10 research outputs found
Age-related delay in information accrual for faces: Evidence from a parametric, single-trial EEG approach
Background: In this study, we quantified age-related changes in the time-course of face processing
by means of an innovative single-trial ERP approach. Unlike analyses used in previous studies, our
approach does not rely on peak measurements and can provide a more sensitive measure of
processing delays. Young and old adults (mean ages 22 and 70 years) performed a non-speeded
discrimination task between two faces. The phase spectrum of these faces was manipulated
parametrically to create pictures that ranged between pure noise (0% phase information) and the
undistorted signal (100% phase information), with five intermediate steps.
Results: Behavioural 75% correct thresholds were on average lower, and maximum accuracy was
higher, in younger than older observers. ERPs from each subject were entered into a single-trial
general linear regression model to identify variations in neural activity statistically associated with
changes in image structure. The earliest age-related ERP differences occurred in the time window
of the N170. Older observers had a significantly stronger N170 in response to noise, but this age
difference decreased with increasing phase information. Overall, manipulating image phase
information had a greater effect on ERPs from younger observers, which was quantified using a
hierarchical modelling approach. Importantly, visual activity was modulated by the same stimulus
parameters in younger and older subjects. The fit of the model, indexed by R2, was computed at
multiple post-stimulus time points. The time-course of the R2 function showed a significantly slower
processing in older observers starting around 120 ms after stimulus onset. This age-related delay
increased over time to reach a maximum around 190 ms, at which latency younger observers had
around 50 ms time lead over older observers.
Conclusion: Using a component-free ERP analysis that provides a precise timing of the visual
system sensitivity to image structure, the current study demonstrates that older observers
accumulate face information more slowly than younger subjects. Additionally, the N170 appears to
be less face-sensitive in older observers
Camouflaging in a Complex Environment—Octopuses Use Specific Features of Their Surroundings for Background Matching
Living under intense predation pressure, octopuses evolved an effective and impressive camouflaging ability that exploits features of their surroundings to enable them to “blend in.” To achieve such background matching, an animal may use general resemblance and reproduce characteristics of its entire surroundings, or it may imitate a specific object in its immediate environment. Using image analysis algorithms, we examined correlations between octopuses and their backgrounds. Field experiments show that when camouflaging, Octopus cyanea and O. vulgaris base their body patterns on selected features of nearby objects rather than attempting to match a large field of view. Such an approach enables the octopus to camouflage in partly occluded environments and to solve the problem of differences in appearance as a function of the viewing inclination of the observer
Automated and accurate segmentation of leaf venation networks via deep learning
Leaf vein network geometry can predict levels of resource transport, defence, and mechanical support that operate at different spatial scales. However, it is challenging to quantify network architecture across scales, due to the difficulties both in segmenting networks from images, and in extracting multi‐scale statistics from subsequent network graph representations.
Here we develop deep learning algorithms using convolutional neural networks (CNNs) to automatically segment leaf vein networks. Thirty‐eight CNNs were trained on subsets of manually‐defined ground‐truth regions from >700 leaves representing 50 southeast Asian plant families. Ensembles of 6 independently trained CNNs were used to segment networks from larger leaf regions (~100 mm2). Segmented networks were analysed using hierarchical loop decomposition to extract a range of statistics describing scale transitions in vein and areole geometry.
The CNN approach gave a precision‐recall harmonic mean of 94.5% ± 6%, outperforming other current network extraction methods, and accurately described the widths, angles, and connectivity of veins. Multi‐scale statistics then enabled identification of previously undescribed variation in network architecture across species.
We provide a LeafVeinCNN software package to enable multi‐scale quantification of leaf vein networks, facilitating comparison across species and exploration of the functional significance of different leaf vein architectures