The paper aims to examine participants’ open-text ‘think aloud’ explanations of their reasoning while making a judgement about an ambiguous scenario. It aims to consider this data in light of frameworks such as causal modelling, intuitive theories, coherence and the story model. Consistent with these frameworks, we find that participants bring in a large amount of world knowledge to connect ambiguous evidence to unobserved, inferred variables and, via these, to the target judgement. We attempt to represent these chains of inferences using causal diagrams and find that participants interpretations of the scenario can be lumped into one of two distinct causal models, each presenting an internally coherent ‘image’ of the ambiguous scenario. Furthermore, participants’ judgement predicts which of those two models they adhere to. We discuss the limitations and merits of this methodological approach for investigating these types of frameworks.

Exploring the Richness of Human Causal Reasoning with Think Aloud Data

The need to update our estimates of probabilities (e.g., the accuracy of a test) given new information is commonplace. Ideally, a new instance (e.g., a correct report) would just be added to the tally, but we are often uncertain whether a new instance has occurred. We present an experiment where participants receive conflicting reports from two early-warning cancer tests, where one has higher historical accuracy (HA). We present a model showing that while uncertain which test is correct, estimates of the accuracy of both tests should be reduced. However, among our participants, we find two dominant approaches: (1) participants increase the more HA test, reducing the other; (2) participants make no change to either. Based on mixed methods we argue that both approaches represent two sides of a ‘binary’ decision i.e., (1) update as if we have complete certainty which test is correct and (2) update as if we have no information.

Categorical Belief Updating Under Uncertainty

Recent work suggests that decisions to detain defendants before trial increase the likelihood of conviction. One reason may be that knowledge of detainment makes jurors more likely to convict. Previous work has claimed this as an example of ‘bounded rationality’ i.e., due to a simple bias. We argue that this inference represents sophisticated causal reasoning e.g. about information hidden from jurors such as criminal history. We examine whether the effect of detainment knowledge on conviction depends on rational inference by presenting participants with a legal vignette in a 2x2 design: a defendant either has or has not been detained, and this detainment decision is either (1) not explained or (2) explained as due to an iron clad rule always used for this class of crimes. We find an effect of detainment when it is not explained, but no effect when explained, providing evidence against the ‘bounded rationality’ view. We provide qualitative extracts of participants’ reasoning, demonstrating sophisticated and nuanced inferences from detainment to hidden information when the decision is not explained.

How does knowledge of detainment affect juror reasoning?

Theories can be designed to predict novel evidence or to accommodate known evidence. Despite the lively debate in philosophy of science whether prediction may hold a superior value over accommodation, people’s intuitions about this issue have not been empirically examined. Within a medical scenario, we assess individuals’ sensitivity to this dilemma. Overall, we find tentative evidence that people favour the predictive account and regard the predicting theorist (i.e., doctor) as more reliable in contrast to the accommodating counterpart. Strikingly, discrepant preference patterns emerged out of their verbal reasoning data echoing the distinct philosophical stances surprisingly well. Possible reasons why people’s reasoning systematically diverges despite the general preference for prediction are discussed.


Stephen Dewitt

4

2

SHORT BIO

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES