Biology subjects

Seak, L. C. U.

Publications and source records attributed to Seak, L. C. U..

8 recordsLinked to original sources

bioRxiv2026

Reuse of learned subtask representations during human planning

The notion of a compositional reuse of previously learned problem solutions predicts that the human brain should reinstate such learned subtask representations during planning. Here, we test this in a value-ranking task implemented in an online and a magnetoencephalography (MEG) experiment, respectively. Participants first learned pairwise rankings and subsequently solved problems where these learned rankings could either be reused or required reordering. Behaviourally, in the online experiment, we show the availability of a reusable subtask structure improved accuracy, with computational modelling indicating a greater weighting of subtask-level value when participants could avail of this learnt structure. In MEG, during planning, we show enhanced reactivation of task-relevant subtask representations in the reusable condition, and particularly so early in the experiment. In a subsequent probe task, we show subtask-identity representations generalised to a novel context and that stimuli belonging to the same learned subtask manifest an increase in their neural similarity. Temporally delayed linear modelling (TDLM) revealed forward and backward sequential reactivation at a subtask level, alongside backward stimulus-level replay, with subtask sequenceness varying according to task context. The findings provide convergent behavioural, computational and neural evidence for reactivation and reuse of learnt relational structures for novel problem solving.

neuroscience↗

bioRxiv2025

Evolving learning state reactivation and value encoding neural dynamics in multi-step planning

Planning in value-based decision making is often dynamic, with reinforcement learning (RL) providing a powerful framework for investigating how value and action at each step change across trials. Surprisingly, the evolving neural signatures of value estimation and state reactivation in multi-step planning, both within and across trials, have received little consideration. Here, using magnetoencephalography (MEG), we detail neural dynamics associated with planning, wherein subjects were tasked to find an optimal path in order to maximise reward. Behavioural evidence showed improved performance across trials, including subjects showing an increasing disregard for low-value states. MEG data captured evolving value estimation signals such that, across trials, there was an emergence of stronger and earlier within trial value encoding linked to boosted vmPFC activity. Value encoding signals showed a positive correlation and individual performance metrics, as reflected in overall task-related reward earnings. Strikingly, across trials, there was an attenuation of state reactivation for negative-value states, an effect that positively correlated with evolving negative-value state avoidance behaviour. The finding linking neural dynamics, including a valence-dependent selective reactivation of negative states, to across-trial behavioural improvement advances an understanding of learning during multi-step planning.

neuroscience↗

bioRxiv2025

Coding of the basic components of subjective value in primate dopamine neurons: subjectively weighted reward amount and probability

Behavioral choices of uncertain rewards suggest that agents construct subjective reward value by combining the basic value components of utility and weighted probability. Despite the general acceptance of this evaluation mechanism for explaining economic choice, knowledge about its neuronal implementation is fractionated and remains essentially unknown. We investigated in monkeys whether reward signals in dopamine neurons might represent subjective reward value based on these two fundamental value components. Despite some heterogeneity across individual neurons, the dopamine population signal reliably represented the axiomatically defined integration of utility and weighted probability into subjective value in a way that closely matched the animal-specific choice behavior. In particular, we identified a crucial contribution of subjectively weighted probability to the dopamine signal of subjective reward value. These data demonstrate a neuronal implementation of subjective value constructed from the two most basic subjective reward components. Preprint ServerAn earlier version of this manuscript has been uploaded to bioRxiv in February 2025 (CC-BY).

neuroscience↗

bioRxiv2023

Systematic comparison of risky choices in humans and monkeys

The past decades have seen tremendous progress in fundamental studies on economic choice in humans. However, elucidation of the underlying neuronal processes requires invasive neurophysiological studies that are met with difficulties in humans. Monkeys as evolutionary closest relatives offer a solution. The animals display sophisticated and well-controllable behavior that allows to implement key constructs of proven economic choice theories. However, the similarity of economic choice between the two species has never been systematically investigated. We investigated compliance with the independence axiom (IA) of expected utility theory as one of the most demanding choice tests and compared IA violations between humans and monkeys. Using generalized linear modeling and cumulative prospect theory (CPT), we found that humans and monkeys made comparable risky choices, although their subjective values (utilities) differed. These results suggest similar fundamental choice mechanism across these primate species and encourage to study their underlying neurophysiological mechanisms.

neuroscience↗

bioRxiv2021

Probability weighting explains Independence Axiom violations of Expected Utility Theory in monkeys

Expected Utility Theory (EUT) provides axioms for maximizing utility in risky choice. The Independence Axiom (IA) is its most demanding axiom: preferences between two options should not change when altering both options equally by mixing them with a common gamble. We tested common consequence (CC) and common ratio (CR) violations of the IA over several months in thousands of stochastic choices using a large variety of binary option sets. Three monkeys showed consistently few outright Preference Reversals (8%) but substantial graded Preference Changes (46%) between the initial preferred gamble and the corresponding altered gamble. Linear Discriminant Analysis (LDA) indicated that gamble probabilities predicted most Preference Changes in CC (72%) and CR (88%) tests. The Akaike Information Criterion indicated that probability weighting within Cumulative Prospect Theory (CPT) explained choices better than models using Expected Value (EV) or EUT. Fitting by utility and probability weighting functions of CPT resulted in nonlinear and non-parallel indifference curves (IC) in the Marschak-Machina triangle and suggested IA non-compliance of models using EV or EUT. Indeed, CPT models predicted Preference Changes better than EV and EUT models. Indifference points in out-of-sample tests were closer to CPT-estimated ICs than EV and EUT ICs. Finally, while the few outright Preference Reversals may reflect the long experience of our monkeys, their more graded Preference Changes corresponded to those reported for humans. In benefitting from the wide testing possibilities in monkeys, our stringent axiomatic tests contribute critical information about risky decision-making and serve as basis for investigating neuronal decision mechanisms.

animal behavior and cognition↗

bioRxiv2021

Next-generation biocomputing: mimicking artificial neural network with genetic circuits

Artificial neural network (ANN) is nowadays one of the most used and researched computational methods. In the field of biocomputing, however, synthetic biologists are still using logic gate technologies to design genetic circuits. We here propose and computationally validate a novel method to mimic ANN with genetic circuits. We first describe the flow and the mathematical expression of this genetic circuit design and then we provide in silico proof to support the functionality of our method in regression and classification analyses. We believe that this de novo genetic circuit design method would have wide applications in biotechnology.

synthetic biology↗

bioRxiv2021

Comparing utility functions between risky and riskless choice in rhesus monkeys

Decisions can be risky or riskless, depending on the outcomes of the choice. Expected Utility Theory describes risky choices as a utility maximization process: we choose the option with the highest subjective value (utility), which we compute considering both the options value and its associated risk. According to the random utility maximization framework, riskless choices could also be based on a utility measure. Neuronal mechanisms of utility-based choice may thus be common to both risky and riskless choices. This assumption would require the existence of a utility function that accounts for both risky and riskless decisions. Here, we investigated whether the choice behavior of macaque monkeys in riskless and risky decisions could be described by a common underlying utility function. We found that the utility functions elicited in the two choice scenarios were different from each other, even after taking into account the contribution of subjective probability weighting. Our results suggest that distinct utility representations exist for riskless and risky choices, which could reflect distinct neuronal representations of the utility quantities, or distinct brain mechanisms for risky and riskless choices. The different utility functions should be taken into account in neuronal investigations of utility-based choice.

animal behavior and cognition↗

bioRxiv2017

Characterization of Azotobacter vinelandii and Kits for Its Synthetic Biology Applications

Azotobacter vinelandii, a Gram-negative aerobic bacterium with an intracellular anaerobic environment that maintains the oxygen-sensitive enzymatic cascades for nitrogen fixation, could be used to express oxygen-sensitive proteins. However, little is known about the properties of A. vinelandii for synthetic biology applications. We therefore first characterized and optimized the conditions for growing and screening BioBrick constructs in A. vinelandii in the presence of 2 antibiotics, ampicillin and chloramphenicol, and then developed two sets of BioBricks for regulated protein expression. The first kit used T7 RNA polymerase, whose expression is under the control of a nitrogen-repressible nifH promoter. The commonly used T7-dependent system in Escherichia coli can then be used in A. vinelandii. Because its intracellular anaerobic environment is favorable for processes such as magnetosome biogenesis, we attempted to migrate the biogenesis machineries from the magnetotactic bacterium Magnetospirillum gryphiswaldense to A. vinelandii. During this undertaking, another insertion kit construct was developed to allow protein conjugation onto magnetosomes. The kit consists of mamC, a gene encoding a transmembrane protein on magnetosomes, and multiple restriction sites downstream of mamC for fusing a gene of interest. This insertion kit allows the attachment of any desired protein onto the magnetosome membrane by fusing with the mamC protein. We demonstrated the function of this kit by fusing mamC to a GFP nanobody. This kit will facilitate the conjugation of any target protein onto magnetosomes for downstream applications in the future.\n\nFinancial DisclosureWe received sponsorship from the 2012-15 Teaching Development Grants Triennium, Faculty of Engineering and Biochemistry Program, School of Life Sciences, The Chinese University of Hong Kong. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.\n\nCompeting InterestsThe authors declare that no competing interests exist.\n\nEthics StatementN/A.\n\nData AvailabilityAll data are fully available without restriction.

synthetic biology↗