Award details

How do ants use encode & identify natural panoramic scenes?

Reference	BB/H013644/1
Principal Investigator / Supervisor	Professor Paul Graham
Co-Investigators / Co-Supervisors	Professor Thomas Collett, Dr David Lent
Institution	University of Sussex
Department	Biology and Environmental Science
Funding type	Research
Value (£)	493,732
Status	Completed
Type	Research Grant
Start date	11/04/2010
End date	10/10/2013
Duration	42 months

Abstract

In field and lab studies, we will examine how ants view, encode and recognise panoramic scenes in two navigational tasks: directional guidance and scene identification. In the directional task ants on a foraging route will be led the centre of an artificial panorama which they can leave in a panorama defined direction. To learn what features of the panorama contribute to setting the ants' aiming direction, their scanning and aiming points will be recorded in training conditions and in tests with the panorama rotated and/or transformed. In the scene identification task, ants in the field and in the lab will be trained to perform one action in one panorama and a second in another panorama. In the field we will examine how each panorama sets the ants' choice of response. In the lab, their scanning and viewing directions will be examined while they identify one or other panorama. By using two panoramas that consist of the same components arranged in different orders or spacing, we will investigate how recognition depends on information integrated across a scene. We will also examine the integration of information within a scene by recording how ants view scenes that are unfamiliar. Naïve ants tend to aim at a scene's centre of mass. By analysing the parameters that contribute to an ant's estimate of the centre of mass of a large set of different scenes, we will explore the horizontal and vertical extents of integration and model the computations involve. The shape of the skyline seems crucial for the identification of whole panoramas and points within a panorama. By training ants to discriminate between different shaped triangles we will determine whether the skyline is encoded as a sequence of differently oriented edges or in terms of the relative positions of the centre of mass of a peak and associated inflections in the skyline. We will investigate the role of compass information in setting viewing direction and in identifying scenes.

Summary

Panoramic scenes are important to many navigating animals in providing directional signals (home is left of the hill), compass direction (valley runs north-south), and a rough sense of place (we are nearly home). But how large natural scenes are encoded or recognised is almost unknown. We will explore this topic in ants for its intrinsic interest, as a new approach to studying insect pattern vision, and more broadly because the way that a low resolution and relatively simple visual system deals with natural scenes relates to current areas of neuroscience, computer vision and robotics. Walking ants are good insects for such a study as their scanning and viewing movements while recognising scenes and approaching patterns can be monitored at high resolution to give both the orientation of an ant's body axis and its position, allowing us to infer how a scene is imaged on the retina. The design of experiments can be kept simple following our finding that the directional responses of desert ants to a real panorama can be elicited by a crude facsimile of the skyline of that panorama. It hints at possible features that ants use for scene identification, such as peaks and troughs in the skyline. Also, as distance and compass information are not essential for scene recognition, it is realistic to study aspects of it in the lab. Additionally, the old but long-ignored result that naïve ants exposed to a large visual stimulus tend to face its centre of mass gives us a new way to analyse how ants integrate information across a large scene. Our major research questions are: How do ants scan and view patterns during recognition? Ants and bees simplify the computational problems of pattern recognition by adopting stereotyped viewing strategies. We will analyse these strategies while ants inspect, recognise and use panoramic visual scenes of different sizes. Pilot data show that desert ants in the field rotate to scan a scene before correcting their course, or if the sceneis unfamiliar. Videos of this scanning will reveal the relationship between viewing direction and guiding elements in the panorama. In the lab, the fixity or variability of viewing behaviour during scene recognition and the changes induced by scene transformations will be examined for clues to the coordinate system of scene encoding (retinotopic, compass-based, or intrinsic to the scene). How do ants integrate information over a large area? As a new approach to analyzing the visual processing that occurs across large scenes, we will study the ants' computation of the centre of mass of a stimulus. Because this aiming behaviour needs no training, we can test a large variety of stimuli and analyse how different areas and features of a scene combine in the estimate of a scene's centre of mass. Our aim is to produce a model of this processing that can predict an ant's directional response to most scenes. How are components of panoramic scenes encoded? That a panorama can be recognised through its simulated skyline means that a significant part of the scene is captured by the skyline contour. One likely form of skyline encoding is as a sequence of oriented edges. Another possibility is that the shape of scene components - peaks or troughs - is encoded by the separation between skyline inflections and the centre of mass of a component. By training ants to distinguish between shapes (initially isosceles and scalene triangles), we will investigate whether ants use these or other visual 'primitives'. How do ants use panoramic scenes? We will examine in the field the ways that information from natural panoramas (1) provides directional information for guidance, (2) acts as a contextual cue for priming the appropriate spatial memories for ants that are trained to two foraging routes, and (3) interacts with compass information in scene recognition.

Impact Summary

Our plan for maximising the impact of this project involves engagement and communication with 3 primary non-academic sectors: (1) Schools; (2) Industry; and, (3), the general public. Outreach to Schools Track Record: PG has an established record of schools-based outreach activities and is registered with STEMNET as a Science and Engineering Ambassador. He has developed a series of ant-themed workshop sessions for children at KeyStage 4 and Higher levels. These sessions have been run for small groups of students visiting from local schools. Additionally, PG has developed material and run workshop sessions for the University of Sussex's AimHigher and Widening Participation schemes where children from families without a tradition of higher education are given a taste of university life, research and teaching methods. Traditionally, this kind of outreach work is dominated by Physics and Chemistry. We feel it is important to increase the visibility of Biology at such events and reinforce a view of biology as a precise science. Planned activities: We will continue with this work and expand our schools outreach by offering summer projects, within the CREST and Nuffield schemes, to gifted and talented students from local schools. Projects will be closely aligned with the objectives of the grant to increase student engagement with current research. Possible projects include: Investigating the visual ecology of local wood ants using our specialist camera equipment; Using off the shelf robot systems (LEGO Mindstorms) to investigate simple algorithms for skyline detection and guidance or simple visual landmark experiments with the ubiquitous garden ant. PG has registered with STEMNET Sussex as a project supervisor. Links with Industry. The impact of this research on industry is within the fields of autonomous robotics and computer vision where applications of real-world robotics will benefit from knowledge of the efficient and economical solutions to complex sensory and computational problems. We will ensure that we communicate the implications of our research findings to an industrial audience by attending and presenting at conferences with an established connection to industry e.g. the British Machine Vision Association and the Applied Vision Association. Both of these organisations have regular meetings with a biological theme. Additionally, as members of the Centre for Computational Neuroscience and Robotics both PG and TSC have strong links to the School of Science and Technology (SciTech). This gives us the opportunity to present our research at SciTech's annual industry and community open days. The objective of this networking will be to find an industrial partner for a CASE studentship or KTP award to begin in Year 3 of this project and to investigate economical algorithms for the encoding of complex natural scenes. Engaging the general public Track Record. PG and TSC have pro-actively engaged with national broadcast and print media to publicise previous funded BBSRC research projects and PG has developed presentations and demos to promote and explain the utility of insect navigation research. These have been shown at the Brighton Science Festival and the Great British R & D show at the Houses of Parliament. Planned activities. We plan to further develop materials and demos for national science events such as the Royal Society Science Festival and RCUK science week. We will maximise the coverage of these events and of key research findings by utilising the University of Sussex's Press Office. Resources. The major resource requirement for all these activities is the PI's time. However, some public engagement activities, especially those at Science Festivals, may require dedicated funds for equipment and materials. These will be sought from the Wellcome Trust, the Royal Society, the BBSRC or RCUK, who all have schemes for funding public engagement activities.

Committee	Research Committee A (Animal disease, health and welfare)
Research Topics	Animal Welfare, Neuroscience and Behaviour
Research Priority	X – Research Priority information not available
Research Initiative	X - not in an Initiative
Funding Scheme	X – not Funded via a specific Funding Scheme

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