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Moca, V. V.

Publications and source records attributed to Moca, V. V..

2 recordsLinked to original sources

Randomness impacts specific priors building, visual exploration, and perception in object recognition

1.Recognising objects is a vital skill on which humans heavily rely to respond quickly and adaptively to their environment. Yet, we lack understanding on the role visual information sampling plays in this process, and its relation to the individuals priors. To bridge this gap, the eye-movements of 18 adult participants were recorded during a free-viewing object-recognition task using Dots stimuli (Moca et al., 2011). Participants viewed the stimuli in one of three orders: from most visible to least (Descending), least visible to most (Ascending) or in a randomised order (Random). This dictated the strength of their priors along the experiment. Adding to Moca et al.s original finding that visibility order influenced participants recognition performance and visual exploration, we found that while orders allowing for stronger priors generally led participants to visually sample more informative locations, this was not the case of Random participants. Indeed, they appeared to behave naively, and their use of specific object-related priors seemed fully impaired, while they seemed to maintain the ability to use general task-related priors to guide their exploration. These findings have important implications for our understanding of perception, which appears markedly subjective even at the basic level of visual sampling and object perception.

neuroscience↗

Superlets: time-frequency super-resolution using wavelet sets

Time-frequency analysis is ubiquitous in many fields of science. Due to the Heisenberg-Gabor uncertainty principle, a single measurement cannot estimate precisely the location of a finite oscillation in both time and frequency. Classical spectral estimators, like the short-time Fourier transform (STFT) or the continuous-wavelet transform (CWT) optimize either temporal or frequency resolution, or find a tradeoff that is suboptimal in both dimensions. Following concepts from optical super-resolution, we introduce a new spectral estimator enabling time-frequency super-resolution. Sets of wavelets with increasing bandwidth are combined geometrically in a superlet to maintain the good temporal resolution of wavelets and gain frequency resolution in the upper bands. Superlets outperform the STFT, CWT, and other super-resolution methods on synthetic data and brain signals recorded in humans and rodents, resolving time-frequency details with unprecedented precision. Importantly, superlets can reveal transient oscillation events that are hidden in the averaged time-frequency spectrum by other methods.

neuroscience↗