Spatial planning with long visual range benefits escape from visual predators in complex naturalistic environments

Collaborative hunting in artificial agents with deep reinforcement learning

Collaborative hunting, in which predators play different and complementary roles to capture prey, has been traditionally believed to be an advanced hunting strategy requiring large brains that involve high-level cognition. However, recent findings that collaborative hunting has also been documented in smaller-brained vertebrates have placed this previous belief under strain. Here, using computational multi-agent simulations based on deep reinforcement learning, we demonstrate that decisions underlying collaborative hunts do not necessarily rely on sophisticated cognitive processes. We found that apparently elaborate coordination can be achieved through a relatively simple decision process of mapping between states and actions related to distance-dependent internal representations formed by prior experience. Furthermore, we confirmed that this decision rule of predators is robust against unknown prey controlled by humans. Our computational ecological results emphasize that collaborative hunting can emerge in various intra- and inter-specific interactions in nature, and provide insights into the evolution of sociality.

A robot-rodent interaction arena with adjustable spatial complexity for ethologically relevant behavioral studies

Outside of the laboratory, animals behave in spaces where they can transition between open areas and coverage as they interact with others. Replicating these conditions in the laboratory can be difficult to control and record. This has led to a dominance of relatively simple, static behavioral paradigms that reduce the ethological relevance of behaviors and may alter the engagement of cognitive processes such as planning and decision-making. Therefore, we developed a method for controllable, repeatable interactions with others in a reconfigurable space. Mice navigate a large honeycomb lattice of adjustable obstacles as they interact with an autonomous robot coupled to their actions. We illustrate the system using the robot as a pseudo-predator, delivering airpuffs to the mice. The combination of obstacles and a mobile threat elicits a diverse set of behaviors, such as increased path diversity, peeking, and baiting, providing a method to explore ethologically relevant behaviors in the laboratory.

Naturalistic reinforcement learning

Humans possess a remarkable ability to make decisions within real-world environments that are expansive, complex, and multidimensional. Human cognitive computational neuroscience has sought to exploit reinforcement learning (RL) as a framework within which to explain human decision-making, often focusing on constrained, artificial experimental tasks. In this article, we review recent efforts that use naturalistic approaches to determine how humans make decisions in complex environments that better approximate the real world, providing a clearer picture of how humans navigate the challenges posed by real-world decisions. These studies purposely embed elements of naturalistic complexity within experimental paradigms, rather than focusing on simplification, generating insights into the processes that likely underpin humans' ability to navigate complex, multidimensional real-world environments so successfully.

Collaborative robots can augment human cognition in regret-sensitive tasks

Despite theoretical benefits of collaborative robots, disappointing outcomes are well documented by clinical studies, spanning rehabilitation, prostheses, and surgery. Cognitive load theory provides a possible explanation for why humans in the real world are not realizing the benefits of collaborative robots: high cognitive loads may be impeding human performance. Measuring cognitive availability using an electrocardiogram, we ask 25 participants to complete a virtual-reality task alongside an invisible agent that determines optimal performance by iteratively updating the Bellman equation. Three robots assist by providing environmental information relevant to task performance. By enabling the robots to act more autonomously-managing more of their own behavior with fewer instructions from the human-here we show that robots can augment participants' cognitive availability and decision-making. The way in which robots describe and achieve their objective can improve the human's cognitive ability to reason about the task and contribute to human-robot collaboration outcomes. Augmenting human cognition provides a path to improve the efficacy of collaborative robots. By demonstrating how robots can improve human cognition, this work paves the way for improving the cognitive capabilities of first responders, manufacturing workers, surgeons, and other future users of collaborative autonomy systems.

From water to land: Evolution of photoreceptor circuits for vision in air

When vertebrates first conquered the land, they encountered a visual world that was radically distinct from that of their aquatic ancestors. Fish exploit the strong wavelength-dependent interactions of light with water by differentially feeding the signals from up to 5 spectral photoreceptor types into distinct behavioural programmes. However, above the water the same spectral rules do not apply, and this called for an update to visual circuit strategies. Early tetrapods soon evolved the double cone, a still poorly understood pair of new photoreceptors that brought the "ancestral terrestrial" complement from 5 to 7. Subsequent nonmammalian lineages differentially adapted this highly parallelised retinal input strategy for their diverse visual ecologies. By contrast, mammals shed most ancestral photoreceptors and converged on an input strategy that is exceptionally general. In eutherian mammals including in humans, parallelisation emerges gradually as the visual signal traverses the layers of the retina and into the brain.

LiDAR Reveals the Process of Vision-Mediated Predator–Prey Relationships

Exploring the processes of interspecific relationships is crucial to understanding the mechanisms of biodiversity maintenance. Visually detecting interspecies relationships of large mammals is limited by the reconstruction accuracy of the environmental structure and the timely detection of animal behavior. Hence, we used backpack laser scanning (BLS) to reconstruct the high-resolution three-dimensional environmental structure to simulate the process of a predator approaching its prey, indicating that predator tigers would reduce their visibility by changing their behavior. Wild boars will nibble off about 5m of branches around the nest in order to create better visibility around the nest, adopting an anti-predation strategy to detect possible predators in advance. Our study not only points out how predator–prey relationships are affected by visibility as the environment mediates it, but also provides an operable framework for exploring interspecific relationships from a more complex dimension. Finally, this study provides a new perspective for exploring the mechanisms of biodiversity maintenance.

Medial and orbital frontal cortex in decision-making and flexible behavior

The medial frontal cortex and adjacent orbitofrontal cortex have been the focus of investigations of decision-making, behavioral flexibility, and social behavior. We review studies conducted in humans, macaques, and rodents and argue that several regions with different functional roles can be identified in the dorsal anterior cingulate cortex, perigenual anterior cingulate cortex, anterior medial frontal cortex, ventromedial prefrontal cortex, and medial and lateral parts of the orbitofrontal cortex. There is increasing evidence that the manner in which these areas represent the value of the environment and specific choices is different from subcortical brain regions and more complex than previously thought. Although activity in some regions reflects distributions of reward and opportunities across the environment, in other cases, activity reflects the structural relationships between features of the environment that animals can use to infer what decision to take even if they have not encountered identical opportunities in the past.

Think Fast!: Vervet Monkeys Assess the Risk of Being Displaced by a Dominant Competitor When Making Foraging Decisions

Foraging animals need to quickly assess the costs and benefits of different foraging decisions, including resource quantity, quality, preference, ease of access, dispersion, distance, and predation risk. Social animals also need to take social context into account and adapt foraging strategies that maximize net resource intake and minimize contest competition with conspecifics. We used an experimental approach to investigate how social context impacts wild vervet monkey (Chlorocebus pygerythrus) foraging decisions in a multi-destination pentagon array. We baited four platforms with less-preferred corn and one platform with a larger, preferred resource (half banana) that required handling time. We ran over 1,000 trials and found that when monkeys foraged alone, they usually took the path that minimized travel distance but prioritized the preferred-food platform when in competition. However, the foraging strategy chosen by low-ranking individuals depended on the handling skill of the decision maker (i.e., time it would take them to retrieve the banana), the relative rank of their audience members (i.e., who has priority-of-access to resources), and the distance audience members were from the experiment site (i.e., their travel time). When the risk of being displaced by a dominant competitor was low (because they were far away and/or because the decision-maker was skilled in retrieving the banana), low-ranking individuals chose a route that minimized travel costs. Conversely, when the risk of losing food to a dominant competitor was high, decision-makers rushed for the preferred-food platform at the onset of the trial. When the risk of displacement was moderate because a dominant audience member was at least 50 m away, low-ranking individuals partly prioritized the preferred-food platform but took the time to stop for one platform of corn on the way. This strategy increased the total amount of food obtained during the trial. These findings suggest that lower-ranking individuals, who experienced high contest competition at the foraging experiment, calculated the risk of being displaced by a dominant competitor when making foraging decisions. This experiment demonstrates that vervets go through a complex decision-making process that simultaneously considers the profitability of different foraging decisions and their social context.

The neuroecology of the water-to-land transition and the evolution of the vertebrate brain

The water-to-land transition in vertebrate evolution offers an unusual opportunity to consider computational affordances of a new ecology for the brain. All sensory modalities are changed, particularly a greatly enlarged visual sensorium owing to air versus water as a medium, and expanded by mobile eyes and neck. The multiplication of limbs, as evolved to exploit aspects of life on land, is a comparable computational challenge. As the total mass of living organisms on land is a hundredfold larger than the mass underwater, computational improvements promise great rewards. In water, the midbrain tectum coordinates approach/avoid decisions, contextualized by water flow and by the animal's body state and learning. On land, the relative motions of sensory surfaces and effectors must be resolved, adding on computational architectures from the dorsal pallium, such as the parietal cortex. For the large-brained and long-living denizens of land, making the right decision when the wrong one means death may be the basis of planning, which allows animals to learn from hypothetical experience before enactment. Integration of value-weighted, memorized panoramas in basal ganglia/frontal cortex circuitry, with allocentric cognitive maps of the hippocampus and its associated cortices becomes a cognitive habit-to-plan transition as substantial as the change in ecology. This article is part of the theme issue 'Systems neuroscience through the lens of evolutionary theory'.

Five Breakthroughs: A First Approximation of Brain Evolution From Early Bilaterians to Humans

Retracing the evolutionary steps by which human brains evolved can offer insights into the underlying mechanisms of human brain function as well as the phylogenetic origin of various features of human behavior. To this end, this article presents a model for interpreting the physical and behavioral modifications throughout major milestones in human brain evolution. This model introduces the concept of a "breakthrough" as a useful tool for interpreting suites of brain modifications and the various adaptive behaviors these modifications enabled. This offers a unique view into the ordered steps by which human brains evolved and suggests several unique hypotheses on the mechanisms of human brain function.

Formalizing planning and information search in naturalistic decision-making

Decisions made by mammals and birds are often temporally extended. They require planning and sampling of decision-relevant information. Our understanding of such decision-making remains in its infancy compared with simpler, forced-choice paradigms. However, recent advances in algorithms supporting planning and information search provide a lens through which we can explain neural and behavioral data in these tasks. We review these advances to obtain a clearer understanding for why planning and curiosity originated in certain species but not others; how activity in the medial temporal lobe, prefrontal and cingulate cortices may support these behaviors; and how planning and information search may complement each other as means to improve future action selection.

What Behavioral Abilities Emerged at Key Milestones in Human Brain Evolution? 13 Hypotheses on the 600-Million-Year Phylogenetic History of Human Intelligence

This paper presents 13 hypotheses regarding the specific behavioral abilities that emerged at key milestones during the 600-million-year phylogenetic history from early bilaterians to extant humans. The behavioral, intellectual, and cognitive faculties of humans are complex and varied: we have abilities as diverse as map-based navigation, theory of mind, counterfactual learning, episodic memory, and language. But these faculties, which emerge from the complex human brain, are likely to have evolved from simpler prototypes in the simpler brains of our ancestors. Understanding the order in which behavioral abilities evolved can shed light on how and why our brains evolved. To propose these hypotheses, I review the available data from comparative psychology and evolutionary neuroscience.

Multicentric tracking of multiple agents by anterior cingulate cortex during pursuit and evasion

Successful pursuit and evasion require rapid and precise coordination of navigation with adaptive motor control. We hypothesize that the dorsal anterior cingulate cortex (dACC), which communicates bidirectionally with both the hippocampal complex and premotor/motor areas, would serve a mapping role in this process. We recorded responses of dACC ensembles in two macaques performing a joystick-controlled continuous pursuit/evasion task. We find that dACC carries two sets of signals, (1) world-centric variables that together form a representation of the position and velocity of all relevant agents (self, prey, and predator) in the virtual world, and (2) avatar-centric variables, i.e. self-prey distance and angle. Both sets of variables are multiplexed within an overlapping set of neurons. Our results suggest that dACC may contribute to pursuit and evasion by computing and continuously updating a multicentric representation of the unfolding task state, and support the hypothesis that it plays a high-level abstract role in the control of behavior.

Efficient Lévy walks in virtual human foraging

Efficient foraging depends on decisions that account for the costs and benefits of various activities like movement, perception, and planning. We conducted a virtual foraging experiment set in the foothills of the Himalayas to examine how time and energy are expended to forage efficiently, and how foraging changes when constrained to a home range. Two hundred players foraged the human-scale landscape with simulated energy expenditure in search of naturally distributed resources. Results showed that efficient foragers produced periods of locomotion interleaved with perception and planning that approached theoretical expectations for Lévy walks, regardless of the home-range constraint. Despite this constancy, efficient home-range foraging trajectories were less diffusive by virtue of restricting locomotive search and spending more time instead scanning the environment to plan movement and detect far-away resources. Altogether, results demonstrate that humans can forage efficiently by arranging and adjusting Lévy-distributed search activities in response to environmental and task constraints.

Long-lasting generalization triggered by a single trial event in the invasive crayfish Procambarus clarkii

Behavioural flexibility allows adaptation to environmental changes, a situation that invasive species have often to face when colonizing new territories. Such flexibility arises from a set of cognitive mechanisms among which generalization plays a key role, as it allows the transfer of past solutions to solve similar new problems. By means of a habituation paradigm, we studied generalization in the invasive crayfish Procambarus clarkii Once crayfish had habituated their defensive response to a specific water jet, we tested whether habituation transferred to a new type of water jet. Although habituation did not generalize when the new stimulus was initially presented, it surprisingly emerged 15 and 45 days later. Hence, remarkably, in Pclarkii, a single presentation of a new event was sufficient to trigger a long-lasting form of learning generalization from previous similar stimuli, a cognitive ability that may concur in providing adaptive advantages to this invasive species.

Tuning movement for sensing in an uncertain world

While animals track or search for targets, sensory organs make small unexplained movements on top of the primary task-related motions. While multiple theories for these movements exist-in that they support infotaxis, gain adaptation, spectral whitening, and high-pass filtering-predicted trajectories show poor fit to measured trajectories. We propose a new theory for these movements called energy-constrained proportional betting, where the probability of moving to a location is proportional to an expectation of how informative it will be balanced against the movement's predicted energetic cost. Trajectories generated in this way show good agreement with measured trajectories of fish tracking an object using electrosense, a mammal and an insect localizing an odor source, and a moth tracking a flower using vision. Our theory unifies the metabolic cost of motion with information theory. It predicts sense organ movements in animals and can prescribe sensor motion for robots to enhance performance.