Like Mother, Like Daughter: Adults’ Judgements about Genetic Inheritance

Do people think about genetic inheritance as a deterministic or probabilistic process? Do adults display systematic biases when reasoning about genetic inheritance? Knowing how adults think about genetic inheritance is valuable, both for understanding the developmental endpoint of these concepts and for identifying biases that persist even after formal education. We examined adults’ reasoning about genetic inheritance for familiar and unfamiliar animals. First, participants were presented with animals that varied in eye color and were asked to judge whether each could be the offspring of a particular set of animal parents that had either the same or different eye colors. The potential offspring had eye colors that were either identical to the parents, blended the parents’ eye colors, or differed from the parents. Next, participants predicted how six offspring of the animal parents would look. Participants judged a variety of choices as possible—not only the ones resembling the parents—suggesting that they thought genetic inheritance was a probabilistic process. Additionally, many participants thought that female offspring would look more like their mothers and male offspring would look more like their fathers. Thus, systemic biases in reasoning about inheritance persist into adulthood.

Dispersal across habitat boundaries: uncovering the demographic fates of populations in unsuitable habitat

Patchy landscapes are characterized by abrupt transitions among distinct habitat types, forcing species to cross habitat boundaries in order to spread. Since seed dispersal is a probabilistic process, with a kernel that decays with distance, most individuals will fail to reach new, suitable habitat. Although failed dispersers are presumed dead in population models, their demographic fates may not be so simple. If transient survival is possible within unsuitable habitat, then through time, individuals may be able to reach distant, suitable habitat, forming new populations and buffering species from extinction. In a fragmented Californian grassland, we explored the fates of individuals that crossed habitat boundaries, and if those fates differed among specialists dispersing from two habitat types: serpentine habitat patches and the invaded non-serpentine matrix. We surveyed the diversity of seedbank and adult life stages along transects that crossed boundaries between patches and the matrix. First, we considered how patch specialists might transiently survive in the matrix via seed dormancy or stepping-stone populations. Second, we investigated the dispersal of an invasive matrix specialist (Avena fatua) into patches, to assess if sink populations existed across the habitat boundary. We found that dormancy maintained populations of patch specialists deep into the matrix, as abundances of seedbanks and of adult plant communities differed with distance into the matrix. We found evidence that these dormant seeds disperse secondarily with vectors of material flows in the landscape, suggesting that they could eventually reach suitable patches even if they first land in the matrix. We found that A. fatua were largely absent deep in patches, where reproductive outputs plummeted and there was no evidence of a dormant seedbank. Our results not only reveal the demographic fates of individuals that land in unsuitable habitat, but that their ecological consequences differ depending on the direction by which the boundary is crossed (patch → matrix ≠ matrix → patch). Dormancy is often understood as a mechanism for persisting in face of temporal variability, but it may serve as a means of traversing unsuitable habitat in patchy systems, warranting its consideration in estimates of habitat connectivity.

Probabilistic Process Algebra and Strategic Interleaving

We first present a probabilistic version of ACP that rests on the principle that probabilistic choices are always resolved before choices involved in alternative composition and parallel composition are resolved and then extend this probabilistic version of ACP with a form of interleaving in which parallel processes are interleaved according to what is known as a process-scheduling policy in the field of operating systems. We use the term strategic interleaving for this more constrained form of interleaving. The extension covers probabilistic process-scheduling policies.

Monte-Carlo simulation of the 1st order hadron-QGP phase transition in heavy ion collisions using a parton model

Quark gluon plasma (QGP) is a special state of nuclear matter where quarks and gluons behave like free particles. Recently, a number of investigations of this state with high temperature and/or density have been conducted using collisions of relativistic and ultra-relativistic heavy nuclei. It is accepted that depending on the temperature and density, 1st or the 2nd order phase transitions take place in hadron matter during the formation of QGP. Herein, we have modeled heavy ion collisions using a HIJING Monte-Carlo generator, taking into account the description of the 1st order phase transition as a probabilistic process. We analyzed the behavior of the fluctuations of the total (N = N+ – N–) and resultant (Q = N+ – N–) electric charges of the system. Different phases were introduced using the BDMPS (Baier – Dokshitzer – Mueller – Piegne – Schiff) model of parton energy loss during crossing through a dense nuclear medium.

Variational Pathway Reasoning for EEG Emotion Recognition

Research on human emotion cognition revealed that connections and pathways exist between spatially-adjacent and functional-related areas during emotion expression (Adolphs 2002a; Bullmore and Sporns 2009). Deeply inspired by this mechanism, we propose a heuristic Variational Pathway Reasoning (VPR) method to deal with EEG-based emotion recognition. We introduce random walk to generate a large number of candidate pathways along electrodes. To encode each pathway, the dynamic sequence model is further used to learn between-electrode dependencies. The encoded pathways around each electrode are aggregated to produce a pseudo maximum-energy pathway, which consists of the most important pair-wise connections. To find those most salient connections, we propose a sparse variational scaling (SVS) module to learn scaling factors of pseudo pathways by using the Bayesian probabilistic process and sparsity constraint, where the former endows good generalization ability while the latter favors adaptive pathway selection. Finally, the salient pathways from those candidates are jointly decided by the pseudo pathways and scaling factors. Extensive experiments on EEG emotion recognition demonstrate that the proposed VPR is superior to those state-of-the-art methods, and could find some interesting pathways w.r.t. different emotions.

The impact of colonization history on the composition of ecological systems

Observational studies of ecological systems have shown that different species compositions can arise from distinct species arrival orders during community assembly—also known as colonization history. However, it is still unclear under which conditions colonization history will dominate community composition. Yet, this is important in order to understand and anticipate the impact of species arrivals on the biodiversity that we observe in both nature and experiments. To address this fundamental question, here we develop a testable theory linking colonization history and community composition. First, we prove two general theorems to determine whether the composition of a community will depend on its colonization history. For communities governed by Lotka-Volterra dynamics, we further simplify the two theorems into a corollary that is easy for numerical test. Second, we show, via extensive numerical simulations, that the probability that community composition is dominated by colonization history increases monotonically with community size and species connectivity. Third, we show that this probability significantly increases with the variation of intrinsic growth rates across species. These results reveal that community composition is a probabilistic process mediated by ecological dynamics via the interspecific variation and the size of regional pools.

Constructing a physical model of the process of acoustic air pollution for brick manufacturing enterprises

The article is devoted to the analysis of the physical nature of the air acoustic pollution process for the clay brick workshop of the brick factory molding department. According to the physical and energy approach, acoustic pollution of the air is considered as a multi-stage probabilistic process, the implementation of which depends on the physical characteristics of the objects involved in it at each stage and the characteristics of their interaction with each other. The physical model of the air acoustic pollution process constructed by the authors clearly reveals the physical essence of each stage of this process.

Rockfall modelling in forested areas: the role of digital terrain model spatial resolution

This article examines how digital terrain model (DTM) spatial resolution influences rockfall modelling using a probabilistic process-based model, RockyFor3D, while taking into account the effect of forest on rockfall propagation and runout area. A rockfall site in the Trenta valley, NW Slovenia, was chosen as a case study. The analysis included DTM spatial resolutions of 1 m, 2 m, 5 m, 10 m, 12.5 m and 25 m, based on lidar data. The highest spatial resolution (1 m) was used to calibrate the surface roughness coefficients of the model while also taking into account the effect of forest since it shapes the rockfall propagation and runout area. The results of the calibration runs were evaluated using goodness-of-fit indices, and the best set of parameters were further used for modelling rockfalls with and without the effect of forest for all spatial resolutions. Accuracy statistics were used to validate the modelled rockfall propagation and runout area for each spatial resolution, with/without the effect of forest. Finally, modelling outputs, such as the mean of the maximum and maximum kinetic energy, the number of block passes and forest parameters in the rockfall propagation area, were compared.

I’ll make you an offer you can refuse: How socially sensitive is the MFN?

Medial frontal negativity (MFN) is an event-related potential thought to originate in the anterior cingulate cortex. It is evoked by outcomes being worse than expected, such as when presented with unfair economic proposals during the Ultimatum Game (UG). This could mean the MFN indexes a social-emotional response, as commonly suggested in accounts that relate it to a violation of a social norm of fairness. To examine the link between MFN and norm violation, we designed an EEG experiment with participants acting as representatives in an UG. Participants responded either as themselves, or as representatives of two charities. Of these, a norm-compatible charity conformed to the participant’s values, while the norm-incompatible charity contrasted to them. The behavioural results showed that norm-incompatible representation reversed behaviour, with almost all fair offers being declined. The MFN, however, was unaffected by the norm representation, with unfair offers consistently evoking MFNs across conditions. We furthermore replicated the curious finding that unexpectedly generous offers evoke as much MFN as unfair offers. Thus, the MFN is not nearly as sensitive to higher-order social-emotional processes as commonly assumed. Instead, the perceived inequality that drives the MFN is likely due to a rational, probabilistic process.