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The purpose of this article is to reconcile the hypotheses that: (1) brain evolution occurred due to a change in diet, and (2) it occurred due to pressures related to understanding more and more about the underlying causes, such as understanding increasingly complex manipulative and cooperative intentions on the part of the other, as well as understanding reality itself (and how to interact with it beyond group issues). I argue that the ingestion of fat, a highly energy-efficient food, would have unlocked the evolutionary process that culminated in the emergence of the practice of reasoning about underlying causes; and that the consolidation of such a practice resulted in a continuous pressure to increase cognition about "whys"; so that many explanations ended up imposing the need for additional ones, and with that came a high level of awareness and the need for the brain to evolve not only in terms of providing a higher level of cognition but also in size., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Causal reasoning is a fundamental cognitive ability that enables individuals to learn about the complex interactions in the world around them. However, the mechanisms that underpin causal reasoning are not well understood. For example, it remains unresolved whether children's causal inferences are best explained by Bayesian inference or associative learning. The two experiments and computational models reported here were designed to examine whether 5- and 6-year-olds will retrospectively reevaluate objects-that is, adjust their beliefs about the causal status of some objects presented at an earlier point in time based on the observed causal status of other objects presented at a later point in time-when asked to reason about 3 and 4 objects and under varying degrees of information processing demands. Additionally, the experiments and models were designed to determine whether children's retrospective reevaluations were best explained by associative learning, Bayesian inference, or some combination of both. The results indicated that participants retrospectively reevaluated causal inferences under minimal information-processing demands (Experiment 1) but failed to do so under greater information processing demands (Experiment 2) and that their performance was better captured by an associative learning mechanism, with less support for descriptions that rely on Bayesian inference. RESEARCH HIGHLIGHTS: Five- and 6-year-old children engage in retrospective reevaluation under minimal information-processing demands (Experiment 1). Five- and 6-year-old children do not engage in retrospective reevaluation under more extensive information-processing demands (Experiment 2). Across both experiments, children's retrospective reevaluations were better explained by a simple associative learning model, with only minimal support for a simple Bayesian model. These data contribute to our understanding of the cognitive mechanisms by which children make causal judgements., (© 2023 John Wiley & Sons Ltd.)

We are said to experience insight when we suddenly and unexpectedly become aware of the solution to a problem that we previously took ourselves to be unable to solve. In the field of comparative cognition, there is rising interest in the question of whether non-human animals are capable of insightful problem-solving. Putative cases of animals demonstrating insight have generally attracted two types of criticism: first, that insight is being conflated with other cognitive capacities (e.g., causal cognition, or mental trial and error); and, second, that the relevant performances merely reflect associative learning-and on the received understanding of insight within comparative cognition, insight necessarily involves non-associative processes. I argue that even if we grant that some cases of animal insight do withstand these two criticisms, these cases of purported animal insight cannot shed light on the nature of insightful problem-solving in humans. For the phenomenon studied by cognitive psychologists under the heading of insight is fundamentally different from that studied in comparative cognition. In light of this impasse, I argue that the reinterpretation of the extant research on animal insight in terms of other high-level cognitive capacities (means-end reasoning in particular) can improve the prospect of a successful comparative research program., (© 2024. The Author(s).)

Planning underpins the impressive flexibility of goal-directed behavior. However, even when planning, people can display surprising rigidity in how they think about problems (e.g., "functional fixedness") that lead them astray. How can our capacity for behavioral flexibility be reconciled with our susceptibility to conceptual inflexibility? We propose that these tendencies reflect avoidance of two cognitive costs: the cost of representing task details and the cost of switching between representations. To test this hypothesis, we developed a novel paradigm that affords participants opportunities to choose different families of simplified representations to plan. In two preregistered, online studies ( N s = 377 and 294 adults), we found that participants' optimal behavior, suboptimal behavior, and reaction time were explained by a computational model that formalized people's avoidance of representational complexity and switching. These results demonstrate how the selection of simplified, rigid representations leads to the otherwise puzzling combination of flexibility and inflexibility observed in problem solving.

Reasoning about causal relations is essential for children's early cognitive development. The current study investigated 4-year-olds' (N = 58) reasoning about complex causal physical interactions in terms of predicting the endpoint of motion. In an online task, children were presented with four configurations that involved different interactions of forces and consequently different patterns of motion. These were Cause (one force moving an object), Enable (a secondary force promoting the motion), Prevent-180° (an opposing force hindering the motion), and Prevent-90° (two-dimensional; a perpendicular force altering the motion). Each prediction was made in terms of either the Distance or Direction of the motion, which was novel in this task compared with previous assessments. Results revealed differences between the configurations, with Cause being the easiest and Prevent-90° being the most difficult to predict. Furthermore, predictions were more accurate when options were about the motion's Direction, whereas Distance options may have aggravated reasoning. The current study extends previous findings on children's intuitive physics and causal cognition by showing that accuracy in reasoning not only is dependent on the number of forces and dimensions at work but also interacts with estimating the motion's Distance and Direction., (Copyright © 2023 Elsevier Inc. All rights reserved.)

My first 30-odd years of research in cognitive science has been driven by an attempt to balance two facts about human thought that seem incompatible and two corresponding ways of understanding information processing. The facts are that, on one hand, human memories serve as sophisticated pattern recognition devices with great flexibility and an ability to generalize and predict as long as circumstances remain sufficiently familiar. On the other hand, we are capable of deploying an enormous variety of representational schemes that map closely onto articulable structure in the world and that support explanation even in unfamiliar circumstances. The contrasting ways of modeling such processes involve, first, more and more sophisticated associative models that capture progressively higher-order statistical structure and, second, more powerful representational languages for other sorts of structure, especially compositional and causal structure. My efforts to rectify these forces have taken me from the study of memory to induction and category knowledge to causal reasoning. In the process, I have consistently appealed to dual systems of thinking. I have come to realize that a key reason for our success as cognizers is that we rely on others for most of our information processing needs; we live in a community of knowledge. We make use of others both intuitively-by outsourcing much of our thinking without knowing we are doing it-and by deliberating with others., (© 2021 Cognitive Science Society LLC.)

People naturally intuit that an agent's ethereal thoughts can cause its body to move. Per intuitive physics; however, one body can only interact with another. Are people, then, covertly puzzled by the capacity of thoughts to command the body? Experiment 1 first confirms that thoughts (e.g., thinking about a cup) are indeed perceived as ethereal-as less detectible in the body (brain), and more likely to exist in the afterlife relative to matched percepts (e.g., seeing a cup). Experiments 2-5 show that thoughts are considered less likely to cause behavior than percepts (e.g., thinking of a cup vs. seeing one). Furthermore, mind-body causation is more remarkable when its bodily consequences are salient (e.g., moving an arm vs. brain activation). Finally, epistemic causes are remarkable only when they are ascribed to mental- (e.g., "thinking") but not to physical states ("activation"). Together, these results suggest that mind-body interactions elicit a latent dualist dissonance., (© 2021 Cognitive Science Society LLC.)

We examined 2- and 3-year-old children's ability to use second-order correlation learning-in which a learned correlation between two pairs of features (e.g., A and B, A and C) is generalized to the noncontiguous features (i.e., B and C)-to make causal inferences. Previous findings showed that 20- and 26-month-old children can use second-order correlation learning to learn about static and dynamic features in category and noncategory contexts. The current behavioral study and computational model extend these findings to show that 2- and 3-year-olds can detect the second-order correlation between an object's surface feature and its capacity to activate a novel machine, but only if the children had encoded the first-order correlations on which the second-order correlation was based. These results have implications for children's developing information-processing capacities on their ability to use second-order correlations to infer causal relations in the world., (Published by Elsevier Inc.)

The development of tool innovation presents a paradox. How do humans have such diverse and complex technology, ranging from smartphones to aircraft, and yet young children find even simple tool innovation challenges, such as fashioning a hook to retrieve a basket from a tube, remarkably difficult? We propose that the solution to this paradox is the cognitive ontogenesis of tool innovation. Using a common measure of children's tool innovation, we describe how multiple cognitive mechanisms work in concert at each step of its process: recognizing the problem, generating appropriate solutions, and the social transmission of innovations. We discuss what the ontogeny of this skill tells us about cognitive and cultural evolution and provide recommendations for future research., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

According to the dominant view of category representation, people preferentially infer that kinds (richly structured categories) reflect essences. Generic language ("Boys like blue") often occupies the central role in accounts of the formation of essentialist interpretations-especially in the context of social categories. In a preregistered study ( n = 240 American children, ages 4 to 9 y), we tested whether children assume essences in the presence of generic language or whether they flexibly assume diverse causal structures. Children learned about a novel social category described with generic statements containing either biological properties or cultural properties. Although generic language always led children to believe that properties were nonaccidental, young children (4 or 5 y) in this sample inferred the nonaccidental structure was socialization. Older children (6 to 9 y) flexibly interpreted the category as essential or socialized depending on the type of properties that generalized. We uncovered early-emerging flexibility and no privileged link between kinds and essences., Competing Interests: The authors declare no competing interest., (Copyright © 2020 the Author(s). Published by PNAS.)

People often prefer simple to complex explanations because they generally have higher prior probability. However, simpler explanations are not always normatively superior because they often do not account for the data as well as complex explanations. How do people negotiate this trade-off between prior probability (favoring simple explanations) and goodness-of-fit (favoring complex explanations)? Here, we argue that people use opponent heuristics to simplify this problem-that people use simplicity as a cue to prior probability but complexity as a cue to goodness-of-fit. Study 1 finds direct evidence for this claim. In subsequent studies, we examine factors that lead one or the other heuristic to predominate in a given context. Studies 2 and 3 find that people have a stronger simplicity preference in deterministic rather than stochastic contexts, while Studies 4 and 5 find that people have a stronger simplicity preference for physical rather than social causal systems, suggesting that people use abstract expectations about causal texture to modulate their explanatory inferences. Together, we argue that these cues and contextual moderators act as powerful constraints that can help to specify the otherwise ill-defined problem of what distributions to use in Bayesian hypothesis comparison., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Teleological explanations, which appeal to a function or purpose (e.g., "kangaroos have long tails for balance"), seem to play a special role within the biological domain. We propose that such explanations are compelling because they are evaluated on the basis of a salient cue: structure-function fit, or the correspondence between a biological feature's form (e.g., tail length) and its function (e.g., balance). Across five studies with 852 participants in total, we find support for three predictions that follow from this proposal. First, we find that function information decreases reliance on mechanistic considerations when evaluating explanations (Experiments 1-3), indicating the presence of a salient, function-based cue. Second, we demonstrate that structure-function fit is the best candidate for this cue (Experiments 3-4). Third, we show that scientifically-unwarranted teleological explanations are more likely to be accepted under speeded and unspeeded conditions when they are high in structure-function fit (Experiment 5). Experiment 5 also finds that structure-function fit extends beyond biology to teleological explanations in other domains. Jointly, these studies provide a new account of how teleological explanations are evaluated and why they are often (but not universally) compelling., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Children aged 5 through 9 years and adults judged the reality status of parallel mundane, improbable, and extraordinary events, generated an explanation for each event, and evaluated explanations purportedly generated by other participants. Participants of all ages claimed that mundane and improbable events could happen, whereas extraordinary events could not. Participants also overwhelmingly generated natural explanations for all three types of events but did so most for mundane, less for improbable, and least for extraordinary events. Supernatural explanations followed the reverse pattern, indicating that an event's possibility affects explanation. Participants also evaluated natural explanations most favorably and evaluated claims that there was no explanation for an event least favorably across all story types. However, significant rejection of the "no explanation" claim did not emerge until age 8 years, indicating that age affects acceptance of the idea that some events might be unexplainable.

Much of cognition allows us to make sense of things by explaining observable evidence in terms of unobservable explanations, such as category memberships and hidden causes. Yet we must often make such explanatory inferences with incomplete evidence, where we are ignorant about some relevant facts or diagnostic features. In seven experiments, we studied how people make explanatory inferences under these uncertain conditions, testing the possibility that people attempt to infer the presence or absence of diagnostic evidence on the basis of other cues such as evidence base rates (even when these cues are normatively irrelevant) and then proceed to make explanatory inferences on the basis of the inferred evidence. Participants followed this strategy in both diagnostic causal reasoning (Experiments 1-4, 7) and in categorization (Experiments 5-6), leading to illusory inferences. Two processing predictions of this account were also confirmed, concerning participants' evidence-seeking behavior (Experiment 4) and their beliefs about the likely presence or absence of the evidence (Experiment 5). These findings reveal deep commonalities between superficially distinct forms of diagnostic reasoning-causal reasoning and classification-and point toward common inferential machinery across explanatory tasks., (Copyright © 2016 Elsevier Inc. All rights reserved.)

We investigated how people design interventions to affect the outcomes of causal systems. We propose that the abstract structural properties of a causal system, in addition to people's content and mechanism knowledge, influence decisions about how to intervene. In Experiment 1, participants preferred to intervene at specific locations (immediate causes, root causes) in a causal chain regardless of which content variables occupied those positions. In Experiment 2, participants were more likely to intervene on root causes versus immediate causes when they were presented with a long-term goal versus a short-term goal. These results show that the structural properties of a causal system can guide the design of interventions., (Copyright © 2014 Cognitive Science Society, Inc.)

Natural and supernatural explanations are used to interpret the same events in a number of predictable and universal ways. Yet little is known about how variation in diverse cultural ecologies influences how people integrate natural and supernatural explanations. Here, we examine explanatory coexistence in three existentially arousing domains of human thought: illness, death, and human origins using qualitative data from interviews conducted in Tanna, Vanuatu. Vanuatu, a Melanesian archipelago, provides a cultural context ideal for examining variation in explanatory coexistence due to the lack of industrialization and the relatively recent introduction of Christianity and Western education. We argue for the integration of interdisciplinary methodologies from cognitive science and anthropology to inform research on explanatory coexistence., (Copyright © 2015 Cognitive Science Society, Inc.)

The transition to a cooked diet represents an important shift in human ecology and evolution. Cooking requires a set of sophisticated cognitive abilities, including causal reasoning, self-control and anticipatory planning. Do humans uniquely possess the cognitive capacities needed to cook food? We address whether one of humans' closest relatives, chimpanzees (Pan troglodytes), possess the domain-general cognitive skills needed to cook. Across nine studies, we show that chimpanzees: (i) prefer cooked foods; (ii) comprehend the transformation of raw food that occurs when cooking, and generalize this causal understanding to new contexts; (iii) will pay temporal costs to acquire cooked foods; (iv) are willing to actively give up possession of raw foods in order to transform them; and (v) can transport raw food as well as save their raw food in anticipation of future opportunities to cook. Together, our results indicate that several of the fundamental psychological abilities necessary to engage in cooking may have been shared with the last common ancestor of apes and humans, predating the control of fire., (© 2015 The Author(s) Published by the Royal Society. All rights reserved.)

Currently, two frameworks of causal reasoning compete: Whereas dependency theories focus on dependencies between causes and effects, dispositional theories model causation as an interaction between agents and patients endowed with intrinsic dispositions. One important finding providing a bridge between these two frameworks is that failures of causes to generate their effects tend to be differentially attributed to agents and patients regardless of their location on either the cause or the effect side. To model different types of error attribution, we augmented a causal Bayes net model with separate error sources for causes and effects. In several experiments, we tested this new model using the size of Markov violations as the empirical indicator of differential assumptions about the sources of error. As predicted by the model, the size of Markov violations was influenced by the location of the agents and was moderated by the causal structure and the type of causal variables., (Copyright © 2014 Cognitive Science Society, Inc.)

Many theories of causal learning and causal induction differ in their assumptions about how people combine the causal impact of several causes presented in compound. Some theories propose that when several causes are present, their joint causal impact is equal to the linear sum of the individual impact of each cause. However, some recent theories propose that the causal impact of several causes needs to be combined by means of a noisy-OR integration rule. In other words, the probability of the effect given several causes would be equal to the sum of the probability of the effect given each cause in isolation minus the overlap between those probabilities. In the present series of experiments, participants were given information about the causal impact of several causes and then they were asked what compounds of those causes they would prefer to use if they wanted to produce the effect. The results of these experiments suggest that participants actually use a variety of strategies, including not only the linear and the noisy-OR integration rules, but also averaging the impact of several causes.

Young children spend a large portion of their time pretending about non-real situations. Why? We answer this question by using the framework of Bayesian causal models to argue that pretending and counterfactual reasoning engage the same component cognitive abilities: disengaging with current reality, making inferences about an alternative representation of reality, and keeping this representation separate from reality. In turn, according to causal models accounts, counterfactual reasoning is a crucial tool that children need to plan for the future and learn about the world. Both planning with causal models and learning about them require the ability to create false premises and generate conclusions from these premises. We argue that pretending allows children to practice these important cognitive skills. We also consider the prevalence of unrealistic scenarios in children's play and explain how they can be useful in learning, despite appearances to the contrary., (© 2013 Cognitive Science Society, Inc.)

We conducted two in vivo studies to explore how scientists respond to anomalies. Based on prior research, we identify three candidate strategies: mental simulation, mental manipulation of an image, and comparison between images. In Study 1, we compared experts in basic and applied domains (physics and meteorology). We found that the basic scientists used mental simulation to resolve an anomaly, whereas applied science practitioners mentally manipulated the image. In Study 2, we compared novice and expert meteorologists. We found that unlike experts, novices used comparison to address anomalies. We discuss the nature of expertise in the two kinds of science, the relationship between the type of science and the task performed, and the relationship of the strategies investigated to scientific creativity., (Copyright © 2009 Cognitive Science Society, Inc.)

Large language models (LLMs) have shown their capabilities in numerical and logical reasoning, yet their capabilities in higher-order cognitive tasks, particularly causal reasoning, remain less explored. Current research on LLMs in causal reasoning has focused primarily on tasks such as identifying simple cause-effect relationships, answering basic "what-if" questions, and generating plausible causal explanations. However, these models often struggle with complex causal structures, confounding variables, and distinguishing correlation from causation. This work addresses these limitations by systematically evaluating LLMs' causal reasoning abilities across three representative scenarios, namely analyzing causation from effects, tracing effects back to causes, and assessing the impact of interventions on causal relationships. These scenarios are designed to challenge LLMs beyond simple associative reasoning and test their ability to handle more nuanced causal problems. For each scenario, we construct four paradigms and employ three types of prompt scheme, namely zero-shot prompting, few-shot prompting, and Chain-of-Thought (CoT) prompting in a set of 36 test cases. Our findings reveal that most LLMs encounter challenges in causal cognition across all prompt schemes, which underscore the need to enhance the cognitive reasoning capabilities of LLMs to better support complex causal reasoning tasks. By identifying these limitations, our study contributes to guiding future research and development efforts in improving LLMs' higher-order reasoning abilities. [ABSTRACT FROM AUTHOR]

Young children tend to deny the possibility of events that violate their expectations, including events that are merely improbable, like making onion-flavored ice cream or owning a crocodile as a pet. Could this tendency be countered by teaching children more valid strategies for judging possibility? We explored this question by training children aged 4–12 (n = 128) to consider either the similarity between the target event and unusual events that have actually occurred or causal mechanisms that might bring the target event about. Both trainings increased children's acceptance of improbable events but only for the types of events addressed during training. Older children were more likely to accept improbable events, as were children who scored higher on a measure of cognitive reflection, but neither age nor cognitive reflection moderated the effects of training. These findings indicate that children can use both similarity and causality to assess possibility, but the use of this information is highly circumscribed, further demonstrating how robustly children conflate improbability with impossibility. Public Significance Statement: Children tend to claim that unusual events are impossible, and this study explored the source of that tendency by training them to consider real events similar to the target events or mechanisms that cause the target events to occur. We find that both types of information increase children's acceptance of unusual events but only for events similar to those covered during training, which suggests that children appreciate the kinds of considerations relevant to possibility but do not seek out those considerations on their own. [ABSTRACT FROM AUTHOR]

Imitation learning allows social robots to learn new skills from human teachers without substantial manual programming, but it is difficult for robotic imitation learning systems to generalize demonstrated skills as well as human learners do. Contemporary neurocomputational approaches to imitation learning achieve limited generalization at the cost of data-intensive training, and often produce opaque models that are difficult to understand and debug. In this study, we explore the viability of developing purely-neural controllers for social robots that learn to imitate by reasoning about the underlying intentions of demonstrated behaviors. We present a novel hypothetico-deductive reasoning algorithm that combines bottom-up abductive inference with top-down predictive verification and captures important aspects of human causal reasoning that are relevant to a broad range of cognitive domains. We also present NeuroCERIL, a neurocognitive architecture that implements this algorithm using only neural computations, and produces generalizable and human-readable explanations for demonstrated behavior. Our empirical results demonstrate that NeuroCERIL can learn various procedural skills in a simulated robotic imitation learning domain. We also show that its causal reasoning procedure is computationally efficient, and that its memory use is dominated by highly transient short-term memories, much like human working memory. We conclude that NeuroCERIL is a viable neural model of human-like imitation learning that can improve human-robot collaboration and contribute to investigations of the neurocomputational basis of human cognition. [ABSTRACT FROM AUTHOR]

Causal cognition – how we perceive, represent and reason about causal events – are fundamental to the human mind, but it has rarely been approached in its cultural specificity. Here, we investigate this core concept among Wichi people, an indigenous group living in Chaco Forest. We focus on the Wichi, because their epistemological orientations and explanatory frameworks about ecosystem differ importantly from those documented among most Western majority-culture populations. We asked participants to reason about causes of events that involve the hunhat lheley (inhabitants of the earth: humans, non-human animals, plants and spiritual beings) and other entities of their ecosystem (e.g., lagoon). We find a native ontological framework that encompasses three interacting organizing principles. This new evidence highlights ways in which native categories guide causal reasoning. Our research challenge long-held assumptions that dichotomies – nature-culture or natural-supernatural – are universal features of the human mind. [ABSTRACT FROM AUTHOR]

How to best understand how animals think about the world? Considering that cognition develops in interactions with the outside world, we need to compare the cognitive performance of individuals of the same species living under different ecological conditions. I propose here to compare the notion of weight studied in captive chimpanzees to the ones observed in a wild chimpanzee population that is using hammers of different weights to crack nuts during many months of the year. Detailing different situations where the chimpanzees have to select a hammer for cracking two species of nuts of different hardness, my colleagues and I could illustrate the flexibility of the causal reasoning performed by the chimpanzees, including stable anticipation of weight, combining weight with density and transport distance, and the gradual shift of selection criteria with increasing transport distance. Combined with results from captive studies on the notion of weight in chimpanzees, this suggests that when individuals face specific ecological challenges, they may acquire sophisticated causal reasoning generalized over different contexts. The evolution of cognition in chimpanzees suggests a complex interplay with ecology, memory demands, individual differences, and technical challenges important to the population.

Stereotypes are cognitive shortcuts that facilitate efficient social judgments about others. Just as causal attributions affect perceptions of people, they may similarly affect perceptions of technology, particularly anthropomorphic technology such as robots. In a scenario-based study, younger and older adults judged the performance and capability of an anthropomorphised robot that appeared young or old. In some cases, the robot successfully performed a task while at other times it failed. Results showed that older adult participants were more susceptible to aging stereotypes as indicated by trust. In addition, both younger and older adult participants succumbed to aging stereotypes when measuring perceived capability of the robots. Finally, a summary of causal reasoning results showed that our participants may have applied aging stereotypes to older-appearing robots: they were most likely to give credit to a properly functioning robot when it appeared young and performed a cognitive task. Our results tentatively suggest that human theories of social cognition do not wholly translate to technology-based contexts and that future work may elaborate on these findings. Practitioner summary: Perception and expectations of the capabilities of robots may influence whether users accept and use them, especially older users. The current results suggest that care must be taken in the design of these robots as users may stereotype them. [ABSTRACT FROM AUTHOR]

When faced with entities with potentially ambiguous category membership, adult category judgments are strongly biased toward dangerous and distinctive properties. For example, a cyanide-water mixture is categorized as cyanide. We used a developmental approach to better understand this cross-domain effect, which we term the asymmetric categorization of mixtures (ACM). According to ACM, attention is biased toward perceived dangerous or distinctive properties, making them prominent in conceptual representation. We consider whether ACM is driven entirely by low-level processes of attention, or whether ACM might require the integration of attention with causal-explanatory reasoning. We argue that ACM requires forms of reasoning that only emerge robustly in middle childhood. Across three studies (N = 270), we find that ACM emerges only after the 7th year for liquids (Studies 1 through 3), and even later for race (Studies 1 and 3). Results are discussed in terms of competing theoretical accounts of ACM. [ABSTRACT FROM AUTHOR]

Institutions make new forms of acting possible: Signing executive orders, scoring goals, and officiating weddings are only possible because of the U.S. government, the rules of soccer, and the institution of marriage. Thus, when an individual occupies a particular social role (president, soccer player, and officiator), they acquire new ways of acting on the world. The present studies investigated children's beliefs about institutional actions, and in particular whether children understand that individuals can only perform institutional actions when their community recognizes them as occupying the appropriate social role. Two studies (Study 1, N = 120 children, ages 4-11; Study 2, N = 90 children, ages 4-9) compared institutional actions to standard actions that do not depend on institutional recognition. In both studies, 4- to 5-year-old children believed all actions were possible regardless of whether an individual was recognized as occupying the social role. In contrast, 8- to 9-year-old children robusdy distinguished between institutional and standard actions; they understood that institutional actions depend on collective recognition by a community. [ABSTRACT FROM AUTHOR]

The last several decades of research on avian cognition have revealed surprising parallels between the abilities of birds — most notably corvids — and great apes. Parrots, albeit far less studied, are cited alongside corvids as "feathered apes", but are these two taxa really that similar cognitively? In this review we aim to take a step back and present the broader picture, focusing on areas where there is now data from both parrots and corvids to facilitate first comparisons on a somewhat wider scale. By charting these birds' performance in cognitive tasks, in many of which corvids perform on par with primates, we hope to highlight understudied areas and promising directions for future research. In reviewing the literature, the general pattern that emerges shows that different corvid and parrot species indeed perform similarly in a range of cognitive tasks to the extent that one may call them "feathered apes". [ABSTRACT FROM AUTHOR]

Previous work has demonstrated a “side-effect effect,” such that intentionality is more likely to be attributed to agents who bring about negatively valenced as opposed to positively valenced side effects. The rational-scientist model explains this by suggesting that norm-violating side effects are more informative for inferring intentionality than norm-conforming side effects. In the present study we reexamined this account, addressing limitations of previous empirical tests (e.g., Uttich & Lombrozo, Cognition 116: 87-100, 2010). Side-effect valence and norm status were manipulated factorially, enabling an examination of the impact of norm status on intentionality judgments in both positively and negatively valenced side effects. Additionally, the impact of side-effect norm status on the perceived valences of side effects and agents was examined. Effects of norm status were found for both positive and negative side effects. Violation of an ostensibly neutral norm led to negative perceptions of the side effect. However, a norm status effect on intentionality judgments persisted when these effects were controlled. These results support the view that the side-effect effect is the result of the rational use of social-cognitive evidence. [ABSTRACT FROM AUTHOR]

Causal cognition is a key part of human learning, reasoning, and decision-making. In particular, people are capable of learning causal relations from data, and then reasoning and planning using those cognitive representations. While there has been significant normative work on the causal structures that ought to be learned from evidence, there has been relatively little on the functional forms that should (normatively) be used or learned for those qualitative causal relations. Moreover, empirical research on causal inference--learning causal relations from observations and interventions--has found support for multiple different functional forms for causal connections. This paper argues that a combination of conceptual and mathematical constraints leads to a privileged (default) functional form for causal relations. This privileged function is shown to provide a theoretical unification of the widely-used noisy-OR/AND models and linear models, thereby showing how they are complementary rather than competing. This unification thus helps to explain the diverse empirical results, as these different functional forms are "merely" special cases of the more general, more privileged function. [ABSTRACT FROM AUTHOR]

A common intuition, often captured in fiction, is that some impossible events (e.g., levitating a stone) are 'more impossible' than others (e.g., levitating a feather). We investigated the source of this intuition, hypothesizing that graded notions of impossibility arise from explanatory considerations logically precluded by the violation at hand but still taken into account. Studies 1-4 involved college undergraduates ( n = 357), and Study 5 involved preschool-aged children ( n = 32). In Studies 1 and 2, participants saw pairs of magical spells that violated one of 18 causal principles-six physical, six biological, and six psychological-and were asked to indicate which spell would be more difficult to learn. Both spells violated the same causal principle but differed in their relation to a subsidiary principle. Participants' judgments of spell difficulty honored the subsidiary principle, even when participants were given the option of judging the two spells equally difficult. Study 3 replicated those effects with Likert-type ratings; Study 4 replicated them in an open-ended version of the task in which participants generated their own causal violations; and Study 5 replicated them with children. Taken together, these findings suggest that events that defy causal explanation are interpreted in terms of explanatory considerations that hold in the absence of such violations. [ABSTRACT FROM AUTHOR]

This is the first in a series of essays that addresses the manifest programmatic interest in developing intelligent systems that help people make good decisions in messy, complex, and uncertain circumstances by exploring several questions: What is an explanation? How do people explain things? How might intelligent systems explain their workings? How might intelligent systems help humans be better understanders as well as better explainers? This article addresses the theoretical foundations. [ABSTRACT FROM AUTHOR]

We conducted five sets of experiments asking whether psychological and physical events are construed in broadly different manners concerning the underlying textures of their causes. In Experiments 1a-1d, we found a robust tendency to estimate fewer causes (but not effects) for psychological than for physical events; Experiment 2 showed a similar pattern of results when participants were asked to generate hypothetical causes and effects; Experiment 3 revealed a greater tendency to ascribe linear chains of causes (but not effects) to physical events; Experiment 4 showed that the expectation of linear chains was related to intuitions about deterministic processes; and Experiment 5 showed that simply framing a given ambiguous event in psychological versus physical terms is sufficient to induce changes in the patterns of causal inferences. Adults therefore consistently show a tendency to think about psychological and physical events as being embedded in different kinds of causal structures. [ABSTRACT FROM AUTHOR]

Christopher Gauker proposes that all cognition can be divided into nonconceptual image-based thought and conceptual language-based thought. The division between the two hinges on the representational powers of their respective mediums. I argue that a richer variety of representational states and processes is necessary in order to explain both human and nonhuman cognition. There are aspects of nonhuman cognition that cannot be explained simply by images, and there are aspects of human conceptual thought, particularly those dealing with causal reasoning, that push us towards positing abstract systems of representation that are neither imagistic nor linguistic. Conceptual thought, then, occurs in its own proprietary inner code, not in images or words. [ABSTRACT FROM PUBLISHER]

In a series of 4 experiments, we tested children’s understanding that the causes of their actions must necessarily be attributed to information known prior to (i.e., “pre-action” information), rather than after (i.e., “post-action” information), the completion of their actions. For example, children were shown a dog, asked to get some cheese to feed the dog, and then returned to discover a mouse. In Experiment 1, the majority of 3-, 4-, and 5-year-olds claimed that they had gotten the cheese to feed the mouse. In Experiments 2 and 3, we ruled out the possibilities that (1) children had forgotten the critical “pre-action” information (e.g., “dog”), and (2) children had merely attributed the cause of their action to the most recent item (e.g., “dog”) that they had seen. Finally, in Experiment 4, we determined that 7-year-olds, but not 6-year-olds, correctly attributed the cause of their action to the pre-action information, suggesting that this is the age at which children are no longer influenced by associative post-action information when explaining the causes of their actions. These results are discussed in terms of their relevance for causal reasoning, action explanation, and memory. [ABSTRACT FROM AUTHOR]