Biology subjects

Wammes, J. D.

Publications and source records attributed to Wammes, J. D..

6 recordsLinked to original sources

bioRxiv2026

Thoughts and cortical responses are shaped by the type of change at event boundaries

Continuous experience is divided into discrete and meaningful events, but different kinds of event transitions may shape thought and brain activity in different ways. We examined how perceived event boundaries relate to visual, auditory, and semantic features, ongoing thought, and large-scale cortical states during movie-watching. We combined three independent samples who viewed the same eight movie clips, aligning these measures to a shared stimulus timeline: One marked event boundaries, one reported ongoing thought, and one was scanned with fMRI. Whole-brain spatial correlations showed that event boundaries correlated positively with visual change and negatively with auditory change, but not with any thought pattern. Time-resolved analyses showed that boundaries were associated with a reduction in distracted thoughts and brain states positioned toward visual processing. Clustering boundaries by movie feature change profile identified visual, semantic, interruption, and narrative boundary types. All four types showed a shift toward visual processing in the brain, but their other associations differed. Visual boundaries were associated with less distraction, semantic boundaries with thoughts related to past experience and prior knowledge, and narrative boundaries with more detailed and deliberate thoughts. Visual and semantic boundaries also showed distinct shifts in brain state that were not apparent in the other two types. These findings suggest that event boundaries mark a set of state shifts in ongoing experience, with a shared reorientation toward visual processing and changes in thought and brain state that vary with the type of transition.

neuroscience↗

bioRxiv2026

A shared functional architecture for error-based and reinforcement-based motor learning in the human brain

The motor system can flexibly learn from fundamentally different teaching signals, from directional errors to rewards. While computational and neurobiological models have long assigned these to distinct error-based learning (EL) and reinforcement-based learning (RL) processes, whether this separation holds at the level of whole-brain network architecture has not been directly tested. Here, we characterized whole-brain functional connectivity manifolds in the same participants as they performed separate EL and RL motor tasks -- differing in both feedback type and motor structure -- during two fMRI sessions. By jointly embedding covariance patterns from both tasks into a common low-dimensional neural space, we isolated task-general from task-specific learning-related network reconfigurations. We show that both forms of motor learning induce converging changes in the manifold structure of higher-order transmodal cortex, with comparatively limited task-specific modulation. Notably, this convergence extended to the cerebellum and basal ganglia, the canonical substrates of error-based and reward-based learning, which reconfigured comparably across both tasks. Against this shared backdrop, we found that the posterior medial cortex exhibited a unique functional geometry, selectively redistributing its connectivity across distinct brain circuits depending on feedback type and learning stage. We further demonstrate that individual differences in domain-general motor learning ability are associated with stage-dependent reconfigurations within limbic, default-mode, and attentional systems. These findings indicate that flexible motor adaptation, irrespective of the nature of the learning signal, is supported less by changes within task-specific sensorimotor circuits than by the dynamic reorganization of higher-order brain networks that coordinate them.

neuroscience↗

bioRxiv2025

Synthesizing images to map neural networks to the human brain

Computational models can be used to generate hypotheses about the brain. In the visual system, this approach has revealed similarities between how natural images are represented in convolutional neural networks and in cortical regions. However, natural images generate highly correlated representations across the hierarchy of model layers, meaning that each brain region will not correspond selectively to a single layer. Because each model layer performs additional image transformations, gaining a more selective mapping between individual layers and regions could reveal how specific algorithmic stages of processing are instantiated in the brain. To enable this mapping, we developed a generative framework for image synthesis that minimizes the similarity of image representational similarity matrices across model layers, aiming to orthogonalize the distances between the patterns of unit activations evoked by the same set of images. With the patterns orthogonalized, the resulting similarity matrix for each layer provides a fingerprint of the unique computational role of that layer. To test this approach, we synthesized 16 artificial images from the Inception-V1/GoogLeNet model and scanned participants with fMRI while they viewed these images repeatedly in random order. Image-specific patterns of voxel activity were used to compute image-by-image similarity matrices across the whole brain with searchlights. Most layers could be mapped to circumscribed cortical regions, and these mappings overlapped less than the mappings obtained with natural images. Given the prevalence of existing fMRI datasets with natural images, we used the synthesis method as a benchmark to develop an alternative residual method that can achieve comparable performance for natural image datasets. These approaches could be extended to other neural network architectures and stimulus modalities for targeted mappings of model computations to the brain and behavior.

neuroscience↗

bioRxiv2023

Inducing representational change in the hippocampus through real-time neurofeedback

AO_SCPLOWBSTRACTC_SCPLOWWhen you perceive or remember one thing, other related things come to mind. This competition has consequences for how these items are later perceived, attended, or remembered. Such behavioral consequences result from changes in how much the neural representations of the items overlap, especially in the hippocampus. These changes can reflect increased (integration) or decreased (differentiation) overlap; previous studies have posited that the amount of coactivation between competing representations in cortex determines which will occur: high coactivation leads to hippocampal integration, medium coactivation leads to differentiation, and low coactivation is inert. However, those studies used indirect proxies for coactivation, by manipulating stimulus similarity or task demands. Here we induce coactivation of competing memories in visual cortex more directly using closed-loop neurofeedback from real-time fMRI. While viewing one object, participants were rewarded for implicitly activating the representation of another object as strongly as possible. Across multiple real-time fMRI training sessions, they succeeded in using the neurofeedback to induce coactivation. Compared with untrained objects, this coactivation led to behavioral and neural integration: The trained objects became harder for participants to discriminate in a categorical perception task and harder to decode from patterns of fMRI activity in the hippocampus.

neuroscience↗

bioRxiv2023

Visual statistical learning is associated with changes in cortical manifold structure

Our brains are in a near constant state of generating predictions, extracting regularities from seemingly random sensory inputs to support later cognition and behavior - a process called statistical learning (SL). Yet, the activity patterns across cortex and subcortex that support this form of associative learning remain unresolved. Here we use human fMRI and a visual SL task to investigate changes in neural activity patterns as participants implicitly learn visual associations from a sequence. By projecting functional connectivity patterns onto a low-dimensional manifold, we reveal that learning is selectively supported by changes along a single neural dimension spanning visual-parietal and perirhinal cortex (PRC). During learning, visual cortex expanded along this dimension, segregating from other networks, while dorsal attention network (DAN) regions contracted, integrating with higher-order transmodal cortex. When we later violated the learned associations, PRC and entorhinal cortex, which initially showed no evidence of learning-related effects, now contracted along this dimension, integrating with the default mode and DAN, while decreasing covariance with visual cortex. Whereas previous studies have linked SL to either broad cortical or medial temporal lobe changes, our findings suggest an integrative view, whereby cortical regions reorganize during association formation, while medial temporal lobe regions respond to their violation.

neuroscience↗

bioRxiv2021

Increasing stimulus similarity drives nonmonotonic representational change in hippocampus

Studies of hippocampal learning have obtained seemingly contradictory results, with manipulations that increase coactivation of memories sometimes leading to differentiation of these memories, but sometimes not. These results could potentially be reconciled using the nonmonotonic plasticity hypothesis, which posits that representational change (memories moving apart or together) is a U-shaped function of the coactivation of these memories during learning. Testing this hypothesis requires manipulating coactivation over a wide enough range to reveal the full U-shape. To accomplish this, we used a novel neural network image synthesis procedure to create pairs of stimuli that varied parametrically in their similarity in high-level visual regions that provide input to the hippocampus. Sequences of these pairs were shown to human participants during high-resolution fMRI. As predicted, learning changed the representations of paired images in the dentate gyrus as a U-shaped function of image similarity, with neural differentiation occurring only for moderately similar images.

neuroscience↗