Biology subjects

Hedge, C.

Publications and source records attributed to Hedge, C..

3 recordsLinked to original sources

bioRxiv2026

Myelin-weighted Core Network Connectivity Supports Trait-Level Future Thinking

Why are some individuals able to recollect past events and imagine future events more richly than others? Here we examine how structural connectivity within a 'core network' supporting mnemonic cognition, substantially overlapping with the canonical default network, underpins inter-individual variation in autobiographical thought. We analysed state-of-the-art magnetic resonance imaging data from 109 adults collected on a high-gradient 3T Connectom scanner, in relation to their Survey of Autobiographical Memory (SAM) scores - a psychometric measure of self-reported episodic autobiographical, semantic, spatial, and future thinking abilities. We anatomically defined a core network, employed streamline tractography to derive participant-specific structural brain networks, and weighted their connections with the myelin water fraction derived from quantitative multicomponent relaxometry. We investigated associations between graph theoretical metrics of the core network and self-reported mnemonic abilities. We found a negative association between future thinking abilities and characteristic path length, and a positive association between future thinking abilities and mean clustering coefficient. Further analysis showed that the node-specific clustering coefficient of most core network nodes was positively correlated with future thinking scores, with frontotemporal nodes showing the strongest associations. Our findings indicate that both efficient integration and local segregation of the core network are important for trait-level future thinking.

neuroscience↗

bioRxiv2023

Non-decision time: the Higg's boson of decision

Generative models of decision now permeate all subfields of psychology, cognitive and clinical neuroscience. To successfully represent decision mechanisms, it is necessary to also assume the presence of delays for sensory and motor information to travel through the brain; but like the Higgs boson in particle physics, directly observing this "non-decision time" from behaviour long appeared beyond reach. Here, we describe and apply a set of methods to empirically measure and characterise the properties of non-decision time in fast visually guided decisions (without requiring modelling assumptions). We gather 11 datasets from humans and monkeys from multiple labs and validate the method by showing that visual properties (brightness, colour, size) consistently affect empirically measured non-decision time, as predicted by neurophysiology. We then show that endogenous factors (pro-active slowing, attention) consistently do not affect non-decision time, in contrast to widespread reports based on model fits. Last, contrasting empirically observed non-decision time with estimates from the EZ, DDM and LBA models, we conclude that models cannot be generally trusted to provide valid estimates, either at a group level or for individual differences, and propose a hybrid approach that combines our empirical method with standard modelling.

animal behavior and cognition↗

bioRxiv2019

Inability to improve performance with control shows limited access to inner states

Any repeatedly performed action is characterised by endogenous variability, affecting both speed and accuracy - for a large part presumably caused by fluctuations in underlying brain and body states. The current research questions were: 1) whether such states are accessible to us, and 2) whether we can act upon this information to reduce variability. For example, when playing a game of darts, there is an implicit assumption that people can wait to throw until they are in the right perceptual-attentional state. If this is true, taking away the ability to self-pace the game should worsen performance. We first tested precisely this assumption asking participants to play darts in a self-paced and a fixed-paced condition. There was no benefit of self-pacing, showing that participants were unable to use such control to improve their performance and reduce their variability. Next, we replicated these findings in two computer-based tasks, in which participants performed a rapid action-selection and a visual detection task in one self-paced and three forced-paced conditions. Over four different empirical tests, we show that the self-paced condition did not lead to improved performance or reduced variability, nor to reduced temporal dependencies in the reaction time series. Overall, it seems that, if people have any access to their fluctuating performance-relevant inner states, this access is limited and not relevant for upcoming performance.

neuroscience↗