|Dog-Eat-Dog by Ruth Graham|
Wednesday, December 18, 2013
Tuesday, December 10, 2013
In order to model something, you first need to measure it. You need to be able to assign a number to an emotion. Since emotions are already either positive or negative, we already have a natural way to assign them values. Most of us can read a text and agree on whether it has a positive or negative tone. But can we automatically assign a number to emotional content on the basis of this assessment? Frank and his colleagues have adapted the software SentiStrength, which claims to be an algorithm to do exactly this. You feed it in a sentence and out pops a ranking. I put in the last sentence of the previous paragraph in to the online version and got negative score -2 for the words 'skeptical' and 'emotional' along with a positive score of 2 for 'interested'. Good to see I was emotionally neutral going in to the seminar.
|Review sentiment distribution for|
'Harry Potter and The Deathly Hollows'
In one study, Garcia & Schweitzer looked at book reviews left on the Amazon website. There was a typical distribution of negative and positive emotions in these reviews (pictured on the right) where positive comments were typically extremely positive and negative criticism was varied. The time pattern of review writing varied, with some books building up a review base over time and others (such as Harry Potter) being hyped from the start.
|Time series of emotions in |
an online chat room.
The team have built up a mathematical model of emotions, which they use to explain the patterns they see. The basic idea of the model is that people change both in how aroused they are and in whether they feel positive or negative emotions. Positive inputs provide positive expressions, while negative inputs enforce negative expressions. The arousal depends on the intensity of the inputs. This model can reproduce many of the properties of the Amazon data and the online chat rooms.
Friday, November 29, 2013
Monday, November 25, 2013
The starting point for our project is the social brain hypothesis: that humans and other primates have big brains because they have complex social lives. It takes a lot of brain power to keep track of all our friends and enemies. Just think about how much of your thoughts during today were dedicated to sorting out interpersonal relationships. You soon realize that you may well possess a social brain. And that brain costs you, not only through the time spent worrying about what other people think about you, but also because of the large amount of energy it consumes.
Our research will look at fish and their brains. Of course, fish do not have as complex brains as we do, but there is good reason to believe that the structure and capacity of the brain will effect social behavior, and visa-versa. Are fish bred for big brains more social than those with small brains? Are fish that are bred on the basis of their social interactions more likely to have smaller or larger brains? These are some of the things we aim to find out.
|Turning response of a guppy as a function |
of the position of its nearest neighbor
(image by Andrea Perna)
Recent debate about the interpretation of Niclas and his co-workers results make my distinction between simple and complicated sociality all the more important. Sue Healy and Candy Rowe argued that differences in the ability of big and small brained fish to learn in a numerical task could be explained by changes in the motivation and stimulation of fish, instead of genuine intelligence differences. The suggestion is that simple behavioral rules can explain apparently complex outcomes. Naturally, Niclas is not convinced by their argument and their team gave a robust response. Our approach should allow us to get to the bottom of exactly these types of details. It will hopefully reveal some of the subtleties of the social brain.
This is a big project, and guppy breeding experiments are just part of it. We will develop better data analysis methods, make more accurate models of fish interactions, and look at in-silico evolution of fish shoaling (hence my recent revived interest in artificial life). The project starts properly in July 2014, once Niclas has totally renovated his lab. We will be recruiting both theoretical and experimental Postdocs in the spring and there should be a possible PhD project too. Watch this space for more details.
Sunday, November 17, 2013
In a week when Daniel Strömbom showed us how beautiful mathematical models can be, Teddy Herbert-Read emailed me the above link. It’s a great video, but has a quote by Bertrand Russell that I’m not so keen on. So I thought I’d write about it.
Sunday, November 10, 2013
Thursday, November 7, 2013
Sunday, November 3, 2013
The paper itself addresses a really fundamental problem in biology: why are there all these complex living forms around us? The answer the authors suggest is that natural selection acts to reduce randomness and make things which are more structured. There is a lot of technical discussion of how to measure randomness and how to define complexity (they define complexity=genome length-genome entropy), but this is the basic result. Natural selection will make genomes less random.
Naturally, the biologists amongst us were not exactly impressed with this revelation. That natural selection eliminates randomness is more or less true by definition. But there are a few additional insights gained in studying evolution of computer programs. For example, there are sudden fitness jumps in the computer simulations, accompanied by decreases in genome randomness. These are reminiscent of 'biological' evolution and are reproduced in the 'artificial' evolution in Avida.
This leads me to my title 'Is Artificial Life still alive?'. It seems to me that research progress since this paper has been pretty slow. Yes, there are Artificial Life conferences and a society with a journal, but the work I have read here is more concerned with engineering challenges and less concerned with the fundamental questions in biology. One nice recent exception to this trend is a paper by Philip Gerlee and Torbjörn Lundh on cross feeding artificial organisms.
One of the reasons for the failure of Aritificial Life to take off in serious biology research is reflected in the reaction of the biologists in our journal club when they read the paper. This research too often looks like Darwin needlessly translated into 'Entroponeese' or some other obscure mathematical language, without providing any new insight. I still think there is potential here, and Philip and Torbjörns' work reflects this potential. I'm interested to hear if anyone else knows of any other signs of life in Artificial Life.
Wednesday, October 30, 2013
What I think is cool when you look at the programme is the diversity of subjects that will be talked about. The list includes everything from 'in-silico ecology and 'neural fields'', through 'fish stocks' and 'fossil dating' back to 'food webs', 'collective decision-making' and 'human fingers'. There is so much variety in how mathematics can be used to study biology.
Being optimistic, I should talk about the common language of mathematics linking all these diverse subjects together. And to some degree this is true. There are common processes in all these parts of biology that are captured by models. It is fun seeing how your favourite model can be used in a completely different way.
Being realistic, however, I know there are going to be a lot of confused (or sometimes sleepy) looks on our faces as we try (or fail) to understand what each other are doing. But that should never stop us from trying. See you in December.