Causal reasoning

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Causal reasoning is the process of identifying causality: the relationship between a cause and its effect. The study of causality extends from ancient philosophy to contemporary neuropsychology; assumptions about the nature of causality may be shown to be functions of a previous event preceding a later one. The first known protoscientific study of cause and effect occurred in Aristotle's Physics.[1]

Understanding cause and effect

Causal relationships may be understood as a transfer of force.[2] If A causes B, then A must transmit a force (or causal power) to B which results in the effect. Causal relationships suggest change over time; cause and effect are temporally related, and the cause precedes the outcome.[3]

Causality may also be inferred in the absence of a force, a less-typical definition.[4] A cause can be removal (or stopping), like removing a support from a structure and causing a collapse or a lack of precipitation causing wilted plants.

Humans can reason about many topics (for example, in social and counterfactual situations and mathematics) with the aid of causal understanding.[3] Understanding depends on the ability to comprehend cause and effect. People must be able to reason about the causes of others’ behavior (to understand their intentions and act appropriately) and understand the likely effects of their own actions. Counterfactual arguments are presented in many situations; humans are predisposed to think about “what might have been”, even when that argument has no bearing on the current situation. Although causality is related to mechanism,[5] an understanding of causality does not necessarily imply an understanding of mechanism.

Cause-and-effect relationships define categories of objects.[6] Wings are a feature of the category "birds"; this feature is causally interconnected with another feature of the category, the ability to fly.[6]

Inferring cause and effect

Humans are predisposed to understand cause and effect, making inferences bi-directionally. Temporal cues demonstrate causality.[7] When observing an event, people assume that things preceding the event cause it, and things following the event are effects of it.[8]

Coincidence of movement and spatial relationships are another way to infer cause and effect. If objects move together (or one object seems to initiate the movement of another), causality is inferred from that relationship.[9] Animacy may also be inferred from such relationships.

Causal reasoning may be activated almost automatically.[3][10] However, inferences about cause and effect do not always demonstrate understanding of mechanisms underlying causality; causality has been described as "cognitive illusion".[11] Much understanding of cause and effect is based on associations, without an understanding of how events are related to one another; this is known as the "illusion of explanatory depth".[5]

A 2013 neuropsychology study [12] demonstrates that humans conform new information to old information. This suggests an inverted causal experience: cause must be attributed to effect a posteriori to understand the causal connection between agent and act. Friedrich Nietzsche argued against Aristotelian causality (that cause precedes effect) in The Will To Power.[13]

Humans understand cause and effect. Research suggests that other animals, such as rats[14] and monkeys,[15] may or may not understand cause and effect. Animals may use information about cause and effect to improve decision-making and make inferences about past and future events.[16] A constant which guides human reasoning and learning about events is causality.[17] Causal considerations are integral to how people reason about their environment.[3] Humans use causal cues and their related effects to make decisions and predictions and to understand mechanisms leading to change.[18]

Types of causal relationships

Several types of causal models are developed as a result of observing causal relationships: common-cause relationships, common-effect relationships, causal chains and causal homeostasis.[5]

  • In common-cause relationships, a single cause has several effects:
Example of a single cause with multiple effects
A virus is an example of a single cause resulting in several effects (fever, headache and nausea).
  • In common-effect relationships, several causes converge in one effect:
Example of multiple causes with a single effect
An increase in government spending is an example of one effect with several causes (high unemployment, increased currency value or civil unrest).
  • In causal chains one cause triggers an effect, which triggers another effect:
Example of a causal chain
An example is poor sleep leading to fatigue, which leads to poor coordination.
  • In causal homeostasis, causal relationships form a stable cycle or reinforcing mechanism:
Example of causal homeostasis
Feathers, hollow bones, high metabolic rate and flight reinforce each other in birds, with adaptation to the whole rather than one instance beginning a causal relationship.[5]

Types of causal reasoning

While causal understanding can be automatic, in complex situations advanced reasoning is necessary. Types of causal reasoning[2] include:

Deduction

Deductive reasoning implies a general rule; an event is a guaranteed conclusion. An outcome may be deduced based on other arguments, which may determine a cause-and-effect relationship.

Induction

Inductive reasoning is an inference made with uncertainty; the conclusion is likely, but not guaranteed. Induction can be used to speculate about causality.

Abduction

In abductive reasoning, the premises do not guarantee a conclusion. Abduction moves from data description to a hypothesis without a necessary relationship between cause and effect.

Models

There are several models of how humans reason about causality.

Dependency

The dependency model asserts that effects are contingent upon causes;[3] cause and effect have a probable relationship.

Covariation

The covariation (regularity) model, a type of dependency model, suggests that humans understand relationships between causes and effects by their coincidence, inferring that change in a cause changes an effect.[19]

Mechanism

This model[2] suggests that cause and effect are mechanistically related. In this situation, there is a basic process underlying the cause and effect.

Dynamics

This model of causal representation[20] suggests that causes are represented by a pattern of forces. The force theory [21] is an extension of the dynamics model that applies to causal representation and reasoning (i.e., drawing inferences from the composition of multiple causal relations).

Development in humans

Children develop an ability to understand causality and make inferences based on cause and effect at an early age;[10] some research suggests that children as young as eight months can understand cause and effect.[22] An understanding of mechanism and causality go hand in hand; children need to understand cause and effect to understand the operation of mechanisms, which allows them to understand causal relationships.[5] Children ask "why?" at an early age to understand mechanism and, in turn, causality. A child’s first "why" question often coincides with their first attempt to explain something, within the first year after acquiring language.[5] Children ask "why" to understanding mechanism and causality.[23]

The ability to understand and reason about causality at a young age allows children to develop naïve theories about many topics. Causality helps children learn about physics, language, concepts and the behavior of others.[10] There is a developmental pattern to the causal understanding children have.[23]

Infants have an understanding of causal power.[5] They know that certain causes have particular effects. Young children, from late infancy to early childhood, understand functional relations:[5] a particular property (or component of a mechanism) has a certain function. They also understand causal density: how causes can interact in a complex way.

Older children and adults continue to develop an understanding of mechanistic fragments.[5] They understand the components of a working system in isolation, although the full mechanistic details of a system do not emerge until adulthood. Jean Piaget defined preoperational, concrete operational and formal operational stages of development.

Across cultures

Causal attributions have been shown to be dissimilar among different cultures in several ways:

Causal attributions

Yan and Gaier[24] investigated causal attributions of college success and failure between two groups of students, American and Asian. The Asian group was from China, Korea, Japan and Southeast Asia. Performance was similar across the four nationalities. The students were asked to make judgments about someone else's successes and failures in schoolwork, and whether those outcomes were attributable to innate ability or to expended effort. American participants were much more likely to attribute academic achievement to ability than Asian participants were. Although Americans tended to rate success as attributable to effort, failure was not perceived as being a result of a lack of effort. Asian students did not show this pattern.

Comparisons between Western and Eastern children and adults suggest differences between the cultures in the causality attributable to particular illnesses.[25] After reading stories of illnesses and making inferences about the causes of those illnesses, both groups showed an understanding of the biological causes of most illnesses. However, all the children and the Eastern adults also attributed some illnesses (and their remedies) to magical causes.

Causal motivations

Members of individualist or collectivist cultures may make different attributions of the origins and motivations of movement on a small scale among animated objects, or what would cause movement within a group of animated objects.[26] Participants from the UK, China and Hong Kong were shown videos of animated fish on a computer screen. The videos depicted a central fish moving toward or away from a group of fish, and participants were asked to determine the relationship among the fish: internally motivated (the central fish was looking for food) or externally motivated (the central fish wanted to join the others). Another set of videos suggested that the group of fish was the predominant agent, with the individual fish being acted upon. These different videos provided an opportunity to determine whether group or individual action is the preferred motivating force among different cultures.

Self-reported results suggested that Asian participants preferred descriptions and situations where the group was the central focus and causal agent, while Westerners preferred situations in which the individual was the agent. These effects also extended to memory processes; collectivist participants had better memories of situations in which the group was primary. This suggests that members of individualistic cultures are more responsive to independent agents and members of collectivist cultures are more responsive when groups guide individual action.

Causal reasoning in non-human animals

Causal reasoning is not unique to humans; animals are often able to use causal information as cues for survival.[11] Rats are able to generalize causal cues to gain food rewards. Animals such as rats can learn the mechanisms required for a reward by reasoning about what could elicit a reward (Sawa, 2009).

File:CorvusMoneduloidesKeulemans.jpg
New Caledonian Crow (Corvus moneduloides)

New Caledonian crows have been studied for their ability to reason about causal events.[27] This intelligent species uses tools in a way that even chimpanzees cannot, making complex tools to bring food within reach.

Experimental work with this species suggests that they can understand hidden causes in a way that was previously believed uniquely human.[28] In the first of two experiments a crow was confined, with food in a tube inaccessible to the crow without some effort. A human entered the enclosure and went behind a curtain, waving a stick near the food tube through a hole in the curtain. When the human left the enclosure the crow confidently moved toward the food area and retrieved the reward, knowing that the human cause of the moving stick (albeit invisible) was gone. In the second experiment, no human entered or exited the enclosure. In this case the crow moved toward the food uncertainly, not knowing what caused the stick to move.

References

  1. ↑ http://plato.stanford.edu/entries/aristotle-causality
  2. ↑ 2.0 2.1 2.2 Ahn, W.-K., & Kalish, C. W. (2000). "The role of mechanism in causal reasoning." In F. C. Keil & R. A. Wilson (Eds.) Explanation and Cognition. (pp. 199-225). Cambridge, MA: The MIT Press.
  3. ↑ 3.0 3.1 3.2 3.3 3.4 Sloman, S. A. (2005). Causal models. New York, NY: Oxford University Press.
  4. ↑ Wolff, P., Barbey, A. K., & Hausknecht, M. (2010). "For want of a nail: How absences cause events." Journal of Experimental Psychology: General, 139, 191-221.
  5. ↑ 5.0 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 Keil, F. C. (2006). "Explanation and understanding." Annual Review in Psychology, 57, 227-254.
  6. ↑ 6.0 6.1 Rehder, B. (2003). "Categorization as causal reasoning." Cognitive Science, 27, 709-748.
  7. ↑ Lagnado, D. A., & Sloman, S. A. (2006). "Time as a guide to cause." Journal of Experimental Psychology: Learning, Memory, and Cognition, 32, 451–460.
  8. ↑ Cheng, P. W., & Novick, L. R. (1990). "A probabilistic contrast model of causal induction." Journal of Personality and Social Psychology, 58, 545-567.
  9. ↑ Scholl, B. J. & Tremoulet, P. D. (2000). "Perceptual causality and animacy." Trends in Cognitive Sciences, 4, 299-309.
  10. ↑ 10.0 10.1 10.2 Corrigan, R. & Denton, P. (1996). "Causal understanding as a developmental primitive." Developmental Review, 16, 162-202.
  11. ↑ 11.0 11.1 Sawa, K. (2009). "Predictive behavior and causal learning in animals and humans." Japanese Psychological Research, 51, 222-233.
  12. ↑ http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2319992
  13. ↑ Friedrich Nietzsche. The Will To Power. circa 1880s, which recent neuropsychological experiments confirm. 477 (Nov. 1887-March 1888) "I maintain the phenomenality of the inner world, too: every- thing of which we become conscious is arranged, simplified, schematized, interpreted through and through — the actual process of inner "perception," the causal connection between thoughts, feelings, desires, between subject and object, are absolutely hidden from us — and are perhaps purely imaginary. The "apparent inner world" is governed by just the same forms and procedures as the "outer" world. We never encounter "facts": pleasure and displeasure are subsequent and derivative intellectual phenomena — "Causality" eludes us; to suppose a direct causal link be ween thoughts, as logic does — that is the consequence of the crudest and clumsiest observation. Between two thoughts all kinds of affects play their game: but their motions are too fast, therefore we fail to recognize them, we deny them — "Thinking" as epistemologists conceive it, simply does not occur: it is a quite arbitrary fiction, arrived at by selecting one element from the process and eliminating all the rest, an artificial arrangement for the purpose of intelligibility — The "spirit," something that thinks: where possible even "absolute, pure spirit" – this conception is a second derivative of that false introspection which believes in "thinking": first an act is imagined which simply does not occur, "thinking," and secondly a subject-substratum in which every act of thinking, and nothing else, has its origin: that is to say, both the deed and the doer are fictions.
  14. ↑ http://news.nationalgeographic.com/news/2006/02/0216_060216_rats.html
  15. ↑ http://www.ncbi.nlm.nih.gov/pubmed/8174341
  16. ↑ Garcia-Retamero, R., Wallin, A., & Dieckmann, A. (2007). "Does causal knowledge help us to be faster and more frugal in our decisions?" Memory & Cognition, 35, 1399–1409.
  17. ↑ Sloman, S. A. & Lagando, D.A. (2003). "Causal invariance in reasoning and learning." The Psychology of Learning and Motivation, 44, 287-325.
  18. ↑ Hagmayer, Y., Sloman, S. A., Lagnado, D. A., & Waldmann, M. R. (2007). "Causal reasoning through intervention." In A. Gopnik & L. Schulz (Eds.), Causal learning: Psychology, philosophy, and computation (pp. 86–100). Oxford, England: Oxford University Press.
  19. ↑ Cheng, P. W. (1997). "From covariation to causation: A causal power theory." Psychological Review, 104, 367–405.
  20. ↑ Wolff, P. (2007). Representing causation. Journal of Experimental Psychology: General, 136, 82-111.
  21. ↑ Wolff, P., Barbey, A.K. (2015). Causal reasoning with forces. Frontiers in Human Neuroscience, doi:10.3389/fnhum.2015.00001.
  22. ↑ Sobel, D. M., & Kirkham, N. Z. (2006). Blickets and babies: The development of causal reasoning in toddlers and infants. Developmental Psychology, 42, 1103-1115.
  23. ↑ 23.0 23.1 Keil, F. C. (2012). Running on empty? How folk science gets by with less. Current Directions in Psychological Science, 21, 329-334.
  24. ↑ Yan, W. & Gaier, E. L. (1994). Causal attributions for college success and failure: An Asian-American comparison. Journal of Cross-Cultural Psychology, 25, 146-158.
  25. ↑ Nguyen, S. P. & Rosengren, K. S. (2004). Causal reasoning about illness: A comparison between European and Vietnamese-American children. Journal of Cognition and Culture, 4, 51-78.
  26. ↑ Ng, S. H. & Zhu, Y. (2001) Attributing causality and remembering events in individual- and group-acting situations: A Beijing, Hong Kong, and Wellington comparison. Asian Journal of Social Psychology, 4, 39-52.
  27. ↑ Hunt, Gavin R. (January 1996). "Manufacture and use of hook-tools by New Caledonian crows". Nature 379: 249–251.
  28. ↑ Taylor, A. H., Miller, R., & Gray, R. D. (2012). New Caledonian crows reason about hidden causal agents. Proceedings of the National Academy of Sciences, 109, 16389-16391.
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