So, just a quick recap – we were hoping to get as much data on the way we process the Pinocchio illusion by measuring different behavioral outcomes as well as the EMG in three timepoints. As far as the behavioral measures are concerned, we questioned participants on the illusion vividity (see my introductory blog post!), the extent of the nose elongation as well as through a recently published Pinocchio questionnaire (Prucell et al., 2021). On the neural side, we were comparing the EMG activity on the biceps and triceps between resting state, state of the illusion and the situation where the participants were actually instructed to contract the muscles.
Firstly, we found that all the participants reported sensation of the illusion and described it as moderately vivid – the average score was 2.6 on the five-point Likert type scale. They felt their nose extending by at least 50% and the questionnaire data suggest that sensations regarding arm tingling, nose and arm elongation represent the best predictors of the illusion vividity, whereas nose widening, pulsation in arm/nose/fingers or tingling in nose and fingers turned out to be less relevant.
When we think about science and technology, we often think about something intricate and sophisticated to comprehend, such as genetic engineering or aerospace astrophysical technology.
However, science and technology are a pivotal part of our mundane life. From us turning off the lights and going to bed at night to curing cancers or genetic disorders, they are all science and technology. We achieved a convenience that might appear to be trivial, and also something that used to deem as a miracle from numerous works and questions of scientists and engineers.
My ceaseless passion for science came from my ignorance of underlying principles; how my body functions, how we get diseases, how we cure them, how we optimize human efficiency, and how we increase the accuracy of data collection. And that is how my endless love for biology and computer science started. My project started from a similar question about the rudimentary concept: attention schema theory, which is elusive and intangible. (See my introductory note here.)
Since the brain is an information-processing device, it has a limitation in processing multiple sources of information. In this project, we investigated attention (visual, primarily) and awareness.
The significance of understanding human consciousness can be also expanded to treatment research and AI research (its consciousness). Linked internal models, cognitive machinery, and the self having a mental possession of the outside objects would be a critical component of awareness. Is it hard to understand? Don’t worry. There are games for it.
Our Y-maze with food on one side and nothing on the other. Guess where they went!
— Written by Amanda Putti & Milica Milosevic —
Well we made it! We’re at the final week of the BYB Fellowship! We faced many challenges throughout this project and had to pivot in order to get results, but we are happy where it ended.
To give updates on our progress, let’s first start where we left off 3 weeks ago. Using a blue light, we took one picture of the slime mold every minute over 24 hours. This allowed us to string the pictures together and make some great time lapse videos! After this, we were able to analyze the videos using a program in Python and create a special kind of video which converts yellow slime branches into lines of directed growth – skeletonized growth video. This was really helpful for understanding how the slime mold grows, explores new areas, and creates a network. But we also think that there’s more to it, and that behavioral analysis opens a lot of questions in the field of biophysics and is a project for itself.
We were very excited to get this imaging. However, we then made the decision to keep the rest of our experiments in the dark and in that manner reduce the amount of imaging done. Slime mold prefers to grow in the dark, and we wanted to make sure light wasn’t inhibiting growth in our experiments.
This means we were more focused on quantifying decision making rather than behavior. We set up a series of three types of experiments and ran many, many tests. (The order of explaining them isn’t coordinated with the chronological order of our work, but makes more sense this way!)
1. Solving Mazes
We wanted to test the ability of slime molds to choose a path that leads them to food, so we set up the easiest of mazes – a Y maze where there was food on one side of the Y and nothing on the other side of the Y (see photo above). Slime molds showed us that they have no intentions of staying hungry and that they’re doing just fine when it comes to finding the food source.
Then we wanted to make things more complicated for them, so we constructed a specific T maze – one side of the letter T was longer and had a food source, and the other was a lot shorter and had an object as a mechanical stimulus (we’ll get more into the mechanical stimulation in a bit). The idea was to check if they can see the difference between the food and something that isn’t food and if they are gonna choose the shorter path towards the no-food region. So, we tried to confuse them, but failed at it – they knew where the food was and grew in that direction almost every time!
