A reservoir of foraging decision variables in the mouse brain

Sex mechanisms as nonbinary influences on cognitive diversity

Essentially all neuropsychiatric diagnoses show some degree of sex and/or gender differences in their etiology, diagnosis, or prognosis. As a result, the roles of sex-related variables in behavior and cognition are of strong interest to many, with several lines of research showing effects on executive functions and value-based decision making in particular. These findings are often framed within a sex binary, with behavior of females described as less optimal than male "defaults"-- a framing that pits males and females against each other and deemphasizes the enormous overlap in fundamental neural mechanisms across sexes. Here, we propose an alternative framework in which sex-related factors encompass just one subset of many sources of valuable diversity in cognition. First, we review literature establishing multidimensional, nonbinary impacts of factors related to sex chromosomes and endocrine mechanisms on cognition, focusing on value- based decision-making tasks. Next, we present two suggestions for nonbinary interpretations and analyses of sex-related data that can be implemented by behavioral neuroscientists without devoting laboratory resources to delving into mechanisms underlying sex differences. We recommend (1) shifting interpretations of behavior away from performance metrics and towards strategy assessments to avoid the fallacy that the performance of one sex is worse than another; and (2) asking how much variance sex explains in measures and whether any differences are mosaic rather than binary, to avoid assuming that sex differences in separate measures are inextricably correlated. Nonbinary frameworks in research on cognition will allow neuroscience to represent the full spectrum of brains and behaviors.

Hierarchical control over foraging behavior by anterior cingulate cortex

Foraging is a natural behavior that involves making sequential decisions to maximize rewards while minimizing the costs incurred when doing so. The prevalence of foraging across species suggests that a common brain computation underlies its implementation. Although anterior cingulate cortex is believed to contribute to foraging behavior, its specific role has been contentious, with predominant theories arguing either that it encodes environmental value or choice difficulty. Additionally, recent attempts to characterize foraging have taken place within the reinforcement learning framework, with increasingly complex models scaling with task complexity. Here we review reinforcement learning foraging models, highlighting the hierarchical structure of many foraging problems. We extend this literature by proposing that ACC guides foraging according to principles of model-based hierarchical reinforcement learning. This idea holds that ACC function is organized hierarchically along a rostral-caudal gradient, with rostral structures monitoring the status and completion of high-level task goals (like finding food), and midcingulate structures overseeing the execution of task options (subgoals, like harvesting fruit) and lower-level actions (such as grabbing an apple).

The maps of meaning consciousness theory

In simple terms, consciousness is constituted by multiple goals for action and the continuous adjudication of such goals to implement action, which is referred to as the maps of meaning (MoM) consciousness theory. The MoM theory triangulates through three parallel corollaries: action (behavior), mechanism (morphology/pathophysiology), and goals (teleology). (1) An organism's consciousness contains fluid, nested goals. These goals are not intentionality, but intersectionality, via the Darwinian byproduct of embodiment meeting the world, i.e., Darwinian inclusive fitness or randomization and then survival of the fittest. (2) These goals are formed via a gradual descent under inclusive fitness and are the abstraction of a "match" between the evolutionary environment and the organism. (3) Human consciousness implements the brain efficiency hypothesis, genetics, epigenetics, and experience-crystallized efficiencies, not necessitating best or objective but fitness, i.e., perceived efficiency based on one's adaptive environment. These efficiencies are objectively arbitrary but determine the operation and level of one's consciousness, termed as extreme thrownness. (4) Since inclusive fitness drives efficiencies in the physiologic mechanism, morphology, and behavior (action) and originates one's goals, embodiment is necessarily entangled to human consciousness as it is at the intersection of mechanism or action (both necessitating embodiment) occurring in the world that determines fitness. (5) Perception is the operant process of consciousness and is the de facto goal adjudication process of consciousness. Goal operationalization is fundamentally efficiency-based via one's unique neuronal mapping as a byproduct of genetics, epigenetics, and experience. (6) Perception involves information intake and information discrimination, equally underpinned by efficiencies of inclusive fitness via extreme thrownness. Perception is not a 'frame rate' but Bayesian priors of efficiency based on one's extreme thrownness. (7) Consciousness and human consciousness are modular (i.e., a scalar level of richness, which builds up like building blocks) and dimensionalized (i.e., cognitive abilities become possibilities as the emergent phenomena at various modularities such as the stratified factors in factor analysis). (8) The meta dimensions of human consciousness seemingly include intelligence quotient, personality (five-factor model), richness of perception intake, and richness of perception discrimination, among other potentialities. (9) Future consciousness research should utilize factor analysis to parse modularities and dimensions of human consciousness and animal models.

Learning attentional templates for value-based decision-making

Attention filters sensory inputs to enhance task-relevant information. It is guided by an "attentional template" that represents the stimulus features that are currently relevant. To understand how the brain learns and uses templates, we trained monkeys to perform a visual search task that required them to repeatedly learn new attentional templates. Neural recordings found that templates were represented across the prefrontal and parietal cortex in a structured manner, such that perceptually neighboring templates had similar neural representations. When the task changed, a new attentional template was learned by incrementally shifting the template toward rewarded features. Finally, we found that attentional templates transformed stimulus features into a common value representation that allowed the same decision-making mechanisms to deploy attention, regardless of the identity of the template. Altogether, our results provide insight into the neural mechanisms by which the brain learns to control attention and how attention can be flexibly deployed across tasks.

Decision-making dynamics are predicted by arousal and uninstructed movements

During sensory-guided behavior, an animal's decision-making dynamics unfold through sequences of distinct performance states, even while stimulus-reward contingencies remain static. Little is known about the factors that underlie these changes in task performance. We hypothesize that these decision-making dynamics can be predicted by externally observable measures, such as uninstructed movements and changes in arousal. Here, using computational modeling of visual and auditory task performance data from mice, we uncovered lawful relationships between transitions in strategic task performance states and an animal's arousal and uninstructed movements. Using hidden Markov models applied to behavioral choices during sensory discrimination tasks, we find that animals fluctuate between minutes-long optimal, sub-optimal, and disengaged performance states. Optimal state epochs are predicted by intermediate levels, and reduced variability, of pupil diameter and movement. Our results demonstrate that externally observable uninstructed behaviors can predict optimal performance states and suggest that mice regulate their arousal during optimal performance.

The rat frontal orienting field dynamically encodes value for economic decisions under risk

Frontal and parietal cortex are implicated in economic decision-making, but their causal roles are untested. Here we silenced the frontal orienting field (FOF) and posterior parietal cortex (PPC) while rats chose between a cued lottery and a small stable surebet. PPC inactivations produced minimal short-lived effects. FOF inactivations reliably reduced lottery choices. A mixed-agent model of choice indicated that silencing the FOF caused a change in the curvature of the rats' utility function (U = Vρ). Consistent with this finding, single-neuron and population analyses of neural activity confirmed that the FOF encodes the lottery value on each trial. A dynamical model, which accounts for electrophysiological and silencing results, suggests that the FOF represents the current lottery value to compare against the remembered surebet value. These results demonstrate that the FOF is a critical node in the neural circuit for the dynamic representation of action values for choice under risk.

Competitive integration of time and reward explains value-sensitive foraging decisions and frontal cortex ramping dynamics

The ability to make advantageous decisions is critical for animals to ensure their survival. Patch foraging is a natural decision-making process in which animals decide when to leave a patch of depleting resources to search for a new one. To study the algorithmic and neural basis of patch foraging behavior in a controlled laboratory setting, we developed a virtual foraging task for head-fixed mice. Mouse behavior could be explained by ramp-to-threshold models integrating time and rewards antagonistically. Accurate behavioral modeling required inclusion of a slowly varying "patience" variable, which modulated sensitivity to time. To investigate the neural basis of this decision-making process, we performed dense electrophysiological recordings with Neuropixels probes broadly throughout frontal cortex and underlying subcortical areas. We found that decision variables from the reward integrator model were represented in neural activity, most robustly in frontal cortical areas. Regression modeling followed by unsupervised clustering identified a subset of neurons with ramping activity. These neurons' firing rates ramped up gradually in single trials over long time scales (up to tens of seconds), were inhibited by rewards, and were better described as being generated by a continuous ramp rather than a discrete stepping process. Together, these results identify reward integration via a continuous ramping process in frontal cortex as a likely candidate for the mechanism by which the mammalian brain solves patch foraging problems.