Biology subjects

Ceolini, E.

Publications and source records attributed to Ceolini, E..

7 recordsLinked to original sources

bioRxiv2026

Emergent smartphone temporal structures reflect cognitive constraints

Fine-grained temporal structures emerge in smartphone behavioral recordings over multi-day periods. Complex systems research suggests that emergent temporal structures reflect underlying resource constraints of the system. Here we test whether cognitive abilities measured through speeded tasks (spanning fractions of a second) are reflected in emergent smartphone temporal structures spanning days, revealing how cognitive resource limitations shape naturalistic behavior. We analyzed smartphone tap interval patterns accumulated over several days and used decision tree regression models to predict performance in simple and choice reaction time tasks from these patterns. Simple reaction time was poorly predicted (R2 = 0.003), indicating that basic sensorimotor constraints play only a marginal role in shaping real-world behavioral timing. In contrast, choice reaction time was moderately predictable (R2 = 0.4), demonstrating that higher-order cognitive constraints prominently influence naturalistic temporal organization. Notably, while task performance operates at sub-second timescales, predictive temporal patterns in smartphone behavior spanned milliseconds to several seconds and was accumulated over days, revealing the broad, multi-scale influence of cognitive resource constraints on everyday behavior. Both predicted and measured choice reaction times showed age-related decline, but the decline was more pronounced in predicted values, suggesting that age-related cognitive changes may be amplified in naturalistic contexts. These findings demonstrate that emergent temporal structures in smartphone use can reveal how cognitive processes measured using speeded tasks manifest, or fail to manifest, in real-world behavior. Complex-systems approaches can bridge laboratory and naturalistic assessments of cognition, revealing which cognitive processes meaningfully constrain real-world behavior.

animal behavior and cognition↗

bioRxiv2024

Age-related behavioral resilience in smartphone touchscreen interaction dynamics

We experience a life that is full of ups and downs. The ability to bounce back after adverse life events such as the loss of a loved one or serious illness declines with age, and such isolated events can even trigger accelerated aging. How humans respond to common day-to-day perturbations is less clear. Here, we infer the aging status from smartphone behavior by using a decision tree regression model trained to accurately estimate the chronological age based on the dynamics of touchscreen interactions. Individuals (N = 280, 21 to 83 years of age) expressed smartphone behavior that appeared younger on certain days and older on other days through the observation period that lasted up to [~]4 years. We captured the essence of these fluctuations by leveraging the mathematical concept of critical transitions and tipping points in complex systems. In most individuals, we find one or more alternative stable aging states separated by tipping points. The older the individual, the lower the resilience to forces that push the behavior across the tipping point into an older state. Traditional accounts of aging based on sparse longitudinal data spanning decades suggest a gradual behavioral decline with age. Taken together with our current results, we propose that the gradual age-related changes are interleaved with more complex dynamics at shorter timescales where the same individual may navigate distinct behavioral aging states from one day to the next. Real-world behavioral data modeled as a complex system can transform how we view and study aging.

animal behavior and cognition↗

bioRxiv2024

Real-time control of a hearing instrument with EEG-based attention decoding

Enhancing speech perception in everyday noisy acoustic environments remains an outstanding challenge for hearing aids. Speech separation technology is improving rapidly, but hearing devices cannot fully exploit this advance without knowing which sound sources the user wants to hear. Even with high-quality source separation, the hearing aid must know which speech streams to enhance and which to suppress. Advances in EEG-based decoding of auditory attention raise the potential of neurosteering, in which a hearing instrument selectively enhances the sound sources that a hearing-impaired listener is focusing their attention on. Here, we present and discuss a real-time brain-computer interface (BCI) system that combines a stimulus-response model based on canonical correlation analysis (CCA) for real-time EEG attention decoding, coupled with a multi-microphone hardware platform enabling low-latency real-time speech separation through spatial beamforming. We provide an overview of the system and its various components, discuss prospects and limitations of the technology, and illustrate its application with case studies of listeners steering acoustic feedback of competing speech streams via real-time attention decoding. A software implementation code of the system is publicly available for further research and explorations.

neuroscience↗

bioRxiv2022

Common multi-day rhythms in smartphone behavior

The idea that abnormal human activities follow multi-day rhythms spans ancient beliefs centered on the moon to modern clinical observations in epilepsy and mood disorders. Multi-day rhythms remain obscure in normal human activities. To address multi-day rhythms in healthy human behavior we analyzed over 300 million smartphone touchscreen interactions logging up to 2 years of day-to-day activities (N = 401 subjects). By using non-negative matrix factorization and data-driven clustering of [~]1 million periodograms, we captured a range of multi-day rhythms spanning periods from 7 to 52 days - cutting across age and gender. Despite their common occurrence, any given multi-day rhythm was observed in different parts of the smartphone behavior from one person to the next. There was little support in the data for ubiquitous rhythm drivers like the moon. We propose that multiple multi-day rhythms are a common trait, but their consequences may be uniquely experienced in day-to-day behavior.

animal behavior and cognition↗

bioRxiv2022

Neural processing of goal and non-goal-directed movements on the smartphone

The discrete behavioral events captured on the smartphone touchscreen may help unravel real-world neural processing. We find that neural signals (EEG) surrounding a touchscreen event show peculiarly contralateral motor preparation, visual processing, and the consolidation of information. We leveraged these events in conjunction with kinematic recordings of the thumb and an artificial neural network to separate highly similar movements according to whether they resulted in a smartphone touch (goal-directed) or not (non-goal-directed). Despite their kinematic similarity underscored by the model, the signatures of neural control of movement and the post-movement processing were substantially dampened for the non-goal-directed movements, and these movements uniquely evoked error-related signals. We speculate that these unnecessary movements are common in the real world and although inconsequential the brain still provides limited motor preparation and tracks the action outcome. Real-world behavior is composed of neural processes that are difficult to capture in conventional laboratory-based tasks.

neuroscience↗

bioRxiv2021

Temporal clusters of age-related behavioral alterations captured in smartphone touchscreen interactions

Cognitive and behavioral abilities alter across the adult life span. Smartphones engage various cognitive functions and the corresponding touchscreen interactions may help resolve if and how the behavior is systematically structured by aging. Here, in a sample spanning the adult lifespan (16 to 86 years, N = 598, accumulating 355 million interactions) we analyzed a range of interaction intervals - from a few milliseconds to a minute. We used probability distributions to cluster the interactions according to their next inter-touch interval dynamics to discover systematic age-related changes at the distinct temporal clusters. There were age-related behavioral losses at the clusters occupying short intervals (~ 100 ms, R2 ~ 0.8) but gains at the long intervals (~ 4 s, R2 ~ 0.4). These correlates were independent of the years of experience on the phone or the choice of fingers used on the screen. We found further evidence for a compartmentalized influence of aging, as individuals simultaneously demonstrated both accelerated and decelerated aging at distant temporal clusters. In contrast to these strong correlations, cognitive tests probing sensorimotor, working memory, and executive processes revealed rather weak age-related decline. Contrary to the common notion of a simple behavioral decline with age based on conventional cognitive tests, we show that real-world behavior does not simply decline and the nature of aging systematically varies according to the underlying temporal dynamics. Of all the imaginable factors determining smartphone interactions in the real world, age-sensitive cognitive and behavioral processes can dominatingly dictate smartphone temporal dynamics.

animal behavior and cognition↗

bioRxiv2018

A Comparison of Temporal Response Function Estimation Methods for Auditory Attention Decoding

The decoding of selective auditory attention from noninvasive electroencephalogram (EEG) data is of interest in brain computer interface and auditory perception research. The current state-of-the-art approaches for decoding the attentional selection of listeners are based on temporal response functions (TRFs). In the current context, a TRF is a function that facilitates a mapping between features of sound streams and EEG responses. It has been shown that when the envelope of attended speech and EEG responses are used to derive TRF mapping functions, the TRF model predictions can be used to discriminate between attended and unattended talkers. However, the predictive performance of the TRF models is dependent on how the TRF model parameters are estimated. There exist a number of TRF estimation methods that have been published, along with a variety of datasets. It is currently unclear if any of these methods perform better than others, as they have not yet been compared side by side on a single standardized dataset in a controlled fashion. Here, we present a comparative study of the ability of different TRF estimation methods to classify attended speakers from multi-channel EEG data. The performance of the TRF estimation methods is evaluated using different performance metrics on a set of labeled EEG data from 18 subjects listening to mixtures of two speech streams.

neuroscience↗