Biology subjects

Chetverikov, A.

Publications and source records attributed to Chetverikov, A..

7 recordsLinked to original sources

bioRxiv2025

Noise in Competing Representations Determines the Direction of Memory Biases

Memories are reconstructions, prone to errors. Historically considered a nuisance, memory errors have recently gained attention when found to be systematically shifted away from or toward non-reported items, promising insights into memory mechanisms. We propose that these biases are optimal and inevitable when the brain disentangles overlapping memory signals, predicting that bias direction depends on the noise distribution between memorized items, not just absolute noise levels. We tested this prediction in four color-memory experiments using novel stimuli with independently varied noise levels. The results support our hypothesis: targets with the same absolute noise level can be repelled from or attracted to non-target items, depending on their relative noise levels. We further show that the model can fit nonlinear bias patterns observed in human data with noise levels as the only free parameters. These findings challenge currently dominant models and support signal disentanglement as a unifying explanation of memory biases.

neuroscience↗

bioRxiv2025

Rapid modulation of choice behavior by ultrasound on the human frontal eye fields

A fundamental challenge in neuroscience is establishing causal brain-function relationships with spatial and temporal precision. Transcranial ultrasonic stimulation (TUS) offers a unique opportunity to modulate deep brain structures non-invasively with high spatial resolution, but temporally precise effects and their neurophysiological foundations have yet to be demonstrated in humans. Here, we develop a temporally precise TUS protocol targeting the frontal eye fields (FEFs) -- a well-characterized circuit critical for saccadic eye movements. We demonstrate that TUS induces robust excitatory behavioral effects. Importantly, individual differences in baseline GABAergic inhibitory tone predict response magnitude. These findings establish TUS as a reliable tool for chronometric circuit interrogation and highlight the importance of neurophysiological state in neuromodulation. This work bridges human and animal research, advancing targeted TUS applications in neuroscience and clinical settings. HighlightsO_LIFocused ultrasound modulates saccades with high spatial and temporal precision C_LIO_LIInhibitory circuits in the frontal eye fields shape choice computations C_LIO_LIGABA levels predict individual variability in ultrasound-induced behavioral changes C_LIO_LIUltrasound can be used to probe fast neural dynamics and individual differences C_LI

neuroscience↗

bioRxiv2023

Motion direction is represented as a bimodal probability distribution in the human visual cortex

How do humans infer motion direction from noisy sensory signals? We hypothesized that motion direction is computed not only from velocity but also spatial orientation signals - a streak created by moving objects. We implemented this hypothesis in a Bayesian model, which quantifies knowledge with probability distributions, and tested its predictions using psychophysics and fMRI. Using a probabilistic pattern-based analysis, we decoded probability distributions of motion direction from trial-by-trial activity in the human visual cortex. Corroborating the predictions, the decoded distributions had a bimodal shape, with peaks that predicted the direction and magnitude of behavioral errors. Interestingly, we observed similar bimodality in the distribution of the observers behavioral responses across trials. Together, these results suggest that observers use spatial orientation signals when estimating motion direction. More broadly, our findings indicate that the cortical representation of low-level visual features, such as motion direction, can reflect a combination of several qualitatively distinct signals.

neuroscience↗

bioRxiv2023

Demixing model: A normative explanation for inter-item biases in memory and perception

Many studies in perception and in the working memory literature demonstrate that human observers systematically deviate from the truth when estimating the features of one item in the presence of another. Such inter-item or contextual biases are well established but lack a coherent explanation at the computational level. Here, I propose a novel normative model showing that such biases exist for any observer striving for optimality when trying to infer the features of multiple similar objects from a mixture of sensory observations. The demixing model predicts that bias strength and direction would vary as a function of the amount of sensory noise and the similarity between items. Crucially, these biases exist not because of the prior knowledge in any form, but simply because the biased solutions to this inference problem are more probable than unbiased ones, counter to the common intuition. The model makes novel predictions about the effect of discriminability along the dimension used to select the item to report (e.g., spatial location) and the relative amount of sensory noise. Although the model is consistent with previously reported data from human observers, more carefully controlled studies are needed for a stringent test of its predictions. The strongest point of the demixing model, however, is that it shows that interitem biases are inevitable when observers lack perfect knowledge of which stimuli caused which sensory observations, which is, arguably, always the case.

neuroscience↗

bioRxiv2023

Auditory confounds can drive online effects of transcranial ultrasonic stimulation in humans

Transcranial ultrasonic stimulation (TUS) is rapidly emerging as a promising non-invasive neuromodulation technique. TUS is already well-established in animal models, providing foundations to now optimize neuromodulatory efficacy for human applications. Across multiple studies, one promising protocol, pulsed at 1000 Hz, has consistently resulted in motor cortical inhibition in humans (Fomenko et al., 2020). At the same time, a parallel research line has highlighted the potentially confounding influence of peripheral auditory stimulation arising from TUS pulsing at audible frequencies. In this study, we disentangle direct neuromodulatory and indirect auditory contributions to motor inhibitory effects of TUS. To this end, we include tightly matched control conditions across four experiments, one preregistered, conducted independently at three institutions. We employed a combined transcranial ultrasonic and magnetic stimulation paradigm, where TMS-elicited motor-evoked potentials (MEPs) served as an index of corticospinal excitability. First, we replicated motor inhibitory effects of TUS but showed through both tight controls and manipulation of stimulation intensity, duration, and auditory masking conditions that this inhibition was driven by peripheral auditory stimulation, not direct neuromodulation. Further, we consider neuromodulation beyond driving overall excitation/inhibition and show preliminary evidence of how TUS might interact with ongoing neural dynamics instead. Primarily, this study highlights the substantial shortcomings in accounting for the auditory confound in prior TUS-TMS work where only a flip-over sham and no active control was used. The field must critically reevaluate previous findings given the demonstrated impact of peripheral confounds. Further, rigorous experimental design via (in)active control conditions is required to make substantiated claims in future TUS studies. Only when direct effects are disentangled from those driven by peripheral confounds can TUS fully realize its potential for research and clinical applications.

neuroscience↗

bioRxiv2021

Effortless integration of probabilistic visual input

Current theories of perception suggest that the brain represents features of the world as probability distributions, but can such uncertain foundations provide the basis for everyday vision? Perceiving objects and scenes requires knowing not just how features (e.g., colors) are distributed but also where they are and which other features they are combined with. Using a Bayesian computational model, we recover probabilistic representations used by human observers to search for odd stimuli among distractors. Importantly, we found that the brain integrates information between feature dimensions and spatial locations, leading to more precise representations compared to when information integration is not possible. We also uncover representational asymmetries and biases, showing their spatial organization and arguing against simplified "summary statistics" accounts. Our results confirm that probabilistically encoded visual features are bound with other features and to particular locations, proving how probabilistic representations can be a foundation for higher-level vision.

neuroscience↗

bioRxiv2020

Dual strategies in human confidence judgments

Although confidence is commonly believed to be an essential element in decision making, it remains unclear what gives rise to ones sense of confidence. Recent Bayesian theories propose that confidence is computed, in part, from the degree of uncertainty in sensory evidence. Alternatively, observers can use physical properties of the stimulus as a heuristic to confidence. In the current study, we developed ideal observer models for either hypothesis and compared their predictions against human data obtained from psychophysical experiments. Participants reported the orientation of a stimulus, and their confidence in this estimate, under varying levels of internal and external noise. As predicted by the Bayesian model, we found a consistent link between confidence and behavioral variability for a given stimulus orientation. Confidence was higher when orientation estimates were more precise, for both internal and external sources of noise. However, we observed the inverse pattern when comparing between stimulus orientations: although observers gave more precise orientation estimates for cardinal orientations (a phenomenon known as the oblique effect), they were more confident about oblique orientations. We show that these results are well explained by a strategy to confidence that is based on the perceived amount of noise in the stimulus. Altogether, our results suggest that confidence is not always computed from the degree of uncertainty in ones perceptual evidence, but can instead be based on visual cues that function as simple heuristics to confidence.

neuroscience↗