International Human Rights Law and the Protection of Medical Scientists against State Inference during COVID-19

The global COVID-19 pandemic has hosted a rising trend of state interference in medical science research against the virus. Such illiberal actions are counterproductive to hopes of addressing the virus because they impede the operations of scientific inquiry and threaten the integrity of scientific findings. State efforts to interfere in pandemic science work involve constraints upon medical scientists researching the virus. Such constraints risk violating the human rights of the scientists involved in COVID-19 research. International human rights law provides means of protecting scientists against state interference, and the reach of international human rights instruments approaches a scale comparable to the global reach of the pandemic. As a result, the exercise of international human rights law on behalf of medical scientists against state interference furthers the global urgency to resolve the COVID-19 pandemic.

Spontaneous State Inferences

Perceivers routinely draw inferences about others from their behavior in an attempt to make sense of the world. Previous research has established that spontaneous inferences include stable characteristics such as traits and a number of variable person-related concepts such as goals, intentions, and motivations. The current research investigated the occurrence of more general spontaneous state inferences. In a series of four preregistered studies (N = 883), we adapted two established experimental paradigms frequently used in spontaneous social inference research to the investigation of spontaneous trait and state inferences. In Studies 1 and 2, we observed evidence for the occurrence of spontaneous state inferences from state-implying statements. In Studies 3 and 4, we observed the simultaneous occurrence of spontaneous trait and state inferences from statements that allowed for both inferences. In a fifth study (N = 97), we provide evidence that people represent states and traits as functionally different: Participants judged the likelihood of behavioral repetition higher when the same behavior was related to a trait-inference than a state inference. The observation of multiple simultaneous spontaneous inferences in the current research suggests that further theory building regarding the underlying mechanisms and processes of spontaneous impression formation in person perception from behavior is warranted.

The Ventrolateral Anterior Temporal Lobe is Commonly Engaged by Both Mental State Inference and Semantic Association Tasks

A key challenge for neurobiological models of social cognition is to elucidate whether brain regions are specialised for that domain. In recent years, discussion surrounding the role of the anterior temporal lobe (ATL) epitomises such debates; some argue it is part of a domain-specific network for social processing, while others claim it is a domain-general hub for semantic representation. In the present study, we used ATL-optimised fMRI to map the contribution of different ATL structures to a variety of paradigms frequently used to probe a crucial social ability, namely ‘theory of mind’ (ToM). Using multiple tasks enables a clearer attribution of activation to ToM as opposed to idiosyncratic features of stimuli. Further, we directly explored whether these same structures are also activated by a non-social task probing semantic representations. We revealed that common to all of the tasks was activation of a key ventrolateral ATL region that is typically invisible to standard fMRI. This constitutes novel evidence in support of the view that the ventrolateral ATL contributes to social cognition via a domain-general role in the retrieval of conceptual knowledge, and against claims of a specialised social function.

Spatiotemporal blocking of the bouncy particle sampler for efficient inference in state-space models

We propose a novel blocked version of the continuous-time bouncy particle sampler of Bouchard-Côté et al. (J Am Stat Assoc 113(522):855–867, 2018) which is applicable to any differentiable probability density. This alternative implementation is motivated by blocked Gibbs sampling for state-space models (Singh et al. in Biometrika 104(4):953–969, 2017) and leads to significant improvement in terms of effective sample size per second, and furthermore, allows for significant parallelization of the resulting algorithm. The new algorithms are particularly efficient for latent state inference in high-dimensional state-space models, where blocking in both space and time is necessary to avoid degeneracy of MCMC. The efficiency of our blocked bouncy particle sampler, in comparison with both the standard implementation of the bouncy particle sampler and the particle Gibbs algorithm of Andrieu et al. (J R Stat Soc Ser B Stat Methodol 72(3):269–342, 2010), is illustrated numerically for both simulated data and a challenging real-world financial dataset.

Dopamine reports reward prediction errors, but does not update policy, during inference-guided choice

Dopamine is thought to carry reward prediction errors (RPEs), which update values and hence modify future behaviour. However, updating values is not always the most efficient way of adapting to change. If previously encountered situations will be revisited in future, inferring that the state of the world has changed allows prior experience to be reused when situations are reencountered. To probe dopamine's involvement in such inference-based behavioural flexibility, we measured and manipulated dopamine while mice solved a sequential decision task using state inference. Dopamine was strongly influenced by the value of states and actions, consistent with RPE signalling, using value information that respected task structure. However, though dopamine responded strongly to rewards, stimulating dopamine at the time of trial outcome had no effect on subsequent choice. Therefore, when inference guides choice, rewards have a dopamine-independent influence on policy through the information they carry about the world's state.

I can tell you know a lot, although I'm not sure what: Modeling broad epistemic inference from minimal action

Inferences about other people's knowledge and beliefs are central to social interaction. In many situations, however, it's not possible to be sure what other people know because their behavior is consistent with a range of potential epistemic states. Nonetheless, this behavior can give us coarse intuitions about how much someone might know, even if we cannot pinpoint the exact nature of this knowledge. We present a computational model of this kind of broad epistemic-state inference, centered on the expectation that agents maximize epistemic utilities. We evaluate our model in a graded inference task where people had to infer how much an agent knew based on the actions they chose. Critically, the agent's behavior was always under-determined, but nonetheless contained information about how much knowledge they possessed. Our model captures nuanced patterns in participant judgments, revealing a quantitative capacity to infer amorphous knowledge from minimal behavioral evidence.

Interacting with volatile environments stabilizes hidden-state inference and its brain signatures

Making accurate decisions in uncertain environments requires identifying the generative cause of sensory cues, but also the expected outcomes of possible actions. Although both cognitive processes can be formalized as Bayesian inference, they are commonly studied using different experimental frameworks, making their formal comparison difficult. Here, by framing a reversal learning task either as cue-based or outcome-based inference, we found that humans perceive the same volatile environment as more stable when inferring its hidden state by interaction with uncertain outcomes than by observation of equally uncertain cues. Multivariate patterns of magnetoencephalographic (MEG) activity reflected this behavioral difference in the neural interaction between inferred beliefs and incoming evidence, an effect originating from associative regions in the temporal lobe. Together, these findings indicate that the degree of control over the sampling of volatile environments shapes human learning and decision-making under uncertainty.