Institut du Cerveau - Paris Brain Institute (ICM), UMR 7225/UMR_S 1127, Sorbonne University/CNRS/INSERM, Paris, France.
Department of Psychiatry, Pitié-Salpêtrière Hospital, Assistance Publique - Hôpitaux de Paris (AP-HP), Paris, France.
Cogn Affect Behav Neurosci. 2021 Dec;21(6):1117-1129. doi: 10.3758/s13415-021-00947-0. Epub 2021 Oct 15.
Newly emerging infectious diseases, such as the coronavirus (COVID-19), create new challenges for public healthcare systems. Before effective treatments, countering the spread of these infections depends on mitigating, protective behaviours such as social distancing, respecting lockdown, wearing masks, frequent handwashing, travel restrictions, and vaccine acceptance. Previous work has shown that the enacting protective behaviours depends on beliefs about individual vulnerability, threat severity, and one's ability to engage in such protective actions. However, little is known about the genesis of these beliefs in response to an infectious disease epidemic, and the cognitive mechanisms that may link these beliefs to decision making. Active inference (AI) is a recent approach to behavioural modelling that integrates embodied perception, action, belief updating, and decision making. This approach provides a framework to understand the behaviour of agents in situations that require planning under uncertainty. It assumes that the brain infers the hidden states that cause sensations, predicts the perceptual feedback produced by adaptive actions, and chooses actions that minimize expected surprise in the future. In this paper, we present a computational account describing how individuals update their beliefs about the risks and thereby commit to protective behaviours. We show how perceived risks, beliefs about future states, sensory uncertainty, and outcomes under each policy can determine individual protective behaviours. We suggest that these mechanisms are crucial to assess how individuals cope with uncertainty during a pandemic, and we show the interest of these new perspectives for public health policies.
新发传染病，如冠状病毒（COVID-19），给公共卫生系统带来了新的挑战。在有效的治疗方法出现之前，遏制这些感染的传播取决于减轻、保护行为，如社交距离、遵守封锁、戴口罩、勤洗手、旅行限制和疫苗接种。以前的工作表明，采取保护行为取决于对个人脆弱性、威胁严重程度以及个人进行此类保护行为的能力的信念。然而，对于这些信念如何在传染病流行时产生，以及可能将这些信念与决策联系起来的认知机制，人们知之甚少。主动推断（AI）是一种新的行为建模方法，它集成了具身感知、行动、信念更新和决策。这种方法提供了一个框架，用于理解在需要在不确定情况下进行规划的情况下代理的行为。它假设大脑推断出导致感觉的隐藏状态，预测自适应行动产生的感知反馈，并选择在未来最小化预期惊喜的行动。在本文中，我们提出了一个计算性描述，说明个人如何更新他们对风险的信念，从而承诺采取保护行为。我们展示了感知风险、对未来状态的信念、感官不确定性以及每种策略下的结果如何决定个人的保护行为。我们认为，这些机制对于评估个人在大流行期间如何应对不确定性至关重要，我们还展示了这些新观点对公共卫生政策的意义。
