Differential recruitment of brain networks following route and cartographic map learning of spatial environments.

An extensive neuroimaging literature has helped characterize the brain regions involved in navigating a spatial environment. Far less is known, however, about the brain networks involved when learning a spatial layout from a cartographic map. To compare the two means of acquiring a spatial representation, participants learned spatial environments either by directly navigating them or learning them from an aerial-view map. While undergoing functional magnetic resonance imaging (fMRI), participants then performed two different tasks to assess knowledge of the spatial environment: a scene and orientation dependent perceptual (SOP) pointing task and a judgment of relative direction (JRD) of landmarks pointing task. We found three brain regions showing significant effects of route vs. map learning during the two tasks. Parahippocampal and retrosplenial cortex showed greater activation following route compared to map learning during the JRD but not SOP task while inferior frontal gyrus showed greater activation following map compared to route learning during the SOP but not JRD task. We interpret our results to suggest that parahippocampal and retrosplenial cortex were involved in translating scene and orientation dependent coordinate information acquired during route learning to a landmark-referenced representation while inferior frontal gyrus played a role in converting primarily landmark-referenced coordinates acquired during map learning to a scene and orientation dependent coordinate system. Together, our results provide novel insight into the different brain networks underlying spatial representations formed during navigation vs. cartographic map learning and provide additional constraints on theoretical models of the neural basis of human spatial representation

How do individuals with Williams syndrome learn a route in a real-world environment?

Individuals with Williams syndrome (WS) show a specific deficit in visuo-spatial abilities. This finding, however, is mainly based on performance on small-scale laboratory-based tasks. This study investigated large-scale route learning in individuals with WS and two matched control groups (moderate learning difficulty group [MLD], typically developing group [TD]). In a non-labelling and a labelling (verbal information provided along the route) condition, participants were guided along one of two unfamiliar 1 km routes with 20 junctions, and then retraced the route themselves (two trials). The WS participants performed less well than the other groups, but given verbal information and repeated experience they learnt nearly all of the turns along the route. The extent of improvement in route knowledge (correct turns) in WS was comparable to that of the control groups. Relational knowledge (correctly identifying spatial relationships between landmarks), compared to the TD group, remained poor for both the WS and MLD groups. Assessment of the relationship between performance on the large-scale route learning task to that on three small-scale tasks (maze learning, perspective taking, map use) showed no relationship for the TD controls, and only a few non-specific associations in the MLD and WS groups

Learning Models for Following Natural Language Directions in Unknown Environments

Natural language offers an intuitive and flexible means for humans to communicate with the robots that we will increasingly work alongside in our homes and workplaces. Recent advancements have given rise to robots that are able to interpret natural language manipulation and navigation commands, but these methods require a prior map of the robot's environment. In this paper, we propose a novel learning framework that enables robots to successfully follow natural language route directions without any previous knowledge of the environment. The algorithm utilizes spatial and semantic information that the human conveys through the command to learn a distribution over the metric and semantic properties of spatially extended environments. Our method uses this distribution in place of the latent world model and interprets the natural language instruction as a distribution over the intended behavior. A novel belief space planner reasons directly over the map and behavior distributions to solve for a policy using imitation learning. We evaluate our framework on a voice-commandable wheelchair. The results demonstrate that by learning and performing inference over a latent environment model, the algorithm is able to successfully follow natural language route directions within novel, extended environments.Comment: ICRA 201

A machine learning route between band mapping and band structure

The electronic band structure (BS) of solid state materials imprints the multidimensional and multi-valued functional relations between energy and momenta of periodically confined electrons. Photoemission spectroscopy is a powerful tool for its comprehensive characterization. A common task in photoemission band mapping is to recover the underlying quasiparticle dispersion, which we call band structure reconstruction. Traditional methods often focus on specific regions of interests yet require extensive human oversight. To cope with the growing size and scale of photoemission data, we develop a generic machine-learning approach leveraging the information within electronic structure calculations for this task. We demonstrate its capability by reconstructing all fourteen valence bands of tungsten diselenide and validate the accuracy on various synthetic data. The reconstruction uncovers previously inaccessible momentum-space structural information on both global and local scales in conjunction with theory, while realizing a path towards integrating band mapping data into materials science databases

Bi-directional route learning in wood ants

Some ants and bees readily learn visually guided routes between their nests and feeding sites. They can learn the appearance of visual landmarks for the food-bound or homeward segment of the route when these landmarks are only present during that particular segment of their round trip. We show here that wood ants can also acquire landmark information for guiding their homeward path while running their food-bound path, and that this information may be picked up, when ants briefly reverse direction and retrace their steps for a short distance. These short periods of looking back tend to occur early in route acquisition and are more frequent on homeward than on food-bound segments

Teaching, learning and technology: An e-route to deep learning?

This is the author's pdf version of an article published in Research into Education.This paper details a research project that considered the extent to which e-learning is congruent with the notion of inculcating and maintaining deep approaches to learning within HE. Also, to explore what actions may be taken to engender and or maintain a deep approach when using e-learning as the central androgogy as knowing what (is possible) and how (it may be achieved) provides a fuller picture. Whilst this paper is designed to help inform practice and professional judgement it is not purporting to provide absolute answers. Whilst I have attempted to provide an honest account of my findings, truth and reality are social constructions (Pring 2000). The research was based upon methodical triangulation and involved thirty-eight undergraduate students who are undertaking study through e-learning and five academic members of staff who utilise e-learning in their programmes. As such, the project was small scale and how much may be inferred as applicable to other groups and other contexts may be contested, as those sampled for this research have their own unique paradigms and perceptions. Finally, it is always worth remembering that effective teaching and learning is contextual (Pring 2000). The research revealed that deep approaches to learning are situational (Biggs 2003) and e-learning can authentically lead to a student adopting and maintaining a deep approach. There are several factors that increase the likelihood of a student adopting this desired approach. These include; where students perceive the programme to be of high quality (Parker 2004), they have feelings of competence and confidence in their ability to study and interact with the technology and others. In addition, students require appropriate, reliable access to technology, associated systems and individualised planned support (Salmon 2004). Further to this deep approaches are more likely to be adopted where programmes are built on a constructivist androgogy, constructive alignment is achieved, interaction at several levels and a steady or systematic style of learning are encouraged (Hwang and Wang 2004). Critically study programmes should have authentic assessment in which deep approaches are intrinsic to their completion. To effectively support students in achieving a deep approach to learning, when employing e-learning, staff require knowledge and skill in three areas: teaching and learning, technology, and subject content (Good 2001). They also require support from leaders at cultural, strategic and structural levels (Elloumi 2004)