Informatics

Lightness illusions are fundamental to human perception, and yet why we see them is still the focus of much research. Here we address the question by modelling not human physiology or perception directly as is typically the case but our natural visual world and the need for robust behaviour. Artificial neural networks were trained to predict the reflectance of surfaces in a synthetic ecology consisting of 3-D “dead-leaves” scenes under non-uniform illumination. The networks learned to solve this task accurately and robustly given only ambiguous sense data. In addition—and as a direct consequence of their experience—the networks also made systematic “errors” in their behaviour commensurate with human illusions, which includes brightness contrast and assimilation—although assimilation (specifically White's illusion) only emerged when the virtual ecology included 3-D, as opposed to 2-D scenes. Subtle variations in these illusions, also found in human perception, were observed, such as the asymmetry of brightness contrast. These data suggest that “illusions” arise in humans because (i) natural stimuli are ambiguous, and (ii) this ambiguity is resolved empirically by encoding the statistical relationship between images and scenes in past visual experience. Since resolving stimulus ambiguity is a challenge faced by all visual systems, a corollary of these findings is that human illusions must be experienced by all visual animals regardless of their particular neural machinery. The data also provide a more formal definition of illusion: the condition in which the true source of a stimulus differs from what is its most likely (and thus perceived) source. As such, illusions are not fundamentally different from non-illusory percepts, all being direct manifestations of the statistical relationship between images and scenes.

Background

The perception of brightness depends on spatial context: the same stimulus can appear light or dark depending on what surrounds it. A less well-known but equally important contextual phenomenon is that the colour of a stimulus can also alter its brightness. Specifically, stimuli that are more saturated (i.e. purer in colour) appear brighter than stimuli that are less saturated at the same luminance. Similarly, stimuli that are red or blue appear brighter than equiluminant yellow and green stimuli. This non-linear relationship between stimulus intensity and brightness, called the Helmholtz-Kohlrausch (HK) effect, was first described in the nineteenth century but has never been explained. Here, we take advantage of the relative simplicity of this ‘illusion’ to explain it and contextual effects more generally, by using a simple Bayesian ideal observer model of the human visual ecology. We also use fMRI brain scans to identify the neural correlates of brightness without changing the spatial context of the stimulus, which has complicated the interpretation of related fMRI studies.

Results
Rather than modelling human vision directly, we use a Bayesian ideal observer to model human visual ecology. We show that the HK effect is a result of encoding the non-linear statistical relationship between retinal images and natural scenes that would have been experienced by the human visual system in the past. We further show that the complexity of this relationship is due to the response functions of the cone photoreceptors, which themselves are thought to represent an efficient solution to encoding the statistics of images. Finally, we show that the locus of the response to the relationship between images and scenes lies in the primary visual cortex (V1), if not earlier in the visual system, since the brightness of colours (as opposed to their luminance) accords with activity in V1 as measured with fMRI.

Conclusions
The data suggest that perceptions of brightness represent a robust visual response to the likely sources of stimuli, as determined, in this instance, by the known statistical relationship between scenes and their retinal responses. While the responses of the early visual system (receptors in this case) may represent specifically the statistics of images, post receptor responses are more likely represent the statistical relationship between images and scenes. A corollary of this suggestion is that the visual cortex is adapted to relate the retinal image to behaviour given the statistics of its past interactions with the sources of retinal images: the visual cortex is adapted to the signals it receives from the eyes, and not directly to the world beyond.

This paper describes a method of training an artificial neural network, specifically a multilayer perceptron (MLP), to act as a tool to help identify plants using morphological characters collected automatically from images of botanical herbarium specimens. A methodology is presented here to provide a practical way for taxonomists to use neural networks as automated identification tools, by collating results from a population of neural networks. A case study is provided using data extracted from specimens of the genus Tilia in the Herbarium of the Royal Botanic Gardens, Kew, UK. A classification accuracy of 44% was achieved on this challenging multiclass problem.

Understanding perception of colour is challenging because what we see is not always what is there, which is a phenomenon we call illusions. Here we review the nature of colour vision, and the problems facing most current models and explanations. Focusing on our recent research on humans, bees and computers, we describe a new, more ecologically based explanation that provides a clear framework for why we see what we do.

Many species of plants produce leaves with distinct teeth around their margins. The presence and nature of these teeth can often help botanists to identify species. Moreover, it has long been known that more species native to colder regions have teeth than species native to warmer regions. It has therefore been suggested that fossilized remains of leaves can be used as a proxy for ancient climate reconstruction. Similar studies on living plants can help our understanding of the relationships.

Abstract: Herbarium specimens are a vital resource in botanical taxonomy. Many herbaria are undertaking large-scale digitization projects to improve access and to preserve delicate specimens, and in doing so are creating large sets of images. Leaf characters are important for describing taxa and distinguishing between them and they can be measured from herbarium specimens. Here, we demonstrate that herbarium images can be analysed using suitable software and that leaf characters can be extracted automatically.

Plants are of fundamental importance to life on Earth. The shapes of leaves, petals and whole plants are of great significance to plant science, as they can help to distinguish between different species, to measure plant health, and even to model climate change. The growing interest in biodiversity and the increasing availability of digital images combine to make this topic timely. The global shortage of expert taxonomists further increases the demand for software tools that can recognize and characterize plants from images.

Understanding perception of colour is challenging because what we see is not always what is there, which is a phenomenon we call illusions. Here we review the nature of colour vision, and the problems facing most current models and explanations. Focusing on our recent research on humans, bees and computers, we describe a new, more ecologically based explanation that provides a clear framework for why we see what we do.

This research is concerned with understanding the importance of novel Perceptible Affordances in the form of visual cues for interaction within emerging NUI (Natural User-Interface) technologies, e.g. haptic, gesture-based and eye tracking for control. A study with eye tracking technology was conducted with participants, which focused only on an observational phase of a digital interface. The iGoogle Personal Web Portal was selected as a case study and drag-and-drop interaction was identified as a non-obvious feature that could be unknown to users. At last we have arrived at some initial findings that demonstrate how previous knowledge is a key factor on spotting specific interface control features; and the interface itself did not present visual cues that would inform users about available features for control and grad-and-drop interactions. Later on, two iPad software versions (one without visual affordances) will be compared by means of a practical investigation with a between-groups design, using eye-tracking followed by retrospective think-aloud for data collection and determining which version users would be more keen to adapt and adopt.

In this research we investigate Perceptible Affordances and their role in drag-and-drop interactions within the iGoogle Personal Web Portal. A usability evaluation with Eye Tracking was conducted in order to better understand how this website signals the possibility for Drag-and-drop interaction with its content. Our protocol for analysis also included participants thinking aloud, so we could better grasp their strategies for identifying potential interactions. We verified that expertise influences how able a user is to identify and understand cues for interaction within an interface. In the case of personal web portals, a general lack of familiarity with this type of website hinders the effective use of these systems by new users. Clearer and stronger cues for perceptible affordance are needed to ensure that users can quickly adopt and adapt these systems.

The move towards touch-based interfaces disrupts the established ways in which users manipulate and control graphical user interfaces. The predominant mode of interaction established by the desktop interface is to ‘double-click’ an icon in order to open an application, file or folder. Icons show users where to click and their shape, colour and graphic style suggests how they respond to user action. In sharp contrast, in a touch-based interface, an action may require a user to form a gesture with a certain number of fingers, a particular movement, and in a specific place. Often, none of this is suggested in the interface.
This thesis adopts the approach of research through design to address the problem of how to inform the user about which gestures are available in a given touch-based interface, how to perform each gesture, and, finally, the effect of each gesture on the underlying system. Its hypothesis is that presenting automatic and animated visual prompts that depict touch and
preview gesture execution will mitigate the problems users encounter when they execute commands within unfamiliar gestural interfaces. Moreover, the thesis claims the need for a new framework to assess the efficiency of gestural UI designs. A significant aspect of this new framework is a rating system that was used to assess distinct phases within the users’
evaluation and execution of a gesture.
In order to support the thesis hypothesis, two empirical studies were conducted. The first introduces the visual prompts in support of training participants in unfamiliar gestures and gauges participants’ interpretation of their meaning. The second study consolidates the design features that yielded fewer error rates in the first study and assesses different interaction
techniques, such as the moment to display the visual prompt. Both studies demonstrate the benefits in providing visual prompts to improve user awareness of available gestures. In addition, both studies confirm the efficiency of the rating system in identifying the most common problems users have with gestures and identifying possible design features to
mitigate such problems.
The thesis contributes: 1) a gesture-and-effect model and a corresponding rating system that can be used to assess gestural user interfaces, 2) the identification of common problems users have with unfamiliar gestural interfaces and design recommendations to mitigate these problems, and 3) a novel design technique that will improve user awareness of unfamiliar gestures within novel gestural interfaces.

This paper investigates new forms of interaction within digital interfaces. It examines the assumption that advances in embodied interaction and natural user interface (NUI) computing will improve and enhance the field of user‐machine interface, bringing features to users that allow a more direct and natural manipulation of digital interfaces and devices (e.g. eye tracking, touch screen, voice and gesture recognition). I will address a set of topics that are more foundational than technical: the investigation on the evolution of digital interfaces will be detailed through the designer & user experience point of view, stressing different aspects and features of operational systems (OS): from Command‐Line Interface (CLI), to Graphic‐User Interface (GUI), towards Natural‐User Interface (NUI), which encompasses Tangible‐User Interface (TUI). Considerations about what the future could bring will be made, with base on an assessment of different moments of Human‐Computer Interaction.