Risk-Sensitivity in Bayesian Sensorimotor Integration

Information processing in the nervous system during sensorimotor tasks with inherent uncertainty has been shown to be consistent with Bayesian integration. Bayes optimal decision-makers are, however, risk-neutral in the sense that they weigh all possibilities based on prior expectation and sensory evidence when they choose the action with highest expected value. In contrast, risk-sensitive decision-makers are sensitive to model uncertainty and bias their decision-making processes when they do inference over unobserved variables. In particular, they allow deviations from their probabilistic model in cases where this model makes imprecise predictions. Here we test for risk-sensitivity in a sensorimotor integration task where subjects exhibit Bayesian information integration when they infer the position of a target from noisy sensory feedback. When introducing a cost associated with subjects'' response, we found that subjects exhibited a characteristic bias towards low cost responses when their uncertainty was high. This result is in accordance with risk-sensitive decision-making processes that allow for deviations from Bayes optimal decision-making in the face of uncertainty. Our results suggest that both Bayesian integration and risk-sensitivity are important factors to understand sensorimotor integration in a quantitative fashion.     

Bayesian Approach to MSD-Based Analysis of Particle Motion in Live Cells

Quantitative tracking of particle motion using live-cell imaging is a powerful approach to understanding the mechanism of transport of biological molecules, organelles, and cells. However, inferring complex stochastic motion models from single-particle trajectories in an objective manner is nontrivial due to noise from sampling limitations and biological heterogeneity. Here, we present a systematic Bayesian approach to multiple-hypothesis testing of a general set of competing motion models based on particle mean-square displacements that automatically classifies particle motion, properly accounting for sampling limitations and correlated noise while appropriately penalizing model complexity according to Occam's Razor to avoid over-fitting. We test the procedure rigorously using simulated trajectories for which the underlying physical process is known, demonstrating that it chooses the simplest physical model that explains the observed data. Further, we show that computed model probabilities provide a reliability test for the downstream biological interpretation of associated parameter values. We subsequently illustrate the broad utility of the approach by applying it to disparate biological systems including experimental particle trajectories from chromosomes, kinetochores, and membrane receptors undergoing a variety of complex motions. This automated and objective Bayesian framework easily scales to large numbers of particle trajectories, making it ideal for classifying the complex motion of large numbers of single molecules and cells from high-throughput screens, as well as single-cell-, tissue-, and organism-level studies.     

Long-Branch Attraction Bias and Inconsistency in Bayesian Phylogenetics

Bayesian inference (BI) of phylogenetic relationships uses the same probabilistic models of evolution as its precursor maximum likelihood (ML), so BI has generally been assumed to share ML''s desirable statistical properties, such as largely unbiased inference of topology given an accurate model and increasingly reliable inferences as the amount of data increases. Here we show that BI, unlike ML, is biased in favor of topologies that group long branches together, even when the true model and prior distributions of evolutionary parameters over a group of phylogenies are known. Using experimental simulation studies and numerical and mathematical analyses, we show that this bias becomes more severe as more data are analyzed, causing BI to infer an incorrect tree as the maximum a posteriori phylogeny with asymptotically high support as sequence length approaches infinity. BI''s long branch attraction bias is relatively weak when the true model is simple but becomes pronounced when sequence sites evolve heterogeneously, even when this complexity is incorporated in the model. This bias—which is apparent under both controlled simulation conditions and in analyses of empirical sequence data—also makes BI less efficient and less robust to the use of an incorrect evolutionary model than ML. Surprisingly, BI''s bias is caused by one of the method''s stated advantages—that it incorporates uncertainty about branch lengths by integrating over a distribution of possible values instead of estimating them from the data, as ML does. Our findings suggest that trees inferred using BI should be interpreted with caution and that ML may be a more reliable framework for modern phylogenetic analysis.     

Learning to Obtain Reward,but Not Avoid Punishment,Is Affected by Presence of PTSD Symptoms in Male Veterans: Empirical Data and Computational Model

Post-traumatic stress disorder (PTSD) symptoms include behavioral avoidance which is acquired and tends to increase with time. This avoidance may represent a general learning bias; indeed, individuals with PTSD are often faster than controls on acquiring conditioned responses based on physiologically-aversive feedback. However, it is not clear whether this learning bias extends to cognitive feedback, or to learning from both reward and punishment. Here, male veterans with self-reported current, severe PTSD symptoms (PTSS group) or with few or no PTSD symptoms (control group) completed a probabilistic classification task that included both reward-based and punishment-based trials, where feedback could take the form of reward, punishment, or an ambiguous “no-feedback” outcome that could signal either successful avoidance of punishment or failure to obtain reward. The PTSS group outperformed the control group in total points obtained; the PTSS group specifically performed better than the control group on reward-based trials, with no difference on punishment-based trials. To better understand possible mechanisms underlying observed performance, we used a reinforcement learning model of the task, and applied maximum likelihood estimation techniques to derive estimated parameters describing individual participants’ behavior. Estimations of the reinforcement value of the no-feedback outcome were significantly greater in the control group than the PTSS group, suggesting that the control group was more likely to value this outcome as positively reinforcing (i.e., signaling successful avoidance of punishment). This is consistent with the control group’s generally poorer performance on reward trials, where reward feedback was to be obtained in preference to the no-feedback outcome. Differences in the interpretation of ambiguous feedback may contribute to the facilitated reinforcement learning often observed in PTSD patients, and may in turn provide new insight into how pathological behaviors are acquired and maintained in PTSD.     

Language Universals Engage Broca's Area

It is well known that natural languages share certain aspects of their design. For example, across languages, syllables like blif are preferred to lbif. But whether language universals are myths or mentally active constraints—linguistic or otherwise—remains controversial. To address this question, we used fMRI to investigate brain response to four syllable types, arrayed on their linguistic well-formedness (e.g., blif≻bnif≻bdif≻lbif, where ≻ indicates preference). Results showed that syllable structure monotonically modulated hemodynamic response in Broca''s area, and its pattern mirrored participants'' behavioral preferences. In contrast, ill-formed syllables did not systematically tax sensorimotor regions—while such syllables engaged primary auditory cortex, they tended to deactivate (rather than engage) articulatory motor regions. The convergence between the cross-linguistic preferences and English participants'' hemodynamic and behavioral responses is remarkable given that most of these syllables are unattested in their language. We conclude that human brains encode broad restrictions on syllable structure.     

Maintaining Homeostasis by Decision-Making

Living organisms need to maintain energetic homeostasis. For many species, this implies taking actions with delayed consequences. For example, humans may have to decide between foraging for high-calorie but hard-to-get, and low-calorie but easy-to-get food, under threat of starvation. Homeostatic principles prescribe decisions that maximize the probability of sustaining appropriate energy levels across the entire foraging trajectory. Here, predictions from biological principles contrast with predictions from economic decision-making models based on maximizing the utility of the endpoint outcome of a choice. To empirically arbitrate between the predictions of biological and economic models for individual human decision-making, we devised a virtual foraging task in which players chose repeatedly between two foraging environments, lost energy by the passage of time, and gained energy probabilistically according to the statistics of the environment they chose. Reaching zero energy was framed as starvation. We used the mathematics of random walks to derive endpoint outcome distributions of the choices. This also furnished equivalent lotteries, presented in a purely economic, casino-like frame, in which starvation corresponded to winning nothing. Bayesian model comparison showed that—in both the foraging and the casino frames—participants’ choices depended jointly on the probability of starvation and the expected endpoint value of the outcome, but could not be explained by economic models based on combinations of statistical moments or on rank-dependent utility. This implies that under precisely defined constraints biological principles are better suited to explain human decision-making than economic models based on endpoint utility maximization.     

Searching for Collective Behavior in a Large Network of Sensory Neurons

Maximum entropy models are the least structured probability distributions that exactly reproduce a chosen set of statistics measured in an interacting network. Here we use this principle to construct probabilistic models which describe the correlated spiking activity of populations of up to 120 neurons in the salamander retina as it responds to natural movies. Already in groups as small as 10 neurons, interactions between spikes can no longer be regarded as small perturbations in an otherwise independent system; for 40 or more neurons pairwise interactions need to be supplemented by a global interaction that controls the distribution of synchrony in the population. Here we show that such “K-pairwise” models—being systematic extensions of the previously used pairwise Ising models—provide an excellent account of the data. We explore the properties of the neural vocabulary by: 1) estimating its entropy, which constrains the population''s capacity to represent visual information; 2) classifying activity patterns into a small set of metastable collective modes; 3) showing that the neural codeword ensembles are extremely inhomogenous; 4) demonstrating that the state of individual neurons is highly predictable from the rest of the population, allowing the capacity for error correction.     

Tinbergen on mirror neurons

Fifty years ago, Niko Tinbergen defined the scope of behavioural biology with his four problems: causation, ontogeny, survival value and evolution. About 20 years ago, there was another highly significant development in behavioural biology—the discovery of mirror neurons (MNs). Here, I use Tinbergen''s original four problems (rather than the list that appears in textbooks) to highlight the differences between two prominent accounts of MNs, the genetic and associative accounts; to suggest that the latter provides the defeasible ‘best explanation’ for current data on the causation and ontogeny of MNs; and to argue that functional analysis, of the kind that Tinbergen identified somewhat misleadingly with studies of ‘survival value’, should be a high priority for future research. In this kind of functional analysis, system-level theories would assign MNs a small, but potentially important, role in the achievement of action understanding—or another social cognitive function—by a production line of interacting component processes. These theories would be tested by experimental intervention in human and non-human animal samples with carefully documented and controlled developmental histories.     

The forestecology R package for fitting and assessing neighborhood models of the effect of interspecific competition on the growth of trees

1. Neighborhood competition models are powerful tools to measure the effect of interspecific competition. Statistical methods to ease the application of these models are currently lacking.
2. We present the forestecology package providing methods to (a) specify neighborhood competition models, (b) evaluate the effect of competitor species identity using permutation tests, and (cs) measure model performance using spatial cross‐validation. Following Allen and Kim (PLoS One, 15, 2020, e0229930), we implement a Bayesian linear regression neighborhood competition model.
3. We demonstrate the package''s functionality using data from the Smithsonian Conservation Biology Institute''s large forest dynamics plot, part of the ForestGEO global network of research sites. Given ForestGEO’s data collection protocols and data formatting standards, the package was designed with cross‐site compatibility in mind. We highlight the importance of spatial cross‐validation when interpreting model results.
4. The package features (a) tidyverse‐like structure whereby verb‐named functions can be modularly “piped” in sequence, (b) functions with standardized inputs/outputs of simple features sf package class, and (c) an S3 object‐oriented implementation of the Bayesian linear regression model. These three facts allow for clear articulation of all the steps in the sequence of analysis and easy wrangling and visualization of the geospatial data. Furthermore, while the package only has Bayesian linear regression implemented, the package was designed with extensibility to other methods in mind.

     

Does the punishment fit the crime? Consequences and diagnosis of misspecified detection functions in Bayesian spatial capture–recapture modeling

Spatial capture–recapture (SCR) analysis is now used routinely to inform wildlife management and conservation decisions. It is therefore imperative that we understand the implications of and can diagnose common SCR model misspecifications, as flawed inferences could propagate to policy and interventions. The detection function of an SCR model describes how an individual''s detections are distributed in space. Despite the detection function''s central role in SCR, little is known about the robustness of SCR‐derived abundance estimates and home range size estimates to misspecifications. Here, we set out to (a) determine whether abundance estimates are robust to a wider range of misspecifications of the detection function than previously explored, (b) quantify the sensitivity of home range size estimates to the choice of detection function, and (c) evaluate commonly used Bayesian p‐values for detecting misspecifications thereof. We simulated SCR data using different circular detection functions to emulate a wide range of space use patterns. We then fit Bayesian SCR models with three detection functions (half‐normal, exponential, and half‐normal plateau) to each simulated data set. While abundance estimates were very robust, estimates of home range size were sensitive to misspecifications of the detection function. When misspecified, SCR models with the half‐normal plateau and exponential detection functions produced the most and least reliable home range size, respectively. Misspecifications with the strongest impact on parameter estimates were easily detected by Bayesian p‐values. Practitioners using SCR exclusively for density estimation are unlikely to be impacted by misspecifications of the detection function. However, the choice of detection function can have substantial consequences for the reliability of inferences about space use. Although Bayesian p‐values can aid the diagnosis of detection function misspecification under certain conditions, we urge the development of additional custom goodness‐of‐fit diagnostics for Bayesian SCR models to identify a wider range of model misspecifications.     

The excuse principle can maintain cooperation through forgivable defection in the Prisoner's Dilemma game

Reciprocal altruism describes a situation in which an organism acts in a manner that temporarily reduces its fitness while increasing another organism''s fitness, but there is an ultimate fitness benefit based on an expectation that the other organism will act in a similar manner at a later time. It creates the obvious dilemma in which there is always a short-term benefit to cheating, therefore cooperating individuals must avoid being exploited by non-cooperating cheaters. This is achieved by following various decision rules, usually variants of the tit-for-tat (TFT) strategy. The strength of TFT, however, is also its weakness—mistakes in implementation or interpretation of moves, or the inability to cooperate, lead to a permanent breakdown in cooperation. We show that pied flycatchers (Ficedula hypoleuca) use a TFT with an embedded ‘excuse principle’ to forgive the neighbours that were perceived as unable to cooperate during mobbing of predators. The excuse principle dramatically increases the stability of TFT-like behavioural strategies within the Prisoner''s Dilemma game.     

The evolution of matrilineal kinship organization

Matrilineal kinship organization is a human social system that emphasizes interactions between matrilineal kin, i.e. individuals related only through females. The ‘matrilineal puzzle’ refers to the potential for tension characteristic of this social system, owing to the conflict between the interests and responsibilities of men in their roles as brother/uncle versus husband/father. From an evolutionary perspective, matrilineal kinship organization is puzzling when it diverts investment of resources from the individuals who provide the potentially highest reproductive returns. I use a game-theoretic framework to investigate a particular form of matrilineal inheritance—the transfer of property from a maternal uncle to a sororal nephew. The analysis reveals two mechanisms that may make this strategy a stable evolutionary outcome. First, a polygynous male has multiple wives, and hence multiple brothers-in-law; with matrilineal inheritance, each additional brother-in-law may transfer resources to the male''s wife''s offspring, thus potentially contributing to the male''s inclusive fitness. Second, the husband of a polyandrous female is effectively ‘sharing’ paternity with other men; depending on the number of husbands, he may be better off investing in his sister''s offspring. I conclude by discussing how these results address the challenges posed by the occurrence of matrilineal kinship organization.     

Inferring on the Intentions of Others by Hierarchical Bayesian Learning

Inferring on others'' (potentially time-varying) intentions is a fundamental problem during many social transactions. To investigate the underlying mechanisms, we applied computational modeling to behavioral data from an economic game in which 16 pairs of volunteers (randomly assigned to “player” or “adviser” roles) interacted. The player performed a probabilistic reinforcement learning task, receiving information about a binary lottery from a visual pie chart. The adviser, who received more predictive information, issued an additional recommendation. Critically, the game was structured such that the adviser''s incentives to provide helpful or misleading information varied in time. Using a meta-Bayesian modeling framework, we found that the players'' behavior was best explained by the deployment of hierarchical learning: they inferred upon the volatility of the advisers'' intentions in order to optimize their predictions about the validity of their advice. Beyond learning, volatility estimates also affected the trial-by-trial variability of decisions: participants were more likely to rely on their estimates of advice accuracy for making choices when they believed that the adviser''s intentions were presently stable. Finally, our model of the players'' inference predicted the players'' interpersonal reactivity index (IRI) scores, explicit ratings of the advisers'' helpfulness and the advisers'' self-reports on their chosen strategy. Overall, our results suggest that humans (i) employ hierarchical generative models to infer on the changing intentions of others, (ii) use volatility estimates to inform decision-making in social interactions, and (iii) integrate estimates of advice accuracy with non-social sources of information. The Bayesian framework presented here can quantify individual differences in these mechanisms from simple behavioral readouts and may prove useful in future clinical studies of maladaptive social cognition.     

An Analysis of Sponsors/Collaborators of 69,160 Drug Trials Registered with ClinicalTrials.gov

Background

Clinical trials have been criticized on various counts. Any attempt to improve how trials are conducted or reported requires—amongst other things—an understanding of the number, the nature and the location of those that sponsor them or collaborate on them. Here we sought to identify the nature and location of each sponsor/collaborator.

Methods and Findings

We examined the ''sponsor/collaborator'' field for the 69,160 drug trials that were registered with ClinicalTrials.gov over a 9-year period (2005–2014). Of the 12,823 unique sponsors, 56% had sponsored only one and 27% had sponsored 2–5 trials each. Just 18% were involved with six or more trials each, and we have (arbitrarily) labeled these organizations as ''more experienced'' in sponsoring/collaborating on trials. These 18% (2,266 sponsors/collaborators) were analyzed further: (a) 951 were corporate organizations and (b) 1,145 were non-corporates (including 31 individuals) with (c) 170 unclassified. Further, we identified the location of each organization in (a) and (b).

Conclusions

Clinical trials are an important part of a nation''s research endeavors, and ultimately contribute to the health of its people. Thus, understanding the clinical trial landscape—including the number and nature of sponsors, and how active they are—is important for every country. We believe that policy makers in particular should be interested in this study to understand the current situation, and to use the numbers as a baseline for the evolving landscape, to assess the impact of their strategies in future.     

Why Do Some People Do “More” to Mitigate Climate Change than Others? Exploring Heterogeneity in Psycho-Social Associations

The urgency of climate change mitigation calls for a profound shift in personal behavior. This paper investigates psycho-social correlates of extra mitigation behavior in response to climate change, while also testing for potential (unobserved) heterogeneity in European citizens'' decision-making. A person''s extra mitigation behavior in response to climate change is conceptualized—and differentiated from common mitigation behavior—as some people''s broader and greater levels of behavioral engagement (compared to others) across specific self-reported mitigation actions and behavioral domains. Regression analyses highlight the importance of environmental psychographics (i.e., attitudes, motivations, and knowledge about climate change) and socio-demographics (especially country-level variables) in understanding extra mitigation behavior. By looking at the data through the lens of segmentation, significant heterogeneity is uncovered in the associations of attitudes and knowledge about climate change—but not in motivational or socio-demographic links—with extra mitigation behavior in response to climate change, across two groups of environmentally active respondents. The study has implications for promoting more ambitious behavioral responses to climate change, both at the individual level and across countries.     

Constraints and variation in food web link-species space

Predicting food web structure in future climates is a pressing goal of ecology. These predictions may be impossible without a solid understanding of the factors that structure current food webs. The most fundamental aspect of food web structure—the relationship between the number of links and species—is still poorly understood. Some species interactions may be physically or physiologically ‘forbidden''—like consumption by non-consumer species—with possible consequences for food web structure. We show that accounting for these ‘forbidden interactions'' constrains the feasible link-species space, in tight agreement with empirical data. Rather than following one particular scaling relationship, food webs are distributed throughout this space according to shared biotic and abiotic features. Our study provides new insights into the long-standing question of which factors determine this fundamental aspect of food web structure.     

The multiple facets of Peto's paradox: a life-history model for the evolution of cancer suppression

Large animals should have higher lifetime probabilities of cancer than small animals because each cell division carries an attendant risk of mutating towards a tumour lineage. However, this is not observed—a (Peto''s) paradox that suggests large and/or long-lived species have evolved effective cancer suppression mechanisms. Using the Euler–Lotka population model, we demonstrate the evolutionary value of cancer suppression as determined by the ‘cost’ (decreased fecundity) of suppression verses the ‘cost’ of cancer (reduced survivorship). Body size per se will not select for sufficient cancer suppression to explain the paradox. Rather, cancer suppression should be most extreme when the probability of non-cancer death decreases with age (e.g. alligators), maturation is delayed, fecundity rates are low and fecundity increases with age. Thus, the value of cancer suppression is predicted to be lowest in the vole (short lifespan, high fecundity) and highest in the naked mole rat (long lived with late female sexual maturity). The life history of pre-industrial humans likely selected for quite low levels of cancer suppression. In modern humans that live much longer, this level results in unusually high lifetime cancer risks. The model predicts a lifetime risk of 49% compared with the current empirical value of 43%.     

A Probabilistic Model for Indel Evolution: Differentiating Insertions from Deletions

Insertions and deletions (indels) are common molecular evolutionary events. However, probabilistic models for indel evolution are under-developed due to their computational complexity. Here, we introduce several improvements to indel modeling: 1) While previous models for indel evolution assumed that the rates and length distributions of insertions and deletions are equal, here we propose a richer model that explicitly distinguishes between the two; 2) we introduce numerous summary statistics that allow approximate Bayesian computation-based parameter estimation; 3) we develop a method to correct for biases introduced by alignment programs, when inferring indel parameters from empirical data sets; and 4) using a model-selection scheme, we test whether the richer model better fits biological data compared with the simpler model. Our analyses suggest that both our inference scheme and the model-selection procedure achieve high accuracy on simulated data. We further demonstrate that our proposed richer model better fits a large number of empirical data sets and that, for the majority of these data sets, the deletion rate is higher than the insertion rate.     

Demographic Inference Reveals African and European Admixture in the North American Drosophila melanogaster Population

Drosophila melanogaster spread from sub-Saharan Africa to the rest of the world colonizing new environments. Here, we modeled the joint demography of African (Zimbabwe), European (The Netherlands), and North American (North Carolina) populations using an approximate Bayesian computation (ABC) approach. By testing different models (including scenarios with continuous migration), we found that admixture between Africa and Europe most likely generated the North American population, with an estimated proportion of African ancestry of 15%. We also revisited the demography of the ancestral population (Africa) and found—in contrast to previous work—that a bottleneck fits the history of the population of Zimbabwe better than expansion. Finally, we compared the site-frequency spectrum of the ancestral population to analytical predictions under the estimated bottleneck model.     

Interrelationships of Meloidogyne Species with Flue-cured Tobacco

Microplot and field experiments were conducted to determine relationships of population densities of Meloidogyne spp. to performance of flue-cured tobacco. A 3-yr microplot study of these interactions involved varying initial nematode numbers (P_i).and use of ethoprop to re-establish ranges of nematode densities. Field experiments included various nematicides at different locations. Regression analyses of microplot data from a loamy sand showed that cured-leaf yield losses on ''Coker 319'' for each 10-fold increase in P_i were as follows: M. javanica and M. arenaria—-13-19%; M. incognita—5-10%; M. hapla—3.4-5%; and 3% for M. incognita on resistant ''Speight G-28'' tobacco. A P_i of 750 eggs and larvae/500 cm³ of soil of all species except M. hapla caused a significant yield loss; only large numbers of M. hapla effected a loss. M. arenaria was the most tolerant species to ethoprop. Root-gall indices for microplot and most field-nematicide tests also were correlated negatively with yield. Relationships of P_i(s) and necrosis indices to yield were best characterized by linear regression models, whereas midseason numbers of eggs plus larvae (P_m) and sometimes gall indices vs. yield were better characterized by quadratic models. The relation of field P_m and yield was also adequately described by the Seinhorst model. Degrees of root galling, root necrosis, yield losses, and basic rates of reproduction on tobacco generally increased from M. hapla to M. incognita to M. arenaria to M. javanica.