Kensington Fieldwork Grant
The Royal Geographical Society offers four annual grants of GBP 25,000 each for ambitious geographical fieldwork that combines rigorous research with communication of its results. Experienced researchers, explorers and storytellers may apply individually or in teams from any country. At least one team member must be a Society Fellow or member throughout the grant period. Proposals should explain field methods, local partnerships, planned outputs and how findings and data will be shared. The current round closes on 8 January 2027, with shortlisted applicants invited to interview on 24 February 2027. The official grant page provides the application requirements and submission instructions.
Sex Differences in Learning from Exploration
Sex-based modulation of cognitive processes could set the stage for individual differences in vulnerability to neuropsychiatric disorders. While value-based decision making processes in particular have been proposed to be influenced by sex differences, the overall correct performance in decision making tasks often show variable or minimal differences across sexes. Computational tools allow us to uncover latent variables that define different decision making approaches, even in animals with similar correct performance. Here, we quantify sex differences in mice in the latent variables underlying behavior in a classic value-based decision making task: a restless two-armed bandit. While male and female mice had similar accuracy, they achieved this performance via different patterns of exploration. Male mice tended to make more exploratory choices overall, largely because they appeared to get ‘stuck’ in exploration once they had started. Female mice tended to explore less but learned more quickly during exploration. Together, these results suggest that sex exerts stronger influences on decision making during periods of learning and exploration than during stable choices. Exploration during decision making is altered in people diagnosed with addictions, depression, and neurodevelopmental disabilities, pinpointing the neural mechanisms of exploration as a highly translational avenue for conferring sex-modulated vulnerability to neuropsychiatric diagnoses.
Object recognition by touch and other senses
Navigation Turing Test: Toward Human-like RL
tbc
Exploration beyond bandits
Machine learning researchers frequently focus on human-level performance, in particular in games. However, in these applications human (or human-level) behavior is commonly reduced to a simple dot on a performance graph. Cognitive science, in particular theories of learning and decision making, could hold the key to unlock what is behind this dot, thereby gaining further insights into human cognition and the design principles of intelligent algorithms. However, cognitive experiments commonly focus on relatively simple paradigms such as restricted multi-armed bandit tasks. In this talk, I will argue that cognitive science can turn its lens to more complex scenarios to study exploration in real-world domains and online games. I will show in one large data set of online food delivery orders and across many online games how current cognitive theories of learning and exploration can describe human behavior in the wild, but also how these tasks demand us to expand our theoretical toolkit to describe a rich repertoire of real-world behaviors such as empowerment and fun.
Generalization guided exploration
How do people learn in real-world environments where the space of possible actions can be vast or even infinite? The study of human learning has made rapid progress in past decades, from discovering the neural substrate of reward prediction errors, to building AI capable of mastering the game of Go. Yet this line of research has primarily focused on learning through repeated interactions with the same stimuli. How are humans able to rapidly adapt to novel situations and learn from such sparse examples? I propose a theory of how generalization guides human learning, by making predictions about which unobserved options are most promising to explore. Inspired by Roger Shepard’s law of generalization, I show how a Bayesian function learning model provides a mechanism for generalizing limited experiences to a wide set of novel possibilities, based on the simple principle that similar actions produce similar outcomes. This model of generalization generates predictions about the expected reward and underlying uncertainty of unexplored options, where both are vital components in how people actively explore the world. This model allows us to explain developmental differences in the explorative behavior of children, and suggests a general principle of learning across spatial, conceptual, and structured domains.
The Desire to Know: Non-Instrumental Information Seeking in Mice
Animals are motivated to acquire knowledge. A particularly striking example is information seeking behavior: animals often seek out sensory cues that will inform them about the properties of uncertain future rewards, even when there is no way for them to use this information to influence the reward outcome, and even when this information comes at a considerable cost. Evidence from monkey electrophysiology and human fMRI studies suggests that orbitofrontal cortex and midbrain dopamine neurons represent the subjective value of knowledge during information seeking behavior. However, it remains unclear how the brain assigns value to information and how it integrates this with other incentives to drive behavior. We have therefore developed a task to test if information preferences are present in mice and study how informational value is imparted on stimuli. Mice are trained to enter a center port and receive an initial odor that instructs them to either go to an informative side port, go to an uninformative side port, or choose freely between them. The chosen side port then yields a second odor cue followed by a delayed probabilistic water reward. The informative port’s odor cue indicates whether the upcoming reward will be big or small. The uninformative port’s odor cue is uncorrelated with the trial outcome. Crucially, the two ports only differ in their odor cues, not in their water value since both offer identical probabilities of big and small rewards. We find that mice prefer the informative port. This preference is evident as a higher percentage choice of the informative port when given a free choice (67% +/- 1.7%, n = 14, p < 0.03), as well as by faster reaction times when instructed to go to the informative port (544ms +/- 21ms vs 795ms +/- 21ms, n = 14, p < 0.001). The preference for information is robust to within-animal reversals of informative and uninformative port locations, and, moreover, mice are willing to pay for information by choosing the informative port even if its reward amount is reduced to be substantially lower than the uninformative port. These behavioral observations suggest that odor stimuli are imparted with informational value as mice learn the information seeking task. We are currently imaging neural activity in orbitofrontal cortex with microendoscopes to identify changes in neural activity that may reflect value associated with the acquisition of knowledge.