Despite the centrality of the notion of representation to its explanations, neuroscience lacks a unified framework for the concepts used to characterize representation, leading to disparate use of terminology and measures associated with representation. To offer clarification, we propose a core set of conceptual dimensions that characterize representations in neuroscience. These dimensions describe relations between a neural response, features that may be represented, and downstream effects of the neural response. A neural response may be shown to be sensitive and specific to a feature, invariant to other features, and functional, which means that it is used downstream in the brain. We use information-theoretic measures to introduce these conceptual dimensions unambiguously and explain how data analysis methods such as correlational analyses, decoding and encoding models, representational similarity analysis, and tests of statistical dependence or adaptation relate to our framework. We consider several canonical examples, including the representation of orientation, numerosity, and spatial location, which illustrate how the evidence put forth in support or criticism of representational conclusions is systematized by our framework. By offering a unified conceptual framework we hope to aid the comparison and integration of results across studies and research groups and to help determine when evidence for a representational conclusion is strong.

Which premisses should we use to start our inquiries? Which transitions during inquiry should we take next? When should we switch lines of inquiry? In this paper, I address these open questions about inquiry, formulating novel norms for such decisions during deductive reasoning. I use the first-order predicate calculus, in combination with Carnap's state description framework, to state such norms. Using that framework, I first demonstrate some properties of sets of sentences used in deduction. I then state some norms for decisions made during deductive reasoning, establishing initial benchmarks for efficient deduction by ideal reasoners. When deciding which transition to make next, reasoners should choose the most informative transition, the one that maximally reduces uncertainty in the sense of ruling out the largest number of state descriptions relevant to their inquiry. Finally, inspired by optimal foraging theory, I show that, under certain assumptions of ignorance, reasoners should change premiss sets when their information intake drops below the global average information intake across premiss sets.

How do theories of the functions of parts of the brain change? I argue that computational hypotheses help explain the nature of theorizing in cognitive neurobiology. I will focus on the orbitofrontal cortex (OFC), a frontal region of the brain implicated in an array of cognitive functions. Different theories of OFC state different principles of OFC function and use different concepts to construct those principles. There are also differences in the patterns of use of evidence across different theories. I briefly survey several extant proposals for understanding theory change in science generally and cognitive neuroscience specifically, including paradigm shifts, tool innovation, mechanism discovery, conceptual innovation, exploratory experimentation, and changes in measurement techniques. While these extant approaches fall short at describing the nature of theory change illustrated by the case of OFC, they are compatible with my proposal that these theoretical changes and differences in the use of evidence result from different computational hypotheses about the region.

The debate over context and neural function pits contextualists, who maintain that neural functions vary by context, against invariantists, who maintain that they do not. In this article, I defend a moderate position that permits some context sensitive yet invariant functions. I distinguish performance from competence as well as different types of contexts, accounting for many cases of variability by context. The discussion suggests changes in networks can sometimes change the competences of parts. I conclude that some context sensitivity of function is consistent with invariant functions whereas other changes in context imply changes in the competence to perform functions.

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.

Foraging is a central competence of all mobile organisms. Models and concepts from foraging theory have been applied widely throughout biology to the search for many kinds of external resources, including food, sexual encounters, minerals, water, and the like. In cognitive science and neuroscience, the tools of foraging theory are increasingly applied to a wide range of other types of search, including for abstract resources like information or for internal resources like memories, concepts, and strategies for problem solving. Despite its importance in ecology and increasing relevance for the study of cognition, the concept of foraging is rarely analyzed. Here, I aim to rectify this situation. I outline three desiderata: first, an analysis should differentiate foraging from search and decision making more generally; second, an analysis should unify different types of foraging; and third, an analysis should help ground predictions. I present an analysis of foraging as the serial search for general resources in accept-or-reject, exclusive, persistent decision contexts. Not all search is serial and not all decision making is exclusive, differentiating foraging from search and decision making generally. With the aid of Markov decision processes and directed cyclical models, I show how the analysis implies a cyclical graph. This cyclical graph is embedded in the description of many types of foraging, unifying the different instances. Finally, I argue that the cyclical graph is also embedded in representations of novel task contexts that have not previously been viewed as foraging. I illustrate this novel application of the concept of foraging by arguing that reasoning is a type of foraging.

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.

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.

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.

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.

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.

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.

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.

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We argue that intelligence — construed as the disposition to perform tasks successfully—is a property of systems composed of agents and their contexts. This is the thesis of extended intelligence. We argue that the performance of an agent will generally not be preserved if its context is allowed to vary. Hence, this disposition is not possessed by an agent alone, but is rather possessed by the system consisting of an agent and its context, which we dub an agent-in-context. An agent’s context may include an environment, other agents, cultural artifacts (like language, technology), or all of these, as is typically the case for humans and artificial intelligence systems, as well as many non-human animals. In virtue of the thesis of extended intelligence, we contend that intelligence is context-bound, task-particular and incommensurable among agents. Our thesis carries strong implications for how intelligence is analyzed in the context of both psychology and artificial intelligence.

Many real world environments contain latent features that animals must learn in order to accomplish their goals. Animals often learn these environments over sequences of choices, but the behavioral mechanisms underlying this complex cognitive competence remain poorly characterized. We investigate this sophisticated behavior in two primate species, humans and macaque monkeys, with a task in which subjects searched for shapes hidden on a grid. Both primate species were adept learners, quickly learning the subset of shapes out of the numerous possibilities. Subjects tended to select tiles earlier in trials that were informative in the past about the hidden shape than those that were rewarding. In addition, we found a surprising signature of foraging behavior over sequences of choices during trials, with human subjects searching local areas of the board until information dropped below the average across all choices, at which time they jumped to a different part of the board. This pattern of choices was not evident for rewards in humans. In contrast, the sequences of choices of monkeys were equally well-described as information or reward foraging. Finally, the rate at which humans learned shapes could be predicted by how well their choice sequences matched foraging behavior. These findings suggest that humans are more tuned to the search for information than reward than monkeys and that foraging competence predicts the capacity to learn complex environments.

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.