arXiv subjects

Miguel Ibáñez-Berganza

Publications and source records attributed to Miguel Ibáñez-Berganza.

13 recordsLinked to original sources

arXiv2026

Inverse generalised spin models of answers to questionnaires

Network psychometrics conceptualises psychological constructs as emergent properties of systems of interacting items. Energy-based probabilistic models have gained popularity as models of these interactions, but their psychometric application has so far been limited to binary responses, bilinear interactions, and approximated inference methods. To fill these gaps, we here infer and analyse three generalized-spin models of ordinal questionnaire data: the Ising, Blume-Capel (BC), and Blume-Emery-Griffiths (BEG) models. These are maximum-entropy models that accommodate ordinal responses on Likert-type scales with an arbitrary number of options, allowing for single-site anisotropy (BC, BEG) and bi-quadratic item interactions (BEG). We prove the concavity of the maximum likelihood estimation of their parameters, as well as the gauge invariance of the Ising and BC models. We introduce a stochastic gradient ascent algorithm for maximum likelihood inference, and apply this procedure to eleven psychometric and sociological questionnaire datasets. Evaluating the predictive ability of the inferred models reveals that the BEG model systematically outperforms Factor Analysis and the other spin models in capturing the distributions of factors and distances of subject answers to the mean, across all datasets. By leveraging a competitive interplay between the quadratic and bi-quadratic energy terms, the BEG model uniquely captures individual average-extremist response styles, alongside standard latent factor positioning. Moreover, only the spin models can account for the non-concavity and multi-modality of factor histograms in the most polarizing questionnaires. Finally, the analysis reveals other highly non-linear traits of ordinal data---such as the fat-tailed distribution of Mahalanobis distances to the mean---that escape satisfactory description by both factor and spin models.

physics.data-an↗

arXiv2026

Assessing imbalance in signed brain networks

Many complex systems - be they financial, natural, or social - are composed of units - such as stocks, neurons, or agents - whose joint activity can be represented as a multivariate time series. An issue of both practical and theoretical importance concerns the possibility of inferring the presence of a static relationship between any two units solely from their dynamic behaviour. The present contribution aims at tackling such an issue within the framework of traditional hypothesis testing: briefly speaking, our suggestion is that of linking any two units if behaving in a sufficiently similar way. To achieve such a goal, we project a multivariate time series onto a signed graph by (1) comparing the empirical properties of the former with those expected under a suitable benchmark and (2) linking any two units with a positive (negative) edge in case the corresponding series shares a significantly large number of concordant (discordant) values. To define our benchmarks, we adopt an information-theoretic approach that is rooted into the constrained maximisation of Shannon entropy, a procedure inducing an ensemble of multivariate time series that preserves some of the empirical properties on average, while randomising everything else. We showcase the possible applications of our method by addressing one of the most timely issues in the domain of neurosciences, i.e. that of determining if brain networks are frustrated or not, and, if so, to what extent. As our results suggest, this is indeed the case, with the major contribution to the underlying negative subgraph coming from the subcortical regions (and, to a lesser extent, from the limbic ones). At the mesoscopic level, the minimisation of the Bayesian information criterion, instantiated with the signed stochastic block model, reveals that brain regions gather into modules aligning with the statistical variant of the relaxed balance theory.

physics.soc-ph↗

arXiv2026

Community detection in subject-subject networks from psychometrics data

Identifying subgroups of respondents in psychometric data is traditionally addressed with Latent Class Analysis, which requires the number of classes to be specified a priori and can perform poorly when strong inter-item correlations violate local independence assumptions. We propose a network-theoretic alternative based on community detection in subject-subject similarity networks. To suppress the systematic artifacts induced by the factor structure of the items, the similarity is computed in a low-dimensional factor-score space and the null model for modularity maximisation is obtained by removing the leading (global) mode of the similarity matrix, rather than via the standard Newman--Girvan model. The significance of a detected partition is then assessed against a column-wise resampling null through four complementary observables: the modularity, the differential entropy of the eigenvector point cloud at two neighbourhood scales, and the overlap of the within- and between-community similarity histograms. On a synthetic benchmark with controlled mixture signal, all four metrics correctly identify the homogeneous case as null-compatible -- including the demanding regime of a dataset dominated by a single factor -- and exhibit a graded departure from the null as the cluster separation grows. Applied to 14 widely used psychometric scales, the pipeline isolates a small group of datasets supporting a genuine and directly interpretable modular structure, while the remaining scales fall either in a mixed-signal regime or in one compatible with a single homogeneous community. The significance analysis is independent of the specific community-detection algorithm and provides an operational way to test for modular subject-level structure in questionnaire data.

physics.soc-ph↗

arXiv2025

Theory of enhanced-by-coincidence neural information transmission

The activity of neurons within brain circuits has been ubiquitously reported to be correlated. The impact of these correlations on brain function has been extensively investigated. Correlations can in principle increase or decrease the information that neural populations carry about sensory stimuli, but experiments in cortical areas have mostly reported information-limiting correlations, which decrease the information encoded in the population. However, a second stream of evidence suggests that temporal correlations between the spiking activity of different neurons may increase the impact of neural activity downstream, implying that temporal correlations affect both the encoding of information and its downstream readout. The principle of how encoding and readout combine are still unclear. Here, we consider a model of transmission of stimulus information encoded in pre-synaptic input spike trains with information-limiting time-correlations to the output firing of a post-synaptic biologicaly-plausible leaky integrate and fire (LIF) readout neuron. We derive an analytical solution of the model in the diffusion approximation, in which the encoding spiking activity is treated as a continuous-time stochastic variable. An ansatz based on a separation of timescales allows us compute the stimulus information transmitted to the readout over a broad range of parameters. Our analytical results reveal that, for sufficiently low input firing rates, large enough difference in input stimulus-specific activity, and moderately large input temporal correlations, the stimulus discriminability of the firing of the LIF readout neuron can be enhanced by the presence of input time correlations, despite they decrease the stimulus information encoded in its inputs.

q-bio.NC↗

arXiv2023

Noise-cleaning the precision matrix of fMRI time series

We present a comparison between various algorithms of inference of covariance and precision matrices in small datasets of real vectors, of the typical length and dimension of human brain activity time series retrieved by functional Magnetic Resonance Imaging (fMRI). Assuming a Gaussian model underlying the neural activity, the problem consists in denoising the empirically observed matrices in order to obtain a better estimator of the true precision and covariance matrices. We consider several standard noise-cleaning algorithms and compare them on two types of datasets. The first type are time series of fMRI brain activity of human subjects at rest. The second type are synthetic time series sampled from a generative Gaussian model of which we can vary the fraction of dimensions per sample q = N/T and the strength of off-diagonal correlations. The reliability of each algorithm is assessed in terms of test-set likelihood and, in the case of synthetic data, of the distance from the true precision matrix. We observe that the so called Optimal Rotationally Invariant Estimator, based on Random Matrix Theory, leads to a significantly lower distance from the true precision matrix in synthetic data, and higher test likelihood in natural fMRI data. We propose a variant of the Optimal Rotationally Invariant Estimator in which one of its parameters is optimised by cross-validation. In the severe undersampling regime (large q) typical of fMRI series, it outperforms all the other estimators. We furthermore propose a simple algorithm based on an iterative likelihood gradient ascent, providing an accurate estimation for weakly correlated datasets.

cond-mat.stat-mech↗

arXiv2023

Information-theoretical analysis of the neural code for decoupled face representation

Processing faces accurately and efficiently is a key capability of humans and other animals that engage in sophisticated social tasks. Recent studies reported a decoupled coding for faces in the primate inferotemporal cortex, with two separate neural populations coding for the geometric position of (texture-free) facial landmarks and for the image texture at fixed landmark positions, respectively. Here, we formally assess the efficiency of this decoupled coding by appealing to the information-theoretic notion of description length, which quantifies the amount of information that is saved when encoding novel facial images, with a given precision. We show that despite decoupled coding describes the facial images in terms of two sets of principal components (of landmark shape and image texture), it is more efficient (i.e., yields more information compression) than the encoding in terms of the image principal components only, which corresponds to the widely used eigenface method. The advantage of decoupled coding over eigenface coding increases with image resolution and is especially prominent when coding variants of training set images that only differ in facial expressions. Moreover, we demonstrate that decoupled coding entails better performance in three different tasks: the representation of facial images, the (daydream) sampling of novel facial images, and the recognition of facial identities and gender. In summary, our study provides a first principle perspective on the efficiency and accuracy of the decoupled coding of facial stimuli reported in the primate inferotemporal cortex.

q-bio.NC↗

arXiv2020

Hamiltonian Modeling of Macro-Economic Urban Dynamics

The ongoing rapid urbanization phenomena make the understanding of the evolution of urban environments of utmost importance to improve the well-being and steer societies towards better futures. Many studies have focused on the emerging properties of cities, leading to the discovery of scaling laws mirroring, for instance, the dependence of socio-economic indicators on city sizes. Though scaling laws allow for the definition of city-size independent socio-economic indicators, only a few efforts have been devoted to the modeling of the dynamical evolution of cities as mirrored through socio-economic variables and their mutual influence. In this work, we propose a Maximum Entropy (ME), non-linear, generative model of cities. We write in particular a Hamiltonian function in terms of a few macro-economic variables, whose coupling parameters we infer from real data corresponding to French towns. We first discover that non-linear dependencies among different indicators are needed for a complete statistical description of the non-Gaussian correlations among them. Furthermore, though the dynamics of individual cities are far from being stationary, we show that the coupling parameters corresponding to different years turn out to be quite robust. The quasi time-invariance of the Hamiltonian model allows proposing an analytic model for the evolution in time of the macro-economic variables, based on the Langevin equation. Despite no temporal information about the evolution of cities has been used to derive this model, its forecast accuracy of the temporal evolution of the system is compatible to that of a model inferred using explicitly such information.

physics.soc-ph↗

arXiv2020

Unsupervised inference approach to facial attractiveness

The perception of facial beauty is a complex phenomenon depending on many, detailed and global facial features influencing each other. In the machine learning community this problem is typically tackled as a problem of supervised inference. However, it has been conjectured that this approach does not capture the complexity of the phenomenon. A recent original experiment (Ibáñez-Berganza et al., Scientific Reports 9, 8364, 2019) allowed different human subjects to navigate the face-space and ``sculpt'' their preferred modification of a reference facial portrait. Here we present an unsupervised inference study of the set of sculpted facial vectors in that experiment. We first infer minimal, interpretable, and faithful probabilistic models (through Maximum Entropy and artificial neural networks) of the preferred facial variations, that capture the origin of the observed inter-subject diversity in the sculpted faces. The application of such generative models to the supervised classification of the gender of the sculpting subjects, reveals an astonishingly high prediction accuracy. This result suggests that much relevant information regarding the subjects may influence (and be elicited from) her/his facial preference criteria, in agreement with the multiple motive theory of attractiveness proposed in previous works.

stat.ML↗

arXiv2019

Subjectivity and complexity of facial attractiveness

The origin and meaning of facial beauty represent a longstanding puzzle. Despite the profuse literature devoted to facial attractiveness, its very nature, its determinants and the nature of inter-person differences remain controversial issues. Here we tackle such questions proposing a novel experimental approach in which human subjects, instead of rating natural faces, are allowed to efficiently explore the face-space and 'sculpt' their favorite variation of a reference facial image. The results reveal that different subjects prefer distinguishable regions of the face-space, highlighting the essential subjectivity of the phenomenon.The different sculpted facial vectors exhibit strong correlations among pairs of facial distances, characterising the underlying universality and complexity of the cognitive processes, and the relative relevance and robustness of the different facial distances.

q-bio.NC↗

arXiv2019

The low-temperature phase in the two-dimensional long-range diluted XY model

The critical behaviour of statistical models with long-range interactions exhibits distinct regimes as a function of $ρ$, the power of the interaction strength decay. For $ρ$ large enough, $ρ>ρ_{\rm sr}$, the critical behaviour is observed to coincide with that of the short-range model. However, there are controversial aspects regarding this picture, one of which is the value of the short-range threshold $ρ_{\rm sr}$ in the case of the long-range XY model in two dimensions. We study the 2d XY model on the {\it diluted} graph, a sparse graph obtained from the 2d lattice by rewiring links with probability decaying with the Euclidean distance of the lattice as $|r|^{-ρ}$, which is expected to feature the same critical behavior of the long range model. Through Monte Carlo sampling and finite-size analysis of the spontaneous magnetisation and of the Binder cumulant, we present numerical evidence that $ρ_{\rm sr}=4$. According to such a result, one expects the model to belong to the Berezinskii-Kosterlitz-Thouless (BKT) universality class for $ρ\ge 4$, and to present a $2^{nd}$-order transition for $ρ<4$.

cond-mat.stat-mech↗

arXiv2017

Chaos and correlated avalanches in excitatory neural networks with synaptic plasticity

A collective chaotic phase with power law scaling of activity events is observed in a disordered mean field network of purely excitatory leaky integrate-and-fire neurons with short-term synaptic plasticity. The dynamical phase diagram exhibits two transitions from quasi-synchronous and asynchronous regimes to the nontrivial, collective, bursty regime with avalanches. In the homogeneous case without disorder, the system synchronizes and the bursty behavior is reflected into a doubling-period transition to chaos for a two dimensional discrete map. Numerical simulations show that the bursty chaotic phase with avalanches exhibits a spontaneous emergence of time correlations and enhanced Kolmogorov complexity. Our analysis reveals a mechanism for the generation of irregular avalanches that emerges from the combination of disorder and deterministic underlying chaotic dynamics.

cond-mat.dis-nn↗

arXiv2016

Fourier-space entanglement of spin chains

Entanglement between different regions in momentum space is studied for ground states of some spin-chain Hamiltonians: the XY model, the Ising model in a transverse field (ITF) and the XXZ models. In the XY and ITF cases, entanglement only takes place between states with opposite momenta. Thus, an anisotropy in the interaction induces entanglement in the momentum pairs. In the ITF case, the ferromagnetic phase is characterized by a total entropy between left- and right-moving modes which is independent on the external field. This result characterizes the Ising phase transition in momentum space. In the critical XXZ case, we provide evidence that the maximal entropy between energy modes around the Fermi point grows logarithmically with the system size, with a prefactor which depends on the compactification radius. The slow growth of the entanglement in Fourier space with the system size provides an explanation for the success of the renormalization techniques in momentum space.

quant-ph↗

arXiv2015

Phenomenological theory of the Potts model evaporation-condensation transition

We present a phenomenological theory describing the finite-size evaporation-condensation transition of the $q$-state Potts model in the microcanonical ensemble. Our arguments rely on the existence of an exponent $σ$, relating the surface and the volume of the condensed phase droplet. The evaporation-condensation transition temperature and energy converge to their infinite-size values with the same power, $a=(1-σ)/(2-σ)$, of the inverse of the system size. For the 2D Potts model we show, by means of efficient simulations up to $q=24$ and $1024^2$ sites, that the exponent $a$ is compatible with $1/4$, in disagreement with previous studies. While this value cannot be addressed by the evaporation-condensation theory developed for the Ising model, it is obtained in the present scheme if $σ=2/3$, in agreement with previous theoretical guesses. The connection with the phenomenon of metastability in the canonical ensemble is also discussed.

cond-mat.stat-mech↗