Improving a gold standard: treating human relevance judgments of MEDLINE document pairs

Given prior human judgments of the condition of an object it is possible to use these judgments to make a maximal likelihood estimate of what future human judgments of the condition of that object will be. However, if one has a reasonably large collection of similar objects and the prior human judgments of a number of judges regarding the condition of each object in the collection, then it is possible to make predictions of future human judgments for the whole collection that are superior to the simple maximal likelihood estimate for each object in isolation. This is possible because the multiple judgments over the collection allow an analysis to determine the relative value of a judge as compared with the other judges in the group and this value can be used to augment or diminish a particular judge's influence in predicting future judgments. Here we study and compare five different methods for making such improved predictions and show that each is superior to simple maximal likelihood estimates.     

An unexpected sequence of events: mismatch detection in the human hippocampus

The ability to identify and react to novelty within the environment is fundamental to survival. Computational models emphasize the potential role of the hippocampus in novelty detection, its unique anatomical circuitry making it ideally suited to act as a comparator between past and present experience. The hippocampus, therefore, is viewed to detect associative mismatches between what is expected based on retrieval of past experience and current sensory input. However, direct evidence that the human hippocampus performs such operations is lacking. We explored brain responses to novel sequences of objects using functional magnetic resonance imaging (fMRI), while subjects performed an incidental target detection task. Our results demonstrate that hippocampal activation was maximal when prior predictions concerning which object would appear next in a sequence were violated by sensory reality. In so doing, we establish the biological reality of associative match-mismatch computations within the human hippocampus, a process widely held to play a cardinal role in novelty detection. Our results also suggest that the hippocampus may generate predictions about how future events will unfold, and critically detect when these expectancies are violated, even when task demands do not require it. The present study also offers broader insights into the nature of essential computations carried out by the hippocampus, which may also underpin its unique contribution to episodic memory.     

Cetacean range and climate in the eastern North Atlantic: future predictions and implications for conservation

There is increasing evidence that the distributions of a large number of species are shifting with global climate change as they track changing surface temperatures that define their thermal niche. Modelling efforts to predict species distributions under future climates have increased with concern about the overall impact of these distribution shifts on species ecology, and especially where barriers to dispersal exist. Here we apply a bio‐climatic envelope modelling technique to investigate the impacts of climate change on the geographic range of ten cetacean species in the eastern North Atlantic and to assess how such modelling can be used to inform conservation and management. The modelling process integrates elements of a species' habitat and thermal niche, and employs “hindcasting” of historical distribution changes in order to verify the accuracy of the modelled relationship between temperature and species range. If this ability is not verified, there is a risk that inappropriate or inaccurate models will be used to make future predictions of species distributions. Of the ten species investigated, we found that while the models for nine could successfully explain current spatial distribution, only four had a good ability to predict distribution changes over time in response to changes in water temperature. Applied to future climate scenarios, the four species‐specific models with good predictive abilities indicated range expansion in one species and range contraction in three others, including the potential loss of up to 80% of suitable white‐beaked dolphin habitat. Model predictions allow identification of affected areas and the likely time‐scales over which impacts will occur. Thus, this work provides important information on both our ability to predict how individual species will respond to future climate change and the applicability of predictive distribution models as a tool to help construct viable conservation and management strategies.     

Making Smart Social Judgments Takes Time: Infants' Recruitment of Goal Information When Generating Action Predictions

Previous research has shown that young infants perceive others'' actions as structured by goals. One open question is whether the recruitment of this understanding when predicting others'' actions imposes a cognitive challenge for young infants. The current study explored infants'' ability to utilize their knowledge of others'' goals to rapidly predict future behavior in complex social environments and distinguish goal-directed actions from other kinds of movements. Fifteen-month-olds (N = 40) viewed videos of an actor engaged in either a goal-directed (grasping) or an ambiguous (brushing the back of her hand) action on a Tobii eye-tracker. At test, critical elements of the scene were changed and infants'' predictive fixations were examined to determine whether they relied on goal information to anticipate the actor''s future behavior. Results revealed that infants reliably generated goal-based visual predictions for the grasping action, but not for the back-of-hand behavior. Moreover, response latencies were longer for goal-based predictions than for location-based predictions, suggesting that goal-based predictions are cognitively taxing. Analyses of areas of interest indicated that heightened attention to the overall scene, as opposed to specific patterns of attention, was the critical indicator of successful judgments regarding an actor''s future goal-directed behavior. These findings shed light on the processes that support “smart” social behavior in infants, as it may be a challenge for young infants to use information about others'' intentions to inform rapid predictions.     

Cognitive neuroscience: trickle-down theories of vision

The visual cortex is not a passive recipient of information: predictions about incoming stimuli are made based on experience, partial information and the consequences of inferences. A combination of imaging studies in the human brain has now led to the proposal that the orbitofrontal cortex is a key source of top-down predictions leading to object recognition.     

Relative Changes from Prior Reward Contingencies Can Constrain Brain Correlates of Outcome Monitoring

It is well-known that the affective value of an environment can be relative to whether it reflects an improvement or a worsening from a previous state. A potential explanation for this phenomenon suggests that relative changes from previous reward contingencies can constrain how brain monitoring systems form predictions about future events. In support of this idea, we found that changes per se relative to previous states of learned reward contingencies modulated the Feedback-Related Negativity (FRN), a human brain potential known to index discrepancies between predictions and affective outcomes. Specifically, we observed that environments with a 50% reward probability yielded different FRN patterns according to whether they reflected an improvement or a worsening from a previous environment. Further, we also found that this pattern of results was driven mainly by variations in the amplitude of ERPs to positive outcomes. Overall, these results suggest that relative changes in reward probability from previous learned environments can constrain how neural systems of outcome monitoring formulate predictions about the likelihood of future rewards and nonrewards.     

Learning to Estimate Dynamical State with Probabilistic Population Codes

Tracking moving objects, including one’s own body, is a fundamental ability of higher organisms, playing a central role in many perceptual and motor tasks. While it is unknown how the brain learns to follow and predict the dynamics of objects, it is known that this process of state estimation can be learned purely from the statistics of noisy observations. When the dynamics are simply linear with additive Gaussian noise, the optimal solution is the well known Kalman filter (KF), the parameters of which can be learned via latent-variable density estimation (the EM algorithm). The brain does not, however, directly manipulate matrices and vectors, but instead appears to represent probability distributions with the firing rates of population of neurons, “probabilistic population codes.” We show that a recurrent neural network—a modified form of an exponential family harmonium (EFH)—that takes a linear probabilistic population code as input can learn, without supervision, to estimate the state of a linear dynamical system. After observing a series of population responses (spike counts) to the position of a moving object, the network learns to represent the velocity of the object and forms nearly optimal predictions about the position at the next time-step. This result builds on our previous work showing that a similar network can learn to perform multisensory integration and coordinate transformations for static stimuli. The receptive fields of the trained network also make qualitative predictions about the developing and learning brain: tuning gradually emerges for higher-order dynamical states not explicitly present in the inputs, appearing as delayed tuning for the lower-order states.     

Time series predictions with neural nets: Application to airborne pollen forecasting

Pollen allergy is a common disease causing rhinoconjunctivitis (hay fever) in 5–10% of the population. Medical studies have indicated that pollen related diseases could be highly reduced if future pollen contents in the air could be predicted. In this work we have developed a new forecasting method that applies the ability of neural nets to predict the future behaviour of chaotic systems in order to make accurate predictions of the airborne pollen concentration. The method requires that the neural net be fed with non-zero values, which restricts the method predictions to the period following the start of pollen flight. The operational method outlined here constitutes a different point of view with respect to the more generally used forecasts of time series analysis, which require input of many meteorological parameters. Excellent forecasts were obtained training a neural net by using only the time series pollen concentration values.     

Grasping Objects with Environmentally Induced Position Uncertainty

Due to noisy motor commands and imprecise and ambiguous sensory information, there is often substantial uncertainty about the relative location between our body and objects in the environment. Little is known about how well people manage and compensate for this uncertainty in purposive movement tasks like grasping. Grasping objects requires reach trajectories to generate object-fingers contacts that permit stable lifting. For objects with position uncertainty, some trajectories are more efficient than others in terms of the probability of producing stable grasps. We hypothesize that people attempt to generate efficient grasp trajectories that produce stable grasps at first contact without requiring post-contact adjustments. We tested this hypothesis by comparing human uncertainty compensation in grasping objects against optimal predictions. Participants grasped and lifted a cylindrical object with position uncertainty, introduced by moving the cylinder with a robotic arm over a sequence of 5 positions sampled from a strongly oriented 2D Gaussian distribution. Preceding each reach, vision of the object was removed for the remainder of the trial and the cylinder was moved one additional time. In accord with optimal predictions, we found that people compensate by aligning the approach direction with covariance angle to maintain grasp efficiency. This compensation results in higher probability to achieve stable grasps at first contact than non-compensation strategies in grasping objects with directional position uncertainty, and the results provide the first demonstration that humans compensate for uncertainty in a complex purposive task.     

Predicting the future of species diversity: macroecological theory, climate change, and direct tests of alternative forecasting methods

Accurate predictions of future shifts in species diversity in response to global change are critical if useful conservation strategies are to be developed. The most widely used prediction method is to model individual species niches from point observations and project these models forward using future climate scenarios. The resulting changes in individual ranges are then summed to predict diversity changes; multiple models can be combined to produce ensemble forecasts. Predictions based on environment-richness regressions are rarer. However, richness regression models, based on macroecological diversity theory, have a long track record of making reliable spatial predictions of diversity patterns. If these empirical theories capture true functional relationships between environment and diversity, then they should make consistent predictions through time as well as space and could complement individual species-based predictions. Here, we use climate change throughout the 20th century to directly test the ability of these different approaches to predict shifts of Canadian butterfly diversity. We found that all approaches performed reasonably well, but the most accurate predictions were made using the single best richness-environment regression model, after accounting for the effects of spatial autocorrelation. Spatially trained regression models based on macroecological theory accurately predict diversity shifts for large species assemblages. Global changes provide pseudo-experimental tests of those macroecological theories that can then generate robust predictions of future conditions.     

Niche modeling for management-ready information in little-studied,threatened frog species assemblages

We use species distribution modeling to create easily testable hypotheses about the current and future distributions of Jamaican frogs, a little studied but highly endangered group. Our models simultaneously represent the best possible current estimate of the frogs’ ranges and provide clear guidelines for future survey work and habitat preservation efforts. We identify areas that contain the highest frog biodiversity, the highest per-unit area frog conservation benefit, and areas that are putative climatic refuges from outbreaks of the frog disease chytridiomycosis. In addition, we use the distribution models to create a set of easily falsifiable predictions about frog presence or absence. Testing these predictions using presence/absence surveys will provide management-ready information about model quality, population trajectories, changes in realized climate tolerance, and disease presence. We present a method of generating targeted conservation recommendations that will be applicable to many little-studied, cryptic taxa worldwide.     

Timing and intensity of herbivory: Its influence on the performance of clonal woodland herbs

Herbaceous woodland species account for a significant amount of the biodiversity of temperate deciduous forests. A wide diversity of life-history strategies is known for woodland herbs, and several systems have been used to categorize the range of life-history characteristics. Clonal growth, one common feature of many woodland herbaceous species, provides several benefits including the ability to respond to disturbances by sharing resources among ramets. There is evidence that resource sharing is common among ramets of some species of woodland herbs but not others. Herbivory is a common form of disturbance among woodland herbs, but little is known about effects of the timing and intensity of herbivory. In this paper, we use an existing system of classification of life-history traits of clonal species to make predictions about how woodland herbs would respond to the timing and intensity of defoliation. Examples from a preliminary study in Maryland, USA, are used to demonstrate that the timing and intensity of herbivory can play an important role in determining patterns of future growth and resource allocation.     

Changes in C uptake in populations of Chlamydomonas reinhardtii selected at high CO2

Estimates of the effect of increased global atmospheric CO(2) levels on oceanic primary productivity depend on the physiological responses of contemporary phytoplankton populations. However, microalgal populations will possibly adapt to rising CO(2) levels in such a way that they become genetically different from contemporary populations. The unknown properties of these future populations introduce an undefined error into predictions of C pool dynamics, especially the presence and size of the biological C pump. To address the bias in predictions introduced by evolution, we measured the kinetics of CO(2) uptake in populations of Chlamydomonas reinhardtii that had been selected for growth at high CO(2) for 1000 generations. Following selection at high CO(2), the populations were unable to induce high-affinity CO(2) uptake, and one line had a lower rate of net CO(2) uptake. We attribute this to conditionally neutral mutations in genes affecting the C concentrating mechanism (CCM). Lower affinity CO(2) uptake, in addition to smaller population sizes, results in a significant reduction in net CO(2) uptake of about 38% relative to contemporary populations under the same conditions. This shows how predictions about the properties of communities in the future can be influenced by the effect of natural selection.     

The human amygdala and orbital prefrontal cortex in behavioural regulation

Survival in complex environments depends on an ability to optimize future behaviour based on past experience. Learning from experience enables an organism to generate predictive expectancies regarding probable future states of the world, enabling deployment of flexible behavioural strategies. However, behavioural flexibility cannot rely on predictive expectancies alone and options for action need to be deployed in a manner that is responsive to a changing environment. Important moderators on learning-based predictions include those provided by context and inputs regarding an organism's current state, including its physiological state. In this paper, I consider human experimental approaches using functional magnetic resonance imaging that have addressed the role of the amygdala and prefrontal cortex (PFC), in particular the orbital PFC, in acquiring predictive information regarding the probable value of future events, updating this information, and shaping behaviour and decision processes on the basis of these value representations.     

Using the satellite-derived NDVI to assess ecological responses to environmental change

Assessing how environmental changes affect the distribution and dynamics of vegetation and animal populations is becoming increasingly important for terrestrial ecologists to enable better predictions of the effects of global warming, biodiversity reduction or habitat degradation. The ability to predict ecological responses has often been hampered by our rather limited understanding of trophic interactions. Indeed, it has proven difficult to discern direct and indirect effects of environmental change on animal populations owing to limited information about vegetation at large temporal and spatial scales. The rapidly increasing use of the Normalized Difference Vegetation Index (NDVI) in ecological studies has recently changed this situation. Here, we review the use of the NDVI in recent ecological studies and outline its possible key role in future research of environmental change in an ecosystem context.     

The ecology and evolution of autotomy

Autotomy, the self‐induced loss of a body part, occurs throughout Animalia. A lizard dropping its tail to escape predation is an iconic example, however, autotomy occurs in a diversity of other organisms. Octopuses can release their arms, crabs can drop their claws, and bugs can amputate their legs. The diversity of organisms that can autotomize body parts has led to a wealth of research and several taxonomically focused reviews. These reviews have played a crucial role in advancing our understanding of autotomy within their respective groups. However, because of their taxonomic focus, these reviews are constrained in their ability to enhance our understanding of autotomy. Here, we aim to synthesize research on the ecology and evolution of autotomy throughout Animalia, building a unified framework on which future studies can expand. We found that the ability to drop an appendage has evolved multiple times throughout Animalia and that once autotomy has evolved, selection appears to act on the removable appendage to increase the efficacy and/or efficiency of autotomy. This could explain why some autotomizable body parts are so elaborate (e.g. brightly coloured). We also show that there are multiple benefits, and variable costs, associated with autotomy. Given this variation, we generate an economic theory of autotomy (modified from the economic theory of escape) which makes predictions about when an individual should resort to autotomy. Finally, we show that the loss of an autotomizable appendage can have numerous consequences on population and community dynamics. By taking this broad taxonomic approach, we identified patterns of autotomy that transcend specific lineages and highlight clear directions for future research.     

Sex Differences in Object Manipulation in Wild Immature Chimpanzees (Pan troglodytes schweinfurthii) and Bonobos (Pan paniscus): Preparation for Tool Use?

Sex differences in immatures predict behavioural differences in adulthood in many mammal species. Because most studies have focused on sex differences in social interactions, little is known about possible sex differences in ‘preparation’ for adult life with regards to tool use skills. We investigated sex and age differences in object manipulation in immature apes. Chimpanzees use a variety of tools across numerous contexts, whereas bonobos use few tools and none in foraging. In both species, a female bias in adult tool use has been reported. We studied object manipulation in immature chimpanzees at Kalinzu (Uganda) and bonobos at Wamba (Democratic Republic of Congo). We tested predictions of the ‘preparation for tool use’ hypothesis. We confirmed that chimpanzees showed higher rates and more diverse types of object manipulation than bonobos. Against expectation, male chimpanzees showed higher object manipulation rates than females, whereas in bonobos no sex difference was found. However, object manipulation by male chimpanzees was play-dominated, whereas manipulation types of female chimpanzees were more diverse (e.g., bite, break, carry). Manipulation by young immatures of both species was similarly dominated by play, but only in chimpanzees did it become more diverse with age. Moreover, in chimpanzees, object types became more tool-like (i.e., sticks) with age, further suggesting preparation for tool use in adulthood. The male bias in object manipulation in immature chimpanzees, along with the late onset of tool-like object manipulation, indicates that not all (early) object manipulation (i.e., object play) in immatures prepares for subsistence tool use. Instead, given the similarity with gender differences in human children, object play may also function in motor skill practice for male-specific behaviours (e.g., dominance displays). In conclusion, even though immature behaviours almost certainly reflect preparation for adult roles, more detailed future work is needed to disentangle possible functions of object manipulation during development.     

Optimal hibernation theory

Hibernation has received considerable attention from physiologists and natural historians, but theoretical and ecological treatments of hibernation are rarer. There is ample recent evidence that costs associated with hibernation affect the degree to which hibernation is expressed in nature, but we currently lack a quantitative framework under which to make predictions about how the costs and benefits of hibernation interact under various environmental conditions. Here, we attempt the first steps towards building an optimal hibernation theory for making specific predictions about the expression of hibernation (i.e. the depth and duration of torpor bouts), metabolic functioning, and the total period of hibernation in mammals and birds. Our current understanding of the costs associated with hibernation do not allow for parameterisation of optimal hibernation theory, but we hope this work provides a roadmap for physiologists and ecologists to collect the necessary data in the future.     

Moving beyond heritability in the search for coral adaptive potential

Global environmental change is happening at unprecedented rates. Coral reefs are among the ecosystems most threatened by global change. For wild populations to persist, they must adapt. Knowledge shortfalls about corals' complex ecological and evolutionary dynamics, however, stymie predictions about potential adaptation to future conditions. Here, we review adaptation through the lens of quantitative genetics. We argue that coral adaptation studies can benefit greatly from “wild” quantitative genetic methods, where traits are studied in wild populations undergoing natural selection, genomic relationship matrices can replace breeding experiments, and analyses can be extended to examine genetic constraints among traits. In addition, individuals with advantageous genotypes for anticipated future conditions can be identified. Finally, genomic genotyping supports simultaneous consideration of how genetic diversity is arrayed across geographic and environmental distances, providing greater context for predictions of phenotypic evolution at a metapopulation scale.