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Are Autonomous Vehicles Blamed Differently?
This study investigates how people assign blame to autonomous vehicles (AVs) when involved in an accident. Our experiment (N= 2647) revealed that people placed more blame on AVs than on human drivers when accident details were unspecified. To examine whether people assess major classes of blame-relevant information differently for AVs and humans, we developed a causal model and introduced a novel concept of prevention effort, which emerged as a crucial factor for blame judgement alongside intentionality. Finally, we addressed the “many hands” problem by exploring how people assign blame to entities associated with AVs and human drivers, such as the car company or an accident victim. Our findings showed that people assigned high blame to these entities in scenarios involving AVs, but not with human drivers. This necessitates adapting a model of blame for AVs to include other agents and thus allow for blame allocation “outside” of autonomous vehicles.
Many Labs 5: Registered Replication of LoBue and DeLoache (2008)
Across three studies, LoBue and DeLoache (2008) provided evidence suggesting that both young children and adults exhibit enhanced visual detection of evolutionarily relevant threat stimuli (as compared with nonthreatening stimuli). A replication of their Experiment 3, conducted by Cramblet Alvarez and Pipitone (2015) as part of the Reproducibility Project: Psychology (RP:P), demonstrated trends similar to those of the original study, but the effect sizes were smaller and not statistically significant. There were, however, some methodological differences (e.g., screen size) and sampling differences (the age of recruited children) between the original study and the RP:P replication study. Additionally, LoBue and DeLoache expressed concern over the choice of stimuli used in the RP:P replication. We sought to explore the possible moderating effects of these factors by conducting two new replications—one using the protocol from the RP:P and the other using a revised protocol. We collected data at four sites, three in Serbia and one in the United States (total N = 553). Overall, participants were not significantly faster at detecting threatening stimuli. Thus, results were not supportive of the hypothesis that visual detection of evolutionarily relevant threat stimuli is enhanced in young children. The effect from the RP:P protocol (d = −0.10, 95% confidence interval = [−1.02, 0.82]) was similar to the effect from the revised protocol (d = −0.09, 95% confidence interval = [−0.33, 0.15]), and the results from both the RP:P and the revised protocols were more similar to those found by Cramblet Alvarez and Pipitone than to those found by LoBue and DeLoache.
Many Labs 5: Testing Pre-Data-Collection Peer Review as an Intervention to Increase Replicability
Replication studies in psychological science sometimes fail to reproduce prior findings. If these studies use methods that are unfaithful to the original study or ineffective in eliciting the phenomenon of interest, then a failure to replicate may be a failure of the protocol rather than a challenge to the original finding. Formal pre-data-collection peer review by experts may address shortcomings and increase replicability rates. We selected 10 replication studies from the Reproducibility Project: Psychology (RP:P; Open Science Collaboration, 2015) for which the original authors had expressed concerns about the replication designs before data collection; only one of these studies had yielded a statistically significant effect (p
Data from the Human Penguin Project, a cross-national dataset testing social thermoregulation principles.
In the Human Penguin Project (N = 1755), 15 research groups from 12 countries collected body temperature, demographic variables, social network indices, seven widely-used psychological scales and two newly developed questionnaires (the Social Thermoregulation and Risk Avoidance Questionnaire (STRAQ-1) and the Kama Muta Frequency Scale (KAMF)). They were collected to investigate the relationship between environmental factors (e.g., geographical, climate etc.) and human behaviors, which is a long-standing inquiry in the scientific community. More specifically, the present project was designed to test principles surrounding the idea of social thermoregulation, which posits that social networks help people to regulate their core body temperature. The results showed that all scales in the current project have sufficient to good psychometrical properties. Unlike previous crowdsourced projects, this dataset includes not only the cleaned raw data but also all the validation of questionnaires in 9 different languages, thus providing a valuable resource for psychological scientists who are interested in cross-national, environment-human interaction studies.
Does distance from the equator predict self-control? Lessons from the Human Penguin Project
We comment on the proposition that lower temperatures and especially greater seasonal variation in temperature call for individuals and societies to adopt ⋯ a greater degree of self-control (Van Lange et al., sect. 3, para. 4) for which we cannot find empirical support in a large data set with data-driven analyses. After providing greater nuance in our theoretical review, we suggest that Van Lange et al. revisit their model with an eye toward the social determinants of self-control.
The hard problem of meta-learning is what-to-learn.
Binz et al. highlight the potential of meta-learning to greatly enhance the flexibility of AI algorithms, as well as to approximate human behavior more accurately than traditional learning methods. We wish to emphasize a basic problem that lies underneath these two objectives, and in turn suggest another perspective of the required notion of "meta" in meta-learning: knowing what to learn.
Modelling how cleaner fish approach an ephemeral reward task demonstrates a role for ecologically tuned chunking in the evolution of advanced cognition.
What makes cognition "advanced" is an open and not precisely defined question. One perspective involves increasing the complexity of associative learning, from conditioning to learning sequences of events ("chaining") to representing various cue combinations as "chunks." Here we develop a weighted graph model to study the mechanism enabling chunking ability and the conditions for its evolution and success, based on the ecology of the cleaner fish Labroides dimidiatus. In some environments, cleaners must learn to serve visitor clients before resident clients, because a visitor leaves if not attended while a resident waits for service. This challenge has been captured in various versions of the ephemeral reward task, which has been proven difficult for a range of cognitively capable species. We show that chaining is the minimal requirement for solving this task in its common simplified laboratory format that involves repeated simultaneous exposure to an ephemeral and permanent food source. Adding ephemeral-ephemeral and permanent-permanent combinations, as cleaners face in the wild, requires individuals to have chunking abilities to solve the task. Importantly, chunking parameters need to be calibrated to ecological conditions in order to produce adaptive decisions. Thus, it is the fine-tuning of this ability, which may be the major target of selection during the evolution of advanced associative learning.
Decision making in foraging bats.
Foraging is a complex and cognitively demanding behavior. Although it is often regarded as a mundane task, foraging requires the continuous weighting and integration of many sources of information with varying levels of credence. Bats are extremely diverse in their ecology and behavior, and thus demonstrate a wide variety of foraging strategies. In this review, we examine the different factors influencing the decision process of bats during foraging. Technological developments of recent years will soon enable real-time tracking of environmental conditions, of the position and quality of food items, the location of conspecifics, and the bat's movement history. Monitoring these variables alongside the continuous movement of the bat will facilitate the testing of different decision-making theories such as the use of reinforcement learning in wild free ranging bats and other animals.
Animals Have No Language, and Humans Are Animals Too.
Language is a cornerstone of human culture, yet the evolution of this cognitive-demanding ability is shrouded in mystery. Studying how different species demonstrate this trait can provide clues for its evolutionary route. Indeed, recent decades saw ample scientific attempts to compare human speech, the prominent behavioral manifestation of language, with other animals' vocalizations. Diligent studies have found only elementary parallels to speech in other animals, fortifying the belief that language is uniquely human. But have we really tested this uniqueness claim? Surprisingly, a true impartial comparison between human speech and other animals' vocalizations has hardly ever been conducted. Here, I illustrate how treating humans as an equal species in vocal-communication research is expected to provide us with no evidence for human superiority in this realm. Thus, novel balanced and unbiased comparative studies are vital for identifying any unique component of human speech and language.
Food for Sex in Bats Revealed as Producer Males Reproduce with Scrounging Females.
Food sharing is often evolutionarily puzzling, because the provider's benefits are not always clear. Sharing among kin may increase indirect fitness [1], but when non-kin are involved, different mechanisms were suggested to act. Occasionally, "tolerated theft" [2, 3] is observed, merely because defending a resource is not cost effective. Sharing may also be explained as "costly signaling" [4, 5], where individuals signal their high qualities by distributing acquired resources, as has been suggested to occur in certain human cultures [6]. Alternatively, a transferred food item might be compensated for in later interactions [7]. In vampire bats, blood sharing reflects reciprocity between non-kin colony members [8-10], and long-term social bonds affect food sharing in chimpanzees [11]. Food may also be exchanged for other goods or social benefits [12-14]. One reciprocity-based explanation for intersexual food sharing is the food-for-sex hypothesis [15-17]. This hypothesis proposes that males share food with females in exchange for mating opportunities. Studies on human hunter-gatherer societies suggest that males with increased foraging success have higher reproductive success [18, 19]. Male chimpanzees, which in contrast to humans do not maintain pair bonds, were suggested to share food with females to increase their mating opportunities [16] (but see [20]). Bats, which are long-lived social mammals [21, 22], provide an opportunity to study long-term social reciprocity mechanisms. We monitored producer-scrounger interactions of a captive Egyptian fruit bat (Rousettus aegyptiacus) colony for more than a year and genetically determined the paternity of the pups that were born in the colony. We found that females carry the young of males from which they used to scrounge food, supporting the food-for-sex hypothesis in this species.
Cultural transmission in an ever-changing world: trial-and-error copying may be more robust than precise imitation.
Cultural transmission facilitates the spread of behaviours within social groups and may lead to the establishment of stable traditions in both human and non-human animals. The fidelity of transmission is frequently emphasized as a core component of cultural evolution and as a prerequisite for cumulative culture. Fidelity is often considered a synonym of precise copying of observed behaviours. However, while precise copying guarantees reliable transmission in an ideal static world, it may be vulnerable to realistic variability in the actual environment. Here, we argue that fidelity may be more naturally achieved when the social learning mechanisms incorporate trial-and-error; and that the robustness of social transmission is thereby increased. We employed a simple model to demonstrate how culture that is produced through exact copying is fragile in an (even slightly) noisy world. When incorporating a certain degree of trial-and-error, however, cultures are more readily formed in a stochastic environment and are less vulnerable to rare ecological changes. We suggest that considering trial-and-error learning as a stabilizing component of social transmission may provide insights into cultural evolution in a realistic, variable, world.This article is part of the theme issue 'Bridging cultural gaps: interdisciplinary studies in human cultural evolution'.
Crowd vocal learning induces vocal dialects in bats: Playback of conspecifics shapes fundamental frequency usage by pups.
Vocal learning, the substrate of human language acquisition, has rarely been described in other mammals. Often, group-specific vocal dialects in wild populations provide the main evidence for vocal learning. While social learning is often the most plausible explanation for these intergroup differences, it is usually impossible to exclude other driving factors, such as genetic or ecological backgrounds. Here, we show the formation of dialects through social vocal learning in fruit bats under controlled conditions. We raised 3 groups of pups in conditions mimicking their natural roosts. Namely, pups could hear their mothers' vocalizations but were also exposed to a manipulation playback. The vocalizations in the 3 playbacks mainly differed in their fundamental frequency. From the age of approximately 6 months and onwards, the pups demonstrated distinct dialects, where each group was biased towards its playback. We demonstrate the emergence of dialects through social learning in a mammalian model in a tightly controlled environment. Unlike in the extensively studied case of songbirds where specific tutors are imitated, we demonstrate that bats do not only learn their vocalizations directly from their mothers, but that they are actually influenced by the sounds of the entire crowd. This process, which we term "crowd vocal learning," might be relevant to many other social animals such as cetaceans and pinnipeds.
An annotated dataset of Egyptian fruit bat vocalizations across varying contexts and during vocal ontogeny.
Animal acoustic communication research depends on our ability to record the vocal behaviour of different species. Only rarely do we have the opportunity to continuously follow the vocal behaviour of a group of individuals of the same species for a long period of time. Here, we provide a database of Egyptian fruit bat vocalizations, which were continuously recorded in the lab in several groups simultaneously for more than a year. The dataset includes almost 300,000 files, a few seconds each, containing social vocalizations and representing the complete vocal repertoire used by the bats in the experiment period. Around 90,000 files are annotated with details about the individuals involved in the vocal interactions, their behaviours and the context. Moreover, the data include the complete vocal ontogeny of pups, from birth to adulthood, in different conditions (e.g., isolated or in a group). We hope that this comprehensive database will stimulate studies that will enhance our understanding of bat, and mammal, social vocal communication.
Everyday bat vocalizations contain information about emitter, addressee, context, and behavior.
Animal vocal communication is often diverse and structured. Yet, the information concealed in animal vocalizations remains elusive. Several studies have shown that animal calls convey information about their emitter and the context. Often, these studies focus on specific types of calls, as it is rarely possible to probe an entire vocal repertoire at once. In this study, we continuously monitored Egyptian fruit bats for months, recording audio and video around-the-clock. We analyzed almost 15,000 vocalizations, which accompanied the everyday interactions of the bats, and were all directed toward specific individuals, rather than broadcast. We found that bat vocalizations carry ample information about the identity of the emitter, the context of the call, the behavioral response to the call, and even the call's addressee. Our results underline the importance of studying the mundane, pairwise, directed, vocal interactions of animals.
Vocal learning in a social mammal: Demonstrated by isolation and playback experiments in bats.
The evolution of human language is shrouded in mystery as it is unparalleled in the animal kingdom. Whereas vocal learning is crucial for the development of speech in humans, it seems rare among nonhuman animals. Songbirds often serve as a model for vocal learning, but the lack of a mammalian model hinders our quest for the origin of this capability. We report the influence of both isolation and playback experiments on the vocal development of a mammal, the Egyptian fruit bat. We continuously recorded pups from birth to adulthood and found that, when raised in a colony, pups acquired the adult repertoire, whereas when acoustically isolated, they exhibited underdeveloped vocalizations. Isolated pups that heard bat recordings exhibited a repertoire that replicated the playbacks they were exposed to. These findings demonstrate vocal learning in a social mammal, and suggest bats as a model for language acquisition.
Recovering key biological constituents through sparse representation of gene expression
Motivation: Large-scale RNA expression measurements are generating enormous quantities of data. During the last two decades, many methods were developed for extracting insights regarding the interrelationships between genes from such data. The mathematical and computational perspectives that underlie these methods are usually algebraic or probabilistic. Results: Here, we introduce an unexplored geometric view point where expression levels of genes in multiple experiments are interpreted as vectors in a high-dimensional space. Specifically, we find, for the expression profile of each particular gene, its approximation as a linear combination of profiles of a few other genes. This method is inspired by recent developments in the realm of compressed sensing in the machine learning domain. To demonstrate the power of our approach in extracting valuable information from the expression data, we independently applied it to large-scale experiments carried out on the yeast and malaria parasite whole transcriptomes. The parameters extracted from the sparse reconstruction of the expression profiles, when fed to a supervised learning platform, were used to successfully predict the relationships between genes throughout the Gene Ontology hierarchy and protein-protein interaction map. Extensive assessment of the biological results shows high accuracy in both recovering known predictions and in yielding accurate predictions missing from the current databases. We suggest that the geometrical approach presented here is suitable for a broad range of high-dimensional experimental data. © The Author 2011. Published by Oxford University Press. All rights reserved.
Geometric Interpretation of Gene Expression by Sparse Reconstruction of Transcript Profiles
Large-scale data collection technologies have come to play a central role in biological and biomedical research in the last decade. Consequently, it has become a major goal of functional genomics to develop, based on such data, a comprehensive description of the functions and interactions of all genes and proteins in a genome. Most large-scale biological data, including gene expression profiles, are usually represented by a matrix, where n genes are examined in d experiments. Here, we view such data as a set of n points (vectors) in d-dimensional space, each of which represents the profile of a given gene over d different experimental conditions. Many known methods that have yielded meaningful biological insights seek geometric or algebraic features of these vectors.