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2019


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Spatial Continuity Effect vs. Spatial Contiguity Failure. Revising the Effects of Spatial Proximity Between Related and Unrelated Representations

Beege, M., Wirzberger, M., Nebel, S., Schneider, S., Schmidt, N., Rey, G. D.

Frontiers in Education, 4:86, 2019 (article)

Abstract
The split-attention effect refers to learning with related representations in multimedia. Spatial proximity and integration of these representations are crucial for learning processes. The influence of varying amounts of proximity between related and unrelated information has not yet been specified. In two experiments (N1 = 98; N2 = 85), spatial proximity between a pictorial presentation and text labels was manipulated (high vs. medium vs. low). Additionally, in experiment 1, a control group with separated picture and text presentation was implemented. The results revealed a significant effect of spatial proximity on learning performance. In contrast to previous studies, the medium condition leads to the highest transfer, and in experiment 2, the highest retention score. These results are interpreted considering cognitive load and instructional efficiency. Findings indicate that transfer efficiency is optimal at a medium distance between representations in experiment 1. Implications regarding the spatial contiguity principle and the spatial contiguity failure are discussed.

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link (url) DOI [BibTex]


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Doing more with less: Meta-reasoning and meta-learning in humans and machines

Griffiths, T., Callaway, F., Chang, M., Grant, E., Krueger, P. M., Lieder, F.

Current Opinion in Behavioral Sciences, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Cognitive Prostheses for Goal Achievement

Lieder, F., Chen, O. X., Krueger, P. M., Griffiths, T.

Nature Human Behavior, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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A rational reinterpretation of dual process theories

Milli, S., Lieder, F., Griffiths, T.

2019 (article)

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DOI [BibTex]

DOI [BibTex]

2016


Probabilistic Duality for Parallel Gibbs Sampling without Graph Coloring
Probabilistic Duality for Parallel Gibbs Sampling without Graph Coloring

Mescheder, L., Nowozin, S., Geiger, A.

Arxiv, 2016 (article)

Abstract
We present a new notion of probabilistic duality for random variables involving mixture distributions. Using this notion, we show how to implement a highly-parallelizable Gibbs sampler for weakly coupled discrete pairwise graphical models with strictly positive factors that requires almost no preprocessing and is easy to implement. Moreover, we show how our method can be combined with blocking to improve mixing. Even though our method leads to inferior mixing times compared to a sequential Gibbs sampler, we argue that our method is still very useful for large dynamic networks, where factors are added and removed on a continuous basis, as it is hard to maintain a graph coloring in this setup. Similarly, our method is useful for parallelizing Gibbs sampling in graphical models that do not allow for graph colorings with a small number of colors such as densely connected graphs.

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pdf [BibTex]


Map-Based Probabilistic Visual Self-Localization
Map-Based Probabilistic Visual Self-Localization

Brubaker, M. A., Geiger, A., Urtasun, R.

IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 2016 (article)

Abstract
Accurate and efficient self-localization is a critical problem for autonomous systems. This paper describes an affordable solution to vehicle self-localization which uses odometry computed from two video cameras and road maps as the sole inputs. The core of the method is a probabilistic model for which an efficient approximate inference algorithm is derived. The inference algorithm is able to utilize distributed computation in order to meet the real-time requirements of autonomous systems in some instances. Because of the probabilistic nature of the model the method is capable of coping with various sources of uncertainty including noise in the visual odometry and inherent ambiguities in the map (e.g., in a Manhattan world). By exploiting freely available, community developed maps and visual odometry measurements, the proposed method is able to localize a vehicle to 4m on average after 52 seconds of driving on maps which contain more than 2,150km of drivable roads.

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pdf Project Page [BibTex]

pdf Project Page [BibTex]