Honeybees solve a multi-comparison ranking task by probability matching.
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ABSTRACT:
SUBMITTER: MaBouDi H
PROVIDER: S-EPMC7542781 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundPretest probability (PTP) assessment plays a central role in diagnosis. This report compares a novel attribute-matching method to generate a PTP for acute coronary syndrome (ACS). We compare the new method with a validated logistic regression equation (LRE).MethodsEight clinical variables (attributes) were chosen by classification and regression tree analysis of a prospectively collected reference database of 14,796 emergency department (ED) patients evaluated for possible ACS. For attribute matching, a computer program identifies patients within the database who have the exact profile defined by clinician input of the eight attributes. The novel method was compared with the LRE for ability to produce PTP estimation <2% in a validation set of 8,120 patients evaluated for possible ACS and did not have ST segment elevation on ECG. 1,061 patients were excluded prior to validation analysis because of ST-segment elevation (713), missing data (77) or being lost to follow-up (271).ResultsIn the validation set, attribute matching produced 267 unique PTP estimates [median PTP value 6%, 1st-3rd quartile 1-10%] compared with the LRE, which produced 96 unique PTP estimates [median 24%, 1st-3rd quartile 10-30%]. The areas under the receiver operating characteristic curves were 0.74 (95% CI 0.65 to 0.82) for the attribute matching curve and 0.68 (95% CI 0.62 to 0.77) for LRE. The attribute matching system categorized 1,670 (24%, 95% CI = 23-25%) patients as having a PTP < 2.0%; 28 developed ACS (1.7% 95% CI = 1.1-2.4%). The LRE categorized 244 (4%, 95% CI = 3-4%) with PTP < 2.0%; four developed ACS (1.6%, 95% CI = 0.4-4.1%).ConclusionAttribute matching estimated a very low PTP for ACS in a significantly larger proportion of ED patients compared with a validated LRE.
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Project description:The prevalence, which is the average fraction of infected nodes, has been studied to evaluate the robustness of a network subject to the spread of epidemics. We explore the vulnerability (infection probability) of each node in the metastable state with a given effective infection rate τ. Specifically, we investigate the ranking of the nodal vulnerability subject to a susceptible-infected-susceptible epidemic, motivated by the fact that the ranking can be crucial for a network operator to assess which nodes are more vulnerable. Via both theoretical and numerical approaches, we unveil that the ranking of nodal vulnerability tends to change more significantly as τ varies when τ is smaller or in Barabási-Albert than Erdős-Rényi random graphs.
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Project description:Probability matching is a classic theory of decision making that was first developed in models of cognition. Posterior probability matching, a variant in which observers match their response probabilities to the posterior probability of each response being correct, is being used increasingly often in models of perception. However, little is known about whether posterior probability matching is consistent with the vast literature on vision and hearing that has developed within signal detection theory. Here we test posterior probability matching models using two tools from detection theory. First, we examine the models' performance in a two-pass experiment, where each block of trials is presented twice, and we measure the proportion of times that the model gives the same response twice to repeated stimuli. We show that at low performance levels, posterior probability matching models give highly inconsistent responses across repeated presentations of identical trials. We find that practised human observers are more consistent across repeated trials than these models predict, and we find some evidence that less practised observers more consistent as well. Second, we compare the performance of posterior probability matching models on a discrimination task to the performance of a theoretical ideal observer that achieves the best possible performance. We find that posterior probability matching is very inefficient at low-to-moderate performance levels, and that human observers can be more efficient than is ever possible according to posterior probability matching models. These findings support classic signal detection models, and rule out a broad class of posterior probability matching models for expert performance on perceptual tasks that range in complexity from contrast discrimination to symmetry detection. However, our findings leave open the possibility that inexperienced observers may show posterior probability matching behaviour, and our methods provide new tools for testing for such a strategy.
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Project description:Correlative evidence suggests that high problem-solving and foraging abilities in a mate are associated with direct fitness advantages, so it would benefit females to prefer problem-solving males. Recent work has also shown that females of several bird species who directly observe males prefer those that can solve a novel foraging task over those that cannot. In addition to or instead of direct observation of cognitive skills, many species utilize assessment signals when choosing a mate. Here, we test whether females can select a problem-solving male over a non-solving male when presented only with a signal known to be used in mate assessment: song. Using an operant conditioning assay, we compared female zebra finch (Taeniopygia guttata) preference for the songs of males that could quickly solve a novel foraging task to the songs of males that could not solve the task. Females were never housed with the test subject males whose song they heard, and the only information provided about the males was their song. We found that females elicited more songs of problem-solving males than of non-solvers, indicating that song may contain information about a male's ability to solve a novel foraging task and that naïve females prefer the songs of problem-solving males.
| S-EPMC7520207 | biostudies-literature
Project description:Animals living in groups make movement decisions that depend, among other factors, on social interactions with other group members. Our present understanding of social rules in animal collectives is mainly based on empirical fits to observations, with less emphasis in obtaining first-principles approaches that allow their derivation. Here we show that patterns of collective decisions can be derived from the basic ability of animals to make probabilistic estimations in the presence of uncertainty. We build a decision-making model with two stages: Bayesian estimation and probabilistic matching. In the first stage, each animal makes a Bayesian estimation of which behavior is best to perform taking into account personal information about the environment and social information collected by observing the behaviors of other animals. In the probability matching stage, each animal chooses a behavior with a probability equal to the Bayesian-estimated probability that this behavior is the most appropriate one. This model derives very simple rules of interaction in animal collectives that depend only on two types of reliability parameters, one that each animal assigns to the other animals and another given by the quality of the non-social information. We test our model by obtaining theoretically a rich set of observed collective patterns of decisions in three-spined sticklebacks, Gasterosteus aculeatus, a shoaling fish species. The quantitative link shown between probabilistic estimation and collective rules of behavior allows a better contact with other fields such as foraging, mate selection, neurobiology and psychology, and gives predictions for experiments directly testing the relationship between estimation and collective behavior.
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Project description:To validate the sequence motifs identified by our multi-task learning model MTtrans, a new 5' UTR library with around 8,000 synthetic 5'UTRs was built to express EGFP. The reads count was used as a proxy of translation rate here to validate the estimated regulatory effect of motifs that we inferred from multiple datasets, proving the robustness of the sequence motifs.
2022-04-28 | GSE201766 | GEO
Project description:The question of which strategy is employed in human decision making has been studied extensively in the context of cognitive tasks; however, this question has not been investigated systematically in the context of perceptual tasks. The goal of this study was to gain insight into the decision-making strategy used by human observers in a low-level perceptual task. Data from more than 100 individuals who participated in an auditory-visual spatial localization task was evaluated to examine which of three plausible strategies could account for each observer's behavior the best. This task is very suitable for exploring this question because it involves an implicit inference about whether the auditory and visual stimuli were caused by the same object or independent objects, and provides different strategies of how using the inference about causes can lead to distinctly different spatial estimates and response patterns. For example, employing the commonly used cost function of minimizing the mean squared error of spatial estimates would result in a weighted averaging of estimates corresponding to different causal structures. A strategy that would minimize the error in the inferred causal structure would result in the selection of the most likely causal structure and sticking with it in the subsequent inference of location-"model selection." A third strategy is one that selects a causal structure in proportion to its probability, thus attempting to match the probability of the inferred causal structure. This type of probability matching strategy has been reported to be used by participants predominantly in cognitive tasks. Comparing these three strategies, the behavior of the vast majority of observers in this perceptual task was most consistent with probability matching. While this appears to be a suboptimal strategy and hence a surprising choice for the perceptual system to adopt, we discuss potential advantages of such a strategy for perception.
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Project description:Chimpanzees and orangutans are able to generate innovative behaviors to solve complicated physical problems. For example, when presented with an out-of-reach peanut at the bottom of a vertical tube (floating peanut task-FPT), some of them spontaneously spit water into the tube until the peanut floats to the top. Yet, it is unclear whether this innovative solution results from repeating those actions that bring the peanut incrementally closer to the top or from anticipating the solution before acting. In the current study, we addressed this question by presenting three naïve orangutans with an opaque version of the FPT that prevented them from obtaining visual information about the effect of their actions on the position of the peanut. One of the subjects solved the opaque FPT in the very first trial: he collected water from the faucet and poured it into the opaque tube repeatedly until the hitherto non-visible peanut reached the top. This provides evidence for the first time that orangutans can potentially solve the FPT without relying on sensorimotor learning, but to some extent by mentally representing the problem.
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Project description:The assignment of a symbolic representation to a specific numerosity is a fundamental requirement for humans solving complex mathematical calculations used in diverse applications such as algebra, accounting, physics and everyday commerce. Here we show that honeybees are able to learn to match a sign to a numerosity, or a numerosity to a sign, and subsequently transfer this knowledge to novel numerosity stimuli changed in colour properties, shape and configuration. While honeybees learned the associations between two quantities (two; three) and two signs (N-shape; inverted T-shape), they failed at reversing their specific task of sign-to-numerosity matching to numerosity-to-sign matching and vice versa (i.e. a honeybee that learned to match a sign to a number of elements was not able to invert this learning to match the numerosity of elements to a sign). Thus, while bees could learn the association between a symbol and numerosity, it was linked to the specific task and bees could not spontaneously extrapolate the association to a novel, reversed task. Our study therefore reveals that the basic requirement for numerical symbolic representation can be fulfilled by an insect brain, suggesting that the absence of its spontaneous emergence in animals is not due to cognitive limitation.
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Project description:Reproductive and task partitioning in large colonies of social insects suggest that colony members belonging to different castes or performing different tasks during their life (polyethism) may produce specific semiochemicals and be differently sensitive to the variety of pheromones involved in intraspecific chemical communication. The main peripheral olfactory organs are the antennal chemosensilla, where the early olfactory processes take place. At this stage, members of two different families of soluble chemosensory proteins [odorant-binding proteins (OBPs) and chemosensory proteins (CSPs)] show a remarkable affinity for different odorants and act as carriers while a further family, the Niemann-Pick type C2 proteins (NPC2) may have a similar function, although this has not been fully demonstrated. Sensillar lymph also contains Odorant degrading enzymes (ODEs) which are involved in inactivation through degradation of the chemical signals, once the message is conveyed. Despite their importance in chemical communication, little is known about how proteins involved in peripheral olfaction and, more generally antennal proteins, differ in honeybees of different caste, task and age. Here, we investigate for the first time, using a shotgun proteomic approach, the antennal profile of honeybees of different castes (queens and workers) and workers performing different tasks (nurses, guards, and foragers) by controlling for the potential confounding effect of age. Regarding olfactory proteins, major differences were observed between queens and workers, some of which were found to be more abundant in queens (OBP3, OBP18, and NPC2-1) and others to be more abundant in workers (OBP15, OBP21, CSP1, and CSP3); while between workers performing different tasks, OBP14 was more abundant in nurses with respect to guards and foragers. Apart from proteins involved in olfaction, we have found that the antennal proteomes are mainly characterized by castes and tasks, while age has no effect on antennal protein profile. Among the main differences, the strong decrease in vitellogenins found in guards and foragers is not associated with age.
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