Causal Reasoning in Non-Human Animals

2017 ◽  
Author(s):  
Christian Schloegl ◽  
Julia Fischer
Keyword(s):  
Causal Reasoning ◽  
Animal Species ◽  
Causal Structure ◽  
Convincing Evidence ◽  
Causal Learning ◽  
Cognitive Evolution ◽  
Selective Pressures ◽  
Cognitive Studies

One goal of comparative cognitive studies is to achieve a better understanding of the selective pressures and constraints that play a role in cognitive evolution. This chapter focuses on the question of causal reasoning in animals, which has mainly been investigated in tool-using and large-brained species. Our survey reveals that numerous animal species appear to be sensitive to violations of causality and may even be tuned to attend to causally relevant features. This, in turn, may facilitate causal learning. The ability to draw logical conclusions and make causal deductions, however, seems to be restricted to few species and limited to (ecologically) relevant contexts. It seems warranted to reject the traditional associationist view that non-human animals lack any understanding of causality, but convincing evidence for human-like abilities is lacking. For instance, animals do not appear to understand the causal structure of interventions.

scholarly journals The Development of Causal Reasoning

2017 ◽  
Author(s):  
Paul Muentener ◽  
Elizabeth Bonawitz
Keyword(s):  
Young Children ◽  
Causal Reasoning ◽  
Causal Structure ◽  
Statistical Evidence ◽  
Causal Learning ◽  
Causal Relationships ◽  
Older Children ◽  
Very Young Children ◽  
The World

Research on the development of causal reasoning has broadly focused on accomplishing two goals: understanding the origins of causal reasoning, and examining how causal reasoning changes with development. This chapter reviews evidence and theory that aim to fulfill both of these objectives. In the first section, it focuses on the research that explores the possible precedents for recognizing causal events in the world, reviewing evidence for three distinct mechanisms in early causal reasoning: physical launching events, agents and their actions, and covariation information. The second portion of the chapter examines the question of how older children learn about specific causal relationships. It focuses on the role of patterns of statistical evidence in guiding learning about causal structure, suggesting that even very young children leverage strong inductive biases with patterns of data to inform their inferences about causal events, and discussing ways in which children’s spontaneous play supports causal learning.

scholarly journals The Psychology of Causal Perception and Reasoning

2010 ◽  
Author(s):  
David Danks
Keyword(s):  
Causal Reasoning ◽  
Causal Structure ◽  
Psychological Research ◽  
Causal Learning ◽  
Causal Beliefs ◽  
Causal Knowledge ◽  
Causal Relations ◽  
Causal Perception ◽  
The World ◽  
Causal Cognition

Causal beliefs and reasoning are deeply embedded in many parts of our cognition. We are clearly ‘causal cognizers’, as we easily and automatically (try to) learn the causal structure of the world, use causal knowledge to make decisions and predictions, generate explanations using our beliefs about the causal structure of the world, and use causal knowledge in many other ways. Because causal cognition is so ubiquitous, psychological research into it is itself an enormous topic, and literally hundreds of people have devoted entire careers to the study of it. Causal cognition can be divided into two rough categories: causal learning and causal reasoning. The former encompasses the processes by which we learn about causal relations in the world at both the type and token levels; the latter refers to the ways in which we use those causal beliefs to make further inferences, decisions, predictions, and so on.

scholarly journals Cyclic quantum causal models

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jonathan Barrett ◽  
Robin Lorenz ◽  
Ognyan Oreshkov
Keyword(s):  
Causal Reasoning ◽  
Causal Structure ◽  
Bell Inequalities ◽  
Quantum Correlations ◽  
Quantum Systems ◽  
Causal Models ◽  
Causal Order ◽  
Quantum Processes ◽  
Causal Explanations ◽  
Causal Structures

AbstractCausal reasoning is essential to science, yet quantum theory challenges it. Quantum correlations violating Bell inequalities defy satisfactory causal explanations within the framework of classical causal models. What is more, a theory encompassing quantum systems and gravity is expected to allow causally nonseparable processes featuring operations in indefinite causal order, defying that events be causally ordered at all. The first challenge has been addressed through the recent development of intrinsically quantum causal models, allowing causal explanations of quantum processes – provided they admit a definite causal order, i.e. have an acyclic causal structure. This work addresses causally nonseparable processes and offers a causal perspective on them through extending quantum causal models to cyclic causal structures. Among other applications of the approach, it is shown that all unitarily extendible bipartite processes are causally separable and that for unitary processes, causal nonseparability and cyclicity of their causal structure are equivalent.

The Role of Causality in Young Children's Memory for Stories

1987 ◽  
Vol 10 (1) ◽  
pp. 71-88
Author(s):  
Claire A. B. Freeland ◽  
Ellin Kofsky Scholnick
Keyword(s):  
Causal Reasoning ◽  
Conceptual Development ◽  
Recall Performance ◽  
Causal Structure ◽  
Cognitive Styles ◽  
Story Recall ◽  
Locus Of Causality ◽  
Causal Understanding ◽  
Children's Memory

This study investigates the conceptual development underlying story recall. Children's memory for stories was examined as a function of subjects' causal understanding and causal structure in stories. Kindergarteners (64 boys and 64 girls) who had scored either high or low on a causal reasoning pretest heard and recalled two stories representing one of four versions which varied in amount and locus of causality. The results supported a developmental view in which recall performance was a complex interaction between characteristics of the learner and characteristics of the story. Depending on the causal structure of the story, boys and girls high in causal reasoning responded differently in employing two alternative cognitive styles. Boys tended to elaborate more on unstructured material and girls tended to assimilate well-structured text more easily. In contrast, boys and girls low in causal reasoning did not respond differently from each other and were not influenced by the causal structure of the story.

scholarly journals The development of causal reasoning

2018 ◽  
Author(s):  
Paul Muentener ◽  
Elizabeth Bonawitz
Keyword(s):  
Early Childhood ◽  
Prior Knowledge ◽  
Causal Reasoning ◽  
Causal Structure ◽  
Object Motion ◽  
Domain Specific ◽  
Reasoning Abilities ◽  
Oxford University ◽  
Early Childhood Research ◽  
Specific Object

This chapter in M. Waldmann (Ed.), Oxford Handbook of Causal Reasoning. Oxford, UK: Oxford University Press, explores the development of causal reasoning in early childhood. We review research on the development of causal reasoning in infancy, toddlerhood, and the preschool years with the broad goals of (1) understanding the origin of our mature causal reasoning abilities and (2) discussing how the process of causal reasoning and discovery may change throughout early childhood. Research on causal reasoning in infancy and toddlerhood provides evidence for both domain-specific (object motion, agent action) as well as domain-general (covariation information) roots to causal reasoning. More recent research suggests that representations of agent’s actions may play a particularly important role in the development of causal reasoning. However, independent from the precise origin of causal reasoning, our review on studies with preschool-aged children leads to the conclusion that by about four-years of age children are integrating domain-general covariation information with domain-specific prior knowledge, as well as with causal inductive constraints and more general inductive biases, to rapidly and effectively represent causal structure.

scholarly journals Evolutionary rescue in vertebrates: evidence, applications and uncertainty

2013 ◽  
Vol 368 (1610) ◽  
pp. 20120090 ◽  
Author(s):  
E. Vander Wal ◽  
D. Garant ◽  
M. Festa-Bianchet ◽  
F. Pelletier
Keyword(s):  
Environmental Change ◽  
Common Species ◽  
Convincing Evidence ◽  
Wild Populations ◽  
Selective Pressures ◽  
Species Of Conservation Concern ◽  
Generation Times ◽  
Evolutionary Rescue ◽  
Conservation Concern ◽  
Available Information

The current rapid rate of human-driven environmental change presents wild populations with novel conditions and stresses. Theory and experimental evidence for evolutionary rescue present a promising case for species facing environmental change persisting via adaptation. Here, we assess the potential for evolutionary rescue in wild vertebrates. Available information on evolutionary rescue was rare and restricted to abundant and highly fecund species that faced severe intentional anthropogenic selective pressures. However, examples from adaptive tracking in common species and genetic rescues in species of conservation concern provide convincing evidence in favour of the mechanisms of evolutionary rescue. We conclude that low population size, long generation times and limited genetic variability will result in evolutionary rescue occurring rarely for endangered species without intervention. Owing to the risks presented by current environmental change and the possibility of evolutionary rescue in nature, we suggest means to study evolutionary rescue by mapping genotype → phenotype → demography → fitness relationships, and priorities for applying evolutionary rescue to wild populations.

scholarly journals Dog eye movements are slower than human eye movements

2020 ◽  
Vol 12 (8) ◽  
Author(s):  
Soon Young Park ◽  
Catarina Espanca Bacelar ◽  
Kenneth Holmqvist
Keyword(s):  
Eye Movements ◽  
Eye Tracking ◽  
Eye Movement ◽  
Animal Species ◽  
Tracking System ◽  
Canis Lupus Familiaris ◽  
Movement Data ◽  
Cognitive Studies ◽  
Systematic Relationships ◽  
Human Eye Movements

Eye movement of a species reflects the visual behavior strategy that it has adapted to during its evolution. What are eye movements of domestic dogs (Canis lupus familiaris) like? Investigations of dog eye movements per se have not been done, despite the increasing number of visuo-cognitive studies in dogs using eye-tracking systems. To fill this gap, we have recorded dog eye movements using a video-based eye-tracking system, and compared the dog data to that of humans. We found dog saccades follow the systematic relationships between saccade metrics previously shown in humans and other animal species. Yet, the details of the relationships, and the quantities of each metric of dog saccades and fixations differed from those of humans. Overall, dog saccades were slower and fixations were longer than those of humans. We hope our findings contribute to existing comparative analyses of eye movement across animal species, and also to improvement of algorithms used for classifying eye movement data of dogs.

Causality in Decision-Making

2017 ◽  
Author(s):  
York Hagmayer ◽  
Philip Fernbach
Keyword(s):  
Decision Making ◽  
Decision Theory ◽  
Attribution Theory ◽  
Causal Reasoning ◽  
Causal Structure ◽  
Empirical Studies ◽  
Normative Theory ◽  
Causal Decision Theory ◽  
Causal Decision

Although causality is rarely discussed in texts on decision-making, decisions often depend on causal knowledge and causal reasoning. This chapter reviews what is known about how people integrate causal considerations into their choice processes. It first introduces causal decision theory, a normative theory of choice based on the idea that rational decision-making requires considering the causal structure underlying a decision problem. It then provides an overview of empirical studies that explore how causal assumptions influence choice and test predictions derived from causal decision theory. Next it reviews three descriptive theories that integrate causal thinking into decision-making, each in a different way: the causal model theory of choice, the story model of decision-making, and attribution theory. It discusses commonalities and differences between the theories and the role of causality in other decision-making theories. It concludes by noting challenges that lie ahead for research on the role of causal reasoning in decision-making.

scholarly journals Causal inference using deep-learning variable selection identifies and incorporates direct and indirect causalities in complex biological systems

2021 ◽  
Author(s):  
Zhenjiang Fan ◽  
Kate F Kernan ◽  
Panayiotis V Benos ◽  
Gregory F Cooper ◽  
Scott W Canna ◽  
...  
Keyword(s):  
Deep Learning ◽  
Variable Selection ◽  
Causal Inference ◽  
Causal Structure ◽  
Complex Diseases ◽  
Therapeutic Agents ◽  
Causal Learning ◽  
Biological Data ◽  
Causal Relations ◽  
Biological Variables

In complex diseases, causal structure learning across biological variables is critical to identify modifiable triggers or potential therapeutic agents. A limitation of existing causal learning methods is that they cannot identify indirect causal relations, those that would interact through latent mediating variables. We developed the first computational method that identifies both direct and indirect causalities, causal inference using deep-learning variable-selection (causalDeepVASE). To accurately identify indirect causalities and incorporate them with direct causalities, causalDeepVASE develops a deep neural network approach and extends a flexible causal inference method. In simulated and biological data of various contexts, causalDeepVASE outperforms existing methods in identifying expected or validated causal relations. Further, causalDeepVASE facilitates a systematic understanding of complex diseases. For example, causalDeepVASE uniquely identified a possible causal relation between IFNγ and creatinine suggested in a polymicrobial sepsis model. In future biomedical studies, causalDeepVASE can facilitate the identification of driver genes and therapeutic agents.

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