Biology subjects

Dolan, R. J.

Publications and source records attributed to Dolan, R. J..

At least 19 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.

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bioRxiv2026

Sensory cortical disinhibition is required for long-term fear memory in humans

The transformation of transient threats into enduring memories is fundamental to survival, yet how the human brain constructs long-term fear memory remains elusive. Early sensory cortex, traditionally viewed as a passive feature analyzer, is increasingly recognized as an active participant in fear processing. Here, using a multimodal approach combining high-definition -frequency transcranial alternating current stimulation (HD -tACS) with simultaneous EEG-fMRI and transformer-based neural decoding, we tracked fear memory development across early formation, consolidation, and long-term retention stages (assessed at 15 min, 24 h, and 7 days post-conditioning). Spatiotemporal memory representations were evident at all three time points, beginning at an early sensory stage (40-60 ms post-stimulus). Conditioning potentiated occipital -oscillation desynchronization (a marker of visual cortical disinhibition), which predicted the strength of subsequent long-term memory traces. Critically, targeted -tACS over early visual cortex induced V1/V2 inhibition, abolished conditioning-potentiated desynchronization, and selectively disrupted fear-memory consolidation and retention while leaving initial acquisition intact. These findings establish sensory cortical disinhibition as a necessary mechanism for long-term fear memory in humans and identify early sensory cortex as a causal node for interventions targeting persistent maladaptive fear.

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bioRxiv2025

Dopamine drives a positive reward bias on human reinforcement learning

Formal theories of reinforcement learning (RL) prescribe a clearly defined function for dopamine, namely modulating learning via reward prediction errors (RPEs). Yet, empirical evidence in humans remains scarce, and recent advances introducing noisy RL cast doubt on a simple one-to-one mapping between neurotransmitters and computational mechanisms. Here, we detail a double-blind, placebo-controlled, randomised pharmacological study using the dopamine precursor L-DOPA, while healthy volunteers performed a volatile two-armed bandit task. Behaviourally, L-DOPA decreased switching behaviour following below-average rewards. Algorithmic RL modelling of human behaviour supported a dual effect of L-DOPA on the rate and precision of learning. By leveraging recurrent neural networks (RNNs) as implementational models of RL, we explain this dual effect through a single inference-time modulation, whereby L-DOPA triggers a positive reward bias at the input of the recurrent layer that implements RL. Our findings highlight a unifying mechanism at the implementation level that explain seemingly disparate algorithmic effects of dopamine.

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bioRxiv2025

Anxious vision: Trait-like visual cortical hyperactivity in trait anxiety

Sensory processing varies across individuals, often manifesting as distinct traits. Some traits, particularly sensory hypersensitivity, even to basic non-valenced stimuli, are closely linked to emotional traits and implicated in a diversity of psychiatric disorders. Extant theories attribute this sensory-emotion linkage to limbic and prefrontal modulation, but empirical support remains limited. Meanwhile, it is increasingly recognized that sensory cortex supports flexible, value-based processing beyond labeled-line sensory analysis. Assaying early visual cortical activity (using visual evoked potentials/VEPs) across four high-density electroencephalogram (hd-EEG) experiments, involving three independent samples (N = 150) and repeated assessments (up to three waves), we identify a reliable and generalizable pattern of visual cortical hyperactivity related to high trait anxiety. Notably, this effect was specific to the parvocellular (vs. magnocellular) pathway, emerging as early as 46 ms and localized to V1. Underscoring its trait-like stability, the effect was reproducible across different arousal states, neutral and negative stimuli, extended time intervals, and varied experimental paradigms. Moreover, its ecological validity was affirmed by its presence in response to both basic low-level stimuli (Gabor patches) and complex real-world images. Crucially, an excitation-inhibition (E/I) ratio (indexed by the resting-state EEG aperiodic exponent; 1/f slope) predicted parvocellular VEP magnitude in subjects with low but not high trait anxiety, suggesting disrupted E/I modulation in trait anxiety underpins this hyperactivity. We conclude that in trait anxiety, early visual processing deviates from strict "fidelity" to physical-world input or mere "subservience" to top-down modulation, instead adapting to an individuals biological propensity at the earliest processing stage. HighlightsO_LIHyperactivity in primary visual cortex is stable, trait-like C_LIO_LIIt occurs as early as 46 ms post-stimulus, during initial feedforward sweep C_LIO_LIIt covaries with trait anxiety, a key personality factor and risk predisposition for psychiatric disorders C_LIO_LIIt is related to disrupted excitation-inhibition modulation of visual cortex in trait anxiety C_LI

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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.

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bioRxiv2025

Differences in LC integrity and fMRI activations in healthy aging and MCI associated with successful memory encoding

INTRODUCTIONWe examined whether the decline of delayed episodic memory in old age and MCI is related to structural integrity of the locus coeruleus (LC). We also tested whether LC integrity was associated with encoding-related brain activity, controlling for emotional salience. METHODSParticipants (28 young adults, 28 older adults, 25 MCI) memorized emotional and neutral images during fMRI. Delayed memory was tested four hours later and compared to immediate memory. LC integrity was measured with a neuromelanin-sensitive sequence. RESULTSMCI showed overall memory decline, independent of delay or emotional valence, and reduced LC integrity compared to older adults. Across participants, LC integrity correlated with delayed memory, but did not explain performance within OA or MCI. LC integrity was associated with lower activity related to encoding success and emotional salience. Groups remembered emotional better than neutral images, though memory was greatly impaired in MCI, matching with their reduce LC integrity. DISCUSSIONLC integrity was associated with lower brain activity during encoding and emotional salience processing, no clear relationship with delayed memory performance was observed in MCI.

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bioRxiv2025

The Semantic Underpinnings of Speech Disorganization in Schizophrenia

Effective communication relies on shared semantic representations and their context-sensitive retrieval. Accordingly, difficulties in communicating coherently, as seen in some patients with schizophrenia, may reflect impairments at the level of semantic structure or the retrieval process operating on this structure. Disentangling these components is challenging. Here, we address this problem using a word association task and magnetoencephalography (MEG), allowing us to examine behavioral markers of semantic structure and retrieval as well as semantic representation in the brain. For the latter, we detail an approach that contrasts the brains encoding of concepts as predicted by generic versus personalized semantic models. In so doing, we provide converging evidence for semantic structure idiosyncrasy in patients with schizophrenia who also exhibit speech disorganization. The findings advance our understanding of the semantic underpinnings of speech disorganization in schizophrenia, while also revealing the potential of using personalized semantic models to explain neural representation and behavior.

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bioRxiv2025

Pupil dilation as a marker of attention/effort in aging and mild cognitive impairment

Pupil dilation (PD) can be easily measured and reflects responses to subjectively salient or cognitively demanding events. It therefore holds promise as a cognitive marker especially for individuals with mild cognitive impairment (MCI) or other neurodegenerative conditions with restricted abilities to respond in cognitive assessments. We assessed PD during two tasks, an oddball task for investigating attentional allocation and a Simon task which additionally allows for investigating cognitive effort in younger adults (YAs), older adults (OAs), and patients with MCI. PD is a useful marker for investigating attention and cognitive effort in MCI, as suggested by elevated PD to salient stimuli in particular of individuals with better attentional control in MCI patients, as well as YAs and OAs. Measurement of PD may serve as an easy-to-administer measure to assess changes in cognitive function in healthy aging and MCI.

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bioRxiv2025

Challenges in Replay Detection by TDLM in Post-Encoding Resting State

Using temporally delayed linear modelling (TDLM) and magnetoencephalography (MEG), we investigated whether items associated with an underlying graph structure are replayed during a post-learning resting state. In these same data, we previously provided evidence for replay during on-line (non-rest) memory retrieval. Despite successful decoding of brain activity during a localizer task, and contrary to predictions, we found no evidence for replay during a post-learning resting state. To better understand this, we performed a hybrid simulation analysis in which we inserted synthetic replay events into a control resting state recorded prior to the actual experiment. This simulation revealed that replay detection using our current pipeline requires an extremely high replay density to reach significance (>1 replay sequence per second, with "replay" defined as a sequence of reactivations within a certain time lag). Furthermore, when scaling the number of replay events with a behavioural measure, we were unable to induce a strong correlation between sequenceness and this measure. We infer that even if replay was present at plausible rates in our resting state dataset, we would lack statistical power to detect it with TDLM. Finally, contrasting our novel hybrid simulation to existing purely synthetic simulations indicated that the latter approaches overestimate the sensitivity of TDLM. We discuss approaches that might optimize the analytic methodology, including identifying boundary conditions under which TDLM can be expected to detect replay. We conclude that solving these methodological constraints will be crucial for optimizing the non-invasive measurement of human replay using MEG.

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bioRxiv2025

Biased replay of aversive and uncertain outcomes underlies irrational decision making

Humans often make irrational decisions when facing uncertain or aversive future events despite careful deliberation. How we make choices, including irrational ones1,2, has been the object of extensive study both behaviourally and neurally3-6 and is the focus of influential behavioural economic theories7-9. Yet, little is known about how these (irrational) decisions are carved out in the brain. Here, using magnetoencephalography (MEG), we show that the construction and outcome evaluation of irrational decisions involves rapid, sequential state reactivation, or "replay."10. During deliberation, we show that forward replay is biased towards choice options with more negative and uncertain outcomes, with this bias further amplified immediately preceding irrational choice. Likewise, post-decision evaluation relates to replay in a choice-dependent manner. Following irrational choices, relief-like signals were evident as stronger backward replay of worse counterfactual options, while after rational decisions, regret-like signals appeared as stronger backward replay of better counterfactual options. Together, these findings suggest that neural replay shapes both the formation and reflection of irrational decisions, and poise replay as a candidate mechanism underlying pervasive decision biases in humans.

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bioRxiv2024

Indecision and recency-weighted evidence integration in non-clinical and clinical settings

Biases in information gathering are common in the general population and can reach pathological extremes in paralysing indecisiveness, as in obsessive-compulsive disorder (OCD). Here, we adopt a new perspective on information gathering and demonstrate an information integration bias where most recent information is over-weighted by means of evidence strength updates ({Delta}ES). In a large, crowd-sourced sample (N=5,237), we find reduced {Delta}ES-weighting drives indecisiveness along an OC spectrum. We replicate the attenuated {Delta}ES- weighting in a second lab-based study (N=105) that includes a transdiagnostic OC spectrum encompassing OCD and generalised anxiety patients. Using magnetoencephalography (MEG), we trace {Delta}ES signals to a late neural signal peaking around 920 ms. Critically, high-OC participants show an attenuated neural {Delta}ES signal in medio-frontal areas while other decision- relevant processes remain intact. Our findings establish biased information-weighting as a key driver of information gathering, where attenuated {Delta}ES can lead to indecisiveness across an OC spectrum.

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bioRxiv2024

Habenula-ventral tegmental area functional coupling and risk-aversion in humans

Maladaptive responses to uncertainty, including excessive risk avoidance, are linked to a range of mental disorders. One expression of these is a pro-variance bias (PVB), wherein risk-seeking manifests in a preference for choosing options with higher variances/uncertainty. Here, using a magnitude learning task, we provide a behavioural and neural account of PVB in humans. We show that individual differences in PVB are captured by a computational model that includes asymmetric learning rates, allowing differential learning from positive prediction errors (PPEs) and negative prediction errors (NPEs). Using high-resolution 7T functional magnetic resonance imaging (fMRI), we identify distinct neural responses to PPEs and NPEs in value-sensitive regions including habenula (Hb), ventral tegmental area (VTA), nucleus accumbens (NAcc), and ventral medial prefrontal cortex (vmPFC). Prediction error signals in NAcc and vmPFC were boosted for high variance options. NPEs responses in NAcc were associated with a negative bias in learning rates linked to a stronger negative Hb-VTA functional coupling during NPE encoding. A mediation analysis revealed this coupling influenced NAcc responses to NPEs via an impact on learning rates. These findings implicate Hb-VTA coupling in the emergence of risk preferences during learning, with implications for psychopathology.

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bioRxiv2024

Interpreting Sleep Activity Through Neural Contrastive Learning

Memories are spontaneously replayed during sleep, a process thought to support memory consolidation. However, capturing this replay in humans has been challenging because unlike wakefulness, sleep EEG is dominated by slow, rhythmic background activity. Moreover, each sleep stage (e.g., NREM, REM) has distinct rhythms, hindering generalisation of models trained on wake-state data. To overcome these challenges, we developed the Sleep Interpreter (SI), a neural network model that decodes memory replay from sleep EEG. In a large dataset comprising 135 participants ([~]1,000 h of overnight sleep; [~]400 h of wake), we employed a TMR-like paradigm with 15 semantically congruent cue-image pairs to tag specific memories. SI was trained separately for NREM and REM using contrastive learning to align neural patterns across wake and sleep, filtering out stage-specific background rhythms. We also examined how slow oscillations and spindle coupling influence decoding in NREM sleep. In a 15-way classification, SI achieved up to 40.02% Top-1 accuracy on unseen subjects. To test generalisability, we followed up with two independent nap experiments in separate samples and applied the trained SI model off-the-shelf. The first probed spontaneous reactivation without auditory cues, while the second used semantic-free sounds with new images. In both, SI successfully decoded reactivation during sleep that correlated with post-nap memory performance. By openly sharing our dataset and SI system, we provide a unique resource for advancing research on memory and learning during sleep, and related disorders.

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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.

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bioRxiv2024

Neural mechanisms of credit assignment for delayed outcomes during contingent learning

Adaptive behavior in complex environments critically relies on the ability to appropriately link specific choices or actions to their outcomes. However, the neural mechanisms that support the ability to credit only those past choices believed to have caused the observed outcomes remain unclear. Here, we leverage multivariate pattern analyses of functional magnetic resonance imaging (fMRI) data and an adaptive learning task to shed light on the underlying neural mechanisms of such specific credit assignment. We find that the lateral orbitofrontal cortex (lOFC) and hippocampus (HC) code for the causal choice identity when credit needs to be assigned for choices that are separated from outcomes by a long delay, even when this delayed transition is punctuated by interim decisions. Further, we show when interim decisions must be made, learning is additionally supported by lateral frontopolar cortex (lFPC). Our results indicate that lFPC holds previous causal choices in a "pending" state until a relevant outcome is observed, and the fidelity of these representations predicts the fidelity of subsequent causal choice representations in lOFC and HC during credit assignment. Together, these results highlight the importance of the timely reinstatement of specific causes in lOFC and HC in learning choice-outcome relationships when delays and choices intervene, a critical component of real-world learning and decision making.

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bioRxiv2023

Reactivation strength during cued recall is modulated by graph distance within cognitive maps

Declarative memory retrieval is thought to involve reinstatement of neuronal activity patterns elicited and encoded during a prior learning episode. Furthermore, it is suggested that two mechanisms operate during reinstatement, dependent on task demands: individual memory items can be reactivated simultaneously as a clustered occurrence or, alternatively, replayed sequentially as temporally separate instances. In the current study, participants learned associations between images that were embedded in a directed graph network and retained this information over a brief 8-minute consolidation period. During a subsequent cued recall session, participants retrieved the learned information while undergoing magnetoencephalographic (MEG) recording. Using a trained stimulus decoder, we found evidence for clustered reactivation of learned material. Reactivation strength of individual items during clustered reactivation decreased as a function of increasing graph distance, an ordering present solely for successful retrieval but not for retrieval failure. In line with previous research, we found evidence that sequential replay was dependent on retrieval performance and was most evident in low performers. The results provide evidence for distinct performance-dependent retrieval mechanisms with graded clustered reactivation emerging as a plausible mechanism to search within abstract cognitive maps.

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bioRxiv2023

Reduced Reverse Replay in Anxious Individuals Impairs Reward Prediction

Understanding how we learn about the value and structure of our environment is central to neurocognitive theories of many psychiatric and neurological disorders. Learning processes have been extensively studied during performance of behavioural tasks (online learning) but less so in relation to "resting" (offline) states. A candidate mechanism for such offline learning is "replay," the sequential neural reactivation of past experiences. Notably, value-based learning is especially tied to replay unfolding in reverse order relative to the original experience ("backward replay"). Here, we demonstrate the utility of EEG-based neural decoding for investigating offline learning, and relate it to trait anxiety. Participants were first required to infer sequential relationships among task objects by using a learned rule to reorganise their visual experiences into distinct sequences. Afterwards, they observed that the final object in one of the sequences was associated with a monetary reward and then entered a post-value resting state. During this rest, we found evidence of backward replay for reward-linked object sequences. The strength of such replay was negatively associated with trait anxiety and positively predicted an increased behavioural preference for reward-predictive stimuli. We also found that high-trait-anxiety individuals showed inefficient credit assignment irrespective of reward magnitude, indicating that this effect does not merely reflect reduced reward sensitivity. Together, these findings suggest a potential aberrant replay mechanism during offline learning in individuals with high trait anxiety. More broadly, our approach illustrates the potential of EEG for measuring structured neural representations in vivo, with implications for studying cognition across a range of neuropsychiatric and neurological disorders.

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bioRxiv2022

Parallel cognitive maps for short-term statistical and long-term semantic relationships in the hippocampal formation

The hippocampal-entorhinal system uses cognitive maps to represent spatial knowledge and other types of relational information, such as the transition probabilities between objects. However, objects can often be characterized in terms of different types of relations simultaneously, e.g. semantic similarities learned over the course of a lifetime as well as transitions experienced over a brief timeframe in an experimental setting. Here we ask how the hippocampal formation handles the embedding of stimuli in multiple relational structures that differ vastly in terms of their mode and timescale of acquisition: Does it integrate the different stimulus dimensions into one conjunctive map, or is each dimension represented in a parallel map? To this end, we reanalyzed functional magnetic resonance imaging (fMRI) data from Garvert et al. (2017) that had previously revealed an entorhinal map which coded for newly learnt statistical regularities. We used a triplet odd-one-out task to construct a semantic distance matrix for presented items and applied fMRI adaptation analysis to show that the degree of similarity of representations in bilateral hippocampus decreases as a function of semantic distance between presented objects. Importantly, while both maps localize to the hippocampal formation, this semantic map is anatomically distinct from the originally described entorhinal map. This finding supports the idea that the hippocampal-entorhinal system forms parallel cognitive maps reflecting the embedding of objects in diverse relational structures.

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