Building Cybiosis Intuition
Cybiosis 2: Essential Elements
In the past few posts we’ve explored two new concepts: the HMI Spectrum which considers various factors important in assessing neurotech, and cybiosis, a user-centred descriptor to define Human-Tech interactions. In this article we will expand on the metrics we use for cybiosis and apply these concepts to assessing MRIs.
To understand the relationship between humans and tech and define a technology’s cybiosis, we look at four main categories: Applicability, Data Quality, Adoptability, and Scalability.
Most of the follow-up discussions to my recent articles on cybiosis have centred around the definition of these categories - so let’s talk about where they came from and why they’re important.
The factors important in Cybiosis, organised into Applicability, Data Quality, Adoptability, and Scalability, are the core of cybiosis as they are central to the impact of the technology, and therefore often guide research, development, and commercialisation.
Applicability
How Useful Is This Data?
Let’s first talk about the type of data we are recording. It’s not just about the amount or quality of the data we collect, but how useful the type of data is.
What does this data actually allow us to do? How does this data improve or grow our understanding? How can we use the information we get out of this data? Will the output help us to refine our daily routines, enhance our health, or support diagnostic processes?
Asking these questions helps understand how certain technologies contribute to the user’s life.
To get a bit more intuition on this, lets look at some Application-Specific Questions:
Health and Wellbeing Applications: How can this data improve our health, productivity, or well-being? How can this inform treatment, management, or diagnostics to help improve prognosis or symptoms?
Building Knowledge: Does this data contribute to long-term health insights and advancements?
Solving Pains: How can this data be applied to make the Hard Things in life easier or more enjoyable?
Technologies that provide practical applications, long-term health insights, and significant life improvements have High Cybiosis. They add substantial value to users' lives, making the integration of the technology impactful.
The stronger the potential usability of the solution, the higher the Cybiosis would be.
Data Quality
What is the Data Quality Like?
Another important concept of the actual data we are recording is the quality of the data. If the type of data has an amazing application but the system conducts low quality recordings, then we may not be able to use the application as regularly or as accurately.
High-quality data is often linked with questions like: How much do I know about where the signal comes from? How certain am I that the signal is the actual signal, and not impacted by noise (other things going on or accidentally measuring nearby things like power lines)? How consistently will I get useable signal that will work correctly for my application?
We need to have a clear signal, that is ideally not too clouded by noise from external factors or internal biases.
These ideas can be summarised by Three Big Questions:
Precision: Is the data detailed and accurate?
Consistency: Is the output from this data reliably similar across different conditions and users?
Reliability: What percentage of the data produced will be usable? Will we be able to use 20% or 80% of the signals from a recording?
Technologies that ensure high precision, consistency, and superior comparison to other methods enhance cybiosis. Reliable, high-quality data directly contributes to more useful data, which can improve health outcomes and be leveraged for more applications, often making the technology more valuable.
Adoptability
How Is This Data Collected?
How we collect data has a massive impact on the user experience and the practicality of the technology in daily life. Adoptability focuses on how simple and convenient it is for users to interact with the technology.
These ideas can be further explored by thinking about ease of use, user comfort, and the cost of data collection. As these questions quickly get complicated, I’ve added more information for each question:
Ease of Use: How simple and safe is the process for users?
The ideal method should be straightforward, safe, and require minimal effort or technical knowledge from the user. High cybiosis ease of use examples include portable wearable devices that can be easily worn and removed, and tech that can be easily and safely implanted and last multiple years.
User Comfort: Is the method non-invasive and comfortable?
Data collection should ideally cause no discomfort or inconvenience. Methods that integrate seamlessly into a user’s routine, like contactless sensors or devices that can be worn comfortably for long periods, enhance cybiosis.
Cost Efficiency: Are the methods cost-effective for widespread use?
Affordability is crucial for broad adoption. If a data collection method is prohibitively expensive, it may be limited to niche markets or clinical settings, reducing its overall cybiosis.
In high cybiosis technologies, users can collect data easily and inexpensively, while doing other things. Ideally, they are effortlessly integrated into our day-to-day lives, causing little to no disruption. They are intuitive, comfortable, and cost-effective.
In low cybiosis technologies, users have to go through a hassle or discomfort, spend money, and may even need professional help to collect the data.
Scalability
How Scalable is This Solution?
Scalability within neurotech is about creating solutions that can be widely adopted and make a significant impact across various populations. Scalable solutions should operate well with different people, using the technologies in a variety of environments and conditions.
There are multiple questions at the heart of scalability:
Market Penetration: Can the technology reach a broad audience?
Accessibility: Is it affordable and easy to use for the average person?
Integration: How well do these methods fit into existing infrastructure?
Global Impact: Can this technology make a difference on a global scale?
As these are important questions that can hard to wrap our heads around, lets answer them directly:
A scalable technology should have the potential to be adopted by a wide range of users, not just a niche segment. This involves considering the diverse needs and circumstances of potential users.
Similarly, scalability is closely tied to affordability and adoptability. If a technology is too expensive or complicated, it will struggle to achieve widespread adoption.
The more seamlessly a technology integrates into existing systems and routines, the higher its scalability. This includes how well it works with other technologies, integrates with existing infrastructure (such as healthcare systems) and how easily users can interpret and act on the data it provides.
The ultimate test of scalability is its potential to improve lives around the world. This includes considerations of how well the technology can be deployed in different regions, with varying levels of infrastructure and resources.
As you can likely tell from the questions above, scalability has a lot of integration with other categories, and gives us a place to directly consider how some of the other categories would be applied on a population level instead of a single user: Are there multiple applications enabled by this device? Is the device comfortable for different populations? Is the data reliable across different groups? Does this potentially democratise a current technology that we use, making it available for more people in a way that is easier to use, cheaper to access, involves less user effort, or is more generalisable?
Scalable technologies are those that can overcome obstacles around cost, complexity, and/or geography to make a meaningful impact on a global scale. They are designed with the broader market in mind, ensuring that they can be adopted and utilized by a wide audience, thus achieving high cybiosis.
Zooming Out
As these factors are often already used in decisions ranging from who gets grants to what bets investors make to what tech gets doctors excited to what tech startups decide to pivot to, it’s important to directly identify and discuss these ideas. I hope that these can help you assess your tech, other people’s tech, and better understand the promise of new and exciting ideas.
Am I missing an idea that is constantly in the back of your mind? Are there any vital questions not included in this overview? Let me know!
My name is Abby Holland. I am a Product Owner at IDUN Technologies, a consumer neurotech company building EEG-augmented headphones. I have been building neurotech since 2018, and have a background in BioMechanical Engineering and Neurological Disorders. Follow me on Twitter @holland_neuro for realtime commentary on new neurotech developments.
Interesting read again! Now i'm wondering about potential feedback loop between these tech & our neural adaptations that might actually enhance cybiosis over time. What timeline comes to your mind?
Great read! I appreciate how you've broken down the assessment of neurotechnology into these four key categories. As someone working in neural security, I find this framework particularly insightful. Your approach reinforces the importance of a holistic, patient/consumer-focused approach to neurotechnology development. It's crucial that privacy, security, and ethics are integrated from the start, rather than being afterthoughts. These considerations should be key drivers in how we assess and advance neurotechnology. Thank you for providing this valuable perspective on evaluating neurotechnology. It's a great tool for ensuring we're developing these technologies responsibly and with the user's best interests in mind.