Biology subjects

Ramot, M.

Publications and source records attributed to Ramot, M..

8 recordsLinked to original sources

bioRxiv2026

From Consensus to Individual Differences: Typicality Links Brain and Behavior Across Naturalistic Contexts

Naturalistic behaviors largely lack objective measures of performance, making it difficult to quantify individual differences and establish links between brain and behavior. Here, we propose typicality, the degree to which an individual's response aligns with the group average, as a framework for identifying and relating stable individual differences across behavioral and neural domains. We propose that, when observers share similar objectives and constraints, convergence toward a consensus response may reflect convergence toward an effective or optimal solution, allowing typicality to approximate optimal processing even when objective ground truth is lacking. To evaluate this framework, we combined naturalistic movie viewing during fMRI with a behavioral battery across multiple tasks spanning social and non-social cognition. Behavioral and neural typicality proved highly stable within individuals while remaining sensitive to the specific computations engaged by different stimuli. Crucially, behavioral typicality was related to neural typicality across multiple domains, with different behavioral measures mapping onto neural systems relevant to the corresponding computations. Neural typicality also predicted objectively measured performance in motion prediction and face recognition tasks, extending the framework beyond consensus-based measures alone. Together, these findings establish typicality as a stable, computation-sensitive measure that links individual differences in behavior to the neural systems supporting them. More broadly, they suggest that the group consensus provides more than a reference for quantifying individual differences: under appropriate conditions, proximity to this shared response may provide an empirical approximation of optimal processing. Typically, therefore, offers a framework for linking brain and behavior in complex naturalistic contexts.

neuroscience↗

bioRxiv2025

Behavioural separation of face memory and face perception

A long-standing debate in neuropsychology concerns whether perception and memory function as independent systems or interact to support cognition. To investigate this, we developed the Face Memory and Perception (FMP) task, a novel paradigm designed to systematically disentangle whether and how these processes interact under different conditions. Across five independent datasets with over 900 participants in total, we observed consistent evidence that face perception and working memory operate independently when task demands are low, but in more complex conditions, these processes appear to interact. Notably, this interaction emerged only when the interference directly involved face-processing mechanisms, and did not arise from a general increase in cognitive load. Rather than the use of shared resources by overlapping cognitive processes, this interaction was driven by a shift in behavioural strategy from holistic to feature-based face processing as a result of maintenance-disrupting interference. These results underscore the fundamental independence of perception and working memory while also explaining some of the conditions under which interactions might be observed.

neuroscience↗

bioRxiv2023

Putting cognitive tasks on trial: A measure of reliability convergence

The surge in interest in individual differences has coincided with the latest replication crisis centered around brain-wide association studies of brain-behavior correlations. Yet the reliability of the measures we use in cognitive neuroscience, a crucial component of this brain-behavior relationship, is often assumed but not directly tested. Here, we evaluate the reliability of different cognitive tasks on a large dataset of over 250 participants, who each completed a multi-day task battery. We show how reliability improves as a function of number of trials, and describe the convergence of the reliability curves for the different tasks, allowing us to score tasks according to their suitability for studies of individual differences. To improve the accessibility of these findings, we designed a simple web-based tool that implements this function to calculate the convergence factor and predict the expected reliability for any given number of trials and participants, even based on limited pilot data.

neuroscience↗

bioRxiv2020

Insufficient Eye Tracking Data Leads to Errors in Evaluating Typical and Atypical Fixation Preferences

Eye tracking provides insights into social processing and its deficits in disorders such as autism spectrum disorder (ASD), especially in conjunction with dynamic, naturalistic stimuli. However, reliance on manual stimuli segmentation severely limits scalability. We assessed how the amount of available data impacts individual reliability of fixation preference for different facial features, and the effect of this reliability on between-group differences. We trained an artificial neural network to segment 22 Hollywood movie clips (7410 frames). We then analyzed fixation preferences in typically developing participants and participants with ASD as we incrementally introduced movie data for analysis. Although fixations were initially variable, results stabilized as more data was added. Additionally, while those with ASD displayed significantly fewer face-centered fixations (p<.001), they did not differ in eye or mouth fixations. Our results highlight the validity of treating fixation preferences as a stable individual trait, and the risk of misinterpretation with insufficient data.

neuroscience↗

bioRxiv2019

Distinct neural mechanisms of social orienting and mentalizing revealed by independent measures of neural and eye movement typicality

Extensive study of typically developing individuals and those on the autism spectrum has identified a large number of brain regions associated with our ability to navigate the social world. Although it is widely appreciated that this so-called social brain is composed of distinct, interacting systems, these component parts have yet to be clearly elucidated. Here we used measures of eye movement and neural typicality - based on the degree to which subjects deviated from the norm - while typically developing (N = 62) and individuals with autism (N = 36) watched a large battery of movies depicting social interactions. Our findings provide clear evidence for distinct, but overlapping, neural systems underpinning two major components of the social brain, social orienting and inferring the mental state of others.

neuroscience↗

bioRxiv2018

Multifaceted integration - memory for faces is subserved by widespread connections between visual, memory, social and auditory networks

Face memory abilities are at the core of human social interaction, yet these vary widely within the general population, ranging from developmental prosopagnosia to \"super-recognizers\". Previous work has focused mainly on the contribution of the well described face network to this variance. However, given the nature of the face memory task, and the social context in which it takes place, we were interested in exploring how the collaboration between different networks outside the face network (measured through resting state connectivity) affects performance on face memory tasks. We discovered that face recognition memory is supported by a wide network of connections between the face patches, memory regions, auditory regions and social networks. Moreover, this network was selective for memory for faces, and did not predict memory for other visual objects, such as cars.

neuroscience↗

bioRxiv2018

A framework for offline evaluation and optimization of real-time algorithms for use in neurofeedback, demonstrated on an instantaneous proxy for correlations

Interest in real-time fMRI neurofeedback has grown exponentially over the past few years, both for use as a basic science research tool, and as part of the search for novel clinical interventions for neurological and psychiatric illnesses. In order to expand the range of questions which can be addressed with this tool however, new neurofeedback methods must be developed, going beyond feedback of activations in a single region. These new methods, several of which have already been proposed, are by their nature complex, involving many possible parameters. Here we suggest a framework for evaluating and optimizing algorithms for use in a real-time setting, before beginning the neurofeedback experiment, by offline simulations of algorithm output using a previously collected dataset. We demonstrate the application of this framework on the instantaneous proxy for correlations which we developed for training connectivity between different network nodes, identify the optimal parameters for use with this algorithm, and compare it to more traditional correlation methods. We also examine the effects of advanced imaging techniques, such as multi-echo acquisition, and the integration of these into the realtime processing stream.

neuroscience↗

bioRxiv2017

Direct Modulation Of Aberrant Brain Network Connectivity Through Real-Time NeuroFeedback

The existence of abnormal connectivity patterns between resting state networks in neuropsychiatric disorders, including Autism Spectrum Disorder (ASD), has been well established. Traditional treatment methods in ASD are limited, and do not address the aberrant network structure. Using real-time fMRI neurofeedback, we directly trained 3 brain nodes in participants with ASD, in which the aberrant connectivity has been shown to correlate with symptom severity. 17 ASD participants and 10 control participants were scanned over multiple sessions (123 sessions in total). Desired network connectivity patterns were reinforced in real-time, without participants awareness of the training taking place. This training regimen produced large, significant long-term changes in correlations at the network level, and whole brain analysis revealed that the greatest changes were focused on the areas being trained. These changes were not found in the control group. Moreover, changes in ASD resting state connectivity following the training were correlated to changes in behavior, suggesting that neurofeedback can be used to directly alter complex, clinically relevant network connectivity patterns.\n\nSignificance StatementMany disorders are characterized by underlying abnormalities in network connectivity. These abnormalities are difficult to address with explicit training procedures (which are unlikely to target the specific abnormalities). Covert neurofeedback however, can directly target these networks, positively reinforcing the desired connections. We have developed a method for reinforcing correlations in real-time, and show that such training is effective, inducing significant, long-lasting changes in connectivity between aberrant networks in Autism Spectrum Disorder. This provides a potential mechanism for modulating aberrant correlation structures in other clinical groups as well.

neuroscience↗