The placebo effect: To explore or to exploit?

How people choose between options with differing outcomes (explore-exploit) is a central question to understanding human behaviour. However, the standard explore-exploit paradigm relies on gamified tasks with low-stake outcomes. Consequently, little is known about decision making for biologically-relevant stimuli.

How people learn about causal influence when there are many possible causes: A model based on informative transitions.

Four experiments tested how people learn cause-effect relations when there are many possible causes of an effect. When there are many cues, even if all the cues together strongly predict the effect, the bivariate relation between each individual cue and the effect can be weak, which can make it difficult to detect the influence of each cue. We hypothesized that when detecting the influence of a cue, in addition to learning from the states of the cues and effect (e.

Bayesian reasoning in residents' preliminary diagnoses.

Whether and when humans in general, and physicians in particular, use their beliefs about base rates in Bayesian reasoning tasks is a long-standing question. Unfortunately, previous research on whether doctors use their beliefs about the prevalence of diseases in diagnostic judgments has critical limitations. In this study, we assessed whether residents' beliefs about the prevalence of a disease are associated with their judgments of the likelihood of the disease in diagnosis, and whether residents' beliefs about the prevalence of diseases change across the 3 years of residency.

Physician Bayesian updating from personal beliefs about the base rate and likelihood ratio.

Whether humans can accurately make decisions in line with Bayes' rule has been one of the most important yet contentious topics in cognitive psychology. Though a number of paradigms have been used for studying Bayesian updating, rarely have subjects been allowed to use their own preexisting beliefs about the prior and the likelihood. A study is reported in which physicians judged the posttest probability of a diagnosis for a patient vignette after receiving a test result, and the physicians' posttest judgments were compared to the normative posttest calculated from their own beliefs in the sensitivity and false positive rate of the test (likelihood ratio) and prior probability of the diagnosis.

Medication adherence as a learning process: insights from cognitive psychology.

Non-adherence to medications is one of the largest contributors to sub-optimal health outcomes. Many theories of adherence include a 'value-expectancy' component in which a patient decides to take a medication partly based on expectations about whether it is effective, necessary, and tolerable. We propose reconceptualising this common theme as a kind of 'causal learning' - the patient learns whether a medication is effective, necessary, and tolerable, from experience with the medication.

Reasoning about causal relationships: Inferences on causal networks.

Over the last decade, a normative framework for making causal inferences, Bayesian Probabilistic Causal Networks, has come to dominate psychological studies of inference based on causal relationships. The following causal networks-[X→Y→Z, X←Y→Z, X→Y←Z]-supply answers for questions like, "Suppose both X and Y occur, what is the probability Z occurs?" or "Suppose you intervene and make Y occur, what is the probability Z occurs?" In this review, we provide a tutorial for how normatively to calculate these inferences. Then, we systematically detail the results of behavioral studies comparing human qualitative and quantitative judgments to the normative calculations for many network structures and for several types of inferences on those networks.

Effect of grouping of evidence types on learning about interactions between observed and unobserved causes.

When a cause interacts with unobserved factors to produce an effect, the contingency between the observed cause and effect cannot be taken at face value to infer causality. Yet it would be computationally intractable to consider all possible unobserved, interacting factors. Nonetheless, 6 experiments found that people can learn about an unobserved cause participating in an interaction with an observed cause when the unobserved cause is stable over time.

Causal learning about tolerance and sensitization.

We introduce two abstract, causal schemata used during causal learning. (1) Tolerance is when an effect diminishes over time, as an entity is repeatedly exposed to the cause (e.g.