Biology subjects

Luettgau, L.

Publications and source records attributed to Luettgau, L..

6 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

Reasoning with programs in replay

Reasoning flexibly composes known elements to solve novel problems. Recent theories suggest the brain uses the axis of time to compose elements for reasoning. In this view, elements are packaged into fast neural sequences, with each sequence exploring the implications of a different composition. Using magnetoencephalography, we tested this idea while participants mentally executed programs. Each program contained a set of steps linked to operations, the implications of which had to be computed. We found that behavioral performance scaled with program complexity, and by the end of execution, inferred program solutions were represented in prefrontal and parietal cortices. We identified a possible mechanism by which these solutions were computed. During reasoning, representations of steps in the program reactivated in fast sequences, consistent with sampling candidate partial solutions. Further evidence suggested these reactivations were accompanied by representations of their operations, and were followed by neural patterns reflecting their computed implications. Together, these results suggest replaying sequences supports program execution and reveal a highly organized temporal microarchitecture of reasoning.

neuroscience↗

bioRxiv2024

A neural mechanism for compositional generalization of structure in humans

A human ability to adapt to the dynamics of novel environments relies on abstracting and generalizing from past experiences. Previous research has focused on how humans generalize from isolated sequential processes, yet we know little about mechanisms that enable adaptation to more complex dynamics, including those that govern much everyday experience. Here, using a novel sequence learning task based on graph factorization, coupled with simultaneous magnetoencephalography (MEG) recordings, we ask whether reuse of experiential "building blocks" enable inference and generalization. Behavioral results were consistent with participants decomposing task experience into subprocesses, abstracting their dynamical structure away from their sensory specifics and transferring these to a new task environment. Neurally, this transfer was associated with a learning-induced, condition-specific, increase in neural similarity among stimuli conditional on their structural role in abstract subprocesses that were shared across task phases. Consistent with this, a complementary dynamical role decoding analysis revealed enhanced neural decodability for stimuli sharing identical structural transition types across experiential contexts, specifically when prior experience included the relevant graph structure. Decoding strength for these role representations was positively related to behavioral success in subprocess knowledge transfer, a relationship that will require future independent replication. These findings provide neural evidence consistent with the idea that a structural scaffolding mechanism supports generalization of experience to new contexts.

neuroscience↗

bioRxiv2020

Reinstatement of cortical outcome representations during higher-order learning

Naturalistic learning scenarios are characterized by infrequent experience of external feedback to guide behavior. Higher-order learning mechanisms like second-order conditioning (SOC) may allow stimuli that were never experienced together with reinforcement to acquire motivational value. Despite its explanatory potential for real-world learning, surprisingly little is known about the neural mechanism underlying such associative transfer of value in SOC. Here, we used multivariate cross-session, cross-modality searchlight classification on functional magnetic resonance imaging data obtained from humans during SOC. We show that visual first-order conditioned stimuli (CS) reinstate cortical patterns representing previously paired gustatory outcomes in the lateral orbitofrontal cortex (OFC). During SOC, this OFC region showed increased functional covariation with amygdala, where neural pattern similarity between second-order CS and outcomes increased from early to late stages of SOC. Our data suggest a mechanism by which motivational value is conferred to stimuli that were never paired with reinforcement.

neuroscience↗

bioRxiv2019

Dissociable roles of cortical excitation-inhibition balance during patch-leaving versus value-guided decisions

In a dynamic world, it is essential to decide when to leave an exploited resource. Such patch-leaving decisions involve balancing the cost of moving against the gain expected from the alternative patch. This is in contrast with value-guided decisions that typically involve maximizing reward by selecting the current best option. Patterns of neuronal activity pertaining to patch-leaving decisions have been reported in the dorsal anterior cingulate cortex (dACC), whereas competition via mutual inhibition in the ventromedial prefrontal cortex (vmPFC) is thought to underlie value-guided choice. Here, we show that the balance between cortical excitation and inhibition (E/I balance), measured by the ratio of GABA and glutamate concentrations, plays a dissociable role for the two kinds of decisions. Patch-leaving decision behaviour was related to E/I balance in dACC. In contrast, value-guided decision making was related to E/I balance in vmPFC. These results support previous mechanistic accounts of value-guided choice and provide novel evidence for a role of dACC E/I balance in patch-leaving decisions.

neuroscience↗

bioRxiv2019

Decisions bias future choices by modifying hippocampal associative memories

Decision making is guided by memories of option values. However, retrieving items from memory renders them malleable. Here, we show that merely retrieving values from memory and making a choice between options is sufficient both to induce changes to stimulus-reward associations in the hippocampus and to bias future decision making. After allowing participants to make repeated choices between reward-conditioned stimuli, in the absence of any outcome, we observed that participants preferred stimuli they had previously chosen, and neglected previously unchosen stimuli, over otherwise identical-valued options. Using functional brain imaging, we show that decisions induced changes to hippocampal representations of stimulus-outcome associations. These changes were correlated with future decision biases. Our results indicate that choice-induced preference changes are partially driven by choice-induced modification of memory representations and suggest that merely making a choice - even without experiencing any outcomes - induces associative plasticity.

neuroscience↗