Colorful brains: 14years of display practice in functional neuroimaging

Christen, Markus; Vitacco, D.; Huber, Lara; Harboe, Julie; Fabrikant, Sara Irina; Brugger, Peter

doi:10.1016/j.neuroimage.2013.01.068

Cited by 19 publications

(16 citation statements)

References 39 publications

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“…However, it is possible that domain experts, with knowledge about the (a) content in colormaps and (b) typical patterns of data in their field, may rely more on spatial structure in their data (e.g., hotspots) than inferred color-quantity mappings when interpreting colormaps. It is also possible that domain experts who are used to looking at colormaps with light-more encoded mappings (e.g., neuroscientists; see Christen et al, 2013) have developed a light-is-more bias instead of a dark-is-more bias. If so, it would be interesting to determine whether that light-is-more bias is domain specific to their area of expertise (e.g., neuroimaging) or extends across domains outside of their area of expertise (e.g., alien animal sighting maps).…”

Section: Effects Of Expertisementioning

confidence: 99%

“…For example, a given color scale can be mapped such that the darker colors correspond to larger quantities or smaller quantities within a data set. One might imagine that the direction of this mapping is inconsequential if specified by a legend, but colormaps in published articles do not always have legends (Christen et al, 2013;Schott, 2010), and when they do, the direction of assignment matters. People are faster at interpreting colormaps when mapping specified by the legend (encoded mapping) matches their expectations (inferred mapping) (Schloss et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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The relation between color and spatial structure for interpreting colormap data visualizations

et al. 2020

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Interpreting colormap visualizations requires determining how dimensions of color in visualizations map onto quantities in data. People have color-based biases that influence their interpretations of colormaps, such as a dark-is-more bias—darker colors map to larger quantities. Previous studies of color-based biases focused on colormaps with weak data spatial structure, but color-based biases may not generalize to colormaps with strong data spatial structure, like “hotspots” typically found in weather maps and neuroimaging brain maps. There may be a hotspot-is-more bias to infer that colors within hotspots represent larger quantities, which may override the dark-is-more bias. We tested this possibility in four experiments. Participants saw colormaps with hotspots and a legend that specified the color-quantity mapping. Their task was to indicate which side of the colormap depicted larger quantities (left/right). We varied whether the legend specified dark-more mapping or light-more mapping across trials and operationalized a dark-is-more bias as faster response time (RT) when the legend specified dark-more mapping. Experiment 1 demonstrated robust evidence for the dark-is-more bias, without evidence for a hotspot-is-more bias. Experiments 2 to 4 suggest that a hotspot-is-more bias becomes relevant when hotspots are a statistically reliable cue to “more” (i.e., the locus of larger quantities) and when hotspots are more perceptually pronounced. Yet, comparing conditions in which the hotspots were “more,” RTs were always faster for dark hotspots than light hotspots. Thus, in the presence of strong spatial cues to the locus of larger quantities, color-based biases still influenced interpretations of colormap data visualizations.

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Section: Effects Of Expertisementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The relation between color and spatial structure for interpreting colormap data visualizations

et al. 2020

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“…Many single-and multi-channel "pseudocoloring" solutions have accordingly been developed [39,187,195], ranging from analog optical [168] to digital frequency domain coloring [3] to the ubiquitous "colormaps" [1]. However, the perceptual intricacies that must be considered even when color mapping single grayscale images [98,154] indicate considerable scope for additional research in this area.…”

Section: Introductionmentioning

confidence: 99%

Personalizing image enhancement for critical visual tasks: improved legibility of papyri using color processing and visual illusions

Atanasiu

Marthot-Santaniello

2021

IJDAR

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This article develops theoretical, algorithmic, perceptual, and interaction aspects of script legibility enhancement in the visible light spectrum for the purpose of scholarly editing of papyri texts. Novel legibility enhancement algorithms based on color processing and visual illusions are compared to classic methods in a user experience experiment. (1) The proposed methods outperformed the comparison methods. (2) Users exhibited a broad behavioral spectrum, under the influence of factors such as personality and social conditioning, tasks and application domains, expertise level and image quality, and affordances of software, hardware, and interfaces. No single enhancement method satisfied all factor configurations. Therefore, it is suggested to offer users a broad choice of methods to facilitate personalization, contextualization, and complementarity. (3) A distinction is made between casual and critical vision on the basis of signal ambiguity and error consequences. The criteria of a paradigm for enhancing images for critical applications comprise: interpreting images skeptically; approaching enhancement as a system problem; considering all image structures as potential information; and making uncertainty and alternative interpretations explicit, both visually and numerically.

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“…This indicates the type of data being shown, the criteria used to threshold the data, the global maxima and minima within the dataset, and the colour mapping between them. Our default colour mapping transitions from red to yellow, which denotes an increase in statistical significance with a lower bound (Christen et al, 2013).…”

Section: Visual Componentsmentioning

confidence: 99%

Porthole and Stormcloud: Tools for Visualisation of Spatiotemporal M/EEG Statistics

Taylor

Garrido

2020

Neuroinform

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Electro-and magneto-encephalography are functional neuroimaging modalities characterised by their ability to quantify dynamic spatiotemporal activity within the brain. However, the visualisation techniques currently used to illustrate these effects are currently limited to single-or multi-channel time series plots, topographic scalp maps and orthographic cross-sections of the spatiotemporal data structure. Whilst these methods each have their own strength and weaknesses, they are only able to show a subset of the data and are suboptimal at articulating one or both of the space-time components.Here, we propose Porthole and Stormcloud, a set of data visualisation tools which can automatically generate context appropriate graphics for both print and screen with the following graphical capabilities: 1) Animated two-dimensional scalp maps with dynamic timeline annotation and optional user interaction;2) Three-dimensional construction of discrete clusters within sparse spatiotemporal volumes, rendered with 'cloud-like' appearance and augmented by crosssectional scalp maps indicating local maxima. These publicly available tools were designed specifically for visualisation of M/EEG spatiotemporal statistical maps, however, we also demonstrate alternate use cases of posterior probability maps and weight maps produced by machine learning classifiers.In principle, the methods employed here are transferrable to visualisation of any spatiotemporal image.

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Colorful brains: 14years of display practice in functional neuroimaging

Cited by 19 publications

References 39 publications

The relation between color and spatial structure for interpreting colormap data visualizations

The relation between color and spatial structure for interpreting colormap data visualizations

Personalizing image enhancement for critical visual tasks: improved legibility of papyri using color processing and visual illusions

Porthole and Stormcloud: Tools for Visualisation of Spatiotemporal M/EEG Statistics

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