Network changes in the transition from initial learning to well-practiced visual categorization

Resting-State Network Plasticity Following Category Learning Depends on Sensory Modality

Learning new categories is fundamental to cognition, occurring in daily life through various sensory modalities. However, it is not well known how acquiring new categories can modulate the brain networks. Resting-state functional connectivity is an effective method for detecting short-term brain alterations induced by various modality-based learning experiences. Using fMRI, our study investigated the intricate link between novel category learning and brain network reorganization. Eighty-four adults participated in an object categorization experiment utilizing visual (n = 41, with 20 females and a mean age of 23.91 ± 3.11 years) or tactile (n = 43, with 21 females and a mean age of 24.57 ± 2.58 years) modalities. Resting-state networks (RSNs) were identified using independent component analysis across the group of participants, and their correlation with individual differences in object category learning across modalities was examined using dual regression. Our results reveal an increased functional connectivity of the frontoparietal network with the left superior frontal gyrus in visual category learning task and with the right superior occipital gyrus and the left middle temporal gyrus after tactile category learning. Moreover, the somatomotor network demonstrated an increased functional connectivity with the left parahippocampus exclusively after tactile category learning. These findings illuminate the neural mechanisms of novel category learning, emphasizing distinct brain networks' roles in diverse modalities. The dynamic nature of RSNs emphasizes the ongoing adaptability of the brain, which is essential for efficient novel object category learning. This research provides valuable insights into the dynamic interplay between sensory learning, brain plasticity, and network reorganization, advancing our understanding of cognitive processes across different modalities.

Individual differences in working memory impact the trajectory of non-native speech category learning

What is the role of working memory over the course of non-native speech category learning? Prior work has predominantly focused on how working memory might influence learning assessed at a single timepoint. Here, we substantially extend this prior work by examining the role of working memory on speech learning performance over time (i.e., over several months) and leverage a multifaceted approach that provides key insights into how working memory influences learning accuracy, maintenance of knowledge over time, generalization ability, and decision processes. We found that the role of working memory in non-native speech learning depends on the timepoint of learning and whether individuals learned the categories at all. Among learners, across all stages of learning, working memory was associated with higher accuracy as well as faster and slightly more cautious decision making. Further, while learners and non-learners did not have substantially different working memory performance, learners had faster evidence accumulation and more cautious decision thresholds throughout all sessions. Working memory may enhance learning by facilitating rapid category acquisition in initial stages and enabling faster and slightly more careful decision-making strategies that may reduce the overall effort needed to learn. Our results have important implications for developing interventions to improve learning in naturalistic language contexts.

Low-Beta Oscillations Turn Up the Gain During Category Judgments

Synchrony between local field potential (LFP) rhythms is thought to boost the signal of attended sensory inputs. Other cognitive functions could benefit from such gain control. One is categorization where decisions can be difficult if categories differ in subtle ways. Monkeys were trained to flexibly categorize smoothly varying morphed stimuli, using orthogonal boundaries to carve up the same stimulus space in 2 different ways. We found evidence for category-specific patterns of low-beta (16-20 Hz) synchrony in the lateral prefrontal cortex (PFC). This synchrony was stronger when a given category scheme was relevant. We also observed an overall increase in low-beta LFP synchrony for stimuli that were near the category boundary and thus more difficult to categorize. Beta category selectivity was evident in partial field-field coherence measurements, which measure local synchrony, but the boundary enhancement was not. Thus, it seemed that category selectivity relied on local interactions while boundary enhancement was a more global effect. The results suggest that beta synchrony helps form category ensembles and may reflect recruitment of additional cortical resources for categorizing challenging stimuli, thus serving as a form of gain control.

Exploring the spatio-temporal neural basis of face learning

Humans are experts at face individuation. Although previous work has identified a network of face-sensitive regions and some of the temporal signatures of face processing, as yet, we do not have a clear understanding of how such face-sensitive regions support learning at different time points. To study the joint spatio-temporal neural basis of face learning, we trained subjects to categorize two groups of novel faces and recorded their neural responses using magnetoencephalography (MEG) throughout learning. A regression analysis of neural responses in face-sensitive regions against behavioral learning curves revealed significant correlations with learning in the majority of the face-sensitive regions in the face network, mostly between 150-250 ms, but also after 300 ms. However, the effect was smaller in nonventral regions (within the superior temporal areas and prefrontal cortex) than that in the ventral regions (within the inferior occipital gyri (IOG), midfusiform gyri (mFUS) and anterior temporal lobes). A multivariate discriminant analysis also revealed that IOG and mFUS, which showed strong correlation effects with learning, exhibited significant discriminability between the two face categories at different time points both between 150-250 ms and after 300 ms. In contrast, the nonventral face-sensitive regions, where correlation effects with learning were smaller, did exhibit some significant discriminability, but mainly after 300 ms. In sum, our findings indicate that early and recurring temporal components arising from ventral face-sensitive regions are critically involved in learning new faces.

The Neural Correlates of Similarity- and Rule-based Generalization

The idea that there are multiple learning systems has become increasingly influential in recent years, with many studies providing evidence that there is both a quick, similarity-based or feature-based system and a more effortful rule-based system. A smaller number of imaging studies have also examined whether neurally dissociable learning systems are detectable. We further investigate this by employing for the first time in an imaging study a combined positive and negative patterning procedure originally developed by Shanks and Darby [Shanks, D. R., & Darby, R. J. Feature- and rule-based generalization in human associative learning. Journal of Experimental Psychology: Animal Behavior Processes, 24, 405-415, 1998]. Unlike previous related studies employing other procedures, rule generalization in the Shanks-Darby task is beyond any simple non-rule-based (e.g., associative) account. We found that rule- and similarity-based generalization evoked common activation in diverse regions including the pFC and the bilateral parietal and occipital lobes indicating that both strategies likely share a range of common processes. No differences between strategies were identified in whole-brain comparisons, but exploratory analyses indicated that rule-based generalization led to greater activation in the right middle frontal cortex than similarity-based generalization. Conversely, the similarity group activated the anterior medial frontal lobe and right inferior parietal lobes more than the rule group did. The implications of these results are discussed.

Dissociable changes in functional network topology underlie early category learning and development of automaticity

Recent work has shown that multimodal association areas-including frontal, temporal, and parietal cortex-are focal points of functional network reconfiguration during human learning and performance of cognitive tasks. On the other hand, neurocomputational theories of category learning suggest that the basal ganglia and related subcortical structures are focal points of functional network reconfiguration during early learning of some categorization tasks but become less so with the development of automatic categorization performance. Using a combination of network science and multilevel regression, we explore how changes in the connectivity of small brain regions can predict behavioral changes during training in a visual categorization task. We find that initial category learning, as indexed by changes in accuracy, is predicted by increasingly efficient integrative processing in subcortical areas, with higher functional specialization, more efficient integration across modules, but a lower cost in terms of redundancy of information processing. The development of automaticity, as indexed by changes in the speed of correct responses, was predicted by lower clustering (particularly in subcortical areas), higher strength (highest in cortical areas), and higher betweenness centrality. By combining neurocomputational theories and network scientific methods, these results synthesize the dissociative roles of multimodal association areas and subcortical structures in the development of automaticity during category learning.

PTSD modifies performance on a task of affective executive control among deployed OEF/OIF veterans with mild traumatic brain injury

Individuals with post-traumatic stress disorder (PTSD) show a cognitive bias for threatening information, reflecting dysregulated executive control for affective stimuli. This study examined whether comorbid mild Traumatic Brain Injury (mTBI) with PTSD exacerbates this bias. A computer-administered Affective Go/No-Go task measured reaction times (RTs) and errors of omission and commission to words with a non-combat-related positive or negative valence in 72 deployed United States service members from the wars in Iraq and Afghanistan. Incidents of military-related mTBI were measured with the Boston Assessment of Traumatic Brain Injury-Lifetime. PTSD symptoms were measured with the Clinician-Administered PTSD Scale. Participants were divided into those with (mTBI+, n = 34) and without a history of military-related mTBI (mTBI-, n = 38). Valence of the target stimuli differentially impacted errors of commission and decision bias (criterion) in the mTBI+ and mTBI- groups. Specifically, within the mTBI+ group, increasing severity of PTSD symptoms was associated with an increasingly liberal response pattern (defined as more commission errors to negative distractors and greater hit rate for positive stimuli) in the positive compared to the negative blocks. This association was not observed in the mTBI- group. This study underscores the importance of considering the impact of a military-related mTBI and PTSD severity upon affective executive control.

Brain activity across the development of automatic categorization: a comparison of categorization tasks using multi-voxel pattern analysis

Previous evidence suggests that relatively separate neural networks underlie initial learning of rule-based and information-integration categorization tasks. With the development of automaticity, categorization behavior in both tasks becomes increasingly similar and exclusively related to activity in cortical regions. The present study uses multi-voxel pattern analysis to directly compare the development of automaticity in different categorization tasks. Each of the three groups of participants received extensive training in a different categorization task: either an information-integration task, or one of two rule-based tasks. Four training sessions were performed inside an MRI scanner. Three different analyses were performed on the imaging data from a number of regions of interest (ROIs). The common patterns analysis had the goal of revealing ROIs with similar patterns of activation across tasks. The unique patterns analysis had the goal of revealing ROIs with dissimilar patterns of activation across tasks. The representational similarity analysis aimed at exploring (1) the similarity of category representations across ROIs and (2) how those patterns of similarities compared across tasks. The results showed that common patterns of activation were present in motor areas and basal ganglia early in training, but only in the former later on. Unique patterns were found in a variety of cortical and subcortical areas early in training, but they were dramatically reduced with training. Finally, patterns of representational similarity between brain regions became increasingly similar across tasks with the development of automaticity.

Training the developing brain: a neurocognitive perspective

Developmental training studies are important to increase our understanding of the potential of the developing brain by providing answers to questions such as: “Which functions can and which functions cannot be improved as a result of practice?,” “Is there a specific period during which training has more impact?,” and “Is it always advantageous to train a particular function?”In addition, neuroimaging methods provide valuable information about the underlying mechanisms that drive cognitive plasticity. In this review, we describe how neuroscientific studies of training effects inform us about the possibilities of the developing brain, pointing out that childhood is a special period during which training may have different effects. We conclude that there is much complexity in interpreting training effects in children. Depending on the type of training and the level of maturation of the individual, training may influence developmental trajectories in different ways. We propose that the immature brain structure might set limits on how much can be achieved with training, but that the immaturity can also have advantages, in terms of flexibility for learning.

Tactile-auditory shape learning engages the lateral occipital complex

Shape is an object property that inherently exists in vision and touch, and is processed in part by the lateral occipital complex (LOC). Recent studies have shown that shape can be artificially coded by sound using sensory substitution algorithms and learned with behavioral training. This finding offers a unique opportunity to test intermodal generalizability of the LOC beyond the sensory modalities in which shape is naturally perceived. Therefore, we investigated the role of the LOC in processing of shape by examining neural activity associated with learning tactile-shape-coded auditory information. Nine blindfolded sighted people learned the tactile-auditory relationship between raised abstract shapes and their corresponding shape-coded sounds over 5 d of training. Using functional magnetic resonance imaging, subjects were scanned before and after training during a task in which they first listened to a shape-coded sound transformation, then touched an embossed shape, and responded whether or not the tactile stimulus matched the auditory stimulus in terms of shape. We found that behavioral scores improved after training and that the LOC was commonly activated during the auditory and tactile conditions both before and after training. However, no significant training-related change was observed in magnitude or size of LOC activity; rather, the auditory cortex and LOC showed strengthened functional connectivity after training. These findings suggest that the LOC is available to different sensory systems for shape processing and that auditory-tactile sensory substitution training leads to neural changes allowing more direct or efficient access to this site by the auditory system.

The prefrontal cortex modulates category selectivity in human extrastriate cortex

Different categories of visual objects evoke distinct stimulus-evoked sensory responses in extrastriate visual cortex. Although numerous lines of evidence support a distinct representational neural architecture, the mechanisms underlying the modulation of the category selectivity by top-down influences remains uncertain. In this study, we investigate the causal role of the PFC in the modulation of evoked activity to face and scene stimuli in the extrastriate cortex. We used two experimental approaches to disrupt prefrontal cortical function-repetitive TMS to PFC in healthy participants (Experiment 1) and focal PFC lesions in stroke patients (Experiment 2). After these perturbations to normal PFC function (pre- vs. post-TMS and lesion vs. intact hemisphere), stimulus-evoked activity in extrastriate cortex exhibited less distinct category selectivity to faces and scenes. These two experiments provide convergent evidence highlighting a direct role of PFC in the top-down modulation of bottom-up visual signals.