Automaticity in the Recognition of Nonverbal Emotional Vocalizations

The ability to perceive the emotions of others is crucial for everyday social interactions. Important aspects of visual socioemotional processing, such as the recognition of facial expressions, are known to depend on largely automatic mechanisms. However, whether and how properties of automaticity extend to the auditory domain remains poorly understood. Here we ask if nonverbal auditory emotion recognition is a controlled deliberate or an automatic efficient process, using vocalizations such as laughter, crying, and screams. In a between-subjects design (N = 112), and covering eight emotions (four positive), we determined whether emotion recognition accuracy (a) is improved when participants actively deliberate about their responses (compared with when they respond as fast as possible) and (b) is impaired when they respond under low and high levels of cognitive load (concurrent task involving memorizing sequences of six or eight digits, respectively). Response latencies were also measured. Mixed-effects models revealed that recognition accuracy was high across emotions, and only minimally affected by deliberation and cognitive load; the benefits of deliberation and costs of cognitive load were significant mostly for positive emotions, notably amusement/laughter, and smaller or absent for negative ones; response latencies did not suffer under low or high cognitive load; and high recognition accuracy (approximately 90%) could be reached within 500 ms after the stimulus onset, with performance exceeding chance-level already between 300 and 360 ms. These findings indicate that key features of automaticity, namely fast and efficient/effortless processing, might be a modality-independent component of emotion recognition

Elevated arousal at time of decision-making is not the arbiter of risk avoidance in chickens

The somatic marker hypothesis proposes that humans recall previously experienced physiological responses to aid decision-making under uncertainty. However, little is known about the mechanisms used by non-human animals to integrate risk perception with predicted gains and losses. We monitored the behaviour and physiology of chickens when the choice between a high-gain (large food quantity), high-risk (1 in 4 probability of receiving an air-puff) option (HGRAP) or a low-gain (small food quantity), no-risk (of an air-puff) (LGNAP) option. We assessed when arousal increased by considering different stages of the decision-making process (baseline, viewing, anticipation, reward periods) and investigated whether autonomic responses influenced choice outcome both immediately and in the subsequent trial. Chickens were faster to choose and their heart-rate significantly increased between the viewing and anticipation (post-decision, pre-outcome) periods when selecting the HGRAP option. This suggests that they responded physiologically to the impending risk. Additionally, arousal was greater following a HGRAP choice that resulted in an air-puff, but this did not deter chickens from subsequently choosing HGRAP. In contrast to human studies, we did not find evidence that somatic markers were activated during the viewing period, suggesting that arousal is not a good measure of avoidance in non-human animals

Application and Validation of Case-Finding Algorithms for Identifying Individuals with Human Immunodeficiency Virus from Administrative Data in British Columbia, Canada

Objective To define a population-level cohort of individuals infected with the human immunodeficiency virus (HIV) in the province of British Columbia from available registries and administrative datasets using a validated case-finding algorithm. Methods Individuals were identified for possible cohort inclusion from the BC Centre for Excellence in HIV/AIDS (CfE) drug treatment program (antiretroviral therapy) and laboratory testing datasets (plasma viral load (pVL) and CD4 diagnostic test results), the BC Centre for Disease Control (CDC) provincial HIV surveillance database (positive HIV tests), as well as databases held by the BC Ministry of Health (MoH); the Discharge Abstract Database (hospitalizations), the Medical Services Plan (physician billing) and PharmaNet databases (additional HIV-related medications). A validated case-finding algorithm was applied to distinguish true HIV cases from those likely to have been misclassified. The sensitivity of the algorithms was assessed as the proportion of confirmed cases (those with records in the CfE, CDC and MoH databases) positively identified by each algorithm. A priori hypotheses were generated and tested to verify excluded cases. Results A total of 25,673 individuals were identified as having at least one HIV-related health record. Among 9,454 unconfirmed cases, the selected case-finding algorithm identified 849 individuals believed to be HIV-positive. The sensitivity of this algorithm among confirmed cases was 88%. Those excluded from the cohort were more likely to be female (44.4% vs. 22.5%; p<0.01), had a lower mortality rate (2.18 per 100 person years (100PY) vs. 3.14/100PY; p<0.01), and had lower median rates of health service utilization (days of medications dispensed: 9745/100PY vs. 10266/100PY; p<0.01; days of inpatient care: 29/100PY vs. 98/100PY; p<0.01; physician billings: 602/100PY vs. 2,056/100PY; p<0.01). Conclusions The application of validated case-finding algorithms and subsequent hypothesis testing provided a strong framework for defining a population-level cohort of HIV infected people in BC using administrative databases

Threat-sensitive anti-predator defence in precocial wader, the northern lapwing Vanellus vanellus

Birds exhibit various forms of anti-predator behaviours to avoid reproductive failure, with mobbing—observation, approach and usually harassment of a predator—being one of the most commonly observed. Here, we investigate patterns of temporal variation in the mobbing response exhibited by a precocial species, the northern lapwing (Vanellus vanellus). We test whether brood age and self-reliance, or the perceived risk posed by various predators, affect mobbing response of lapwings. We quantified aggressive interactions between lapwings and their natural avian predators and used generalized additive models to test how timing and predator species identity are related to the mobbing response of lapwings. Lapwings diversified mobbing response within the breeding season and depending on predator species. Raven Corvus corax, hooded crow Corvus cornix and harriers evoked the strongest response, while common buzzard Buteo buteo, white stork Ciconia ciconia, black-headed gull Chroicocephalus ridibundus and rook Corvus frugilegus were less frequently attacked. Lapwings increased their mobbing response against raven, common buzzard, white stork and rook throughout the breeding season, while defence against hooded crow, harriers and black-headed gull did not exhibit clear temporal patterns. Mobbing behaviour of lapwings apparently constitutes a flexible anti-predator strategy. The anti-predator response depends on predator species, which may suggest that lapwings distinguish between predator types and match mobbing response to the perceived hazard at different stages of the breeding cycle. We conclude that a single species may exhibit various patterns of temporal variation in anti-predator defence, which may correspond with various hypotheses derived from parental investment theory

Learning Temporal Patterns of Risk in a Predator-Diverse Environment

Predation plays a major role in shaping prey behaviour. Temporal patterns of predation risk have been shown to drive daily activity and foraging patterns in prey. Yet the ability to respond to temporal patterns of predation risk in environments inhabited by highly diverse predator communities, such as rainforests and coral reefs, has received surprisingly little attention. In this study, we investigated whether juvenile marine fish, Pomacentrus moluccensis (lemon damselfish), have the ability to learn to adjust the intensity of their antipredator response to match the daily temporal patterns of predation risk they experience. Groups of lemon damselfish were exposed to one of two predictable temporal risk patterns for six days. “Morning risk” treatment prey were exposed to the odour of Cephalopholis cyanostigma (rockcod) paired with conspecific chemical alarm cues (simulating a rockcod present and feeding) during the morning, and rockcod odour only in the evening (simulating a rockcod present but not feeding). “Evening risk” treatment prey had the two stimuli presented to them in the opposite order. When tested individually for their response to rockcod odour alone, lemon damselfish from the morning risk treatment responded with a greater antipredator response intensity in the morning than in the evening. In contrast, those lemon damselfish previously exposed to the evening risk treatment subsequently responded with a greater antipredator response when tested in the evening. The results of this experiment demonstrate that P. moluccensis have the ability to learn temporal patterns of predation risk and can adjust their foraging patterns to match the threat posed by predators at a given time of day. Our results provide the first experimental demonstration of a mechanism by which prey in a complex, multi-predator environment can learn and respond to daily patterns of predation risk

Interspecific Variation in Life History Relates to Antipredator Decisions by Marine Mesopredators on Temperate Reefs

As upper-level predatory fishes become overfished, mesopredators rise to become the new ‘top’ predators of over-exploited marine communities. To gain insight into ensuing mechanisms that might alter indirect species interactions, we examined how behavioural responses to an upper-level predatory fish might differ between mesopredator species with different life histories. In rocky reefs of the northeast Pacific Ocean, adult lingcod (Ophiodon elongatus) are upper-level predators that use a sit-and-wait hunting mode. Reef mesopredators that are prey to adult lingcod include kelp greenling (Hexagrammos decagrammus), younger lingcod, copper rockfish (Sebastes caurinus) and quillback rockfish (S. maliger). Across these mesopredators species, longevity and age at maturity increases and, consequently, the annual proportion of lifetime reproductive output decreases in the order just listed. Therefore, we hypothesized that the level of risk taken to acquire resources would vary interspecifically in that same order. During field experiments we manipulated predation risk with a model adult lingcod and used fixed video cameras to quantify interactions between mesopredators and tethered prey (Pandalus shrimps). We predicted that the probabilities of inspecting and attacking tethered prey would rank from highest to lowest and the timing of these behaviours would rank from earliest to latest as follows: kelp greenling, lingcod, copper rockfish, and quillback rockfish. We also predicted that responses to the model lingcod, such as avoidance of interactions with tethered prey, would rank from weakest to strongest in the same order. Results were consistent with our predictions suggesting that, despite occupying similar trophic levels, longer-lived mesopredators with late maturity have stronger antipredator responses and therefore experience lower foraging rates in the presence of predators than mesopredators with faster life histories. The corollary is that the fishery removal of top predators, which relaxes predation risk, could potentially lead to stronger increases in foraging rates for mesopredators with slower life histories

Collective Animal Behavior from Bayesian Estimation and Probability Matching

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 based on empirical fits to observations and we lack 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 given by the Bayesian estimation 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

Phenotypic redshifts with self-organizing maps: A novel method to characterize redshift distributions of source galaxies for weak lensing

Wide-field imaging surveys such as the Dark Energy Survey (DES) rely on coarse measurements of spectral energy distributions in a few filters to estimate the redshift distribution of source galaxies. In this regime, sample variance, shot noise, and selection effects limit the attainable accuracy of redshift calibration and thus of cosmological constraints. We present a new method to combine wide-field, few-filter measurements with catalogs from deep fields with additional filters and sufficiently low photometric noise to break degeneracies in photometric redshifts. The multi-band deep field is used as an intermediary between wide-field observations and accurate redshifts, greatly reducing sample variance, shot noise, and selection effects. Our implementation of the method uses self-organizing maps to group galaxies into phenotypes based on their observed fluxes, and is tested using a mock DES catalog created from N-body simulations. It yields a typical uncertainty on the mean redshift in each of five tomographic bins for an idealized simulation of the DES Year 3 weak-lensing tomographic analysis of $\sigma_{\Delta z} = 0.007$, which is a 60% improvement compared to the Year 1 analysis. Although the implementation of the method is tailored to DES, its formalism can be applied to other large photometric surveys with a similar observing strategy.Comment: 24 pages, 11 figures; matches version accepted to MNRA