Neural Mechanisms of Memory Enhancement and Impairment Induced by Visual Statistical Learning

Frontal Deactivation and the Efficacy of Statistical Learning: Neural Mechanisms Accompanying Exposure to Visual Statistical Sequences

Statistical learning is the cognitive ability to rapidly identify structure and meaning in unfamiliar streams of sensory experience, even in the absence of feedback. Despite extensive studies, the neurocognitive mechanisms underlying this phenomenon still require further clarification under varying cognitive conditions. Here, we examined neural mechanisms during the first exposure to visually presented sequences in 47 healthy participants. We used two types of visual objects: abstract symbols and pictures of cartoon-like animals. This allowed us to compare informational processing mechanisms with defined distinguishing features. Participants achieved better performance for sequences with easy-to-name than difficult-to-name abstract stimuli. fMRI results revealed greater activation in widespread brain regions in response to random versus statistical sequences for all stimuli types. Behavioral accuracy was associated with increased deactivation of the ventromedial PFC for easy-to-name statistical versus random sequences. For difficult-to-name statistical versus random sequences, performance correlated with dorsomedial prefrontal cortex deactivation. ROI analysis showed a generally positive involvement of the caudate head in sequence processing with significantly stronger activity during the first run of performing the task. Functional connectivity analysis of prefrontal deactivation regions revealed significant connectivity with nodes of the salience network for both object types and inverse connectivity with the caudate head only for easy-to-name objects. The results indicated that distinct subregions of PFC modulate task performance depending on the visual stimulus characteristic. They also showed that among striatal regions, only the head of the caudate was sensitive to initial exposure to visual statistical information.

Reduced functional connectivity supports statistical learning of temporally distributed regularities

Statistical learning is a powerful ability that extracts regularities from our environment and makes predictions about future events. Using functional magnetic resonance imaging, we aimed to probe how a wide range of brain areas are intertwined to support statistical learning, characterising its architecture in the whole-brain functional connectivity (FC). Participants performed a statistical learning task of temporally distributed regularities. We used refined behavioural learning scores to associate individuals' learning performances with the FC changed by statistical learning. As a result, the learning performance was mediated by the activation strength in the lateral occipital cortex, angular gyrus, precuneus, anterior cingulate cortex, and superior frontal gyrus. Through a group independent component analysis, activations of the superior frontal network showed the largest correlation with the statistical learning performances. Seed-to-voxel whole-brain and seed-to-ROI FC analyses revealed that the FC between the superior frontal gyrus and the salience, language, and dorsal attention networks were reduced during statistical learning. We suggest that the weakened functional connections between the superior frontal gyrus and brain regions involved in top-down control processes serve a pivotal role in statistical learning, supporting better processing of novel information such as the extraction of new patterns from the environment.

Visual experience modulates whole-brain connectivity dynamics: A resting-state fMRI study using the model of radiologists

Visual expertise refers to proficiency in visual recognition. It is attributed to accumulated visual experience in a specific domain and manifests in widespread neural activities that extend well beyond the visual cortex to multiple high‐level brain areas. An extensive body of studies has centered on the neural mechanisms underlying a distinctive domain of visual expertise, while few studies elucidated how visual experience modulates resting‐state whole‐brain connectivity dynamics. The current study bridged this gap by modeling the subtle alterations in interregional spontaneous connectivity patterns with a group of superior radiological interns. Functional connectivity analysis was based on functional brain segmentation, which was derived from a data‐driven clustering approach to discriminate subtle changes in connectivity dynamics. Our results showed there was radiographic visual experience accompanied with integration within brain circuits supporting visual processing and decision making, integration across brain circuits supporting high‐order functions, and segregation between high‐order and low‐order brain functions. Also, most of these alterations were significantly correlated with individual nodule identification performance. Our results implied that visual expertise is a controlled, interactive process that develops from reciprocal interactions between the visual system and multiple top‐down factors, including semantic knowledge, top‐down attentional control, and task relevance, which may enhance participants' local brain functional integration to promote their acquisition of specific visual information and modulate the activity of some regions for lower‐order visual feature processing to filter out nonrelevant visual details. The current findings may provide new ideas for understanding the central mechanism underlying the formation of visual expertise.