Research & Play, Thoughts About Life

My Teaching Philosophy

“The best way to learn is to teach.” (Richard Feynman)

I usually introduce myself as an animation researcher and storyteller. Animation is the field I am most passionate about, as it combines both art and science. Art manifests itself through drawing, sculpting, designing characters and worlds as well as storytelling, theatre and character arcs. Science combines mathematics, programming as well as elements of physics, like optics and mechanics. My teaching philosophy thus starts from a point of infinite curiosity for how the world is connected from both a rational (science) and a spiritual (art) perspective.

Arm waving

My current role is as unit leader for two units on the BSc Computer Animation and Visual Effects course at Manchester Metropolitan University. The units are 3D Character Development for Level 5 students and Character Animation Techniques for Level 6 students. Before this lecturing post, I undertook an Engineering Doctorate research role with the Centre for Digital Entertainment at The University of Bath. There my research in the field of computer animation was combined with lab tutoring in adjacent subjects. I also taught one year on the aforementioned units before becoming unit leader.

Teaching in Higher Education (HE)

To facilitate learning in higher education (HE) I believe the most important factors are to have a passion for the subjects being taught and to share knowledge adaptively, regarding students with empathy and respect for their individual traits. A passion for learning is essential for refreshing the material, encouraging multiple perspectives (Jonassen 1991) and increasing students’ retention though a plethora of resources (Black and William 2009).

Being able to adapt the information difficulty level to the audience members is a technique for improving learners’ retention (Campbell 2020). Learners’ retention can be further enhanced by the three tier model described in Sundqvist et. al (2019), where support is adapted to the students’ needs. Tier one represents general support, available to all students, followed by tier two of intensified support for smaller groups and finally tier three of special support for individuals that need more detailed explanations.

Also, by including a level of empathy in the delivery method, information becomes relevant to the individual, rather than a set of abstract concepts. This is linked to inclusive practice through understanding and getting to know students as individuals. This practice has been shown to create a sense of safety and belonging among groups of students (Hockings et al 2012). Furthermore, empathy allows the material to be made relevant to the learning orientation of each student (Entwistle and Peterson 2004), leading to a deeper understanding of the subject.

Learning Theories

An influential piece of reading for me was Illeris (2018) because it summarized the most popular learning theories from the past century. The learning theory proposed by the author also resonated well with my teaching methods. Considering content, incentive and interaction dimensions when designing and delivering a course allows me to offer a holistic learning experience. The incentive dimension has the highest priority in my opinion, followed by content and finally the interaction dimension.

Incentive comprises motivation, emotion and volition (Illeris 2018) and is linked to the “why” metaphor described by Simon Sinek (TED Talk 2010) and to the learning orientation described by Entwistle and Peterson (2004). I believe the reason why people learn is due to an inner drive, that ignites their curiosity both academically and vocationally (Entwistle and Peterson 2004). This can be linked to personal beliefs, innate talents or discovered passions and career goals. This “inner spark” then leads to content acquisition and the desire to meet learners with similar interests. Although useful to some extent, external motivators like marks and positive praise are used sparingly as this type of behaviourist approach to learning can lead to negative, competitive effects (Palmer 2005).

Regarding the content dimension, there are a few theories that influence my teaching. I relate to constructivism theories proposed by authors like Piaget (1926/1959), Jonassen (1991) and Palmer (2005) where knowledge is cumulated from previous experiences and is context, sometimes motivation driven. Before a student can reach mastery, however, cognitivism approaches (Ertmer and Newby 2013) help shape the introductory stages of knowledge (Jonassen 1991).

The method I use frequently to organize content is to find the building blocks that compose a complex piece of knowledge and to rebuild the latter using both logical scaffolding (Bruner 2002) and storytelling (narrative) methods. A data visualization pipeline, for example, can be explained through a story of a pirate using a map to look for treasure.

My teaching process usually starts with a holistic view (Pask 1988) of the broader picture, identifying patterns and analysing their component parts or building blocks. A serialist procedure (Pask 1988) then follows to further refine understanding of each component and how it fits into the overall structure of the piece of knowledge we wish to understand. Both theoretical and practical or experiential learning (Kolb 1984) are used to achieve this.

Afterwards, I like to bend the structures created by breaking and making new patterns (De Bono 1970), allowing students to view theory and practice from multiple angles (Jonassen 1991). Both lateral and vertical thinking (De Bono 1970) are thus employed to shape the knowledge concepts. The building blocks of knowledge can then be repeated in the design, delivery and assessment steps using constructive alignment techniques (Biggs and Tang, 2011) to consolidate the information.

It is important for me that students go beyond memorizing information and strive to analyse, create and find different applications for it (Bloom, 1956, Anderson 2000). Although previously learnt concepts influence the acquisition of new content (Hockings et al 2012), I also believe the brain can form new patterns due to its plasticity (Amen 2011), when employing a growth mindset.

Allowing students to reach beyond the facts and into a deeper understanding is a combination of individual motivation (Palmer 2005) and creating a safe and encouraging environment for learning. The latter implies interaction with peers and the teacher, who can influence each student’s experience, since learning is both an individual and a relational process (Murphy and Brown 2012).

Learning by Playing

A safe, inclusive environment for learning starts by viewing diversity of skills, knowledge and background as strengths (Thomas and May 2010). Each student should be given equal opportunities to express and share their opinions through diverse means (eg. text, speech, images). Some of the techniques I use to encourage students to collaborate are taken from improvised theatre (Johnstone 1999), where play is used for learning and connecting with others.

An example of an improv technique is endowment, or playing to each other’s strengths, which is great for group projects. To end, I would like to quote Dr Karin Purvis’ professional opinion on play, to show that accurate theoretical concepts, should always be combined with a fresh, personal and playful approach to knowledge:   

“Scientists have recently determined that it takes approximately 400 repetitions to create a new synapse in the brain – unless it is done with play, in which case, it takes between 10 – 20 repetitions.” (Dr Karin Purvis)


  • Amen, D. G. (2011). Change your brain, change your life. [Place of publication not identified], CMI/Premier Education Solutions.
  • Anderson, L W.; Krathwohl, D. R., eds. (2000). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. Allyn and Bacon. 
  • Biggs, J. B., and Tang, C. S.-K. (2011). Teaching for quality learning at university: what the student does. [Philadelphia, Pa.], McGraw-Hill/Society for Research into Higher Education.
  • Black, P., Wiliam, D. (2009). Developing the theory of formative assessmentEduc Asse Eval Acc 21, 5
  • Bloom, B. S.; Engelhart, M. D.; Furst, E. J.; Hill, W. H.; Krathwohl, D. R. (1956). Taxonomy of educational objectives: The classification of educational goals. Handbook I: Cognitive domain. David McKay Company.
  • Bruner, J. (2002). Making stories: Law, literature, life. Farrar, Straus and Giroux.
  • Campbell, Anthony J. (2020). The Feynman Technique: The Best Way to Learn is to Teach. [Accessed 29/10/2020] [Link here]
  • De Bono, E. (1970). Lateral thinking: a textbook of creativity. London, Ward Lock Educational.
  • Entwistle, Noel & Peterson, Elizabeth. (2004). Conceptions of Learning and Knowledge in Higher Education: Relationships with Study Behaviour and Influences of Learning Environments. International Journal of Educational Research. 41. 407-428. 10.1016/j.ijer.2005.08.009.
  • Ertmer, P. A. and Newby, T. J. (2013). Behaviourism, cognitivism, constructivism: Comparing critical features from an instructional design perspective. Performance Improvement Quarterly, 26(2), 43-71.
  • Hadjianastasis, Marios. (2017). Learning outcomes in higher education: assumptions, positions and the views of early-career staff in the UK system. Studies in Higher Education, 42:12, 2250-2266.
  • Hockings, Christine & Brett, Paul & Terentjevs, Mat. (2012). Making a difference—inclusive learning and teaching in higher education through open educational resources. Distance Education – DISTANCE EDUC. 33. 237-252. 10.1080/01587919.2012.692066.
  • Illeris, Knud. (2018). An overview of the history of learning theory. European Journal of Education. Research, Development and Policy, 53 (2018), pp. 86-101
  • Johnstone, Keith. (1999). Impro for Storytellers, Theatresports and the Art of Making Things Happen. Published by Faber and Faber Limited.
  • Jonassen, D. H. (1991a). Evaluating constructivistic learning. Educational Technology, 31(9), 28-33.
  • Kolb, D. A. (1984). Experiential Learning: Experience as the Source of Learning and Development. Englewood Cliffs, NJ: Prentice Hall.
  • McLeod, S. A. (2020, March 20). Maslow’s hierarchy of needs. Simply Psychology.
  • Murphy, Mark. and Brown, Tony. (2012). Learning as relational: intersubjectivity and pedagogy in higher education. International Journal of Lifelong Education, 31:5, 643-654, DOI: 10.1080/02601370.2012.700648
  • Pask, G. (1988). Learning Strategies, teaching strategies and conceptual or learning style. In R. R. Schmeck (Ed.), Learning strategies and learning styles (pp. 83 – 100), New York: Plenum Press.
  • Palmer, D. (2005). A motivational view of constructivistinformed teaching. International Journal of Science Education, 27(15), 1853-1881. doi:10.1080/09500690500339654
  • Piaget, J. (1926/1959). The Psychology of Intelligence. London: Routledge & Kegan Paul.
  • Sundqvist, Christel & Björk-Åman, Camilla & Ström, K. (2019). The three-tiered support system and the special education teachers’ role in Swedish-speaking schools in Finland. European Journal of Special Needs Education. 34. 1-16. 10.1080/08856257.2019.1572094.
  • TED Talks. (2010). How Great Leaders Inspire Action | Simon Sinek. [Accessed 29/10/2020] [Link here]
  • The Harriet W. Sheridan Center for Teaching and Learning. (2019). Inclusive Teaching Through Active Learning. Brown University.
  • Thomas, L and May, H. (2010). Inclusive Learning and Teaching in Higher Education. The Higher Education Academy. 
Animation, Character Essences, Research & Coding, Research & Play

Le Quack Walker v1.0

“People’s movements can change your impression of them.” 

(Isao Takahata)




Code: This blog post is a piece of personal research and development, which is very close to my heart. I encourage you to read, experiment with the code and Maya file available on Github and reference this post if you use it in your own work.

Research: I believe academic findings should be shared beyond the borders of peer reviewed journals and this is a small attempt at achieving that. I do recognize, however, the importance of published work. The current post does not aim to replace the latter, but to supplement it with findings that stem from curiosity rather than academic rigour. 

Learn: If you wish to learn more about designing, modelling and procedurally animating simple characters, please have a look at my upcoming course, Little Creatures with a Personality on Thinkific. 



Patterns of movement, patterns of laughter, patterns of movement that make us laugh. There are many patterns that connect us, but the ones that truly matter speak the truth of our human nature. And if the truth is unique, otherwise it wouldn’t be called “the truth”, then it should have clear characteristics. There should be a code behind truthful behaviours that generate similar reactions in people, regardless of where they come from.

This project started as a question: What is it that makes ducks funny? Moreover, I wanted to know if there was a code behind the movement of a funny duck. After much thought, experimentation and scripting, I realized that the notion of funniness is too complex. Rather than attempting to understand everything at once, I chose a simple behaviour, walking, and investigated its “funny” potential. Since emotions are also seen among laughing people, they were chosen as nuances for the walking behaviour. This allowed a palette of walk cycles to be experimented with.

This report is part of the Character Essences project, which focuses on recreating believable actions using procedural animation. Actions are often hard to describe, but techniques like Laban Motion Analysis allow dividing complex behaviours into simple motions. Behaviours can thus be described and recreated as a cohesion of individual movements.

This observation is connected to emergence theory, where complex systems emerge from apparently simple rules. One example is Craig Reynolds’ flocking system (1987), where three simple rules govern the complexity of a moving flock of boids (ie. birds or fish). These rules are cohesion (boids must stick together), alignment (boids must travel in the same direction) and separation (boids mustn’t collide with each other).


Before delving into the complexity of human behaviour, I wanted to have a look at a simple creature, a duck. Ducks are funny little birds, with their wagging tails and wobbly walks. Everything about a duck feels like out of a cartoon, even its brilliantly coloured feathers and beak. So what is it that makes a duck funny? Also, can I find the simple motions which form the complex behaviour of a wobbly duck? If the answers to these questions are found, I can then recreate a duck as a procedurally animated character.

Moreover, the feeling a procedural character conveys could inspire a similar effect in an observer. In other words, if we consider a walking duck to be funny, a similar reconstruction done for a procedural duck should also be classified as funny. This could extend to more types of characteristics and behaviours, which can lead to applications in video games, films and psychology.

Procedural characters could be used in simulations and interactions with users to entertain and aid them. If the psychological effect and believability are controllable to some extent, characters can react according to the context of a scene. In a video game, for example, a procedural character can display an angry walk if a user breaks the rules or it can have a joyful jump if they haven’t seen the player in a long time. 


Firstly, let’s discuss the concept of “movement code”. I was introduced to this notion in Stephen Mottram’s puppeteering workshop, The Logic of Movement (2017). He spoke about every creature having a well defined method for moving, which is linked to their size, weight and emotion. As an example, the reason why a chicken thrusts its head forward when walking is to balance out the larger body weight left behind when taking a step.

The “movement code” is also linked to the more comprehensive Laban Motion Analysis (LMA) technique defined by Rudolf Laban and his students (2011). Laban was a movement theorist who studied and classified complex movement into a simple set of qualities. He looked at the shape of the body and space it moves in, as well as the conscious efforts humans make when performing an action.

The efforts described in LMA are weight, space, time and flow (Bishko 2014). Each effort varies between two movement qualities. Weight can vary between light and strong/heavy, space between indirect and direct, time between sudden and sustained, while flow varies between free and bound. Combinations of two qualities form states (awake, remote, stable, dream, rhythm and mobile) and three qualities form drives (action, passion, vision and spell).

By focusing on the four efforts, the question is whether these elements can form the basis of complex, emergent behaviour. In Melzer et. al (2019) basic emotions (Ekman 1992) like happiness, sadness and anger were recreated through sets of simple motions. The goal of the performing actors was to display core movements, without the knowledge of which emotion they were attempting to recreate. Participants then ranked the overall movement as emotions. The experiments showed not only that emotions can be recreated in the human body by simply repeating certain simple movements, but also that others recognise and empathize with such emotions.

The work I attempted relies on the aforementioned paper, but is not rigorous in its academic methodology. It is more of an early prototype, a hypothesis formed in the imagination, if you will. I wished to know whether similar techniques could be applied to a duck walk and whether people found the results funny. The duck walk was to be generated using mathematical functions, thus forming a repeatable movement code. 


The software used to create the Le Quack Walker V1.0 prototype was Autodesk Maya 2018. A simple 3D mesh was modelled, textured and rigged to approximate the look and mechanics of a duck’s walk. NURBS controllers parent constrained joints in the feet, spine and neck areas to allow movement of the skinned joints.


Simple duck mesh, texturing and rig prototype.

Instead of keyframing curves by hand, a Python script plugin was written to generate keyframes depending on the desired parameters (code available on Github). The GUI below shows the options the user has when running the Python script. First the Animation Start and End Frames are established, together with the Frames per Second (FPS). By default, these values are 0, 120 and 24 respectively. When generating the walk cycle, a keyframe is added automatically for all the controllers every 3 frames to help create a smooth animation.



The next values, Amplitude, Speed, Weight and Direction control the qualities of movement for the duck walk cycle. Weight and Direction are directly linked to the Laban efforts, mentioned in the Background section. Amplitude is the length of the stride, while Speed is how fast the duck goes. Unfortunately the latter parameter didn’t work out as expected and the default value of 5 that the plugin starts with is the best looking option.


Amplitude compensates for the issue with the Speed parameter, as a large stride coincides with a faster walk, since more ground is covered in the same amount of time as a smaller stride. The slider value varies between a low and a high Amplitude. This is mapped to the respective small and large step sizes. The forward translation is then calculated as a function of the amplitude and speed of the character. The side view images below show two frames from the Amplitude = 1 and Amplitude = 10 respectively. An amplitude of zero would result in no movement as the step size is 0.


Frames 1 (right) and 36 (left) of the generated walk at Amplitude = 1


Frames 1 (right) and 36 (left) of the generated walk at Amplitude = 10


A low Weight value on the available slider represents a light weight, while a high value is a strong/heavy weight. A light weight is similar to a feather floating through the air, while a heavy weight is like the sturdy step of an elephant. I added some additional bounce in the duck’s step for a lightweight animation. When the weight is heavy, the duck’s movement is closer to the ground, since it’s more affected by gravity. The side view images below show two frames from the minimum and maximum Weight values, 0 and 10 respectively. Notice the bounce in the step for the low weight value.


Two frames of the generated walk at Weight = 0


Two frames of the generated walk at Weight = 10


Direction is direct for low slider values and indirect for high values. A direct motion is converted into little or no body rotation around the vertical (Y) axis. An indirect motion has more rotation around the Y axis, as well as some supporting side to side X axis translation. The side view images below show two frames from the minimum and maximum Direction values, 0 and 10 respectively. Notice the exaggerated sway in the second image when the movement is indirect.


Two frames of the generated walk at Direction = 1


Two frames of the generated walk at Direction = 10


Once these parameters were established, the question was whether combinations of them would reveal complex behaviour. There are many ways to express behaviour and personality, but among the most common ones are emotions. Two out of the six basic emotions described by Paul Ekman (1992) were chosen, joy and sadness. Attempts were made to recreate these emotions on top of the neutral walk cycle of the duck. The neutral state was estimated at Amplitude = 5, Speed = 5, Weight = 5 and Direction = 0. 

The available parameters were mapped to the parameters suggested in Melzer et. al (2019) for recreating emotions in humans. For example, their paper mentions that joy was recognized by participants in their study when elements like lightness, jumping and rising movements were observed. These could be replicated easily with small weight value, specifically Weight = 1.

Sadness, on the other hand, was recognized in Melzer et. al (2019) as passive, sinking weight along with other parameters. A high value, Weight = 9, was used for recreating this effect. Amplitude and Direction were also experimented with, but did not offer significant results in expressing joy or sadness.

Animation Graphs and Code

Once the desired parameters are established, for example Amplitude = 5, Speed = 5, Weight = 1, Direction = 0 the Generate button is pressed in the GUI Python plugin. This activates a sequence of functions that reset controller values and extract values from the GUI fields. These values are then fed into the generateWalk() function. A snipped of this function is shown below. 

Notice that trigonometric functions like sine and cosine are used with an angle theta as a parameter. This angle increases depending on the current frame and frames per second. The Amplitude parameter influences the amplitude of the trigonometric functions, resulting in the step size. Looking at lines 4 and 5 below, the variables currentFootTranslationY and currentFootTranslationZ are the coordinates for a point moving along an ellipse.

The ellipse flattens when touching the ground, as conditioned in lines 8 to 11. The resulting curve is the trajectory for the left foot inverse kinematics (IK) handle. The joint angles for the rest of the leg are calculated automatically by Maya’s Rotate Plane IK Solver. An example of the left foot animation graphs can be seen in the first image below.

The spine translation along the Y axis factors in the inverse Weight parameter. The sine wave graph that results shifts between higher and lower average values depending on whether the Weight is low or high respectively. An example of the Translate Y animation graph for a low Weight value can be seen in the second image below.

Notice that the maximum value is 1.5, while the minimum value is -0.5. This translates visually to the character bouncing up more than it gravitates towards the ground. Finally, in the third image you can see the animation graph for the Rotate Z values for the spine, which is directly proportional to the Rotate Y variable. The latter is the side to side movement of the spine, given by a cosine function.    

rotationAmplitude = amplitude * extraAmpFactor
currentLeftFootTranslationX = (amplitude / 3.0) * weight * math.fabs(math.sin(0.5 * teta))
currentRightFootTranslationX = currentLeftFootTranslationX - amplitude                
currentFootTranslationY = -amplitude * math.sin(teta) / asq
currentFootTranslationZ = amplitude * math.cos(teta) / bsq
currentLeftFootRotationX = -rotationAmplitude * math.sin(teta) / 2.0

if (currentFootTranslationY < 0):
    currentFootTranslationY = 0
if (currentLeftFootRotationX < 0):
    currentLeftFootRotationX = 0  
currentLeftToeTranslationY = -currentFootTranslationY / asq
currentLeftFootTranslationZ = currentLeftToeTranslationY / asq                           
currentSpineTranslationX = currentLeftFootTranslationX - amplitude / 2.0                
currentSpineTranslationY = (weightCosValue / 2.0) + invWeight * math.sin(2 * teta) / asq 
currentSpineRotationY = -weight * rotationAmplitude * math.cos(teta)
currentSpineRotationZ = currentSpineRotationY / 3.0
currentTailRotationY = currentSpineRotationY / 2.0
#Assign values to controllers


Fourteen combinations of low and high parameter values for Amplitude, Weights and Direction were made. The resulting animations were playblasted out of Autodesk Maya and uploaded as private videos on Youtube. These videos were then inserted into a Google Forms survey with thirty questions.

At the start and end of the survey, participants were asked how happy they were. This was to check whether the duck animations had any effect on the overall state of the observers. At the start, 73.5% of the participants were above 5 on a scale from 1 (not happy) to 10 (super happy). At the end, 79.4% of the participants were above 5. Although the change is not significant, it does show a tendency towards a more cheerful disposition after watching procedurally animated ducks.   

For each of the fourteen videos, participants were asked to name the emotion they thought the video expressed, with Happy, Sad, Angry, Fearful, Disgusted, Neutral and Other as potential answers. They were then asked whether the duck in the video was funny.

Thirty four participants answered the questionnaire anonymously. The most successful question was the one for video four (Amplitude = 5, Speed = 5, Weight = 1, Direction = 0). Over 90% of the participants recognized the light Weight animation as a Happy movement. In the graph below Excited was classified as Happy.

94% of the participants also found this animation as funny, with a score of 5 or above, where 1 is not funny, while 10 is super funny. 61% of participants gave a score of 7 or above to the same question. This result repeated itself for both the emotion and the degree of funniness for duck 12 (Amplitude = 7, Speed = 5, Weight = 1, Direction = 5), but to a lesser degree. About 67% of participants found the duck Happy, while over 85% said the duck was funny.

Duck 4 walk cycle results

Sadness and fear were often found at similar percentages of influence. For example, video thirteen (Amplitude = 3, Speed = 5, Weight = 9, Direction = 0) was classified as Sad by 38% of the participants, while 41.2% classified it as Fear. This was triggered by a high weight while walking, with the respective parameter Weight = 9. This observation is backed up by the sinking motion described by Melzer et al (2019) when defining sadness.

It is worth noting that Amplitude has an influence in the results. Videos five and thirteen both had Weight = 9, but only the latter was classified as sad and fearful. Amplitude = 5 for video five, while Amplitude = 3 for video thirteen. This might be linked to the enclosing behaviour recognized in fear and passive weight specific to sadness (Melzer et. al 2019).

Discussion and Conclusion

This report illustrated the creation of a procedural walk cycle for a duck character with the option of varying the movement style through a set of parameters (Amplitude, Weight and Direction). Two of these parameters, Weight and Direction, are linked to Laban’s efforts of movement. In specific combinations, Laban’s efforts have been shown to convey emotions. Thus the duck walk cycles can be nuanced through such emotions.

The survey results were conclusive only for the expression of joy, with sadness coming second. The most indicative parameter of such emotions was Weight. Low weights have been found to illustrate happiness, while high weights are more representative of sadness. These results are similar to the characteristics given to such emotions in a study on human movement and the link to emotions (Melzer et. al 2019). Moreover, people were more prone to find a duck funny when it was displaying a happy walk cycle.

More work is needed, however, to further understand the mechanics of stylized walk cycles, the emergent theories behind emotions and what comprises a funny behaviour. In the future, comparisons can be done with similar techniques from the field of physics simulations or machine learning algorithms, rather than purely mathematical procedural animation.

It must be said, however, that this report shows how simple movements have the potential to convey complex behaviours. Along with emergent theories, procedural animation could unlock nature’s hidden patterns of movement using the simplest of tools. In other words, we are slightly closer to discovering the “movement code” of a duck, which opens possibilities for other, more complicated beings, maybe even humans.


  • Bishko, L. 2014. Animation Principles and Laban Movement Analysis: Movement Frameworks for Creating Empathic Character Performances. Research Showcase at Carnegie Mellon University: Nonverbal Communication in Virtual Worlds: Understanding and Designing Expressive Characters.
  • Ekman, P. (1992). An argument for basic emotionsCognition and Emotion, 6(3-4), 169–200. [Link here]
  • Laban, R., Ullmann, L. (2011). The Mastery of Movement, Fourth Edition. A Dance Books Publication.
  • Melzer Ayelet, Shafir Tal, Tsachor Rachelle Palnick. (2019). How Do We Recognize Emotion From Movement? Specific Motor Components Contribute to the Recognition of Each Emotion. Frontiers in Psychology, Volume 10, 2019, Pages 1389, DOI=10.3389/fpsyg.2019.01389, ISSN=1664-1078 [Link here]
  • Reynolds, Craig W. (1987). Flocks, herds and schools: A distributed behavioral model. SIGGRAPH Comput. Graph. 21, 4 (July 1987), 25–34. DOI:
  • Laughing Matters | Comedy Documentary  | Earful Comedy. (1985). Redistributed by Earful Comedy, narrated and starring Rowan Atkinson [Video] [Link here]
  • The Logic of Movement. Workshop by Stephen Mottram as part of the Pupeteering Festival, Bristol 2017.
Research & Play

Tech for Good

Dramatic intro: It is quiet on the streets of Manchester. Fewer and fewer cars flow rapidly over the rain spotted streets. The trees are in full bloom, but few are there to see them. At least birds can now take a breath of fresh air. Ironically, the climate will benefit from the world’s cry. Greta kept saying that our house is on fire, but only now do we see the fire. It’s a microscopic fire, spreading more fear than fevers, but a threat nevertheless.

A few days ago I thought the world doesn’t need anymore technology. We are too disconnected, mentally and spiritually distanced from each other. And now, in the middle of a pandemic, we will be even more isolated. Again, ironically, the root of our social distancing can bring us together during our physical distancing. Although we might end up having walls between each other, we can break the virtual walls and say hello once more! Or if you have balconies, sing together like our friends from Italy.

Here is a list (that can be updated by me or you) of ways technology can bring us together during this world isolation experiment. Disclaimer, I am not advertising these apps! 🙂

  1. Teach your grandma to use Whatsapp before going into lockdown.
  2. Pray together using conferencing applications like Zoom (up to 50 people, free for up to 40 minutes / ~15 $/month per host for unlimited time) [Useful link here].
  3. Improvise together using Google Hangouts (up to 25 people, free). [Useful link here]
  4. Sing together using Skype or other apps (up to 25 people, free). [TED Talk inspiration here]
  5. Create animations online with anyone from anywhere with Artella (free to sign up). [Website link here]
  6. Play video games online with your buddies. [Useful link here]
  7. Share stories with your writer’s group using Google Docs/Dropbox and comment on each other’s work.
  8. Read dramatic stories to each other using Discord (free). Or join an online book club. [Useful link here]
  9. Watch films with 3 friends (at the same time) using a Premium Netflix package (~3 pounds/person per month).
  10. Solve maths problems using a collaborative online whiteboard (free, up to 5 people/ 5$/month for unlimited participants). [Useful link here]
  11. Artists, teachers, scientists, gregarious opinionated debaters, live stream your work and ask for donations, especially if you’re going through a hard time! [Useful link on apps | Useful links on getting paid]
  12. Write your own novel, inspired by the events from today by using online writing tools like Ulysses. [Useful links here]


Please comment if you have more ideas and I can add them to the list! Take care of each other my friends!

Acting & Improv, Research & Coding, Research & Play

World Problems: Ep.1 – Global Warming and the Magic Box Designs

“Scientists have recently determined that it takes approximately 400 repetitions to create a new synapse in the brain – unless it is done with play, in which case, it takes between 10 – 20 repetitions.” (Dr Karin Purvis)

Motivation of World Problems Series

I’m starting Ana’s Research and Play with Episode 1 of the World Problems (WP) series. WP will have longer episodes (~15 mins) that combine ideation, design, prototyping and testing of sometimes crazy inventions. It is intended to experiment with possible solutions to help “save” the world. The approach is a playful one, rather than a worried and tense one. The reasoning is my belief that people achieve their best when fear of failure is out of the way.

The inventions that result from this series might or might not be viable. In this sense, WP presents a humble method to saving the world. My ambition is not to come up with precise inventions that will give accurate results (although they are very welcome). In my experience, having such pressures, under the constraint of limited time, leads to mediocre solutions and headaches. What I am trying to do is follow my curiosity and allowing myself to both innovate and fail (first attempt at learning).     

In the best case scenario, the world will benefit from an invention. Worse case scenario, I will have brainstormed some ideas that fill people with such indignation at my nerve, that they’ll just go and make their own creations. Empathy also motivates me and it is necessary to prevent an attitude of carelessness and lack of responsibility. It is important, however, to use empathy as a driving energy rather than an energy draining one. We should all make a contribution to saving the world we live in, but it mustn’t destroy us in the process – unless it’s a sacrifice of love, but that’s a different story. Let’s begin!  

Episode 1 Summary

In this episode I come up with a few crazy designs to help save the world from global warming, by using random household items. It all starts with choosing the problem out of a list of possible world problems. I then have a warm up (of my mind, not the world) by finding different uses for household items via lateral thinking.

The Magic Box, which is often seen in clowning exercises comes into play. This leads to shotfire brainstorms from Experimental Ana, who gives up grammar for creativity. It all ends with a set of crazy invention designs (see below). One of them or a combination of up to three of them could be prototyped in the future.

The Research

Episode 1 is linked more to brainstorming ideas, but research elements also find their way through. Please see the video description for the references used. Here are some research inspired elements from the video.

  • Choosing the problem
  • Motivation of play based approach
  • Review of a few accidental discoveries
  • Background on Lateral Thinking
  • Ideation of designs
  • Designing possible prototypes

The Play

The structure of Episode 1 is linked to an improv game called Fix it MacGyver! In this game, a character called MacGyver is given a problem and three random items. He or she has to come up with a solution to fix the problem by utilizing the given items.

For example, let’s say someone’s house is on fire. MacGyver has a cat, a sandwich and a chainsaw. One solution is of course to use the cat as a scout to check if there are any survivors. The chainsaw can be used to cut through the fallen parts of the house, so that the trapped victims can be reached. Once they are out, a sandwich is provided for nutrition, while waiting for the firemen.

The idea of the game is not to “get it right”, since there are “no mistakes, just opportunities in improv” (Tina Fey). Letting your thoughts imagine the wildest solutions is very liberating because it cuts out inner criticism. What improvisers experience with this game is also linked to Julia Cameron’s theory, described in her book The Artist’s Way. She recommends evading the inner critic by free writing three pages of whatever comes to mind every morning.

My Experimental Ana from the video uses this technique of free and spontaneous thought. Censoring of ideas is kept to a minimum, giving priority to the joy of discovering where my own thoughts take me. In the paraphrased words of Keith Johnstone, one of the pillars of improv, “You must trust that your mind, God or the giant moose will tell you what to say.”

The elements of play in Episode 1 are the following:

  • Defining the game guidelines (box of objects + find different uses for them)
  • Magic box game linked to clowning exercise
  • Lateral thinking solutions to a problem breaks patterns of thinking
  • Experimental Ana uses free and spontaneous thought
  • Experimental Ana uses jump and justify improv technique (say the word first and then justify its meaning)
  • Creating designs with commitment


After the research and play collaboration, seven designs emerged. These are not necessarily viable designs, but they open up a world of possibilities! Please have a look and tell me which of these designs you would like prototyped in the future!


Research & Play

Research is not Academia

After a few years of a rather painful experience as a PhD student, or should I say Engineering Doctorate (EngD) student, which is even more demanding, I have some thoughts I’d like to share. I do not wish to be ungrateful for the experience and support I have been given, financially and on an advisory level, but there are some things that have changed my feeling towards what I though academia was.

Firstly, why do I say doing an EngD is even more demanding than doing a PhD? It’s because after one year of confusion, where you’re deciding on which topic you would like to pursue, you end up doing something marginally close to your heart. Don’t get me wrong, I have colleagues who absolutely love what they do and it is a pleasure to see their progress throughout the 4 years. But these cases are very rare and often people settle to working on a project they like, but don’t feel passionate about. One year at university, one year where you make friends and where you find out what makes you tick and then you move to a different city where you have to start over. When it works, it can be miraculous and you also get industry experience, awesome! But when it doesn’t work so well, one can feel very isolated.

I feel I’m complaining a lot these days and I do not like this sobby version of Ana, I do apologize. I wish I could restore myself to a previous version, the version that fell in love with animation after watching Finding Nemo in 2004. My life, unfortunately, does not have version control so I have to keep overriding my mind with happier experiences. If doing an EngD isn’t hard enough, try adding a broken relationship, changing supervisors 3 times and your company shutting down after 2 years on placement (for an EngD, placement lasts for 3 years).

The biggest obstacle, however, is realizing that there is a huge difference between research and academia. The word itself, “research” means to search again, to fail a lot (Thomas Edison comes to mind), to discover, to explore, to create. Academia means to publish. Research is, or at least should be, the biggest part of academic studies. Students should be encouraged and helped, especially at the beginning of their PhD to “do the scary thing, fight the monster” (Jacob Banigan) and fail happily. Linking in to the philosophy I have learnt in improvised theatre, “There are no mistakes, just opportunities.” (Tina Fey).

Constraints like time running out, feeling guilty for wasting resources, the impostor syndrome generate fear or at least stress. A mind subdued to long periods of negative emotions such as these will not be inclided to create. I am currently talking from personal experience and from what I have observed, but I am sure there are a lot of references out there, for the more scrutinous amongst us. The high level of depression in academia is very real and has been overlooked far enough.

I believe there should be a change, a revolution even, in the way academia is structured. My inspiration is improvised theatre (impro), where I have found a supportive and fun atmosphere to explore storytelling and ideas. Keith Johnstone, one of the founders of this art form introduced the notion of “happy fail”, where we actually celebrate failure. In impro, people say “yes and”, in academia people say “yes but”. In impro people collaborate, in academia people compete. In impro people actively play and interact, in academia people sit at conferences with an invisible wall between them and the presenter. Lastly, in impro, people feel happy to explore, in academia, people feel fearful to share their work.

I don’t know about you, but I am starting to see a pattern emerging. Academia should be about research and research should be about exploration, collaboration, discovery, creation and, most importantly, happy fails. Results should be just as important as failures. Ideas should be just as important as publications. Funding shouldn’t be seen as a salary, but as support towards the creative process. People shouldn’t be telling audience members what they’re doing, but should be actively showing them and bringing them into their circle.

Alas, where are the days when pubs used to be public places, where people would share their amazing discoveries informally, where the truth really comes out? Of course, some level of formality is required, we don’t want a group of drunk academics playing football with the audience, while explaining relativity. But we do need to make research fun again and only then will it become truthful, because “the truth is funny” (Del Close) and also memorable.