Attention deficits linked with proclivity to explore while foraging.

All mobile organisms forage for resources, choosing how and when to search for new opportunities by comparing current returns with the average for the environment. In humans, nomadic lifestyles favouring exploration have been associated with genetic mutations implicated in attention deficit hyperactivity disorder (ADHD), inviting the hypothesis that this condition may impact foraging decisions in the general population. Here we tested this pre-registered hypothesis by examining how human participants collected resources in an online foraging task. On every trial, participants chose either to continue to collect rewards from a depleting patch of resources or to replenish the patch. Participants also completed a well-validated ADHD self-report screening assessment at the end of sessions. Participants departed resource patches sooner when travel times between patches were shorter than when they were longer, as predicted by optimal foraging theory. Participants whose scores on the ADHD scale crossed the threshold for a positive screen departed patches significantly sooner than participants who did not meet this criterion. Participants meeting this threshold for ADHD also achieved higher reward rates than individuals who did not. Our findings suggest that ADHD attributes may confer foraging advantages in some environments and invite the possibility that this condition may reflect an adaptation favouring exploration over exploitation.

The Dynamicist Landscape.

The dynamical hypothesis states that cognitive systems are dynamical systems. While dynamical systems play an important role in many cognitive phenomena, the dynamical hypothesis as stated applies to every system and so fails both to specify what makes cognitive systems distinct and to distinguish between proposals regarding the nature of cognitive systems. To avoid this problem, I distinguish several different types of dynamical systems, outlining four dimensions along which dynamical systems can vary: total-state versus partial-state, internal versus external, macroscopic versus microscopic, and systemic versus componential, and illustrate these with examples. I conclude with two illustrations of partial-state, internal, microscopic, componential dynamicism.

Studying the neural representations of uncertainty.

The study of the brain's representations of uncertainty is a central topic in neuroscience. Unlike most quantities of which the neural representation is studied, uncertainty is a property of an observer's beliefs about the world, which poses specific methodological challenges. We analyze how the literature on the neural representations of uncertainty addresses those challenges and distinguish between 'code-driven' and 'correlational' approaches. Code-driven approaches make assumptions about the neural code for representing world states and the associated uncertainty. By contrast, correlational approaches search for relationships between uncertainty and neural activity without constraints on the neural representation of the world state that this uncertainty accompanies. To compare these two approaches, we apply several criteria for neural representations: sensitivity, specificity, invariance and functionality. Our analysis reveals that the two approaches lead to different but complementary findings, shaping new research questions and guiding future experiments.

Information gathering explains decision dynamics during human and monkey reward foraging.

Foraging in humans and other animals requires a delicate balance between exploitation of current resources and exploration for new ones. The tendency to overharvest-lingering too long in depleting patches-is a routine behavioral deviation from predictions of optimal foraging theories. To characterize the computational mechanisms driving these deviations, we modeled foraging behavior using a virtual patch-leaving task with human participants and validated our findings in an analogous foraging task in two monkeys. Both humans and monkeys overharvested and stayed longer in patches with longer travel times compared to shorter ones. Critically, patch residence times in both species declined over the course of sessions, enhancing reward rates in humans. These decisions were best explained by a logistic transformation that integrated both current rewards and information about declining rewards. This parsimonious model demystifies both the occurrence and dynamics of overharvesting, highlighting the role of information gathering in foraging. Our findings provide insight into computational mechanisms shaped by ubiquitous foraging dilemmas, underscoring how behavioral modeling can reveal underlying motivations of seemingly irrational decisions.

Tasks and their role in visual neuroscience.

Vision is widely used as a model system to gain insights into how sensory inputs are processed and interpreted by the brain. Historically, careful quantification and control of visual stimuli have served as the backbone of visual neuroscience. There has been less emphasis, however, on how an observer's task influences the processing of sensory inputs. Motivated by diverse observations of task-dependent activity in the visual system, we propose a framework for thinking about tasks, their role in sensory processing, and how we might formally incorporate tasks into our models of vision.

Visuospatial information foraging describes search behavior in learning latent environmental features.

In the real world, making sequences of decisions to achieve goals often depends upon the ability to learn aspects of the environment that are not directly perceptible. Learning these so-called latent features requires seeking information about them. Prior efforts to study latent feature learning often used single decisions, used few features, and failed to distinguish between reward-seeking and information-seeking. To overcome this, we designed a task in which humans and monkeys made a series of choices to search for shapes hidden on a grid. On our task, the effects of reward and information outcomes from uncovering parts of shapes could be disentangled. Members of both species adeptly learned the shapes and preferred to select tiles expected to be informative earlier in trials than previously rewarding ones, searching a part of the grid until their outcomes dropped below the average information outcome-a pattern consistent with foraging behavior. In addition, how quickly humans learned the shapes was predicted by how well their choice sequences matched the foraging pattern, revealing an unexpected connection between foraging and learning. This adaptive search for information may underlie the ability in humans and monkeys to learn latent features to support goal-directed behavior in the long run.

Geometrical representation of serial order in working memory.

Encoding a sequence relies on one's memory for ordinal succession of events and is critical for episodic memory, spatial navigation, language, and other cognitive functions. Investigating the neural mechanisms underlying sequence working memory in the macaque prefrontal cortex, Xie et al. (Science, 375, 632-639, 2022) uncovered a novel integrated representation of temporal and spatial information in different subspaces of a high-dimensional neural state space, offering broad implications across comparative cognition and neuroscience.

A call for more clarity around causality in neuroscience.

In neuroscience, the term 'causality' is used to refer to different concepts, leading to confusion. Here we illustrate some of those variations, and we suggest names for them. We then introduce four ways to enhance clarity around causality in neuroscience.

Two views on the cognitive brain.

Cognition can be defined as computation over meaningful representations in the brain to produce adaptive behaviour. There are two views on the relationship between cognition and the brain that are largely implicit in the literature. The Sherringtonian view seeks to explain cognition as the result of operations on signals performed at nodes in a network and passed between them that are implemented by specific neurons and their connections in circuits in the brain. The contrasting Hopfieldian view explains cognition as the result of transformations between or movement within representational spaces that are implemented by neural populations. Thus, the Hopfieldian view relegates details regarding the identity of and connections between specific neurons to the status of secondary explainers. Only the Hopfieldian approach has the representational and computational resources needed to develop novel neurofunctional objects that can serve as primary explainers of cognition.

Neuronal Activity in the Posterior Cingulate Cortex Signals Environmental Information and Predicts Behavioral Variability during Trapline Foraging.

Animals engage in routine behavior to efficiently navigate their environments. This routine behavior may be influenced by the state of the environment, such as the location and size of rewards. The neural circuits tracking environmental information and how that information impacts decisions to deviate from routines remain unexplored. To investigate the representation of environmental information during routine foraging, we recorded the activity of single neurons in posterior cingulate cortex (PCC) in 2 male monkeys searching through an array of targets in which the location of rewards was unknown. Outside the laboratory, people and animals solve such traveling salesman problems by following routine traplines that connect nearest-neighbor locations. In our task, monkeys also deployed traplining routines; but as the environment became better known, they deviate from them despite the reduction in foraging efficiency. While foraging, PCC neurons tracked environmental information but not reward and predicted variability in the pattern of choices. Together, these findings suggest that PCC may mediate the influence of information on variability in choice behavior.SIGNIFICANCE STATEMENT Many animals seek information to better guide their decisions and update behavioral routines. In our study, subjects visually searched through a set of targets on every trial to gather two rewards. Greater amounts of information about the distribution of rewards predicted less variability in choice patterns, whereas smaller amounts predicted greater variability. We recorded from the posterior cingulate cortex, an area implicated in the coding of reward and uncertainty, and discovered that these neurons signaled the expected information about the distribution of rewards instead of signaling expected rewards. The activity in these cells also predicted the amount of variability in choice behavior. These findings suggest that the posterior cingulate helps direct the search for information to augment routines.

Posterior Cingulate Neurons Dynamically Signal Decisions to Disengage during Foraging.

Foraging for resources is a fundamental behavior balancing systematic search and strategic disengagement. The foraging behavior of primates is especially complex and requires long-term memory, value comparison, strategic planning, and decision-making. Here we provide evidence from two different foraging tasks that neurons in primate posterior cingulate cortex (PCC) signal decision salience during foraging to motivate disengagement from the current strategy. In our foraging tasks, salience refers to the difference between decision thresholds and the net harvested reward. Salience signals were stronger in poor foraging contexts than rich ones, suggesting low harvest rates recruit mechanisms in PCC that regulate strategic disengagement and exploration during foraging.

Neurocomputational Nosology: Malfunctions of Models and Mechanisms.

Executive dysfunctions, psychopathologies arising from problems in the control and regulation of behavior, can occur as a result of the faulty execution of formal information processing models or as a result of malfunctioning neural mechanisms. The models correspond to the formal descriptions of how signals in the environment must be transformed in order to behave adaptively, and the mechanisms correspond to the signal transformations that nervous systems implement in order to execute those cognitive functions. Mechanisms in the form of repeated patterns of neural dynamics execute information processing models. Two distinct modes of malfunction can occur when neural dynamics execute models of information processing. The processing models describing behavior may fail to be executed correctly by neural mechanisms. Or, the neural mechanisms may malfunction, failing to implement the right computation. As an example of malfunctioning models in executive cognition, purported failures of rule following can be understood as failures to appropriately execute a suite of processing models. As an example of malfunctioning mechanisms of executive cognition, maladaptive behavior resulting from dysfunction in the medial prefrontal cortex (mPFC) can be understood as failures in the signal transformations carried out therein. The purpose of these examples is to illustrate the potential benefits of considering models and mechanisms in the diagnosis and etiology of neuropsychological illness and dysfunction, especially disorders of executive cognition.

Temporal trade-offs in psychophysics.

Psychophysical techniques typically assume straightforward relationships between manipulations of real-world events, their effects on the brain, and behavioral reports of those effects. However, these relationships can be influenced by many complex, strategic factors that contribute to task performance. Here we discuss several of these factors that share two key features. First, they involve subjects making flexible use of time to process information. Second, this flexibility can reflect the rational regulation of information-processing trade-offs that can play prominent roles in particular temporal epochs: sensitivity to stability versus change for past information, speed versus accuracy for current information, and exploitation versus exploration for future goals. Understanding how subjects manage these trade-offs can be used to help design and interpret psychophysical studies.

Mechanistic classification of neural circuit dysfunctions: insights from neuroeconomics research in animals.

Many psychiatric conditions present complex behavioral symptoms, and the type and magnitude of underlying neural dysfunction may vary drastically. This review introduces a classification scheme for psychiatric symptoms, describing them in terms of the state of a dysfunctional neural circuit. We provide examples of two kinds of functional deficits: variance-shifted functionality, in which a damaged circuit continues to function albeit suboptimally, and state-shifted functionality, resulting in an absent or qualitatively different functional state. We discuss, from the perspective of neuroeconomics and related areas of behavioral investigation, three broad classes of commonly occurring symptoms in psychopathology based on selected studies of decision making in animals: temporal discounting, social preferences, and decision making under environmental volatility. We conclude that the proposed mechanistic categorization scheme offers promise for understanding neural circuit dysfunctions underlying psychopathology.

Posterior cingulate cortex: adapting behavior to a changing world.

When has the world changed enough to warrant a new approach? The answer depends on current needs, behavioral flexibility and prior knowledge about the environment. Formal approaches solve the problem by integrating the recent history of rewards, errors, uncertainty and context via Bayesian inference to detect changes in the world and alter behavioral policy. Neuronal activity in posterior cingulate cortex - a key node in the default network - is known to vary with learning, memory, reward and task engagement. We propose that these modulations reflect the underlying process of change detection and motivate subsequent shifts in behavior.