Biology subjects

Aminoff, E. M.

Publications and source records attributed to Aminoff, E. M..

3 recordsLinked to original sources

bioRxiv2022

Contextual associations represented both in neural networks and human behavior

Contextual associations facilitate object recognition in human vision. However, the role of context in artificial vision remains elusive as does the characteristics that humans use to define context. We investigated whether contextually related objects (bicycle-helmet) are represented more similarly in convolutional neural networks (CNNs) used for image understanding than unrelated objects (bicycle-fork). Stimuli were of objects against a white background and consisted of a diverse set of contexts (N=73). CNN representations of contextually related objects were more similar to one another than to unrelated objects across all CNN layers. Critically, the similarity found in CNNs correlated with human behavior across three experiments assessing contextual relatedness, emerging significant only in the later layers. The results demonstrate that context is inherently represented in CNNs as a result of object recognition training, and that the representation in the later layers of the network tap into the contextual regularities that predict human behavior.

neuroscience↗

bioRxiv2020

Functional Context Affects Scene Processing

Rapid visual perception is often viewed as a bottom-up process. Category-preferred neural regions are often characterized as automatic, default processing mechanisms for visual inputs of their categorical preference. To explore the sensitivity of such regions to top-down information, we examined three scene-preferring brain regions, the occipital place area (OPA), the parahippocampal place area (PPA), and the retrosplenial complex (RSC), and tested whether the processing of outdoor scenes is influenced by the functional contexts in which they are seen. Context was manipulated by presenting real-world landscape images as if being viewed through a window or within a picture frame; manipulations that do not affect scene content but do affect ones functional knowledge regarding the scene. This manipulation influences neural scene processing (as measured by fMRI): the OPA and PPA exhibited greater neural activity when participants viewed images as if through a window as compared to within a picture frame, while the RSC did not show this difference. In a separate behavioral experiment, functional context affected scene memory in predictable directions (boundary extension). Our interpretation is that the window context denotes three-dimensionality, therefore rendering the perceptual experience of viewing landscapes as more realistic. Conversely, the frame context denotes a two-dimensional image. As such, more spatially-biased scene representations in the OPA and the PPA are influenced by differences in top-down, perceptual expectations generated from context. In contrast, more semantically-biased scene representations in the RSC are likely to be less affected by top-down signals that carry information about the physical layout of a scene.

neuroscience↗

bioRxiv2018

Exploring spatio-temporal neural dynamics of the human visual cortex

The human visual cortex is organized in a hierarchical manner. Although a significant body of evidence has been accumulated in support of this hypothesis, specific details regarding the spatial and temporal information flow remain open. Here we present detailed spatio-temporal correlation profiles of neural activity with low-level and high-level features derived from a \"deep\" (8-layer) neural network pre-trained for object recognition. These correlation profiles indicate an early-to-late shift from low-level features to high-level features and from low-level regions to higher-level regions along the visual hierarchy, consistent with feedforward information flow. To refine our understanding of information flow, we computed three sets of features from the low-and high-level features provided by the neural network: object-category-relevant low-level features (the common components between low-level and high-level features), low-level features roughly orthogonal to high-level features (the residual Layer 1 features), and unique high-level features that were roughly orthogonal to low-level features (the residual Layer 7 features). Contrasting the correlation effects of the common components and the residual Layer 1 features, we observed that the early visual cortex exhibits a similar amount of correlation with the two feature sets early in time (60 to 120 ms), but in a later time window, the early visual cortex exhibits a higher and longer correlation effect with the common components/low-level task-relevant features as compared to the low-level residual features--an effect unlikely to arise from purely feedforward information flow. Overall, our results indicate that non-feedforward processes, for example, top-down influences from mental representations of categories, may facilitate differentiation between these two types of low-level features within the early visual cortex.

neuroscience↗