Biology subjects

Hall-McMaster, S.

Publications and source records attributed to Hall-McMaster, S..

7 recordsLinked to original sources

bioRxiv2025

Entorhinal cortex signals dimensions of past experience that can be generalised in a novel environment

No two situations are identical. They can be similar in some aspects but different in others. This poses a key challenge when attempting to generalise our experience from one situation to another. How do we distinguish the aspects that transfer across situations from those that do not? One hypothesis is that the entorhinal cortex (EC) meets this challenge by forming factorised representations that allow for increased neural similarity between events that share generalisable features. We tested this hypothesis using functional magnetic resonance imaging. Female and male participants (n=40) were trained to report behavioural sequences based on an underlying graph structure. Participants then made decisions in a new environment where some, but not all graph transitions from the previous structure could be generalised. Behavioural results showed that participants distinguished the generalisable transition information. Accuracy was significantly higher in blocks where sequence transitions were shared across environments, than those in which transitions differed. This boost in accuracy was especially pronounced during early exposure to the novel environment. Throughout this early phase, neural patterns in EC showed a corresponding differentiation of the generalisable aspects. Neural patterns representing starting locations in familiar and novel environments were significantly more similar in EC on trials where sequences could be generalised from prior experience, compared to trials with new sequential transitions. This signalling was associated with improved performance when prior sequence knowledge could be reused. Our results suggest that during early exposure to novel environments, EC may signal dimensions of past experience that can be generalised. Significance StatementGeneralisation is the process of using our experience to solve new challenges. A central problem we face when attempting to generalise is determining which aspects of a new situation are similar to what we have experienced before and which are different. This allows us to generalise our knowledge selectively, transferring insights to the aspects that are similar and relevant in novel scenarios, while avoiding over-generalisation to the aspects that differ. The results from this experiment suggest that the entorhinal cortex may distinguish these aspects during early exposure to new environments, supporting complex generalisation behaviour.

neuroscience↗

bioRxiv2025

Alpha and Beta Oscillations Mediate the Effect of Motivation on Neural Coding of Cognitive Flexibility

Cognitive flexibility is crucial for adaptive human behaviour. Prior studies have analysed the effect of reward on cognitive flexibility; however, the neural mechanisms underlying these effects remain largely unknown. This study explores how reward influences neural oscillations and how these changes impact behavioural performance. Using time-frequency decomposition, we examined electroencephalographic data from participants engaged in rule-guided task-switching with varying reward prospects. Higher anticipated rewards lead to greater desynchronisation of alpha (8-12Hz) and beta (20-30Hz) oscillations, which in turn correlated with improved task performance. Both alpha power and event-related potential (ERP) coding of reward independently predicted reward-based performance improvements, suggesting distinct mechanisms supporting proactive control. These findings underscore the unique contributions of neural oscillations in mediating motivational effects on cognitive flexibility.

neuroscience↗

bioRxiv2024

Neural Prioritisation of Past Solutions Supports Generalisation

Generalisation from past experience is an important feature of intelligent systems. When faced with a new task, one efficient computational approach is to evaluate solutions to earlier tasks as candidates for reuse. Consistent with this idea, we found that human participants (n=38) learned optimal solutions to a set of training tasks and generalised them to novel test tasks in a reward selective manner. This behaviour was consistent with a computational process based on the successor representation known as successor features and generalised policy improvement (SF&GPI). Neither model-free perseveration or model-based control using a complete model of the environment could explain choice behaviour. Decoding from functional magnetic resonance imaging data revealed that solutions from the SF&GPI algorithm were activated on test tasks in visual and prefrontal cortex. This activation had a functional connection to behaviour in that stronger activation of SF&GPI solutions in visual areas was associated with increased behavioural reuse. These findings point to a possible neural implementation of an adaptive algorithm for generalisation across tasks.

neuroscience↗

bioRxiv2024

An inductive bias for slowly changing features in human reinforcement learning

Identifying goal-relevant features in novel environments is a central challenge for efficient behaviour. We asked whether humans address this challenge by relying on prior knowledge about common properties of reward-predicting features. One such property is the rate of change of features, given that behaviourally relevant processes tend to change on a slower timescale than noise. Hence, we asked whether humans are biased to learn more when task-relevant features are slow rather than fast. To test this idea, 295 human participants were asked to learn the rewards of two-dimensional bandits when either a slowly or quickly changing feature of the bandit predicted reward. Across two experiments and one preregistered replication, participants accrued more reward when a bandits relevant feature changed slowly, and its irrelevant feature quickly, as compared to the opposite. We did not find a difference in the ability to generalise to unseen feature values between conditions. Testing how feature speed could affect learning with a set of four function approximation Kalman filter models revealed that participants had a higher learning rate for the slow feature, and adjusted their learning to both the relevance and the speed of feature changes. The larger the improvement in participants performance for slow compared to fast bandits, the more strongly they adjusted their learning rates. These results provide evidence that human reinforcement learning favours slower features, suggesting a bias in how humans approach reward learning. Author summaryLearning experiments in the laboratory are often assumed to exist in a vacuum, where participants solve a given task independently of how they learn in more natural circumstances. But humans and other animals are in fact well known to "meta learn", i.e. to leverage generalisable assumptions about how to learn from other experiences. Taking inspiration from a well-known machine learning technique known as slow feature analysis, we investigated one specific instance of such an assumption in learning: the possibility that humans tend to focus on slowly rather than quickly changing features when learning about rewards. To test this, we developed a task where participants had to learn the value of stimuli composed of two features. Participants indeed learned better from a slowly rather than quickly changing feature that predicted reward. Computational modelling of participant behaviour indicated that participants had a higher learning rate for slowly changing features from the outset. Hence, our results support the idea that human reinforcement learning reflects a priori assumptions about the reward structure in natural environments.

neuroscience↗

bioRxiv2021

Integrating reward information for prospective behaviour

Value-based decision-making is often studied in a static context, where participants decide which option to select from those currently available. However, everyday life often involves an additional dimension: deciding when to select to maximise reward. Recent evidence suggests that agents track the latent reward of an option, updating changes in their latent reward estimate, to achieve appropriate selection timing (latent reward tracking). However, this strategy can be difficult to distinguish from one in which the optimal selection time is estimated in advance, allowing an agent to wait a pre-determined amount of time before selecting, without needing to monitor an options latent reward (distance-to-goal tracking). Here we show that these strategies can in principle be dissociated. Human brain activity was recorded using electroencephalography (EEG) while female and male participants performed a novel decision task. Participants were shown an option and decided when to select it, as its latent reward changed from trial-to-trial. While the latent reward was uncued, it could be estimated using cued information about the options starting value and value growth rate. We then used representational similarity analysis to assess whether EEG signals more closely resembled latent reward tracking or distance-to-goal tracking. This approach successfully dissociated the strategies in this task. Starting value and growth rate were translated into a distance-to-goal signal, far in advance of selecting the option. Latent reward could not be independently decoded. These results demonstrate the feasibility of using high temporal resolution neural recordings to identify internally computed decision variables in the human brain. Significance StatementReward-seeking behaviour involves acting at the right time. However, the external world does not always tell us when an action is most rewarding, necessitating internal representations that guide action timing. Specifying this internal neural representation is challenging because it might stem from a variety of strategies, many of which make similar predictions about brain activity. This study used a novel approach to test whether alternative strategies could be dissociated in principle. Using representational similarity analysis, we were able to distinguish between candidate internal representations for selection timing. This shows how pattern analysis methods can be used to measure latent decision information in non-invasive neural data.

neuroscience↗

bioRxiv2021

Control over patch encounters changes foraging behaviour

Foraging is a common decision problem in natural environments. When new exploitable sites are always available, a simple optimal strategy is to leave a current site when its return falls below a single average reward rate. Here, we examined foraging in a more structured environment, with a limited number of sites that replenished at different rates and had to be revisited. When participants could choose sites, they visited fast-replenishing sites more often, left sites at higher levels of reward, and achieved a higher net reward rate. Decisions to exploit-or-leave a site were best explained with a computational model that included both the average reward rate for the environment and reward information about the unattended sites. This suggests that unattended sites influence leave decisions, in foraging environments where sites can be revisited. HighlightsO_LIBeing able to select sites during foraging increased visits to high value sites C_LIO_LIThis visitation pattern was efficient, producing higher average reward rates C_LIO_LIDecisions to leave a site were influenced by information about alternative sites C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/426950v3_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@2e662org.highwire.dtl.DTLVardef@6352eaorg.highwire.dtl.DTLVardef@d8aae7org.highwire.dtl.DTLVardef@bc60a6_HPS_FORMAT_FIGEXP M_FIG C_FIG

animal behavior and cognition↗

bioRxiv2019

Reward boosts neural coding of task rules to optimise cognitive flexibility

Cognitive flexibility is critical for intelligent behaviour. However, its execution is effortful and often suboptimal. Recent work indicates that flexible behaviour can be improved by the prospect of reward, which suggests that rewards optimise flexible control processes. Here we investigated how different reward prospects influence neural encoding of task rule information to optimise cognitive flexibility. We applied representational similarity analysis (RSA) to human electroencephalograms, recorded while female and male participants performed a rule-guided decision-making task. During the task, the prospect of reward varied from trial to trial. Participants made faster, more accurate judgements on high reward trials. Critically, high reward boosted neural coding of the active task rule and the extent of this increase was associated with improvements in task performance. Additionally, the effect of high reward on task rule coding was most pronounced on switch trials, where rules were updated relative to the previous trial. These results suggest that reward prospect can promote cognitive performance by strengthening neural coding of task rule information, helping to improve cognitive flexibility during complex behaviour.\n\nSignificance StatementThe importance of motivation is evident in the ubiquity with which reward prospect guides adaptive behaviour and the striking number of neurological conditions associated with motivational impairments. In this study, we investigated how dynamic changes in motivation, as manipulated through reward, shape neural coding for task rules during a flexible decision-making task. The results of this work suggest that motivation to obtain reward modulates encoding of task rules needed for flexible behaviour. The extent to which reward increased task rule coding also tracked improvements in behavioural performance under high reward conditions. These findings help inform how motivation shapes neural processing in the healthy human brain.

neuroscience↗