arXiv subjects

Francesco Fumarola

Publications and source records attributed to Francesco Fumarola.

9 recordsLinked to original sources

arXiv2024

"Bayesian anchoring" and the fourfold pattern of risk attitudes

Experiments on decision making under uncertainty are known to display a classical pattern of risk aversion and risk seeking referred to as "fourfold pattern" (or "reflection effect") , but recent experiments varying the speed and order of mental processing have brought to light a more nuanced phenomenology. We model experiments though a Bayesian formalization of the anchor-and-adjust heuristic observed in empirical studies on cognitive bias. Using only elementary assumptions on constrained information processing, we are able to infer three separate effects found in recent observations: (1) the reported enhancement of the fourfold pattern for quicker decision processes; (2) the observed decrease of fluctuations for slower decision-making trials; (3) the reported dependence of the outcome on the order in which options are processed. The application of Bayesian modeling offers a solution to recent empirical riddles by bridging two heretofore separate domains of experimental inquiry on bounded rationality.

q-bio.NC↗

arXiv2022

Multi-tasking via baseline control in recurrent neural networks

Changes in an animal's behavioral state, such as arousal and movements, induce {complex modulations of the baseline input currents to sensory areas, eliciting sensory modality-specific effects. A simple computational principle explaining the effects of baseline modulations to recurrent cortical circuits is lacking. We investigate the benefits of baseline modulations using a reservoir computing approach in recurrent neural networks with random couplings. Baseline modulations unlock a set of new network phases and phenomena, including chaos enhancement, neural hysteresis and ergodicity breaking. Strikingly, baseline modulations enable reservoir networks to perform multiple tasks, without any optimization of the network couplings.} Baseline control of network dynamics opens new directions for brain-inspired artificial intelligence and sheds new light on behavioral modulations of cortical activity.

q-bio.NC↗

arXiv2021

Mechanisms for spontaneous symmetry breaking in developing visual cortex

For the brain to recognize local orientations within images, neurons must spontaneously break the translation and rotation symmetry of their response functions -- an archetypal example of unsupervised learning. The dominant framework for unsupervised learning in biology is Hebb's principle, but how Hebbian learning could break such symmetries is a longstanding biophysical riddle. Theoretical studies agree that this should require inputs to visual cortex to invert the relative magnitude of their correlations at long distances. Empirical measurements have searched in vain for such an inversion, and report the opposite to be true. We formally approach the question through the hermitianization of a multi-layer model, which maps it into a problem of zero-temperature phase transitions. In the emerging phase diagram, both symmetries break spontaneously as long as (1) recurrent interactions are sufficiently long-range and (2) Hebbian competition is duly accounted for. The relevant mechanism for symmetry breaking is competition among connections sprouting from the same afferent cell. Such competition, and not the structure of the inputs, is capable of triggering the broken-symmetry phase required by image processing. We provide analytic predictions on the relative magnitudes of the relevant length-scales needed for this novel mechanism to occur. These results reconcile experimental observations to the Hebbian paradigm, shed light on a new mechanism for visual cortex development, and contribute to our growing understanding of the relationship between learning and symmetry breaking.

q-bio.NC↗

arXiv2017

Hierarchical searching in episodic memory

An analysis of free-recall datasets from two independent experiments allows to identify two anomalous instances of non-monotonicity in free recall: a maximum in the dependence of the inter-response intervals on the serial-position lags, and a minimum in the rate of contiguous recall near the beginning of the recall process. Both effects, it is argued, may stem from a hierarchical search protocol in the space of memories. An elementary random-walk model on binary strings is used to test this hypothesis.

q-bio.NC↗

arXiv2017

A theory of interpretive clustering in free recall

A stochastic model of short-term verbal memory is proposed, in which the psychological state of the subject is encoded as the instantaneous position of a particle diffusing over a semantic graph with a probabilistic structure. The model is particularly suitable for studying the dependence of free-recall observables on semantic properties of the words to be recalled. Besides predicting some well-known experimental features (contiguity effect, forward asymmetry, word-length effect), a novel prediction is obtained on the relationship between the contiguity effect and the syllabic length of words; shorter words, by way of their wider semantic range, are predicted to be characterized by stronger forward contiguity. A fresh analysis of archival data allows to confirm this prediction.

q-bio.NC↗

arXiv2016

The Role of Word Length in Semantic Topology

A topological argument is presented concering the structure of semantic space, based on the negative correlation between polysemy and word length. The resulting graph structure is applied to the modeling of free-recall experiments, resulting in predictions on the comparative values of recall probabilities. Associative recall is found to favor longer words whereas sequential recall is found to favor shorter words. Data from the PEERS experiments of Lohnas et al. (2015) and Healey and Kahana (2016) confirm both predictons, with correlation coefficients $r_{seq}= -0.17$ and $r_{ass}= +0.17$. The argument is then applied to predicting global properties of list recall, which leads to a novel explanation for the word-length effect based on the optimization of retrieval strategies.

q-bio.NC↗

arXiv2016

Verbal Perception and the Word Length Effect

A theoretical framework is proposed for the understanding of verbal perception -- the conversion of words into meaning, modeled as a compromise between lexical demands and contextual constraints -- and the theory is tested against experiments on short-term memory. The observation that lists of short words are recalled better than lists of long ones has been a long-standing subject of controversy, further complicated by the apparent inversion of the effect for mixed lists. In the framework here proposed, these behaviors emerge as an effect of the different level of localization of short and long words in semantic space. Events corresponding to the recognition of a nonlocal word have a clustering property in phase space, which facilitates associative retrieval. The standard word-length effect arises directly from this property, and the inverse effect from its breakdown. An analysis of data from the PEERS experiments (Healey and Kahana, 2016) confirms the main predictions of the theory. Further predictions are listed and new experiments are proposed. Finally, an interpretation of the above results is presented.

q-bio.NC↗

arXiv2015

Properties of networks with partially structured and partially random connectivity

We provide a general formula for the eigenvalue density of large random $N\times N$ matrices of the form $A = M + LJR$, where $M$, $L$ and $R$ are arbitrary deterministic matrices and $J$ is a random matrix of zero-mean independent and identically distributed elements. For $A$ nonnormal, the eigenvalues do not suffice to specify the dynamics induced by $A$, so we also provide general formulae for the transient evolution of the magnitude of activity and frequency power spectrum in an $N$-dimensional linear dynamical system with a coupling matrix given by $A$. These quantities can also be thought of as characterizing the stability and the magnitude of the linear response of a nonlinear network to small perturbations about a fixed point. We derive these formulae and work them out analytically for some examples of $M$, $L$ and $R$ motivated by neurobiological models. We also argue that the persistence as $N\rightarrow\infty$ of a finite number of randomly distributed outlying eigenvalues outside the support of the eigenvalue density of $A$, as previously observed, arises in regions of the complex plane $Ω$ where there are nonzero singular values of $L^{-1} (z\mathbf{1} - M) R^{-1}$ (for $z\inΩ$) that vanish as $N\rightarrow\infty$. When such singular values do not exist and $L$ and $R$ are equal to the identity, there is a correspondence in the normalized Frobenius norm (but not in the operator norm) between the support of the spectrum of $A$ for $J$ of norm $σ$ and the $σ$-pseudospectrum of $M$.

q-bio.NC↗

arXiv2007

Single particle spectrum of resonant population imbalanced Fermi gases

We use a T-matrix approximation to calculate the single particle spectrum of the normal state of a gas of Fermionic atoms at low temperature. In the strongly interacting regime of the polarized gas, we find that the spectrum is separated in two branches, leading to a double-peaked radiofrequency spectral feature.

cond-mat.other↗