Causal Cognition Research Articles

Agency at a distance: learning causal connections

AbstractIn a series of papers, we have argued that causal cognition has coevolved with the use of various tools. Animals use tools, but only as extensions of their own bodies, while humans use tools that act at a distance in space and time. This means that we must learn new types of causal mappings between causes and effects. The aim of this article is to account for what is required for such learning of causal relations. Following a proposal by Grush and Springle, we argue that learning of inverse mappings from effects to causes is central. Learning such mappings also involves constraints based on monotonicity, continuity and convexity. In order for causal thinking to extend beyond space and time, mental simulations are required that predict the effects of actions. More advanced forms of causal reasoning involve more complicated forms of simulations.

Reasoning about actual causation in reversible and irreversible causal structures.

This article investigates people's judgments of actual causation in the context of a previously neglected property of causal structures-their reversibility, that is, whether an effect persists or returns to its original state if its causes are removed. Causal reversibility, and its potential impact on causal judgment, was recently analyzed theoretically by Ross and Woodward (2022). They hypothesized that reversibility might affect people's evaluation of causes in late-preemption scenarios. The typical finding in preemption scenarios is that events happening earlier are considered to be actual causes, while events happening later are regarded as noncauses. The hypothesis is that this robust intuition depends on causal reversibility and that in reversible structures later events are regarded as actual causes. Across three main experiments and one supplementary study (N = 590), it is shown that reversibility has the predicted effect: later causes are perceived to make an actual causal contribution to the effect. It is also shown that Henne et al. (2023), in a first study, did not find evidence for Ross and Woodward's hypothesis because they did not test whether people regard later causes in preemption-like sequences of reversible structures as maintainers and not as triggers of their effect. Because they used test questions that asked explicitly for triggering rather than maintaining or were at least ambiguous, their results seemed to show that people think that later events have no causal impact. Maintaining is a relevant causal concept deserving more attention in both philosophical theories and psychological studies on causal cognition. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Unraveling Moral Evaluations: A Qualitative Exploration of Young Individuals’ Causal Beliefs and Moral Emotions

This article presents an empirical examination of attribution and appraisal theories, focusing on young individuals’ beliefs about causality and feelings toward two social issues impacting the welfare of others: food insecurity and opioid addiction. Using a deductive qualitative approach, this investigation deepens and expands our theoretical understanding of how causal beliefs shape moral evaluations and corresponding emotions. Through focus group discussions ( N = 9), participants revealed distinct patterns of causal cognitions and specific negative, other-oriented moral emotions. Causes of individuals’ hardships were characterized along four themes (personal choices, inherited circumstances, systemic issues, unexpected events), linking beliefs with corresponding discrete emotional experiences resembling contempt, pity, resentment, and compassion. Findings provide a description of attributions that supports a nuanced understanding of the complex relationship between causality and emotion, thus contributing to the current theoretical perspective. Practical implications and opportunities for further investigation are discussed in view of the broader goal of developing strategies to amplify civic engagement.

The irreconcilability of insight

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.

Language models and psychological sciences.

Large language models (LLMs) are demonstrating impressive performance on many reasoning and problem-solving tasks from cognitive psychology. When tested, their accuracy is often on par with average neurotypical adults, challenging long-standing critiques of associative models. Here we analyse recent findings at the intersection of LLMs and cognitive science. Here we discuss how modern LLMs resurrect associationist principles, with abilities like long-distance associations enabling complex reasoning. While limitations remain in areas like causal cognition and planning, phenomena like emergence suggest room for growth. Providing examples and increasing the dimensions of the network are methods that further improve LLM abilities, mirroring facilitation effects in human cognition. Analysis of LLMs errors provides insight into human cognitive biases. Overall, we argue LLMs represent a promising development for cognitive modelling, enabling new explorations of the mechanisms underlying intelligence and reasoning from an associationist point of view. Carefully evaluating LLMs with the tools of cognitive psychology will further understand the building blocks of the human mind.

The causal mind: An affordance-based account of causal engagement

Causal cognition is a core aspect of how we deal with the world; however, existing psychological theories tend not to target intuitive causal engagement that is done in daily life. To fill this gap, we propose an Ecological-Enactive (E-E) affordance-based account of situated causal engagement, that is, causal judgments and perceptions. We develop this account to improve our understanding of this way of dealing with the world, which includes making progress on the causal selection problem, and to extend the scope of embodied cognitive science to causal cognition. We characterize identifying causes as selectively attending to the relevant ecological information to engage with relevant affordances, where these affordances are dependent on individual abilities. Based on this we construe causal engagement as based on a learned skill. Moreover, we argue that to understand judgments of causation as we make them in our daily lives, we need to see them as situated in sociocultural practices. Practices are about doing, and so this view helps us understand why people make these judgments so ubiquitously: to get things done, to provide an effective path to intervening in the world. Ultimately this view on causal engagement allows us to account for individual differences in causal perceptions, judgments, and selections by appealing to differences in learned skills and sociocultural practices.

Combining forces for causal reasoning: Children’s predictions about physical interactions

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.

Active causal structure learning in continuous time

Research on causal cognition has largely focused on learning and reasoning about contingency data aggregated across discrete observations or experiments. However, this setting represents only the tip of the causal cognition iceberg. A more general problem lurking beneath is that of learning the latent causal structure that connects events and actions as they unfold in continuous time. In this paper, we examine how people actively learn about causal structure in a continuous-time setting, focusing on when and where they intervene and how this shapes their learning. Across two experiments, we find that participants’ accuracy depends on both the informativeness and evidential complexity of the data they generate. Moreover, participants’ intervention choices strike a balance between maximizing expected information and minimizing inferential complexity. People time and target their interventions to create simple yet informative causal dynamics. We discuss how the continuous-time setting challenges existing computational accounts of active causal learning, and argue that metacognitive awareness of one’s inferential limitations plays a critical role for successful learning in the wild.

The Paradox of Time in Dynamic Causal Systems.

Recent work has shown that people use temporal information including order, delay, and variability to infer causality between events. In this study, we build on this work by investigating the role of time in dynamic systems, where causes take continuous values and also continually influence their effects. Recent studies of learning in these systems explored short interactions in a setting with rapidly evolving dynamics and modeled people as relying on simpler, resource-limited strategies to grapple with the stream of information. A natural question that arises from such an account is whether interacting with systems that unfold more slowly might reduce the systematic errors that result from these strategies. Paradoxically, we find that slowing the task indeed reduced the frequency of one type of error, albeit at the cost of increasing the overall error rate. To explain these results we posit that human learners analyze continuous dynamics into discrete events and use the observed relationships between events to draw conclusions about causal structure. We formalize this intuition in terms of a novel Causal Event ion model and show that this model indeed captures the observed pattern of errors. We comment on the implications these results have for causal cognition.

A cognitive account of manipulative sympathetic magic

ABSTRACT Frazer’s theory of sympathetic magic has been extremely influential in both anthropology and comparative religion, yet the manipulative aspect has not been adequately theorized. In this paper, I formalize sympathetic magical action and offer a naturalistic explanation of manipulative sympathetic magic by attributing it to a combination of environmental regularities (i.e., things that are similar and/or physically proximate tend to co-vary) and human causal cognition (i.e., the tendency to mistake correlation as causation), and supply ample ethnographic and historical evidence for my arguments. In doing so I also specify the variables involved and re-classify sympathetic magic into four distinct types for analytic convenience.

Do the more flexible individuals rely more on causal cognition? Observation versus intervention in causal inference in great-tailed grackles

Behavioral flexibility, the ability to change behavior when circumstances change based on learning from previous experience, is thought to play an important role in a species ability to successfully adapt to new environments and expand its geographic range. It is alternatively or additionally possible that causal cognition, the ability to understand relationships beyond their statistical covariations, could play a significant role in rapid range expansions via the ability to learn faster: causal cognition could lead to making better predictions about outcomes through exerting more control over events. We aim to determine whether great-tailed grackles (Quiscalus mexicanus), a species that is rapidly expanding its geographic range, use causal inference and whether this ability relates to their behavioral flexibility (flexibility measured in these individuals by Logan et al. (2019): reversal learning of a color discrimination and solution switching on a puzzle box). Causal cognition was measured using a touchscreen where individuals learned about the relationships between a star, a tone, a clicking noise, and food. They were then tested on their expectations about which of these causes the food to become available. We found that eight grackles showed no evidence of making causal inferences when given the opportunity to intervene on observed events using a touchscreen apparatus, and that performance on the causal cognition task did not correlate with behavioral flexibility measures. This could indicate that our test was inadequate to assess causal cognition. Because of this, we are unable to speculate about the potential role of causal cognition in a species that is rapidly expanding its geographic range. We suggest further exploration of this hypothesis using larger sample sizes and multiple test paradigms.

Causal Pluralism in Philosophy: Empirical Challenges and Alternative Proposals

An increasing number of arguments for causal pluralism invoke empirical psychological data. Different aspects of causal cognition—specifically, causal perception and causal inference—are thought to involve distinct cognitive processes and representations, and they thereby distinctively support transference and dependency theories of causation, respectively. We argue that this dualistic picture of causal concepts arises from methodological differences, rather than from an actual plurality of concepts. Hence, philosophical causal pluralism is not particularly supported by the empirical data. Serious engagement with cognitive science reveals that the connection between psychological concepts of causation and philosophical notions is substantially more complicated than is traditionally presumed.

A causal view of the sense of agency

ABSTRACT If you expect that your action causes a near effect, you perceive the action and the effect as closer in time than they really are. This phenomenon is called temporal binding and is considered an implicit measure of the sense of agency, namely the sense of being the author of an action or action awareness. Recent studies, however, show that temporal binding occurs even without the agent executing any action and depends on the capacity to represent one event as the cause of another one. These studies demand the reexamination of the sense of agency, and of temporal binding as its diagnostic tool. I propose a causal view of the sense of agency, according to which action awareness arises when your action is represented as causing an effect. Because representing an action as causing outcomes affects time perception creating the illusion of event proximity, the causal view explains and operationalizes the sense of agency through the connection between causality and time, thus overcoming the indeterminacy of previous accounts. The causal view can pave the way to novel experimental perspectives in development and evolution and stimulate new thinking on the relationship between subjectivity, causal cognition, and time perception.

Causal Reasoning and Event Cognition as Evolutionary Determinants of Language Structure.

The aim of this article is to provide an evolutionarily grounded explanation of central aspects of the structure of language. It begins with an account of the evolution of human causal reasoning. A comparison between humans and non-human primates suggests that human causal cognition is based on reasoning about the underlying forces that are involved in events, while other primates hardly understand external forces. This is illustrated by an analysis of the causal cognition required for early hominin tool use. Second, the thinking concerning forces in causation is used to motivate a model of human event cognition. A mental representation of an event contains two vectors representing a cause as well as a result but also entities such as agents, patients, instruments and locations. The fundamental connection between event representations and language is that declarative sentences express events (or states). The event structure also explains why sentences are constituted of noun phrases and verb phrases. Finally, the components of the event representation show up in language, where causes and effects are expressed by verbs, agents and patients by nouns (modified by adjectives), locations by prepositions, etc. Thus, the evolution of the complexity of mental event representations also provides insight into the evolution of the structure of language.

A simple definition of ‘intentionally’

Cognitive scientists have been debating how the folk concept of intentional action works. We suggest a simple account: people consider that an agent did X intentionally to the extent that X was causally dependent on how much the agent wanted X to happen (or not to happen). Combined with recent models of human causal cognition, this definition provides a good account of the way people use the concept of intentional action, and offers natural explanations for puzzling phenomena such as the side-effect effect. We provide empirical support for our theory, in studies where we show that people's causation and intentionality judgments track each other closely, in everyday situations as well as in scenarios with unusual causal structures. Study 5 additionally shows that the effect of norm violations on intentionality judgments depends on the causal structure of the situation, in a way uniquely predicted by our theory. Taken together, these results suggest that the folk concept of intentional action has been difficult to define because it is made of cognitive building blocks, such as our intuitive concept of causation, whose logic cognitive scientists are just starting to understand.

Causal judgments about atypical actions are influenced by agents' epistemic states

A prominent finding in causal cognition research is people’s tendency to attribute increased causality to atypical actions. If two agents jointly cause an outcome (conjunctive causation), but differ in how frequently they have performed the causal action before, people judge the atypically acting agent to have caused the outcome to a greater extent. In this paper, we argue that it is the epistemic state of an abnormally acting agent, rather than the abnormality of their action, that is driving people's causal judgments. Given the predictability of the normally acting agent's behaviour, the abnormal agent is in a better position to foresee the consequences of their action. We put this hypothesis to test in four experiments. In Experiment 1, we show that people judge the atypical agent as more causal than the normally acting agent, but also judge the atypical agent to have an epistemic advantage. In Experiment 2, we find that people do not judge a causal difference if no epistemic advantage for the abnormal agent arises. In Experiment 3, we replicate these findings in a scenario in which the abnormal agent's epistemic advantage generalises to a novel context. In Experiment 4, we extend these findings to mental states more broadly construed and develop a Bayesian network model that predicts the degree of outcome-oriented mental states based on action normality and epistemic states. We find that people infer mental states like desire and intention to a greater extent from abnormal behaviour when this behaviour is accompanied by an epistemic advantage. We discuss these results in light of current theories and research on people's preference for abnormal causes.

The relevance of causation in robotics: A review, categorization, and analysis

Abstract In this article, we investigate the role of causal reasoning in robotics research. Inspired by a categorization of human causal cognition, we propose a categorization of robot causal cognition. For each category, we identify related earlier work in robotics and also connect to research in other sciences. While the proposed categories mainly cover the sense–plan–act level of robotics, we also identify a number of higher-level aspects and areas of robotics research where causation plays an important role, for example, understandability, machine ethics, and robotics research methodology. Overall, we conclude that causation underlies several problem formulations in robotics, but it is still surprisingly absent in published research, in particular when it comes to explicit mentioning and using of causal concepts and terms. We discuss the reasons for, and consequences of, this and hope that this article clarifies the broad and deep connections between causal reasoning and robotics and also by pointing at the close connections to other research areas. At best, this will also contribute to a “causal revolution” in robotics.

From cognition to range dynamics – and from preregistration to peer-reviewed preprint

A recommendation of: Blaisdell A, Seitz B, Rowney C, Folsom M, MacPherson M, Deffner D, Logan CJ Do the more flexible individuals rely more on causal cognition? Observation versus intervention in causal inference in great-tailed grackles https://doi.org/10.31234/osf.io/z4p6s

Towards ending the animal cognition war: a three-dimensional model of causal cognition

Debates in animal cognition are frequently polarized between the romantic view that some species have human-like causal understanding and the killjoy view that human causal reasoning is unique. These apparently endless debates are often characterized by conceptual confusions and accusations of straw-men positions. What is needed is an account of causal understanding that enables researchers to investigate both similarities and differences in cognitive abilities in an incremental evolutionary framework. Here we outline the ways in which a three-dimensional model of causal understanding fulfills these criteria. We describe how this approach clarifies what is at stake, illuminates recent experiments on both physical and social cognition, and plots a path for productive future research that avoids the romantic/killjoy dichotomy.

Causal Cognition and Skillful Tool Use

An epistemological account of tool use is fundamental for a better comprehension of technical objects within the philosophy of technology. In this paper, we put forward an answer to the question “What is the role of causal cognition in skillful tool use?” We argue that an interventionist account of causal representation enables us to see how cases of skillful tool use presuppose the acquisition of representations of the causal relationships between direct interventions on a tool and the desired effects. This approach allows us to explain two of the main features of skillful tool use: systematicity and generality.