Preferences for Explanation Generality Develop Early in Biology But Not Physics

Johnston, Angie M.; Sheskin, Mark; Johnson, Samuel G. B.; Keil, Frank C.

doi:10.1111/cdev.12804

Cited by 21 publications

(7 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The notion that "good begets good" (Leiser & Aroch, 2009) can be thought of a highly generalized causal schema, with narratives failing to match this schema (e.g., actions that decrease inflation are likely to increase unemployment) fighting an uphill battle for plausibility. In other cases, we have domain-specific expectations (Johnston, Sheskin, Johnson, & Keil, 2018), such as the belief that physical causation follows more linear causal pathways compared to the more web-like structure of social causation (Strickland, Silver, & Keil, 2016).…”

Section: Explanationmentioning

confidence: 99%

Conviction Narrative Theory: A theory of choice under radical uncertainty

Johnson

Bilovich²,

Tuckett

2022

Behav Brain Sci

Self Cite

View full text Add to dashboard Cite

Conviction Narrative Theory (CNT) is a theory of choice under radical uncertainty—situations where outcomes cannot be enumerated and probabilities cannot be assigned. Whereas most theories of choice assume that people rely on (potentially biased) probabilistic judgments, such theories cannot account for adaptive decision-making when probabilities cannot be assigned. CNT proposes that people use narratives—structured representations of causal, temporal, analogical, and valence relationships—rather than probabilities, as the currency of thought that unifies our sense-making and decision-making faculties. According to CNT, narratives arise from the interplay between individual cognition and the social environment, with reasoners adopting a narrative that feels ‘right’ to explain the available data; using that narrative to imagine plausible futures; and affectively evaluating those imagined futures to make a choice. Evidence from many areas of the cognitive, behavioral, and social sciences supports this basic model, including lab experiments, interview studies, and econometric analyses. We propose 12 principles to explain how the mental representations (narratives) interact with four inter-related processes (explanation, simulation, affective evaluation, communication), examining the theoretical and empirical basis for each. We conclude by discussing how CNT can provide a common vocabulary for researchers studying everyday choices across areas of the decision sciences.

show abstract

Section: Explanationmentioning

confidence: 99%

Conviction Narrative Theory: A theory of choice under radical uncertainty

Johnson

Bilovich²,

Tuckett

2022

Behav Brain Sci

Self Cite

View full text Add to dashboard Cite

show abstract

“…This dual consideration reveals the importance that causal learners place both on identifying causal relationships that are most reliable and knowing the contexts in which they may be relied on. Empirical evidence supporting interventionism's role for invariance in our causal thinking comes primarily from studies looking at highly similar concepts, such as 'explanation generality' (e.g., Friedman, 1974;Gelman, Star, & Flukes, 2002;Johnston, Sheskin, Johnson, & Keil, 2018;Kitcher, 1981;Strevens, 2009;Walker, Lombrozo, Legare, & Gopnik, 2014). Very recently, computational (Morris et al, 2018) and behavioral (Vasilyeva et al, 2018) studies have begun to look explicitly at interventionist invariance and find that it both reflects and influences our causal judgments.…”

Section: Causal Invariance and Interventionismmentioning

confidence: 99%

The Search for Invariance: Repeated Positive Testing Serves the Goals of Causal Learning

Lapidow

Walker

2020

Language and Concept Acquisition From Infancy Through Childhood

View full text Add to dashboard Cite

To appear in Childers, J.B., Graham, S.A. & Namy, L. (Eds). (2019). Learning Language and Concepts from Multiple Examples in Infancy and Childhood What is invariant does not emerge unequivocally except with a flux. The essentials become evident in the context of changing nonessentials.-James Gibson, 1979 Human learners are intuitively exploratory: We acquire new knowledge from the outcomes of our actions. However, in order for exploration to support learning, at least some of these actions must serve to evaluate our existing knowledge. Despite this need for informative 'hypothesis testing' in everyday learning, decades of research examining self-directed experimentation suggests that learners rarely choose informative tests. That is, instead of selecting actions to test whether their current hypothesis is correct, both children and adults tend to prefer 'positive tests': actions that will produce an effect assuming their current hypothesis is correct (see Klayman, 1995; Zimmerman, 2007). To illustrate, suppose you drop an ice cube on the floor and it shatters. As a learner, you might form an initial hypothesis that 'impact with an unyielding surface causes ice to shatter.' This hypothesis is also a causal explanation for your observation: indicating how one variable (X) makes a difference to the state of another variable (Y). According to traditional interpretations of Popper's (1959) falsificationist approach, testing this hypothesis would require disconfirming its alternatives. That is, assessing whether 'X is the cause of Y,' requires 'negative tests,' or actions to determine whether Y occurs in the absence of X. Here, since Y is 'shattering' and X is 'impacting an unyielding surface,' you should drop an ice cube on a yielding surface (not-X), like rubber or cotton, to determine whether it will shatter. However, learners rarely choose this kind of disconfirming action during their exploration. Instead, they are much more likely to repeat the initial observation: e.g., to pick up another ice-cube and drop it on the same surface, or a similar one. This tendency to generate multiple positive examples is a puzzling characteristic of self-directed learning, since it does not initially appear to be informative. After all, these repeated demonstrations often produce the same evidence, and do not distinguish between the current hypothesis (i.e., impacting an unyielding surface) and potential alternatives (e.g., impacting any surface at a particular speed), since they are consistent with both. Why then, do self-directed learners consistently and repeatedly conduct positive tests? In this chapter, we propose a novel answer to this question: the Search for Invarinace (SI) hypothesis, which suggests that observing muliple, positive examples may facilitate learning by allowing us to assess the invariance of our causal theories. That is, by repeatedly activating a hypothesized cause and checking if its anticipated effect occurs, positive tests generate information about the degree to which this relationship holds across tim...

show abstract

“…Because previous studies have found domain differences in explanatory preferences (e.g. Johnston, Sheskin, Johnson & Keil, ), half of the children evaluated biology explanations about animals and the other half evaluated physics explanations about machines (see Supplementary Materials for sample stimuli).…”

Section: Methodsmentioning

confidence: 99%

Little Bayesians or little Einsteins? Probability and explanatory virtue in children's inferences

Johnston

Johnson

Koven

et al. 2016

Developmental Science

Self Cite

View full text Add to dashboard Cite

Like scientists, children seek ways to explain causal systems in the world. But are children scientists in the strict Bayesian tradition of maximizing posterior probability? Or do they attend to other explanatory considerations, as laypeople and scientists - such as Einstein - do? Four experiments support the latter possibility. In particular, we demonstrate in four experiments that 4- to 8-year-old children, like adults, have a robust latent scope bias that leads to inferences that do not maximize posterior probability. When faced with two explanations equally consistent with observed data, where one explanation makes an unverified prediction, children consistently preferred the explanation that does not make this prediction (Experiment 1), even if the prior probabilities are identical (Experiment 3). Additional evidence suggests that this latent scope bias may result from the same explanatory strategies used by adults (Experiments 1 and 2), and can be attenuated by strong prior odds (Experiment 4). We argue that children, like adults, rely on 'explanatory virtues' in inference - a strategy that often leads to normative responses, but can also lead to systematic error.

show abstract

Preferences for Explanation Generality Develop Early in Biology But Not Physics

Cited by 21 publications

References 34 publications

Conviction Narrative Theory: A theory of choice under radical uncertainty

Conviction Narrative Theory: A theory of choice under radical uncertainty

The Search for Invariance: Repeated Positive Testing Serves the Goals of Causal Learning

Little Bayesians or little Einsteins? Probability and explanatory virtue in children's inferences

Contact Info

Product

Resources

About