Reward masks the learning of cognitive control demand.

Unlabelled: Cognitive control refers to a set of cognitive functions that modulate other cognitive processes to align with internal goals. Recent research has shown that cognitive control can flexibly adapt to internal and external factors such as reward, effort, and environmental demands. This suggests that learning processes track changes in these factors and drive an optimization process to determine how cognitive control should be applied in changing situations.

Free recall is shaped by inference and scaffolded by event structure.

Though everyday life is continuous, people understand and remember experiences as discrete events separated by boundaries. Event boundaries influence the temporal structure of memory, and have been proposed to enhance encoding of boundary-adjacent information. However, the extent to which event boundaries influence memory for specific items, and their effect on memory in interactive environments are not well understood.

Evidence for shallow cognitive maps in schizophrenia.

Individuals with schizophrenia can have marked deficits in goal-directed decision making. Prominent theories differ in whether schizophrenia (SZ) affects the ability to exert cognitive control or the motivation to exert control. An alternative explanation is that schizophrenia negatively impacts the formation of cognitive maps, the internal representations of the way the world is structured, necessary for the formation of effective action plans.

Distractor-specific control adaptation in multidimensional environments.

Goal-directed behaviour requires humans to constantly manage and switch between multiple, independent and conflicting sources of information. Conventional cognitive control tasks, however, only feature one task and one source of distraction. Therefore, it is unclear how control is allocated in multidimensional environments.

Choosing the right frame: how context preferences facilitate subsequent decisions.

Context shapes how we perceive choices and, therefore, how we decide between them. For instance, a large body of literature on the "framing effect" demonstrates that people become more risk-seeking when choices are framed in terms of losses. Despite this research, it remains unknown how people make choices between contexts and how these choices affect subsequent decision making.

The consequences of AI training on human decision-making.

AI is now an integral part of everyday decision-making, assisting us in both routine and high-stakes choices. These AI models often learn from human behavior, assuming this training data is unbiased. However, we report five studies that show that people change their behavior to instill desired routines into AI, indicating this assumption is invalid.

Humans do not avoid reactively implementing cognitive control.

The ability to exert cognitive control allows us to achieve goals in the face of distraction and competing actions. However, control is costly-people generally aim to minimize its demands. Because control takes many forms, it is important to understand whether such costs apply universally.

Evidence for shallow cognitive maps in schizophrenia.

Individuals with schizophrenia can have marked deficits in goal-directed decision making. Prominent theories differ in whether schizophrenia (SZ) affects the ability to exert cognitive control, or the motivation to exert control. An alternative explanation is that schizophrenia negatively impacts the formation of cognitive maps, the internal representations of the way the world is structured, necessary for the formation of effective action plans.

The construction and use of cognitive maps in model-based control.

When making decisions, we sometimes rely on habit and at other times plan toward goals. Planning requires the construction and use of an internal representation of the environment, a cognitive map. How are these maps constructed, and how do they guide goal-directed decisions? We coupled a sequential decision-making task with a behavioral representational similarity analysis approach to examine how relationships between choice options change when people build a cognitive map of the task structure.

Need for cognition moderates the relief of avoiding cognitive effort.

When making decisions, humans aim to maximize rewards while minimizing costs. The exertion of mental or physical effort has been proposed to be one those costs, translating into avoidance of behaviors carrying effort demands. This motivational framework also predicts that people should experience positive affect when anticipating demand that is subsequently avoided (i.

Computational and behavioral markers of model-based decision making in childhood.

Human decision-making is underpinned by distinct systems that differ in flexibility and associated cognitive cost. A widely accepted dichotomy distinguishes between a cheap but rigid model-free system and a flexible but costly model-based system. Typically, humans use a hybrid of both types of decision-making depending on environmental demands.

A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning.

The Gumbel-max trick is a method to draw a sample from a categorical distribution, given by its unnormalized (log-)probabilities. Over the past years, the machine learning community has proposed several extensions of this trick to facilitate, e.g.

Reasoning supports utilitarian resolutions to moral dilemmas across diverse measures.

Sacrificial moral dilemmas elicit a strong conflict between the motive to not personally harm someone and the competing motive to achieving the greater good, which is often described as the "utilitarian" response. Some prior research suggests that reasoning abilities and deliberative cognitive style are associated with endorsement of utilitarian solutions, but, as has more recently been emphasized, both conceptual and methodological issues leave open the possibility that utilitarian responses are due instead to a reduced emotional response to harm. Across 8 studies, using self-report, behavioral performance, and neuroanatomical measures, we show that individual differences in reasoning ability and cognitive style of thinking are positively associated with a preference for utilitarian solutions, but bear no relationship to harm-relevant concerns.

Metacontrol of decision-making strategies in human aging.

Humans employ different strategies when making decisions. Previous research has reported reduced reliance on model-based strategies with aging, but it remains unclear whether this is due to cognitive or motivational factors. Moreover, it is not clear how aging affects the metacontrol of decision making, that is the dynamic adaptation of decision-making strategies to varying situational demands.

Mental labour.

Mental effort is an elementary notion in our folk psychology and a familiar fixture in everyday introspective experience. However, as an object of scientific study, mental effort has remained rather elusive. Cognitive psychology has provided some tools for understanding how effort impacts performance, by linking effort with cognitive control function.

The transdiagnostic structure of mental effort avoidance.

The law of least mental effort states that, everything else being equal, the brain tries to minimize mental effort expenditure during task performance by avoiding decisions that require greater cognitive demands. Prior studies have shown associations between disruptions in effort expenditure and specific psychiatric illnesses (e.g.

Incentives Boost Model-Based Control Across a Range of Severity on Several Psychiatric Constructs.

Background: Human decision making exhibits a mixture of model-based and model-free control. Recent evidence indicates that arbitration between these two modes of control ("metacontrol") is based on their relative costs and benefits. While model-based control may increase accuracy, it requires greater computational resources, so people invoke model-based control only when potential rewards exceed those of model-free control.

Planning Complexity Registers as a Cost in Metacontrol.

Decision-making algorithms face a basic tradeoff between accuracy and effort (i.e., computational demands).

Cost-Benefit Arbitration Between Multiple Reinforcement-Learning Systems.

Human behavior is sometimes determined by habit and other times by goal-directed planning. Modern reinforcement-learning theories formalize this distinction as a competition between a computationally cheap but inaccurate model-free system that gives rise to habits and a computationally expensive but accurate model-based system that implements planning. It is unclear, however, how people choose to allocate control between these systems.

Toward a Rational and Mechanistic Account of Mental Effort.

In spite of its familiar phenomenology, the mechanistic basis for mental effort remains poorly understood. Although most researchers agree that mental effort is aversive and stems from limitations in our capacity to exercise cognitive control, it is unclear what gives rise to those limitations and why they result in an experience of control as costly. The presence of these control costs also raises further questions regarding how best to allocate mental effort to minimize those costs and maximize the attendant benefits.

When Does Model-Based Control Pay Off?

Many accounts of decision making and reinforcement learning posit the existence of two distinct systems that control choice: a fast, automatic system and a slow, deliberative system. Recent research formalizes this distinction by mapping these systems to "model-free" and "model-based" strategies in reinforcement learning. Model-free strategies are computationally cheap, but sometimes inaccurate, because action values can be accessed by inspecting a look-up table constructed through trial-and-error.

Sequential dynamics in visual short-term memory.

Visual short-term memory (VSTM) is thought to help bridge across changes in visual input, and yet many studies of VSTM employ static displays. Here we investigate how VSTM copes with sequential input. In particular, we characterize the temporal dynamics of several different components of VSTM performance, including: storage probability, precision, variability in precision, guessing, and swapping.

Cognitive effort avoidance and detection in people with schizophrenia.

Many people with schizophrenia exhibit avolition, a difficulty initiating and maintaining goal-directed behavior, considered to be a key negative symptom of the disorder. Recent evidence indicates that patients with higher levels of negative symptoms differ from healthy controls in showing an exaggerated cost of the physical effort needed to obtain a potential reward. We examined whether patients show an exaggerated avoidance of cognitive effort, using the demand selection task developed by Kool, McGuire, Rosen, and Botvinick (Journal of Experimental Psychology.

Mechanisms of motivation-cognition interaction: challenges and opportunities.

Recent years have seen a rejuvenation of interest in studies of motivation-cognition interactions arising from many different areas of psychology and neuroscience. The present issue of Cognitive, Affective, & Behavioral Neuroscience provides a sampling of some of the latest research from a number of these different areas. In this introductory article, we provide an overview of the current state of the field, in terms of key research developments and candidate neural mechanisms receiving focused investigation as potential sources of motivation-cognition interaction.