the microbial community.

Herbivore-induced maize leaf volatiles affect attraction and feeding behavior of Spodoptera littoralis caterpillars.

Plants under herbivore attack emit volatile organic compounds (VOCs) that can serve as foraging cues for natural enemies. Adult females of Lepidoptera, when foraging for host plants to deposit eggs, are commonly repelled by herbivore-induced VOCs, probably to avoid competition and natural enemies. Their larval stages, on the other hand, have been shown to be attracted to inducible VOCs. We speculate that this contradicting behavior of lepidopteran larvae is due to a need to quickly find a new suitable host plant if they have fallen to the ground. However, once they are on a plant they might avoid the sites with fresh damage to limit competition and risk of cannibalism by conspecifics, as well as exposure to natural enemies. To test this we studied the effect of herbivore-induced VOCs on the attraction of larvae of the moth Spodoptera littoralis and on their feeding behavior. The experiments further considered the importance of previous feeding experience on the responses of the larvae. It was confirmed that herbivore-induced VOCs emitted by maize plants are attractive to the larvae, but exposure to the volatiles decreased the growth rate of caterpillars at early developmental stages. Larvae that had fed on maize previously were more attracted by VOCs of induced maize than larvae that had fed on artificial diet. At relatively high concentrations synthetic green leaf volatiles, indicative of fresh damage, also negatively affected the growth rate of caterpillars, but not at low concentrations. In all cases, feeding by the later stages of the larvae was not affected by the VOCs. The results are discussed in the context of larval foraging behavior under natural conditions, where there may be a trade-off between using available host plant signals and avoiding competitors and natural enemies.

Age at Death of Creutzfeldt-Jakob disease in subsequent family generation carrying the E200K mutation of the prion protein gene.

BACKGROUND: The E200K mutation of the prion protein gene (PRNP) is the most frequent amino acid substitution in genetic Creutzfeldt-Jakob disease and is the only one responsible for the appearance of clustered cases in the world. In the Israel and Slovakian clusters, age of disease onset was reduced in successive generations but the absence of a clear molecular basis raised the possibility that this event was an observational bias. The aim of the present study was to investigate possible selection biases or confounding factors related to anticipation in E200K CJD patients belonging to a cluster in Southern Italy. METHODS: Clinical and demographical data of 41 parent-offspring pairs from 19 pedigrees of the Italian cluster of E200K patients were collected. Age at death of parents was compared with age at death of E200K CJD offspring. Subgroup analyses were performed for controlling possible selection biases, confounding factors, or both. RESULTS: The mean age at death/last follow-up of the parent generation was 71.4 years while that of CJD offspring was 59.3 years with an estimated anticipation of 12.1 years. When the same analysis was performed including only parents with CJD or carrying the E200K mutation (n = 26), the difference between offspring and parents increased to 14.8 years. CONCLUSIONS: These results show that early age at death occurs in offspring of families carrying the E200K PRNP mutation and that this event is not linked to observational biases. Although molecular or environmental bases for this occurrence remain unsettled, this information is important for improving the accuracy of information to give to mutated carriers.

Regional risk assessment for contaminated sites part 3: spatial decision support system.

Large scale (e.g. regional or national) assessments of contaminated sites may be very costly in terms of investigation and methodological (i.e. risk assessment procedures) requirements and may produce a quantity of information that usually discourages examination by decision-makers. Moreover, most of the existing tools effectively support local environmental risk assessment and management, but lack the capabilities of larger scale analysis, not mentioning the absence of the relevant component of socio-economic prioritization. To respond to the concerns and the management needs of experts and decision makers, the Spatial decision support sYstem for Regional rIsk Assessment of Degraded land (SYRIADE DSS) was developed and is presented according to its three modules: Regional Risk Assessment, Socio-economic Assessment and Integrated Assessment, respectively. The system allows to rank potentially contaminated sites for priority of investigation, when no information on characterization and risk by site specific methodologies is available. This GIS-based system embeds an innovative spatial and relative risk assessment procedure, and proposes the integrated analysis of different data (environmental and socio-economic) for the concerned sites, eliciting when necessary experts' knowledge and stakeholders' values (through Multi Criteria Decision Analysis, MCDA, methodologies). The application to a Polish case-study shows the performance and the flexibility of the system in investigating and mapping (potentially) contaminated sites at the regional scale.

Maximum-entropy Monte Carlo method for the inversion of the structure factor in simple classical systems.

We present a method for the evaluation of the interaction potential of an equilibrium classical system starting from the (partial) knowledge of its structure factor. The procedure is divided into two phases, both of which are based on the maximum entropy principle of information theory. First we determine the maximum entropy estimate of the radial distribution function constrained by the information contained in the structure factor. Next we invert the pair function and extract the interaction potential. The method is tested on a Lennard-Jones fluid at high density and the reliability of its results with respect to the missing information in the structure factor data are discussed. Finally, it is applied to the experimental data of liquid sodium at 100 °C.

Dispensing synthetic green leaf volatiles in maize fields increases the release of sesquiterpenes by the plants, but has little effect on the attraction of pest and beneficial insects.

Maize plants respond to feeding by arthropod herbivores by producing a number of secondary plant compounds, including volatile organic compounds (VOCs). These herbivore-induced VOCs are not only known to attract natural enemies of the herbivores, but they may also prime inducible defences in neighbouring plants, resulting in stronger and faster defence responses in these VOC-exposed plants. Among the compounds that cause this priming effect, green leaf volatiles (GLVs) have received particular attention, as they are ubiquitous and rapidly emitted upon damage. In this study, we investigated their effects under realistic conditions by applying specially devised dispensers to release four synthetic GLVs at physiologically relevant concentrations in a series of experiments in maize fields. We compared the VOC emission of GLV-exposed maize plants to non-exposed plants and monitored the attraction of herbivores and predators, as well as parasitism of the caterpillar Spodoptera frugiperda, the most common herbivore in the experimental maize fields. We found that maize plants that were exposed to GLVs emitted increased quantities of sesquiterpenes compared to non-exposed plants. In several replicates, herbivorous insects, such as adult Diabrotica beetles and S. frugiperda larvae, were observed more frequently in GLV-treated plots and caused more damage to GLV-exposed plants than to non-exposed plants. Parasitism of S. frugiperda was only weakly affected by GLVs and overall parasitism rates of S. frugiperda were similar in GLV-exposed and non-exposed plots. The effects on insect presence depended on the distance from the GLV-dispensers at which the plants were located. The results are discussed in the context of strategies to improve biological control by enhancing plant-mediated attraction of natural enemies.