Meta-control: From psychology to computational neuroscience

Abstract

Research in the past decades shed light on the different mechanisms that underlie our capacity for cognitive control. However, the meta-level processes that regulate cognitive control itself remain poorly understood. Following the terminology from artificial intelligence, meta-control can be defined as a collection of mechanisms that (a) monitor the progress of controlled processing and (b) regulate the underlying control parameters in the service of current task goals and in response to internal or external constraints. From a psychological perspective, meta-control is an important concept because it may help explain and predict how and when human agents select different types of behavioral strategies. From a cognitive neuroscience viewpoint, meta-control is a useful concept for understanding the complex networks in the prefrontal cortex that guide higher-level behavior as well as their interactions with neuromodulatory systems (such as the dopamine or norepinephrine system). The purpose of the special issue is to integrate hitherto segregated strands of research across three different perspectives: 1) a psychological perspective that specifies meta-control processes on a functional level and aims to operationalize them in experimental tasks; 2) a computational perspective that builds on ideas from artificial intelligence to formalize normative solutions to meta-control problems; and 3) a cognitive neuroscience perspective that identifies neural correlates of and mechanisms underlying meta-control.

Valence bias in metacontrol of decision making in adolescents and young adults

“…In this study, we ask how the ability for metacontrol of decision making (i.e. the dynamic adaptation of decision-making strategies; Eppinger et al, 2021;Ruel, Devine, et al, 2021) develops from adolescence into young adulthood and whether framing effects differentially affect the flexible usage of decision-making strategies in adolescents as compared to young adults.To study metacontrol, we draw on previous work that dissociates two major decision-making strategies: modelbased and model-free decision making (Daw et al, 2011;Dayan & Niv, 2008). Model-based decision making represents a deliberative, prospective strategy that evaluates different choice options by means of forward planning based on knowledge about the structure of the environment (a cognitive model).…”

“…In this study, we ask how the ability for metacontrol of decision making (i.e. the dynamic adaptation of decision‐making strategies; Eppinger et al, 2021; Ruel, Devine, et al, 2021) develops from adolescence into young adulthood and whether framing effects differentially affect the flexible usage of decision‐making strategies in adolescents as compared to young adults.…”

Mapping the dose–response relationship between monetary reward and cognitive performance

“…Second, the predicted inverted-U only emerged on repeat trials. If we interpret this finding in terms of meta-control models (Eppinger et al, 2021;Hommel, 2015), extreme rewards seem to have biased people's control state towards flexibility. Although this bias facilitates performance on switch trials, it also impairs people's ability to maintain a stable task representation, increasing distractibility and impairing performance on repeat trials.…”

“…Third, if extremely valuable rewards can harm cognitive performance, different cognitive control mechanisms may explain this interference effect. Specifically, based on meta-control models (Dreisbach & Fröber, 2019;Eppinger et al, 2021;Hommel, 2015), we assume that rewards can affect cognitive control by biasing the control state towards either stability or flexibility. One the one hand, studies show that rewards can increase stability (Hefer & Dreisbach, 2017).…”

Meta-control of social learning strategies

“…Recent studies in neuroscience suggest that the brain can find a compromise between these learning strategies via a process called meta-control [ 3 – 6 ]. Meta-control is based on the premise that learning strategies have different levels of sensitivity to environment variability and this can be measured by perceptual uncertainty concerning the association of actions and rewards [ 3 ].…”