# New paper: Unifying saliency metrics

How well can we predict where people will look in an image? A large variety of models have been proposed that try to predict where people look using only the information provided by the image itself. The MIT Saliency Benchmark, for example, compares 47 models.

So which model is doing the best? Well, it depends which metric you use to compare them. That particular benchmark lists 7 metrics; ordering by a new one changes the model rankings. That’s a bit confusing. Ideally we would find a metric that unambiguously tells us what we want to know.

Over a year ago, I wrote this blog post telling you about our preprint How close are we to understanding image-based saliency?. After reflecting on the work, we realised that the most useful contribution of that paper was buried in the appendix (Figure 10). Specifically, putting the models onto a common probabalistic scale makes all the metrics* agree. It also allows the model performance per se to be separated from nuisance factors like centre bias and spatial precision, and for model predictions to be evaluated within individual images.

We re-wrote the paper to highlight this contribution, and it’s now available hereThe code is available here.

Citation:

Kümmerer, M., Wallis, T.S.A. and Bethge, M. (2015). Information-theoretic model comparison unifies saliency metrics. Proceedings of the National Academy of Sciences.

*all the metrics we evaluated, at least.

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# Exploring GLMs with Python

A few months ago Philipp Berens and I ran a six week course introducing the Generalised Linear Model to graduate students. We decided do run the course in Python and to use the IPython Notebook to present the lectures. You can view the lecture notes here and get the source code here.

The six lectures were:

1. Introduction by us, then student group presentations of a points of significance article that we hoped would provide a refresher on basic statistics.
2. Finishing off student presentations (which took too long in the first week), followed by an introduction to Python, dataframes and exploratory plotting.
3. Design matrices
4. Interactions
6. Current issues in statistical practice, in which we touched on things like exploratory vs confirmatory data analysis, p-hacking, and the idea that statistics is not simply a cookbook one can mechanically follow.

## What worked

• In general the feedback suggests that most students felt they benefitted from the course. Many reported that they enjoyed using the notebook and working through the course materials with a few exercises themselves.
• The main goal of the course was to give students a broad understanding of how GLMs work and how many statistical procedures can be thought of as special cases of the GLM. Our teaching evaluations suggest that many people felt they achieved this.
• The notebooks allow tying the theory and equations very concretely to the computations one performs to do the analysis. I think many students found this helpful, particularly in working through the design matrices

## What didn’t

• Many students felt the course was too short for the material that we wanted to cover. I agree.
• Some students found the lectures (for which weeks 2–5 involved me working through notebooks and presenting extra material) boring.
• Despite the niceness of the Anaconda distribution, Python is unfortunately still not as easy to set up as (for example) MATLAB across different environments (primarily Windows, some OSX). We spent more time than we would have liked troubleshooting issues (mainly to do with different Windows versions not playing nicely with other things).
• We didn’t spend a lot of time discussing the homework assignments in class.
• Some students (graduate students in the neural behavioural school) are more familiar with MATLAB and strongly preferred that we teach the course using that.

## If I were to run the course again

I think the main thing I would change if I ran the course again would be to have it run longer. As a “taste test” I think six weeks was fine, but to really get into the materials would require more lectures.

I also think it would be beneficial to split the content into lectures and practicals. I think the IPython notebooks in the way I used them would be excellent for teaching practical / small class tutorials, but that the lectures probably benefit from higher-level overviews and less scrolling over code*.

I would also plan the homework projects better. One student suggested that rather than having the introduction presentations at the beginning of the course, it would be nice to have the last lecture dedicated to students running through their analysis of a particular dataset in the notebook. I think that’s a great idea.

Finally, I would ignore requests to do the course in MATLAB. Part of why we wanted to use Python or R to do the course was to equip students with tools that they could continue using (for free) if they leave the university environment. Perhaps this is more valuable to undergraduates than PhD students (who already have some MATLAB experience), but I think it’s good for those students to get exposed to free alternatives as well. Plus, the IPython notebooks are just, well, pretty boss.

*I know you can hide cells using the slide mode, but I found slide mode in general quite clunky and so I opted not to use it.