Biology subjects

Wu, C. M.

Publications and source records attributed to Wu, C. M..

14 recordsLinked to original sources

bioRxiv2023

Visual-spatial dynamics drive adaptive social learning in immersive environments

Human cognition is distinguished by our ability to adapt to different environments and circumstances. Yet the mechanisms driving adaptive behavior have predominantly been studied in separate asocial and social contexts, with an integrated framework remaining elusive. Here, we use a collective foraging task in a virtual Minecraft environment to integrate these two fields, by leveraging automated transcriptions of visual field data combined with high-resolution spatial trajectories. Our behavioral analyses capture both the structure and temporal dynamics of social interactions, which are then directly tested using computational models sequentially predicting each foraging decision. These results reveal that adaptation mechanisms of both asocial foraging and selective social learning are driven by individual foraging success (rather than social factors). Furthermore, it is the degree of adaptivity--of both asocial and social learning--that best predicts individual performance. These findings not only integrate theories across asocial and social domains, but also provide key insights into the adaptability of human decision-making in complex and dynamic social landscapes.

animal behavior and cognition↗

bioRxiv2021

Asymmetric learning facilitates human inference of transitive relations

Humans and other animals are capable of inferring never-experienced relations (e.g., A>C) from other relational observations (e.g., A>B and B>C). The processes behind such transitive inference are subject to intense research. Here, we demonstrate a new aspect of relational learning, building on previous evidence that transitive inference can be accomplished through simple reinforcement learning mechanisms. We show in simulations that inference of novel relations benefits from an asymmetric learning policy, where observers update only their belief about the winner (or loser) in a pair. Across 4 experiments (n=145), we find substantial empirical support for such asymmetries in inferential learning. The learning policy favoured by our simulations and experiments gives rise to a compression of values which is routinely observed in psychophysics and behavioural economics. In other words, a seemingly biased learning strategy that yields well-known cognitive distortions can be beneficial for transitive inferential judgments.

neuroscience↗

bioRxiv2021

Specialization and selective social attention establishes the balance between individual and social learning

A key question individuals face in any social learning environment is when to innovate alone and when to imitate others. Previous simulation results have found that the best performing groups exhibit an intermediate balance, yet it is still largely unknown how individuals collectively negotiate this balance. We use an immersive collective foraging experiment, implemented in the Minecraft game engine, facilitating unprecedented access to spatial trajectories and visual field data. The virtual environment imposes a limited field of view, creating a natural trade-off between allocating visual attention towards individual search or to look towards peers for social imitation. By analyzing foraging patterns, social interactions (visual and spatial), and social influence, we shine new light on how groups collectively adapt to the fluctuating demands of the environment through specialization and selective imitation, rather than homogeneity and indiscriminate copying of others.

animal behavior and cognition↗

bioRxiv2020

Mutations in SARS-CoV-2 spike protein and RNA polymerase complex are associated with COVID-19 mortality risk

BackgroundSARS-CoV-2 mortality has been extensively studied in relation to host susceptibility. How sequence variations in the SARS-CoV-2 genome affect pathogenicity is poorly understood. Whole-genome sequencing (WGS) of the virus with death in SARS-CoV-2 patients is one potential method of early identification of highly pathogenic strains to target for containment. MethodsWe analyzed 7,548 single stranded RNA-genomes of SARS-CoV-2 patients in the GISAID database (Elbe and Buckland-Merrett, 2017; Shu and McCauley, 2017) and associated variants with reported patients health status from COVID-19, i.e. deceased versus non-deceased. We probed each locus of the single stranded RNA of the SARS-CoV-2 virus for direct association with host/patient mortality using a logistic regression. ResultsIn total, evaluating 29,891 loci of the viral genome for association with patient/host mortality, two loci, at 12,053bp and 25,088bp, achieved genome-wide significance (p-values of 4.09e-09 and 4.41e-23, respectively). ConclusionsMutations at 25,088bp occur in the S2 subunit of the SARS-CoV-2 spike protein, which plays a key role in viral entry of target host cells. Additionally, mutations at 12,053bp are within the ORF1ab gene, in a region encoding for the protein nsp7, which is necessary to form the RNA polymerase complex responsible for viral replication and transcription. Both mutations altered amino acid coding sequences, potentially imposing structural changes that could enhance viral infectivity and symptom severity, and may be important to consider as targets for therapeutic development. Identification of these highly significant associations, unlikely to occur by chance, may assist with COVID-19 early containment of strains that are potentially highly pathogenic.

genomics↗

bioRxiv2020

Inference and search on graph-structured spaces

How do people learn functions on structured spaces? And how do they use this knowledge to guide their search for rewards in situations where the number of options is large? We study human behavior on structures with graph-correlated values and propose a Bayesian model of function learning to describe and predict their behavior. Across two experiments, one assessing function learning and one assessing the search for rewards, we find that our model captures human predictions and sampling behavior better than several alternatives, generates human-like learning curves, and also captures participants confidence judgements. Our results extend past models of human function learning and reward learning to more complex, graph-structured domains.

animal behavior and cognition↗

bioRxiv2020

Similarities and differences in spatial and non-spatial cognitive maps

Learning and generalization in spatial domains is often thought to rely on a "cognitive map", representing relationships between spatial locations. Recent research suggests that this same neural machinery is also recruited for reasoning about more abstract, conceptual forms of knowledge. Yet, to what extent do spatial and conceptual reasoning share common computational principles, and what are the implications for behavior? Using a within-subject design we studied how participants used spatial or conceptual distances to generalize and search for correlated rewards in successive multi-armed bandit tasks. Participant behavior indicated sensitivity to both spatial and conceptual distance, and was best captured using a Bayesian model of generalization that formalized distance-dependent generalization and uncertainty-guided exploration as a Gaussian Process regression with a radial basis function kernel. The same Gaussian Process model best captured human search decisions and judgments in both domains, and could simulate realistic learning curves, where we found equivalent levels of generalization in spatial and conceptual tasks. At the same time, we also find characteristic differences between domains. Relative to the spatial domain, participants showed reduced levels of uncertainty-directed exploration and increased levels of random exploration in the conceptual domain. Participants also displayed a one-directional transfer effect, where experience in the spatial task boosted performance in the conceptual task, but not vice versa. While confidence judgments indicated that participants were sensitive to the uncertainty of their knowledge in both tasks, they did not or could not leverage their estimates of uncertainty to guide exploration in the conceptual task. These results support the notion that value-guided learning and generalization recruit cognitive-map dependent computational mechanisms in spatial and conceptual domains. Yet both behavioral and model-based analyses suggest domain specific differences in how these representations map onto actions. Author summaryThere is a resurgence of interest in "cognitive maps" based on recent evidence that the hippocampal-entorhinal system encodes both spatial and non-spatial relational information, with far-reaching implications for human behavior. Yet little is known about the commonalities and differences in the computational principles underlying human learning and decision making in spatial and non-spatial domains. We use a within-subject design to examine how humans search for either spatially or conceptually correlated rewards. Using a Bayesian learning model, we find evidence for the same computational mechanisms of generalization across domains. While participants were sensitive to expected rewards and uncertainty in both tasks, how they leveraged this knowledge to guide exploration was different: participants displayed less uncertainty-directed and more random exploration in the conceptual domain. Moreover, experience with the spatial task improved conceptual performance, but not vice versa. These results provide important insights about the degree of overlap between spatial and conceptual cognition.

animal behavior and cognition↗

bioRxiv2019

Generalization as diffusion: human function learning on graphs

From social networks to public transportation, graph structures are a ubiquitous feature of life. Yet little is known about how humans learn functions on graphs, where relationships are defined by the connectivity structure. We adapt a Bayesian framework for function learning to graph structures, and propose that people perform generalization by diffusing observed function values across the graph. We test the predictions of this model by asking participants to make predictions about passenger volume in a virtual subway network. The model captures both generalization and confidence judgments, and is a quantitatively superior account relative to several heuristic models. Our work suggests that people exploit graph structure to make generalizations about functions in complex discrete spaces.

animal behavior and cognition↗

bioRxiv2019

The Evolutionary Dynamics of Cooperation in Collective Search

How does cooperation arise in an evolutionary context? We approach this problem using a collective search paradigm where interactions are dynamic and there is competition for rewards. Using evolutionary simulations, we find that the unconditional sharing of information can be an evolutionary advantageous strategy without the need for conditional strategies or explicit reciprocation. Shared information acts as a recruitment signal and facilitates the formation of a self-organized group. Thus, the improved search efficiency of the collective grants byproduct benefits to the original sharer by altering the interaction structure. A key mechanism is a visibility radius, where individuals have access to information about neighbors within a limited distance. Our results show that for a variety of initial conditions and across both static and dynamic fitness landscapes, there is strong selection pressure for unconditional sharing.

animal behavior and cognition↗

bioRxiv2018

Searching for rewards like a child means less generalization and more directed exploration

How do children and adults differ in their search for rewards? We consider three different hypotheses that attribute developmental differences to either childrens increased random sampling, more directed exploration towards uncertain options, or narrower generalization. Using a search task in which noisy rewards are spatially correlated on a grid, we compare 55 younger children (age 7-8), 55 older children (age 9-11), and 50 adults (age 19-55) in their ability to successfully generalize about unobserved outcomes and balance the exploration-exploitation dilemma. Our results show that children explore more eagerly than adults, but obtain lower rewards. Building a predictive model of search to disentangle the unique contributions of the three hypotheses of developmental differences, we find robust and recoverable parameter estimates indicating that children generalize less and rely on directed exploration more than adults. We do not, however, find reliable differences in terms of random sampling.

animal behavior and cognition↗

bioRxiv2018

Connecting conceptual and spatial search via a model of generalization

The idea of a \"cognitive map\" was originally developed to explain planning and generalization in spatial domains through a representation of inferred relationships between experiences. Recently, new research has suggested similar principles may also govern the representation of more abstract, conceptual knowledge in the brain. We test whether the search for rewards in conceptual spaces follows similar computational principles as in spatial environments. Using a within-subject design, participants searched for both spatially and conceptually correlated rewards in multi-armed bandit tasks. We use a Gaussian Process model combining generalization with an optimistic sampling strategy to capture human search decisions and judgments in both domains, and to simulate human-level performance when specified with participant parameter estimates. In line with the notion of a domain-general generalization mechanism, parameter estimates correlate across spatial and conceptual search, yet some differences also emerged, with participants generalizing less and exploiting more in the conceptual domain.

animal behavior and cognition↗

bioRxiv2018

Sharing is not erring: Pseudo-reciprocity in collective search

Information sharing in competitive environments may seem counterintuitive, yet it is widely observed in humans and other animals. For instance, the open-source software movement has led to new and valuable technologies being released publicly to facilitate broader collaboration and further innovation. What drives this behavior and under which conditions can it be beneficial for an individual? Using simulations in both static and dynamic environments, we show that sharing information can lead to individual benefits through the mechanisms of pseudo-reciprocity, whereby shared information leads to by-product benefits for an individual without the need for explicit reciprocation. Crucially, imitation with a certain level of innovation is required to avoid a tragedy of the commons, while the mechanism of a local visibility radius allows for the coordination of self-organizing collectives of agents. When these two mechanisms are present, we find robust evidence for the benefits of sharing--even when others do not reciprocate.

animal behavior and cognition↗

bioRxiv2017

Generalization and search in risky environments

How do people pursue rewards in risky environments, where some outcomes should be avoided at all costs? We investigate how participant search for spatially correlated rewards in scenarios where one must avoid sampling rewards below a given threshold. This requires not only the balancing of exploration and exploitation, but also reasoning about how to avoid potentially risky areas of the search space. Within risky versions of the spatially correlated multi-armed bandit task, we show that participants behavior is aligned well with a Gaussian process function learning algorithm, which chooses points based on a safe optimization routine. Moreover, using leave-one-block-out cross-validation, we find that participants adapt their sampling behavior to the riskiness of the task, although the underlying function learning mechanism remains relatively unchanged. These results show that participants can adapt their search behavior to the adversity of the environment and enrich our understanding of adaptive behavior in the face of risk and uncertainty.

animal behavior and cognition↗

bioRxiv2017

Exploration and generalization in vast spaces

From foraging for food to learning complex games, many aspects of human behaviour can be framed as a search problem with a vast space of possible actions. Under finite search horizons, optimal solutions are generally unobtainable. Yet how do humans navigate vast problem spaces, which require intelligent exploration of unobserved actions? Using a variety of bandit tasks with up to 121 arms, we study how humans search for rewards under limited search horizons, where the spatial correlation of rewards (in both generated and natural environments) provides traction for generalization. Across a variety of diifferent probabilistic and heuristic models, we find evidence that Gaussian Process function learning--combined with an optimistic Upper Confidence Bound sampling strategy--provides a robust account of how people use generalization to guide search. Our modelling results and parameter estimates are recoverable, and can be used to simulate human-like performance, providing insights about human behaviour in complex environments.

animal behavior and cognition↗

bioRxiv2017

Mapping the unknown: The spatially correlated multi-armed bandit

We introduce the spatially correlated multi-armed bandit as a task coupling function learning with the exploration-exploitation trade-off. Participants interacted with bi-variate reward functions on a two-dimensional grid, with the goal of either gaining the largest average score or finding the largest payoff. By providing an opportunity to learn the underlying reward function through spatial correlations, we model to what extent people form beliefs about unexplored payoffs and how that guides search behavior. Participants adapted to assigned payoff conditions, performed better in smooth than in rough environments, and--surprisingly--sometimes performed equally well in short as in long search horizons. Our modeling results indicate a preference for local search options, which when accounted for, still suggests participants were best-described as forming local inferences about unexplored regions, combined with a search strategy that directly traded off between exploiting high expected rewards and exploring to reduce uncertainty about the spatial structure of rewards.

animal behavior and cognition↗