Idea: Methodology for evaluation of a person's brain change relative to a population standard.

Abstract: Longitudinal neuroimaging studies offer valuable insight into intricate dynamics of brain development, aging, and disease progression over time. However, prevailing analytical approaches rooted in our understanding of population variation are primarily tailored for cross-sectional studies. To fully harness the potential of longitudinal neuroimaging data, we have to develop and refine methodologies that are adapted to longitudinal designs, considering the complex interplay between population variation and individual dynamics.

We build on normative modeling framework, which enables the evaluation of an individual's position compared to a population standard. We extend this framework to evaluate an individual's change compared to standard dynamics. Thus, we exploit the existing normative models pre-trained on over 58,000 individuals and adapt the framework so that they can also be used in the evaluation of longitudinal studies. Specifically, we introduce a quantitative metric termed "z-diff" score, which serves as an indicator of change of an individual compared to a population standard. Notably, our framework offers advantages such as flexibility in dataset size and ease of implementation.

To illustrate our approach, we applied it to a longitudinal dataset of 98 patients diagnosed with early-stage schizophrenia who underwent MRI examinations shortly after diagnosis and one year later.

Compared to cross-sectional analyses, which showed global thinning of grey matter at the first visit, our method revealed a significant normalisation of grey matter thickness in the frontal lobe over time. Furthermore, this result was not observed when using more traditional methods of longitudinal analysis, making our approach more sensitive to temporal changes. 

Overall, our framework presents a flexible and effective methodology for analyzing longitudinal neuroimaging data, providing insights into the progression of a disease that would otherwise be missed when using more traditional approaches.

Idea: When is it optimal to base decision confidence on the same evidence as the decision itself and when is it optimal to gather more evidence?

Abstract: We study informational dissociations between decisions and decision confidence. We explore the consequences of a dual-system model: the decision system and confidence system have distinct goals, but share access to a source of noisy and costly information about a decision-relevant variable. The decision system aims to maximize utility while the confidence system monitors the decision system and aims to provide good feedback about the correctness of the decision. In line with existing experimental evidence showing the importance of post-decisional information in confidence formation, we allow the confidence system to accumulate information after the decision. We aim to base the post-decisional stage (used in descriptive models of confidence) in the optimal learning theory. However, we find that it is not always optimal to engage in the second stage, even for a given individual in a given decision environment. In particular, there is scope for post-decisional information acquisition only for relatively fast decisions. Hence, a strict distinction between one-stage and two-stage theories of decision confidence may be misleading because both may manifest themselves under one underlying mechanism in a non-trivial manner.

Idea: People discriminate in the information they share about job candidates of different gender and nationality.

Abstract: We focus on communication among hiring team members and document the existence of discrimination in the disclosure of information about candidates. In particular, we conduct an online experiment with a nationally representative sample of Czech individuals who act as human resource assistants and hiring managers in our online labor market. The main novel feature of our experiment is the monitoring of information flow between human resource assistants and hiring managers. We exogenously manipulate candidates' names to explore the causal effects of their gender and nationality on information that assistants select for managers. Our findings reveal that assistants disclose more information about family and less information about work for female candidates relative to male candidates. An in-depth analysis of the disclosed information suggests that gender stereotypes play an important role in this disclosure discrimination. Furthermore, assistants disclose less information about foreigners overall. This effect appears to be driven by the less attention assistants are willing to devote to the CVs of foreigners, measured by the extra effort to learn more about the candidates.

[AEA RCT pre-registration]

Idea: A graph procedure for finding what states a sender wants to pool to manipulate a receiver.

Abstract: We study a Bayesian persuasion model in which the state space is finite, the sender and the receiver have state-dependent quadratic loss functions, and their disagreement regarding the preferred action is of arbitrary form. This framework enables us to focus on the understudied sender’s trade-off between the informativeness of the signal and the concealment of the state-dependent disagreement about the preferred action. In particular, we study which states are pooled together in the supports of posteriors of the optimal signal. We provide an illustrative graph procedure that takes the form of preference misalignment and outputs potential representations of the state-pooling structure. Our model provides insights into situations in which the sender and the receiver care about two different but connected issues, for example, the interaction of a political advisor who cares about the state of the economy with a politician who cares about the political situation.