- Proceedings of the National Academy of Sciences of the United States of America
- Published almost 3 years ago
The unprecedented scale of the Ebola outbreak in Western Africa (2014-2015) has prompted an explosion of efforts to understand the transmission dynamics of the virus and to analyze the performance of possible containment strategies. Models have focused primarily on the reproductive numbers of the disease that represent the average number of secondary infections produced by a random infectious individual. However, these population-level estimates may conflate important systematic variation in the number of cases generated by infected individuals, particularly found in spatially localized transmission and superspreading events. Although superspreading features prominently in first-hand narratives of Ebola transmission, its dynamics have not been systematically characterized, hindering refinements of future epidemic predictions and explorations of targeted interventions. We used Bayesian model inference to integrate individual-level spatial information with other epidemiological data of community-based (undetected within clinical-care systems) cases and to explicitly infer distribution of the cases generated by each infected individual. Our results show that superspreaders play a key role in sustaining onward transmission of the epidemic, and they are responsible for a significant proportion ([Formula: see text]61%) of the infections. Our results also suggest age as a key demographic predictor for superspreading. We also show that community-based cases may have progressed more rapidly than those notified within clinical-care systems, and most transmission events occurred in a relatively short distance (with median value of 2.51 km). Our results stress the importance of characterizing superspreading of Ebola, enhance our current understanding of its spatiotemporal dynamics, and highlight the potential importance of targeted control measures.
Transformative applications in biomedicine require the discovery of complex regulatory networks that explain the development and regeneration of anatomical structures, and reveal what external signals will trigger desired changes of large-scale pattern. Despite recent advances in bioinformatics, extracting mechanistic pathway models from experimental morphological data is a key open challenge that has resisted automation. The fundamental difficulty of manually predicting emergent behavior of even simple networks has limited the models invented by human scientists to pathway diagrams that show necessary subunit interactions but do not reveal the dynamics that are sufficient for complex, self-regulating pattern to emerge. To finally bridge the gap between high-resolution genetic data and the ability to understand and control patterning, it is critical to develop computational tools to efficiently extract regulatory pathways from the resultant experimental shape phenotypes. For example, planarian regeneration has been studied for over a century, but despite increasing insight into the pathways that control its stem cells, no constructive, mechanistic model has yet been found by human scientists that explains more than one or two key features of its remarkable ability to regenerate its correct anatomical pattern after drastic perturbations. We present a method to infer the molecular products, topology, and spatial and temporal non-linear dynamics of regulatory networks recapitulating in silico the rich dataset of morphological phenotypes resulting from genetic, surgical, and pharmacological experiments. We demonstrated our approach by inferring complete regulatory networks explaining the outcomes of the main functional regeneration experiments in the planarian literature; By analyzing all the datasets together, our system inferred the first systems-biology comprehensive dynamical model explaining patterning in planarian regeneration. This method provides an automated, highly generalizable framework for identifying the underlying control mechanisms responsible for the dynamic regulation of growth and form.
In the present work, we investigated the pop cultural idea that people have a sixth sense, called “gaydar,” to detect who is gay. We propose that “gaydar” is an alternate label for using stereotypes to infer orientation (e.g., inferring that fashionable men are gay). Another account, however, argues that people possess a facial perception process that enables them to identify sexual orientation from facial structure. We report five experiments testing these accounts. Participants made gay-or-straight judgments about fictional targets that were constructed using experimentally manipulated stereotypic cues and real gay/straight people’s face cues. These studies revealed that orientation is not visible from the face-purportedly “face-based” gaydar arises from a third-variable confound. People do, however, readily infer orientation from stereotypic attributes (e.g., fashion, career). Furthermore, the folk concept of gaydar serves as a legitimizing myth: Compared to a control group, people stereotyped more often when led to believe in gaydar, whereas people stereotyped less when told gaydar is an alternate label for stereotyping. Discussion focuses on the implications of the gaydar myth and why, contrary to some prior claims, stereotyping is highly unlikely to result in accurate judgments about orientation.
We used an expectancy violation procedure to ask whether cats could use a causal rule to infer the presence of an unseen object on hearing the noise it made inside a container and predict its appearance when the container was turned over. We presented cats with either an object dropping out of an opaque container or no object dropping out (turning-over phase) after producing either a rattling sound by shaking the container with the object inside, or no sound (shaking phase). The cats were then allowed to freely explore the experimental environment (exploration phase). The relation between the sound and the object matched with physical laws in half of the trials (congruent condition) and mismatched in the other half (incongruent condition). Inferring the presence of an unseen object from the noise was predicted to result in longer looking time in the incongruent condition. The prediction was supported by the cats' behavior during the turning-over phase. The results suggest that cats used a causal-logical understanding of auditory stimuli to predict the appearance of invisible objects. The ecology of cats' natural hunting style may favor the ability for inference on the basis of sounds.
When inferring the presence of a specific cognitive process from observed brain activation a kind of reasoning is applied that is called reverse inference. Poldrack (2006) rightly criticized the careless use of reverse inference. As a consequence, reverse inference is assumed as intrinsically weak by many and its validity has been increasingly regarded as limited. Although it is undisputed that the careless use of reverse inference is a problematic practice, the current view of reverse inference is to the author’s opinion overly pessimistic. The present manuscript provides a revised formulation of reverse inference that includes an additional conditional constraint that has been previously acknowledged, but so far not implemented: the task-setting. This revised formulation I.) reveals that reverse inference can have high predictive power (as demonstrated by an example estimation) and II.) allows an estimation of reverse inference on the basis of meta-analyses instead of large-scale databases. It is concluded that reverse inference cannot be disregarded as a fallacy per se. Rather, the predictive power of reverse inference can even be “decisive” - dependent on the cognitive process of interest, the specific brain region activated, and the task-setting used.
- Journal of experimental psychology. Learning, memory, and cognition
- Published about 7 years ago
In 2 experiments, we tested a strong version of a dual process theory of conditional inference (cf. Verschueren et al., 2005a, 2005b) that assumes that most reasoners have 2 strategies available, the choice of which is determined by situational variables, cognitive capacity, and metacognitive control. The statistical strategy evaluates inferences probabilistically, accepting those with high conditional probability. The counterexample strategy rejects inferences when a counterexample shows the inference to be invalid. To discriminate strategy use, we presented reasoners with conditional statements (if p, then q) and explicit statistical information about the relative frequency of the probability of p/q (50% vs. 90%). A statistical strategy would accept the more probable inferences more frequently, whereas the counterexample one would reject both. In Experiment 1, reasoners under time pressure used the statistical strategy more, but switched to the counterexample strategy when time constraints were removed; the former took less time than the latter. These data are consistent with the hypothesis that the statistical strategy is the default heuristic. Under a free-time condition, reasoners preferred the counterexample strategy and kept it when put under time pressure. Thus, it is not simply a lack of capacity that produces a statistical strategy; instead, it seems that time pressure disrupts the ability to make good metacognitive choices. In line with this conclusion, in a 2nd experiment, we measured reasoners' confidence in their performance; those under time pressure were less confident in the statistical than the counterexample strategy and more likely to switch strategies under free-time conditions. (PsycINFO Database Record © 2012 APA, all rights reserved).
Neuroimaging findings are often interpreted in terms of affective experience, but researchers disagree about the advisability or even possibility of such inferences, and few frameworks explicitly link these levels of analysis. Here, we suggest that the spatial and temporal resolution of functional magnetic resonance imaging (fMRI) data could support inferences about affective states. Specifically, we propose that fMRI nucleus accumbens (NAcc) activity is associated with positive arousal, whereas a combination of anterior insula activity and NAcc activity is associated with negative arousal. This framework implies quantifiable and testable inferences about affect from fMRI data, which may ultimately inform predictions about approach and avoidance behavior. We consider potential limits on neurally inferred affect before highlighting theoretical and practical benefits.
This study investigates the ERP components associated with the processing of words that are critical to generating and rejecting deductive conditional Modus Ponens arguments (If P then Q; P//Therefore, Q). The generation of a logical inference is investigated by placing a verb in the minor premise that matches the one used in the antecedent of the conditional premise so that the inference can be carried out (If John is sleeping then he is snoring; John is sleeping). Rejections are examined by placing verbs that are associates of the verb that would make the conclusion valid (Conclusion ‘therefore John is dreaming’ in the example above). The inference generation phase was characterized by two ERP components, namely the P3b and the PSW. Rejections were associated with an N2 and a late positive component. The implications of these results regarding the processing of words in an inferential context are discussed.
We examined inferential reasoning by exclusion in the Clark’s nutcracker (Nucifraga columbiana) using two-way object-choice procedures. While other social scatter-hoarding corvids appear capable of engaging in inferential reasoning, it remains unclear if the relatively less social nutcracker is able to do so. In an initial experiment, food was hidden in one of two opaque containers. All of the birds immediately selected the baited container when shown only the empty container during testing. We subsequently examined the nutcrackers in two follow-up experiments using a task that may have been less likely to be solved by associative processes. The birds were trained that two distinctive objects were always found hidden in opaque containers that were always positioned at the same two locations. During testing, one of the two objects was found in a transparent “trash bin” and was unavailable. The birds were required to infer that if one of the objects was in the “trash,” then the other object should still be available in its hidden location. Five out of six birds were unable to make this inference, suggesting that associative mechanisms likely accounted for our earlier results. However, one bird consistently chose the object that was not seen in the “trash,” demonstrating that nutcrackers may have the ability to use inferential reasoning by exclusion to solve inference tasks. The role of scatter hoarding and social organization is discussed as factors in the ability of corvid birds to reason.
A recent study has inferred that the red fox (Vulpes vulpes) is now widespread in Tasmania as of 2010, based on the extraction of fox DNA from predator scats. Heuristically, this inference appears at first glance to be at odds with the lack of recent confirmed discoveries of either road-killed foxes-the last of which occurred in 2006, or hunter killed foxes-the most recent in 2001. This paper demonstrates a method to codify this heuristic analysis and produce inferences consistent with assumptions and data. It does this by formalising the analysis in a transparent and repeatable manner to make inference on the past, present and future distribution of an invasive species. It utilizes Approximate Bayesian Computation to make inferences. Importantly, the method is able to inform management of invasive species within realistic time frames, and can be applied widely. We illustrate the technique using the Tasmanian fox data. Based on the pattern of carcass discoveries of foxes in Tasmania, we infer that the population of foxes in Tasmania is most likely extinct, or restricted in distribution and demographically weak as of 2013. It is possible, though unlikely, that that population is widespread and/or demographically robust. This inference is largely at odds with the inference from the predator scat survey data. Our results suggest the chances of successfully eradicating the introduced red fox population in Tasmania may be significantly higher than previously thought.