Does causal knowledge help us be faster and more frugal in our decisions?

One challenge that has to be addressed by the fast and frugal heuristics program is how people manage to select, from the abundance of cues that exist in the environment, those to rely on when making decisions. We hypothesize that causal knowledge helps people target particular cues and estimate their validities. This hypothesis was tested in three experiments. Results show that when causal information about some cues was available (Experiment 1), participants preferred to search for these cues first and to base their decisions on them. When allowed to learn cue validities in addition to causal information (Experiment 2), participants also became more frugal (i.e., they searched fewer of the available cues), made more accurate decisions, and were more precise in estimating cue validities than was a control group that did not receive causal information. These results can be attributed to the causal relation between the cues and the criterion, rather than to greater saliency of the causal cues (Experiment 3). Overall, our results support the hypothesis that causal knowledge aids in the learning of cue validities and is treated as a meta-cue for identifying highly valid cues.     

Inferring action structure and causal relationships in continuous sequences of human action

In the real world, causal variables do not come pre-identified or occur in isolation, but instead are embedded within a continuous temporal stream of events. A challenge faced by both human learners and machine learning algorithms is identifying subsequences that correspond to the appropriate variables for causal inference. A specific instance of this problem is action segmentation: dividing a sequence of observed behavior into meaningful actions, and determining which of those actions lead to effects in the world. Here we present a Bayesian analysis of how statistical and causal cues to segmentation should optimally be combined, as well as four experiments investigating human action segmentation and causal inference. We find that both people and our model are sensitive to statistical regularities and causal structure in continuous action, and are able to combine these sources of information in order to correctly infer both causal relationships and segmentation boundaries.     

Verbalization of Decision Strategies in Multiple‐Cue Probabilistic Inference

In multiple‐cue probabilistic inference, people choose between alternatives based on several cues, each of which is differentially associated with an alternative's overall value. Various strategies have been proposed for probabilistic inference (e.g., weighted additive, tally, and take‐the‐best). These strategies differ in how many cue values they require to enact and in how they weight each cue. Do decision makers actually use any of these strategies? Ways to investigate this question include analyzing people's choices and the cues that they reveal. However, different strategies often predict the same decisions, and search behavior says nothing about whether or how people use the information that they acquire. In this research, we attempt to elucidate which strategies participants use in a multiple‐cue probabilistic inference task by examining verbal protocols, a high‐density source of process data. The promise of verbal data is in their utility for testing detailed information processing models. To that end, we apply protocol analysis in conjunction with computational simulations. We find converging evidence across outcome measures, search measures, and verbal reports that most participants use simplifying heuristics, namely take‐the‐best. Copyright © 2015 John Wiley & Sons, Ltd.     

On the generation and evaluation of inferences from single premises

A theory of how individuals construct mental models to draw inferences from single premises was tested in three experiments. Experiment 1 confirmed a counterintuitive prediction that it is easier to generate inferences between conditionals and disjunctions than it is to evaluate them. Experiment 2 replicated this finding, but an advantage found in the first experiment for conditional-to-disjunction over disjunction-to-conditional inferences was removed with different sentence contents. Experiment 3 showed that disjunction-to-conditional inferences were facilitated when premises expressed familiar indicative relations, whereas conditional-to-disjunction inferences were facilitated when premises expressed causal relations. The results indicate that small changes in task format can have large effects on the strategies that people use to represent and reason about different sentential connectives. We discuss the potential for theories other than mental models to account for these results. We argue that, despite the important role played by single-premise inferences in paraphrasing logical forms during inference, mental logic theories cannot account for the results reported here.     

A Bayesian Theory of Sequential Causal Learning and Abstract Transfer

Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause‐effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that learning involves abstract transfer, and such transfer effects involve sequential presentation of distinct sets of causal cues. It has been demonstrated that pre‐training (or even post‐training) can modulate classic causal learning phenomena such as forward and backward blocking. To account for these effects, we propose a Bayesian theory of sequential causal learning. The theory assumes that humans are able to consider and use several alternative causal generative models, each instantiating a different causal integration rule. Model selection is used to decide which integration rule to use in a given learning environment in order to infer causal knowledge from sequential data. Detailed computer simulations demonstrate that humans rely on the abstract characteristics of outcome variables (e.g., binary vs. continuous) to select a causal integration rule, which in turn alters causal learning in a variety of blocking and overshadowing paradigms. When the nature of the outcome variable is ambiguous, humans select the model that yields the best fit with the recent environment, and then apply it to subsequent learning tasks. Based on sequential patterns of cue‐outcome co‐occurrence, the theory can account for a range of phenomena in sequential causal learning, including various blocking effects, primacy effects in some experimental conditions, and apparently abstract transfer of causal knowledge.     

Distinguishing Between Causes and Enabling Conditions—Through Mental Models or Linguistic Cues?

The mental model theory of naive causal understanding and reasoning (Goldvarg & Johnson-Laird, 2001, Cognitive Science, 25, 565–610) claims that people distinguish between causes and enabling conditions on the basis of sets of models that represent possible causal situations. In the tasks used to test this hypothesis, however, the proposed set of models was confounded with linguistic cues that frame which event to assume as given (the enabling condition) and which to consider as responsible for the effect under this assumption (the cause). By disentangling these two factors, we were able to show that when identifying causes and enabling conditions in these tasks, people rely strongly on the linguistic cues but not on the proposed set of models and that this set of models does not even reflect people's typical interpretation of the tasks. We propose an alternative explanation that integrates syntactic and causal considerations.     

Time as a guide to cause

How do people learn causal structure? In 2 studies, the authors investigated the interplay between temporal-order, intervention, and covariational cues. In Study 1, temporal order overrode covariation information, leading to spurious causal inferences when the temporal cues were misleading. In Study 2, both temporal order and intervention contributed to accurate causal inference well beyond that achievable through covariational data alone. Together, the studies show that people use both temporal-order and interventional cues to infer causal structure and that these cues dominate the available statistical information. A hypothesis-driven account of learning is endorsed, whereby people use cues such as temporal order to generate initial models and then test these models against the incoming covariational data.     

Do People Order Cues by Retrieval Fluency when Making Probabilistic Inferences?

In multiple‐cue probabilistic inferences, people infer alternatives' unknown values on decision criteria, using alternatives' attributes as cues. Some inferential strategies, like take‐the‐best, assume that people consider relevant cues sequentially in order of decreasing validity. This assumption has been deemed cognitively implausible by some, who suggest memory retrieval principles to guide cue order. We test whether memory‐based inferences are better described by a model considering cues in order of validity or in order of memory retrieval. In an experiment, we manipulated the frequency with which cues appeared in a learning phase, increasing retrieval fluency of cue values related to the more frequently appearing cue. In a subsequent decision phase, participants made a series of two‐alternative decisions based on the learned cue values. We compared two sequential sampling models, which differed in whether cues are sampled in order of subjective cue validity or in order of retrieval fluency. To model retrieval order of cues in the fluency sampling model, we used the declarative memory theory embedded in the ACT‐R cognitive architecture. Most participants' decisions were best described by the model sampling cues in order of memory retrieval. Only a minority of participants were classified as sampling cues by validity. Our result suggests that retrieval fluency is the primary driver of cue order in inferences from memory, irrespective of the cues' validities. Copyright © 2017 John Wiley & Sons, Ltd.     

The integration of knowledge and experimentation strategies in understanding a physical system

This study investigated how people integrate different kinds of knowledge in coming to understand the functions of components of a physical system. Twenty-three undergraduates worked on a hypothesis-testing task in which they constructed test circuits to decide the identity of electrical components hidden inside boxes. The students' problem-solving was driven by qualitative causal models that included ideas about cause and effect, task demands, and functions of circuits and circuit components. There was a hierarchy of four causal models, with higher levels representing increasing understanding and supporting increasingly successful problem solution. These causal models were associated with characteristic experimentation strategies, including strategies for generating evidence, interpreting evidence, and managing memory requirements. Some of these strategies followed directly from the causal model a student held, whereas others appeared to be more general. The level of understanding a student eventually attained about this physical system was a function of both domain knowledge and proficiency in experimentation strategies.     

Individual differences in causal learning and decision making

In judgment and decision making tasks, people tend to neglect the overall frequency of base-rates when they estimate the probability of an event; this is known as the base-rate fallacy. In causal learning, despite people's accuracy at judging causal strength according to one or other normative model (i.e., Power PC, DeltaP), they tend to misperceive base-rate information (e.g., the cause density effect). The present study investigates the relationship between causal learning and decision making by asking whether people weight base-rate information in the same way when estimating causal strength and when making judgments or inferences about the likelihood of an event. The results suggest that people differ according to the weight they place on base-rate information, but the way individuals do this is consistent across causal and decision making tasks. We interpret the results as reflecting a tendency to differentially weight base-rate information which generalizes to a variety of tasks. Additionally, this study provides evidence that causal learning and decision making share some component processes.     

Compound cue processing within the fast and frugal heuristics approach in nonlinearly separable environments

Three experiments investigated whether participants used Take The Best (TTB) Configural, a fast and frugal heuristic that processes configurations of cues when making inferences concerning which of two alternatives has a higher criterion value. Participants were presented with a compound cue that was nonlinearly separable from its elements. The compound was highly valid in Experiments 1 and 2, but invalid in Experiment 3. Participants’ causal mental models were manipulated via instructions: participants were either told that cues acted through the same causal mechanism (configural causal model), through different causal mechanisms (elemental causal model), or the causal mechanisms were not specified (neutral causal model). A high percentage of participants used TTB-Configural when they had a configural causal model and a highly valid compound existed, suggesting that causal knowledge can be incorporated in otherwise very basic cognitive mechanisms to allow fine-grained adaptation to complex task structures.     

Identifying expectations about the strength of causal relationships

When we try to identify causal relationships, how strong do we expect that relationship to be? Bayesian models of causal induction rely on assumptions regarding people’s a priori beliefs about causal systems, with recent research focusing on people’s expectations about the strength of causes. These expectations are expressed in terms of prior probability distributions. While proposals about the form of such prior distributions have been made previously, many different distributions are possible, making it difficult to test such proposals exhaustively. In Experiment 1 we used iterated learning—a method in which participants make inferences about data generated based on their own responses in previous trials—to estimate participants’ prior beliefs about the strengths of causes. This method produced estimated prior distributions that were quite different from those previously proposed in the literature. Experiment 2 collected a large set of human judgments on the strength of causal relationships to be used as a benchmark for evaluating different models, using stimuli that cover a wider and more systematic set of contingencies than previous research. Using these judgments, we evaluated the predictions of various Bayesian models. The Bayesian model with priors estimated via iterated learning compared favorably against the others. Experiment 3 estimated participants’ prior beliefs concerning different causal systems, revealing key similarities in their expectations across diverse scenarios.     

An integrated account of generalization across objects and features

Humans routinely make inductive generalizations about unobserved features of objects. Previous accounts of inductive reasoning often focus on inferences about a single object or feature: accounts of causal reasoning often focus on a single object with one or more unobserved features, and accounts of property induction often focus on a single feature that is unobserved for one or more objects. We explore problems where people must make inferences about multiple objects and features, and propose that people solve these problems by integrating knowledge about features with knowledge about objects. We evaluate three computational methods for integrating multiple systems of knowledge: the output combination approach combines the outputs produced by these systems, the distribution combination approach combines the probability distributions captured by these systems, and the structure combination approach combines a graph structure over features with a graph structure over objects. Three experiments explore problems where participants make inferences that draw on causal relationships between features and taxonomic relationships between animals, and we find that the structure combination approach provides the best account of our data.     

Causal models frame interpretation of mathematical equations

We offer evidence that people can construe mathematical relations as causal. The studies show that people can select the causal versions of equations and that their selections predict both what they consider most understandable and how they expect variables to influence one another. When asked to write down equations, people have a strong preference for the version that matches their causal model. Causal models serve to structure equations by determining the preferred order of variables: Causes should be on one side of an equality, and a single effect should appear on the other.     

Sufficiency and Necessity Assumptions in Causal Structure Induction

Research on human causal induction has shown that people have general prior assumptions about causal strength and about how causes interact with the background. We propose that these prior assumptions about the parameters of causal systems do not only manifest themselves in estimations of causal strength or the selection of causes but also when deciding between alternative causal structures. In three experiments, we requested subjects to choose which of two observable variables was the cause and which the effect. We found strong evidence that learners have interindividually variable but intraindividually stable priors about causal parameters that express a preference for causal determinism (sufficiency or necessity; Experiment 1). These priors predict which structure subjects preferentially select. The priors can be manipulated experimentally (Experiment 2) and appear to be domain‐general (Experiment 3). Heuristic strategies of structure induction are suggested that can be viewed as simplified implementations of the priors.     

Inferring causal networks from observations and interventions

Information about the structure of a causal system can come in the form of observational data—random samples of the system's autonomous behavior—or interventional data—samples conditioned on the particular values of one or more variables that have been experimentally manipulated. Here we study people's ability to infer causal structure from both observation and intervention, and to choose informative interventions on the basis of observational data. In three causal inference tasks, participants were to some degree capable of distinguishing between competing causal hypotheses on the basis of purely observational data. Performance improved substantially when participants were allowed to observe the effects of interventions that they performed on the systems. We develop computational models of how people infer causal structure from data and how they plan intervention experiments, based on the representational framework of causal graphical models and the inferential principles of optimal Bayesian decision‐making and maximizing expected information gain. These analyses suggest that people can make rational causal inferences, subject to psychologically reasonable representational assumptions and computationally reasonable processing constraints.     

Complexity of culture: the role of identity and context in bicultural individuals' body ideals

Culture plays an important role in shaping body image, and people from different cultures have different beliefs about what constitutes the "ideal" body type. This study examines the relationship between culture and body ideals in Asian-American and Black-American women. Results from two studies show that subjective cultural identity and situational cultural cues had different relationships with body ideals. Among Asian-American women, identification with Asian culture was related to a thinner body ideal, but exposure to Asian cultural cues (relative to American cultural cues) was related to a thicker body ideal. Among Black-American women, identification with Black culture was related to a thicker body ideal, but exposure to Black cultural cues (relative to American cultural cues) was related to a thinner body ideal. These results have theoretical and practical implications for understanding how internal and external manifestations of culture can differentially influence body image.     

Why versus how often: Causal reasoning and the incidence of judgmental bias

When faced with the task of making a prediction or estimating a likelihood, it is argued that people often reason about the presence or absence and relative strength of possible causal mechanisms for the production of relevant outcomes. In so doing people rely on “causal cues” or properties of an inferential problem which indicate the nature of the particular causal processes which give rise to specific outcomes. It is hypothesized that causal cues, precisely because they focus attention and thought on specific causal mechanisms, can obscure the relevance of mathematical laws of probability and lead to statistically biased judgment. Two experiments were conducted. Their results support the hypothesis, showing that the incidence of the conjunction fallacy and the base rate fallacy depend on task-specific cues for causal reasoning.     

Event structure in perception and conception

Events can be understood in terms of their temporal structure. The authors first draw on several bodies of research to construct an analysis of how people use event structure in perception, understanding, planning, and action. Philosophy provides a grounding for the basic units of events and actions. Perceptual psychology provides an analogy to object perception: Like objects, events belong to categories, and, like objects, events have parts. These relationships generate 2 hierarchical organizations for events: taxonomies and partonomies. Event partonomies have been studied by looking at how people segment activity as it happens. Structured representations of events can relate partonomy to goal relationships and causal structure; such representations have been shown to drive narrative comprehension, memory, and planning. Computational models provide insight into how mental representations might be organized and transformed. These different approaches to event structure converge on an explanation of how multiple sources of information interact in event perception and conception.