Biology subjects

Bejjanki, V. R.

Publications and source records attributed to Bejjanki, V. R..

7 recordsLinked to original sources

bioRxiv2026

On the limit: Working memory capacity constrains learning of complex visuomotor mappings

Human sensorimotor adaptation critically depends on the ability to map sensory inputs onto motor outputs. While this process was once thought to rely primarily on cerebellar-dependent implicit recalibration, recent work has revealed that explicit, cognitive strategies, relying on working memory and executive function, play a substantial role. However, it remains unclear whether explicit strategies can scale to support learning of complex sensorimotor mappings. Here, we parametrically tested the capacity of explicit strategies to solve a complex visuomotor rotation task by varying the number of target-rotation pairings that participants had to acquire. In experiment 1, participants were tasked with learning four visuomotor mappings - well within working memory capacity, based on prior studies. We found that participants achieved near-perfect compensation, which was best explained by the retrieval of stored target-rotation associations, rather than by computationally demanding algorithmic strategies. In experiment 2, in an attempt to push beyond working memory capacity, participants were tasked with learning eight mappings. Unlike in experiment 1, here we found that participants failed to fully compensate for the rotations, reaching asymptotic performance of only [~]50%, despite evidence of continued strategic engagement. This performance limit was fully predicted by a parameter-free working-memory model where performance is a mixture of a fixed number of stored target-rotation associations and random guessing. These findings reveal a cognitive "bandwidth limit" on the effectiveness of strategies for sensorimotor adaptation: when task complexity exceeds this limit, adaptation plateaus, defining a fundamental constraint on how far higher-order cognition can go to support learning. Significance StatementHuman learners often compensate for perturbations in sensorimotor mappings by deploying explicit, cognitive strategies, but the scalability of these strategies for more complex tasks remains unknown. Here, we tested how performance changes as people must simultaneously learn an increasing number of distinct visuomotor rotation mappings. When presented with only a small set, participants have little difficulty - they learn by opting to forgo computationally demanding algorithmic strategies in favor of storing and retrieving target-specific solutions from working memory. However, as the number of mappings grows, this response caching strategy fails, with performance plateauing in a manner that is consistent with a capacity-limited memory bottleneck. These results highlight the critical constraint that working memory imposes on the utility of explicit strategies for sensorimotor adaptation in complex tasks.

neuroscience↗

bioRxiv2025

Visual perceptual learning enhances functional connectivity in retinotopic space

Repeated exposure to perceptual tasks improves behavioral performance. Several neural mechanisms have been proposed to account for such perceptual learning. Computational modeling suggests that plasticity in the connectivity between cortical sites may be responsible, by increasing the fidelity with which task-relevant information is transmitted through sensory hierarchies. Here we explore this theory in humans using fMRI, testing the hypothesis that perceptual learning at one location in space will increase functional connectivity between voxels in visual areas that are tuned to that retinotopic location. Participants learned to detect one of two novel visual shape contours embedded in a noisy background in different visual quadrants. At baseline, there was no difference in behavioral sensitivity for the two shapes, nor a difference in functional connectivity between voxels in V1 and V4 responsive to the retinotopic locations of the two shapes. After training, there was a robust and selective improvement in perceptual detection of the trained shape relative to the control shape, along with increased functional connectivity between V1 and V4 voxels coding for the location of the trained shape in retinotopic space. Moreover, this increase in functional connectivity for the trained versus control shape predicted the improvement in behavioral sensitivity across participants. These results are consistent with the proposal that perceptual learning alters network dynamics so as to enhance the processing of behaviorally relevant information.

neuroscience↗

bioRxiv2021

Sequence learning attenuates cortical responses in both frontal and perceptual cortices in early infancy

Prior work has found that the frontal lobe is involved in higher-order sequential and statistical learning in young infants. Separate lines of work have found evidence of modulation of posterior sensory cortices during and after learning tasks. How do these processes relate? Here, we build evidence the infant frontal lobe was modulated during sequential learning and ask whether posterior perceptual cortices show corresponding modulation. First, replicating and extending past work, we found evidence of frontal lobe involvement in this task. Second, consistent with our hypotheses, we found that there is a corresponding attenuation of neural responses in the posterior perceptual cortices (temporal and occipital) to predictable compared to unpredictable audiovisual sequences. This study provides convergent evidence that the frontal lobe is crucial for higher-level learning in young infants but that it likely works as part of a large, distributed network of regions to modulate infant neural responses during learning. Overall, this work challenges the view that the infant brain is not dynamic and disconnected, lacking in long-range neural connections. Instead, this paper reveals patterns of a highly dynamic and interconnected infant brain that change rapidly as a result of new, learnable experiences.

neuroscience↗

bioRxiv2020

Retinotopic organization of visual cortex in human infants

Vision develops rapidly during infancy, yet how visual cortex is organized during this period is unclear. One possibility is that the retinotopic organization of visual cortex emerges gradually as perceptual abilities improve. This may result in a hierarchical maturation of visual areas from striate to extrastriate cortex. Another possibility is that retinotopic organization is present from early infancy. This early maturation of area boundaries and tuning could scaffold further developmental changes. Here we test the functional maturity of infant visual cortex by performing retinotopic mapping with fMRI. Infants aged 5-23 months had retinotopic maps, with alternating preferences for vertical and horizontal meridians indicative of area boundaries from V1 to V4, and an orthogonal gradient of preferences from high to low spatial frequencies indicative of growing receptive field sizes. Although present in the youngest infants, these retinotopic maps showed subtle agerelated changes, suggesting that early maturation undergoes continued refinement.

neuroscience↗

bioRxiv2020

Evidence of hippocampal learning in human infants

The hippocampus is essential for human memory. Thus, memory deficiencies in infants are often attributed to hippocampal immaturity. However, the functionality of the infant hippocampus has never been tested directly. Here we report that the human hippocampus is indeed active in infancy. We recorded hippocampal activity using fMRI while awake infants aged 3-24 months viewed sequences of objects. Greater activity was observed when the order of the sequence contained regularities that could be learned compared to when the order was random. The involvement of the hippocampus in such statistical learning, with additional recruitment of the medial prefrontal cortex, is consistent with findings from adults. These results suggest that the hippocampus supports the important ability of infants to extract the structure of their environment through experience.

neuroscience↗

bioRxiv2020

How to read a baby's mind: Re-imagining fMRI for awake, behaving infants

Thousands of functional magnetic resonance imaging (fMRI) studies have provided important insight into the human brain. However, only a handful of these studies tested infants while they were awake, because of the significant and unique methodological challenges involved. We report our efforts over the past five years to address these challenges, with the goal of creating methods for infant fMRI that can reveal the inner workings of the developing, preverbal mind. We use these methods to collect and analyze two fMRI datasets obtained from infants during cognitive tasks, released publicly with this paper. In these datasets, we explore data quantity and quality, task-evoked activity, and preprocessing decisions to derive and evaluate recommendations for infant fMRI. We disseminate these methods by sharing two software packages that integrate infant-friendly cognitive tasks and behavioral monitoring with fMRI acquisition and analysis. These resources make fMRI a feasible and accessible technique for cognitive neuroscience in human infants.

neuroscience↗

bioRxiv2019

Young children combine sensory cues with learned information in a statistically efficient manner: But task complexity matters

Human adults are adept at mitigating the influence of sensory uncertainty on task performance by integrating sensory cues with learned prior information, in a Bayes-optimal fashion. Previous research has shown that young children and infants are sensitive to environmental regularities, and that the ability to learn and use such regularities is involved in the development of several cognitive abilities. However, it has also been reported that children younger than 8 do not combine simultaneously available sensory cues in a Bayes-optimal fashion. Thus, it remains unclear whether, and by what age, children can combine sensory cues with learned regularities in an adult manner. Here, we examine the performance of 6-7-year old children when tasked with localizing a hidden target by combining uncertain sensory information with prior information learned over repeated exposure to the task. We demonstrate that 6-7-year olds learn task-relevant statistics at a rate on-par with adults, and like adults, are capable of integrating learned regularities with sensory information in a statistically efficient manner. We also show that variables such as task complexity can influence young childrens behavior to a greater extent than that of adults, leading their behavior to look sub-optimal. Our findings have important implications for how we should interpret failures in young childrens ability to carry out sophisticated computations. These failures need not be attributed to deficits in the fundamental computational capacity available to children early in development, but rather to ancillary immaturities in general cognitive abilities that mask the operation of these computations in specific situations.\n\nResearch HighlightsO_LIYoung children are sensitive to, and can learn, environmental regularities. Can they also utilize such learned regularities in a statistically efficient manner?\nC_LIO_LIWe demonstrate that 6-7-year olds are capable of learning and utilizing regularities in a statistically efficient fashion, and in a manner indistinguishable from adult behavior.\nC_LIO_LIHowever, variables such as task complexity can influence young childrens behavior to a greater extent than that of adults, leading their behavior to look sub-optimal.\nC_LIO_LIThese findings have important implications for how we should interpret failures in young childrens ability to carry out sophisticated computations.\nC_LI

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