Category: neurodivergence

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Note: If you haven’t already read my previous post, I’d recommend you give it a quick scan as I cover some material which I make reference to below.

So you’ve got some access to AI tools and sort of know how they work. But what are they for? I know sometimes big tech can meet education with a solution looking for a problem and I’m keen to be clear-eyed about how we review “innovation”. I think there are some genuine use cases which I’ll outline a bit below. It’s worth noting that engagement with AI tech is deceptively simple. You can just write a question and get an (uncannily good sounding) answer. However, if you put in some time to craft your interaction, you’ll find that the quality rises sharply. Most people don’t bother, but I think that in academia we have enough bespoke situations that this might be warranted. In this article I’ll also detail a bit of the learning and investment of time that might be rewarded for each scenario. Here are, as I see them, some of those use caess:

1. Transcribe audio/video

AI tools like whisper-AI, which can be easily self-hosted with a fairly standard laptop, enable you to take a video or audio file and convert it very quickly to very accurate text. It’s accurate enough that I think the days of qualitative researchers paying for transcription are probably over. There are additional tools being crafted which can separate text into appropriate paragraphs and indicate specific speakers on the transcript (person 1, person 2, etc.). I think that it’s faster for most of us to read / skim a transcript, but also, for an academic with some kind of hearing or visual impairment, this is an amazingly useful tool. See: MacWhisper for a local install you can run on your Mac, or a full-stack app you can run as a WebUI via docker in Whishper (formerly FrogBase / whisper-ui).

Quick note: the way that whisper has been developed makes it very bad at distinguishing separate speakers, so development work is quite actively underway to add on additional layers of analysis which can do this for us. You can get a sense of the state of play here: https://github.com/openai/whisper/discussions/264. There are a number of implementations which supplement whisper-ai with pyannote-audio, including WhisperX and whisperer. I haven’t seen a WebUI version yet, but will add a note here when I see one emerge (I think this is underway with V4 of whishper (https://github.com/pluja/whishper/tree/v4). Good install guide here: https://dmnfarrell.github.io/general/whisper-diarization.

2. Summarise text

Large language models are very good at taking a long chunk of text and reducing it to something more manageable. And  it is reasonably straight-forward to self-host this kind of service using one of the 7B models I mentioned in the previous host. You can simply paste in the text of a transcript produced by whisper and ask a Mistral-7B model to summarise it for you using LMStudio without too much hassle. You can ask things like, “Please provide a summary of the following text: <paste>”. But you might also benefit from different kinds of presentation, and can add on additional instructions like: “Please provide your output in a manner that a 13 year old would understand” or “return your response in bullet points that summarise the key points of the text”. You can also encourage more analytical assessment of a given chunk of text, as, if properly coaxed, LLMs can also do things like sentiment analysis. You might ask: “output the 10 most important points of the provided text as a list with no more than 20 words per point.” You can also encourage the model to strive for literal or accurate results: “Using exact quote text from the input, please provide five key points from the selected text”. Because the underlying data that LLMs are trained on is full of colloquialisms, you should experiment with different terms: “provide me with three key hot takes from this essay” and even emojis. In terms of digital accessibility, you should consider whether you find it easier to get information in prose or in bulleted lists. You can ask for certain kinds of terms to be highlighted or boldface.

All of this work writing out questions in careful ways to draw out more accurate or readable information is referred to by experts as prompt engineering, and there is a lot of really interesting work being done which demonstrates how a carefully worded prompt can really mobilise an AI chatbot in some impressive ways. To learn more about prompt engineering, I highly recommend this guide: https://www.promptingguide.ai.

It’s also worth noting that the questions we bring to AI chatbots can also be quite lengthy. Bear in mind that there are limits on the number of tokens an AI can take in at once (e.g. the context length), often limited to around 2k or 4k words, but then you can encourage your AI chatbot to take on personality or role and set some specific guidelines for the kind of information you’d like to receive. You can see a master at work on this if you want to check out the fabric project. One example is their “extract wisdom” prompt: https://github.com/danielmiessler/fabric/blob/main/patterns/extract_wisdom/system.md.

You can also encourage a chatbot to take on a character, e.g. be the book, something like this:

System Prompt:


You are a book about botany, here are your contents:
<context>

User Query: "What are you about?"

There are an infinite number of combinations of long-form prose, rule writing, role-playing, custom pre-prompt and pre-fix/suffix writing which you can combine and I’d encourage people to play with all of these things to get a sense of how they work and develop your own style. It’s likely that the kid of flow and interaction you benefit from is quite bespoke, and the concept of neurodiversity encourages us to anticipate that this will be the case.

There are some emerging tools which do transcription, diarisation of speakers and summarisation in real time, like Otter.AI. I’m discouraged by how proprietary and expensive (e.g. extractive) these tools are so far, and I think there’s a quite clear use case for Universities to invest time and energy, perhaps in a cross-sector way, to develop some open source tools we can use with videoconferencing, and even live meetings, to make them more accessible to participation from staff with sensory sensitivies and central auditory processing challenges.

3. Getting creative

One of the hard things for me is often the “getting started” part of a project. Once I’m going with an idea (provided I’m not interrupted, gasp!) I can really move things along. But where do I start? Scoping can stretch out endlessly, and some days there just isn’t extra energy for big ideas and catalysts for thinking. It’s also the case that in academia we increasingly have less opportunities for interacting with other scholars. On one hand this is because there might not be others with our specialisation at a given university and we’re limited to conferences to have those big catalytic converastions. But on the other hand, it’s possible that the neoliberalisation of the University and marketisation of education has stripped out the time you used to have for casual non-directed converastions. On my campus, even the common areas where we might once have sat around and thought about those things are also gone. So it’s hard to find spaces, time and companions for creativity. Sometimes all you’ve got is the late hours of the night and you realise there’s a bit of spare capacity to try something out.

The previous two tasks are pretty mechanical, so I think you’ll need to stick with me for a moment, but I want to suggest that you can benefit from an AI chatbot to clear the logjam and help get things flowing. LLMs are designed to be responsive to user input, they absorb everything you throw at them and take on a persona that will be increasingly companionable. There are fascinating ethical implications for how we afford agency to these digital personas and the valence of our relationships with them. But I think for those who are patient and creative, you can have a quite free-flowing and sympathetic conversation with a chatbot. Fire up a 7B model, maybe Mistral, and start sharing ideas and open up an unstructured converastion and see where it takes you. Or perhaps see if you can just get a quick list to start: “give me ten ideas for X”.

Do beware the underlying censorship in some models, especially if your research area might be sensitive, and consider drawing on models which have been fine-tuned to be uncensored. Consider doing some of the previous section work on your own writing: “can you summarise the key points in this essay?” “what are the unsubstantiated claims that might need further development?”

There’s a lot more to cover, but this should be enough to highlight some of the places to get started, and the modes of working with the tools which will really open up the possibilities. In my next post, I’ll talk a bit about LLM long-term memory and vector databases. If you’re interested in working with a large corpus of text, or having a long-winded conversation preserved across time, you might be interested in reading more!

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I’ve spent the last several months playing with AI tools, more specifically large language (and other adjacent data) models and the underlying corpus of data that formed them, trying to see if there are some ways that AI can help an academic like me. More particularly, I’m curious to know if AI can help neurodivergent scholars in large bureaucratic Universities make their path a bit easier. The answer is a qualified “yes”. In this article, I’ll cover some of the possible use cases, comment on the maturity, accessiblity and availability of the tech involved and explain some of the technological landscape you’ll need to know if you want to make the most of this tech and not embarrass yourself. I’ll begin with the caveats…

First I really need to emphasise that AI will not fix the problems that our organisations have with accessiblity – digital or otherwise – for disabled staff. We must confront the ways that our cultures and processes are founded on ableist and homogenous patterns of working, dismantle unnecessary hierarchies, and reduce gratuitous beurocracy. Implementing AI tools on top of these scenarios unchanged will very likely intensify vulnerability and oppression of particular staff and students and we have a LOT of work to do in the modern neoliberal University before we’re there. My worst case scenario would be for HR departments to get a site license to otter.AI and fire their disability support teams. This is actually a pretty likely outcome in practice given past patterns (such as the many University executives which used the pandemic as cover to implement redundancies and strip back resource devoted to staff mental health support). So let’s do the work please? In the meantime, individual staff will need to make their way as best as they can, and I’m hoping that this article will be of some use to those folx.

The second point I need to emphasise at the outset is that AI need not be provided through SAAS or other-subscription led outsourcing.Part of my experimentation has been about tinkering with open source and locally hosted models, to see about whether these are a viable alternative to overpriced subscription models. I’m happy to say that “yes”! these tools are relatively easy to host on your own PC, provided it has a bit of horsepower. Even more, there’s no reason that Universities can’t host LLM services on a local basis at very low cost per end user, vastly below what many services are charging like otter.AI’s $6/mo fee per user. All you need is basically just a bank of GPUs, a server, and electricity required to run them.

What Are the Major Open Source Models?

There are a number of foundational AI models. These are the “Big Ones” created at significant cost running over billions of data points, by large tech firms like OpenAI, Microsoft, Google, Meta etc. It’s worth emphasising that cost and effort are not exclusively bourne by these tech firms. All of these models are generated on the back of freely available intellectual deposit of decades of scholarly research into AI and NLP. I know of none which do not make copious use of open source software “under the hood.” They’re all trained on data which the general public has deposited and curated through free labour into platforms like wikipedia, stackexchange, youtube, etc., and models are developed in public-private partnerships with a range of University academics whose salaries are often publicly funded. So I think there is a strong basis for ethically oriented AI firms to “share alike” and make their models freely available, and end users should demand this. Happily, there have been some firms which recognise this. OpenAI has made their GPT1 and GPT2 models available for download, though GPT3 and 4 remain locked behind a subscription fee. Many Universities are purchasing GPT subscriptions implicitly as this provides the backbone for a vast number of services including Microsoft’s CoPilot chatbot, which have under deployment to University staff this last year as a part of Microsoft’s ongoing project to extract wealth from the education sector in the context of subscription fees for software (Microsoft Teams anyone?). But it doesn’t have to be this way – there are equally performant foundational models which have been made freely available to users who are willing to hack a bit and get them working. These include:

  • LLaMA (Language Learning through Multimodal Adaptation), a foundation model developed by Meta
  • Mistral (a foundation model designed for mathematical reasoning and problem-solving), which has been the basis for many other models such as NeuralChat by Intel.
  • Google’s Gemini and BERT models
  • BLOOM, developed by a consortium called BigScience (led by huggingface primarily)
  • Falcon, which has been funded by the Abu Dhabi sovereign wealth fund under the auspices of Technology Innovation Institute (TII)
  • Pythia by EleutherAI
  • Grok 1 developed by X.ai

These are the “biggies” but there are many more smaller models. You can train your own models on a £2k consumer PC, so long as it has a bit of horsepower and a strong GPU. But the above models would take, in some cases, years of CPU time for you to train on a consumer PC and have billions or even trillions (in the case of GPT4) parameters.

What Do I Need to Know About Models? What can I run on my own PC?

To get a much broader sense of how these models are made and what they are I’d recommend a very helpful and accessible write-up by Andreas Stöffelbauer. For now it’s worth focussing on the concept of “parameters” which reflects the complexity of the AI model.You’ll usually see this listed next to the model’s name, like Llama7B. And some models have been released with different parameter levels, 7B, 14B, 30B and so on. Given our interest in self-hosting, it’s worth noting that parameter levels are also often taken as a proxy for what kind of hardware is required to run the model. While it’s unlikely that any individual person is going to train a 30B model from scratch on their PC, it’s far more likely that you may be able to run the model after it has been produced by one of these large consortia that open source their models.

Consumer laptops with a strong GPU and 16GB of RAM can generally run most 7B parameter models and some 10G models. You’ll need 32GB of memory and a GPU with 16GB of VRAM to get access to 14B models, and running 30B or 70B models will require a LOT of horsepower, probably 24/40+ GB RAM which in some cases can only be achieved using a dual-GPU setup. If you want to run a 70B model on consumer hardware, you’ll need to dive the hardware discussion a bit as there are some issues that make things more complex in practice (like a dual-GPU setup), but to provide a ballpark, you can get second hand NVidia RTX 3090 GPU for £600-1000 and two of these will enable you to run 70B models relatively efficiently. Four will support 100B+ models which is veering close to GPT4 level work. Research is actively underway to find new ways to optimise models at 1B or 2B so that they can run with less memory and processing power, even on mobile phones. However, higher parameter levels can help with complex or long-winded tasks like analysing and summarising books, preventing LLM “hallucination” an effect where the model will invent fictional information as part of its response. I’ve found that 7B models used well can do an amazing range of tasks accurately and efficiently.

While we’re on the subject of self-hosting, it’s worth noting that when you attempt to access them models are also often compressed to make them more feasible to run on consumer hardware, using a form of compression called “quantization“. Quantization levels are represented with “Q” values, that is a Llama2 7B model might come in Q4, Q5 and Q8 flavours. As you’ll notice lower Q levels require less memory to run. But they’re also more likely to fail and hallucinate. As a general rule of thumb, I’d advise you stick with Q5 or Q6 as a minimum for models you run locally if you’re going to work with quantized models.

The units that large language models work with are called tokens. In the world of natural language processing, a token is the smallest unit that can be analyzed, often separated by punctuation or white space. In most cases tokens correspond to individual words. This helps to breaks down complex text into manageable units and enables things like part-of-speech tagging and named entity recognition. A general rule of thumb is that 130 tokens correspond to roughly 100 words. Models are trained to handle a maximum number of array elements, e.g. tokens in what is called the “context length“. Humans do this too – we work with sentences, paragraphs, pages of text, etc. We work with smaller units and build up from there. Context length limits have implications for memory use on the computers you use for an LLM, so it’s good not to go too high or the model will stop working. Llama 1 had a maximum context length of 2,024 tokens and Llama 2 stops at 4,096 tokens. Mistral 7B stops at 8k tokens. If we assume a page has 250 words, this means that Llama2 can only work with a chunk of data that is around 16 pages long. Some model makers have been pushing the boundaries of context length, as with GPT4-32K which aims to support a context length of 32K or about 128 pages of text. So if you want to have an LLM summarise a whole book, this might be pretty relevant.

There are only a few dozen foundational models available and probably only a few I’d bother with right now. Add in quantization and there’s a bit more to sift through. But the current end-user actually has thousands of models to sift through (and do follow that link to the huggingface database which is pretty stellar) for one important reason: fine-tuning.

As any academic will already anticipate, model training is not a neutral exercise. They have the biases and anxieties of their creators baked into them. In some cases this is harmless, but in other cases, it’s pretty problematic. It’s well known that many models are racist, given a lack of diversity in training data and carelessness on behalf of developers. They are often biased against vernacular versions of languages (like humans are! see my other post on the ways that the British government has sharpened the hazards of bias against vernacular English in marking). And in some other instances, models can produce outputs which veer towards some of the toxicity embedded in the (cough, cough, reddit, cough) training data used. But then attempts to address this by developers have presented some pretty bizarre results, like the instance of Google’s gemini model producing a bit too much diversity in an overcorrection that resulted in racially diverse image depictions of nazis. For someone like me who is a scholar in religion, it’s also worth noting that some models have been trained on data with problematic biases around religion, or conversely aversion to discussing it at all! These are wonderful tools, but they come with a big warning label.

One can’t just have a “redo” of the millions of CPU hours used to train these massive models, so one of the ways that developers attempt to surmount these issues is with fine-tuning. Essentially, you take the pre-trained model and train it a bit more using a smaller dataset related to a specific task. This process helps the model get better at solving particular problems and inflecting the responses you get. Fine-tuning takes a LOT less power than training models, and there are a lot of edge cases, where users have taken models after they’ve been developed and attempted to steer them in a new direction or a more focussed one. So when you have a browse on the huggingface database, this is why there aren’t just a couple dozen models to download but thousands as models like Mistral have been fine-tuned to do a zillion different tasks, including some that LLM creators have deliberately bracketed to avoid liability like offering medical advice, cooking LSD, or discussing religion. Uncensoring models is a massive discussion, which I won’t dive into here, but IMHO it’s better for academics (we’re all adults here, right?) to work with an uncensored version of a model which won’t avoid discussing your research topic in practice and might even hone in on some special interests you have. Some great examples of how censoring can be strange and problematic here and here.

Deciding which models to run is quite an adventure. I find it’s best to start with the basics, like llama2, mistral and codellama, and then extend outwards as you find omissions and niche cases. The tools I’ll highlight below are great at this.

There’s one more feature of LLMs I want to emphasise, as I know many people are going to want to work with their PDF library using a model. You may be thinking that you’d like to do your own fine-tuning, and this is certainly possible. You can use tools like LLaMA-Factory or axolotl to do your own fine-tuning of an LLM.

How Can I Run LLMs on My Pc?

There is a mess of software out there you can use to run LLMs locally.

In general you’ll find that you can do nearly anything in Python. LLM work is not as complex as you might expect if you know how to code a bit. There are amazing libraries and tutorials (like this set I’d highly recommend on langchain) you can access to learn and get up to speed fairly quickly working with LLMs in a variety of use-cases.

But let’s assume you don’t want to write code for every single instance where you use an LLM. Fair enough. I’ve worked with quite a wide range of open source software, starting with GPT4All and open-webui. But there are some better options available. I’ve also tried out a few open source software stacks, which basically create a locally hosted website you can use to interface with LLM models which can be easily run through docker. Some examples include Fooocus, InvokeAI and Whishper. The top tools “out there” right now seem to be:

Note: the most comprehensive list of open source tools you can use to run an AI chatbot I’ve seen to date can be found here.

I have a few tools on my MacBook now and these are the ones I’d recommend after a bit of trial and error. They are reasonably straight-forward GUI-driven applications with some extensability. As a starting point, I’d recommend lmstudio. This tool works directly with the huggingface database I mentioned above and allows you to download and keep models organised. Fair warning, these take a lot of space and you’ll want to keep an eye on your hard disks. LMStudio will let you fine tune the models you’re using in a lot of really interesting ways, lowering temperature for example (which will press the model for more literal answers) or raising the context length (see above). You can also start up an ad hoc server which other applications can connect to, just like if you were using the OpenAI API. Alongside LMStudio, I run a copy of Faraday which is a totally different use case. Faraday aims to offer you characters for your chatbots, such as Sigmund Freud or Thomas Aquinas (running on a fine-tuned version of Mistral of course). I find that these character AIs offer a different kind of experience which I’ll comment on a bit more in the follow-up post along with mention of other tools that can enhance this kind of AI agent interactivity like memgpt.

There are real limits to fine-tuning and context-length hacking and another option I haven’t mentioned yet, which may be better for those of you who want to dump in a large library of PDFs is to ingest all your PDF files into a separate vector database which the LLM can access in parallel. This is referred to as RAG (Retrieval-Augmented Generation). My experimenting and reading has indicated that working with RAG is a better way to bring PDF files to your LLM journey. As above, there are python ways to do this, and also a few UI-based software solutions. My current favourite is AnythingLLM, a platform agnostic open source tool which will enable you to have your own vector database fired up in just a few minutes. You can easily point AnythingLLM to LMStudio to use the models you’ve loaded there and the interoperability is pretty seamless.

That’s a pretty thorough introduction to how to get up and running with AI, and also some of the key parameters you’ll want to know about to get started. Now that you know how to get access up and running, in my second post, I’ll explain a bit about how I think these tools might be useful and what sort of use cases we might be able to bring them to.

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Forging and living in community is hard work. Anyone who tells you otherwise is either not aware of the suffering or hard work of other members in their community which is ongoing on their behalf or inexperienced. But this labour can also be part of the joy and beauty of it, a kind of craft taken up in the interest of establishing and maintaining spaces of loving mutual care for one another.

While people can sometimes tend to view church as a form of consumerised therapy, e.g. something that is undemanding (or only so demanding as to avoid revealing our interpersonal shortcomings) and meets a person’s (able-bodied) needs implicitly, there are other examples in recent years of reflection on ecclesia which focusses on areas where there is hard work to be done and towards the ways that we might sharpen the tools we bring to community-as-craft:

  • confronting racism and the need for forms of repentance and reconciliation as white ethno-nationalism has crept into particular instances of Christian self-identity
  • confronting institutional and interpersonal forms of misogyny and exclusion of women
  • addressing the ways that elitism and class (un)consciousness, in the form of wealth, literacy, or social status can subvert worship
  • unpacking the phenomenon of spiritual abuse and the ways that other more adjacent forms of trauma should be drawn into our reflection on life together
  • …and finally, assessing the ways that we have created spaces where the disabled God is unwelcome

As a theologian who occupies a small slice of these intersectional categories, I am wary of the ways that in some of these fields, reflection about justice can come in speculative forms – where you might imagine how it is for another person who is oppressed whilst not experiencing oppression yourself – rather than arising from lived experience or wisdom. I’m working on a book, God is Weird where I’ll unpack some of my own analysis as an autistic theologian and Christian ethicist and I’ve also begun setting up an ethnographic project around autistic spirituality. These projects won’t be complete for some years now, I expect, so in the meantime I thought it might be helpful to put down a series of posts on what resources I’ve found (some of them are written, but not all!), areas where I see opportunities for practitioners to change their approaches, and ways that our core theological and doctrinal thinking might need to change in light of the forms of exclusion of privilege which have narrowed theological reflection in the 20th century.

It is worth emphasising that the task here is quite severe: Christians have been at the forefront of conversion therapy and eugenics movements which have sought to eradicate and hide autistic lives, usually achieved through breathtaking levels of interpersonal violence. So while there is joy abounding in a neurodivergent theology, this is very much not a cheery lighthearted conversation for autists who are sharply aware of these legacies and hold them as personal trauma. For this post, I’m going to frame out some of the parameters that I think might be salient for autistic church for the sake of later reflection where I’ll unpack them, so here goes:

(1) Queering church: the starting point for all of this has to be an accounting for weirdness and unconventionality in forms of Christian life and worship, doctrine and collectivity. I note that sometimes neurodivergent (“ND”) thinkers might try to avoid getting caught up in debates around sexual orientation and attempt more covert or disentangled ways to engage this subject, but after a long season of covert work as a theologian, I’ve really begun to question the wisdown of such an approach. Queerness has always been about wider concerns beyond sexual orientation, but also “unconventional” sexual orientation and gender identity have always been a co-occuring feature of ND lives and bodies. As a result, I tend to view adjacent queering projects as complementary. Following on from this, there are some hard questions we need to ask in opening up a conversation about queering church for ND neighbours:

  • Why do we obsessively focus on categories of normality and flee from reflection on embodied difference?
  • Why have Christians led movements to eradicate or convert forms of embodied difference (like down syndrome or autism)? It is really important to reckon with the intensity of violence that some Christian communities have sustained towards disabled members of their communities. Christians were among those at the forefront of the eugenics movement in the 20th century and still are via the proxy of genetic research into “causes” of autism and gene therapies in the 21st century. But in other contexts, it is equally the case that conservative Christians have been at the forefront of pushing for access to behavioural therapies (like ABA) which seek to convert (I’m not exaggerating when I suggest we shoud substitute language here of “torture”) autistic children and young adults so that they conceal conspicuous behaviours. I know of many ND children who were pressed into narratives of “normal” behaviour as part of sunday school teaching. I know of no disavowal of ABA by any Christian denomination. As Laura MacGregor and Allen G. Jorgenson observe in one recent journal article, especially for parents and carers of children with disabilities, for a variety of reasons, churches feel “unsafe” with the consequence that those families “[withdraw] entirely from church”.
  • While the first-hand accounts of caregivers form an important body of knowledge which we should take very seriously in our reflection on the ways that churches exclude disabled persons, there is a hazard here in that these accounts of caregiver suffering and burnout can displace first-person accounts of chuch. I observe that much of the underwriting support for ABA and eugenics charities like Autism Speaks tends to be led by caregivers desperate for support which can ultimately cause further harm and marginalisation of those persons they are caring for. Why have we tended to focus on the (very real, to be fair) suffering of able-bodied caregivers and ignored the trauma and stolen agency of care-receivers?
  • I have also observed that conversations about accommodations and support start off very easy and meet hard limits very quickly. Too often, after a brief burst of enthusiasm (again, perhaps not appreciating the full scale of work that may be involved), we often tend to drift eventually towards a framing of conversations about forms of accommodation, conversations about rights and entitlements as a zero-sum game, emphasising limits to what is “practical” and in sharper cases supporting rearguard action to centre and celebrate privileged people who are ceding those “rights” for the sake of another person (e.g.  men’s rights, or white experiences of “racism”).

(2) There is an urgent need to centre embodied diversity as we reflect on life together. We’ve done a bit of thinking (thanks in no small part to the legacy of Nancy Eiesland) around accessibilty for wheelchair users in the built environment, but there are many other forms of inaccessiblity that don’t get attention, including some straight-forward aspects of ND embodied experience like sensory sensitivities around sound, light, texture and food. How do we worship corporately in ways that start from the assumption that auditory, visual, and sensorimotor differences are going to abound? Formulaic approaches to worship music aren’t likely to work. And how can we create pathways for people to share their negative experiences without fear of marginalisation, shame or sanction? Two autistic people in the same church may have conflicting experiences: loud music can produce suffering for some people whilst intense bodily vibration can generate deep joy for another. This is a paradox we haven’t spent much time exploring in the church or in academic theology. In a similar way, we’ve done a bit of work to accommodate some forms of hearing impairment with AV equipment, but other forms of neurological difference like central auditory processing disorder (CAPD) or just plain autistic cognition can lead to challenges around pace and flow of auditory information: a 30 minute long eloquent sermon may be a brilliant canvas for some people but a blank page for many others. Should the sermon or song really be the centerpiece of a church service? There are interesting questions about the love feast and other forms of liturgical meals which seem to have fallen off the radar as we seek to emulate para-ecclesial genres of oratory and performance.

(3) There is an obsession, especially within Protestant forms of worship on speech and oratory. This can often crowd out silence. Or worse-still this preference can lead to a commodification of silence, where it becomes seen as especially holy (mindfulness workshop anyone?) rather than part of someone’s mundane everyday experience. ND church needs to be able to accommodate and celebrate persons who are non-verbal or non-speaking (even if this is occasional rather than persistent as is often the case with masking autistic people). This is more common than you expect, especially given the ways we can tend to use invalidated pseudo-social scientific instruments like the Myers-Briggs tests to badge non-communication as a personality trait (and thus not requiring a person’s energy as attention as a prolific communicator). We also need to reflect on ways of accommodating different forms of speech, which may involve gesture, sounds, or echolalia. I was particularly struck by the account by Eli Clare (Brilliant Imperfection: Grappling with Cure) of having shaking and erratic body motions as a form of embodiment which can be celebrated. Think about the number of places across a worship service where perfection is upheld (even if not achieved) as a pursuit: music production, scholarly and eloquent but accessible sermons, clever spontaneous announcements. Would it feel perfectly normal if a person with cerebal palsy delivered a sermon in your church? So often we assume that effective communication is the problem of the communicator. But what if listening was sanctified as a challenging liturgical practice?

There’s more I’m sure, including thinking about how we can confront and dismantle ablist hierarchies, but that’s a good start for now. More from me on this in weeks to come!

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Another love letter to curious neurotypical allies

Another way that autism is often pathologised relates to characterisations of us as having a limited number of interests (sometimes described as “monotropism”), having “weak central coherence,” or as being inflexible or rigid around activities and conversations. More recently, autistic people have taken the reins of research and started to characterise this aspect of our cognition in different ways, particularly around the importance thinking through and tending to flow states and inertia. There is also some pretty interesting research emerging around cognitive difference around “predictive coding”.

Of course we can flip the pathologisation, asking why non-autistic people have to be so flighty, swapping from one topic to the next and correspondingly slow to complete tasks and notice connections across different domains. Of course this is just as unfair, but it highlights the ways that different forms of cognition are really just that: different.

We all experience times when we hit mental friction, e.g. trying to think through a problem in a way that just isn’t working, to such a degree that all your mental gears grind to a halt and you need to regroup. My ideal way of working through something is to work on tasks or topics, one at a time. I really want to understand and consolidate something (even if just provisionally, to highlight what has been left undone, to highlight what gaps in knowledge have been identified, what work might remain, etc.) before I move on. And I really like to set things up properly, getting the ambiance right, ensuring the right tools are ready, and the stage is set for effective work. For a simple thing like washing dishes, I want to make sure I’ve got a nice clear space for drying, a good soundtrack playing, and enough time set aside so that once I’ve begun, I can reasonably expect to finish the washing in one go. The upside of this is that once I’ve got things moving, I can go very fast – faster than most. But constantly stopping and starting, interrupting flow and having to hit the brakes and stop that inertia, can be really uncomfortable. That discomfort can be mitigated, when I anticipate I’m going to be interrupted, I’ll split tasks up into sub-groups, folding just the kids laundry and leaving towels for another day, or washing silverware and plates and stopping before I get to the pots.

It’s hard to convey the bodily sensation of having inertia stopped abrubtly, but it’s important to stress that it’s way beyond what you might think of as normal levels of uncomfortable annoyance. If I’m trapped in a situation where we are constantly stopping and starting, failing to take time to set up and define parameters, leaving things unfinished and never consolidating conversations or marking progress before moving on, it can feel like being forced to hold my breath or being exposed to loud music for hours at a time: uncomfortable, veering towards tortuous or traumatic. Poorly designed academic workshops can sometimes be like this or a dinner party with expectations that we all engage in small talk about topics that none of us actually care about (yes, ND folx are often not big fans of small talk). Again, I have ways of mitigating this – taking on the task of note-taker for a conversation and trying to consolidate the conversation for our small group; being the person at the party who gently invites “deep” conversations to those who are interested.

What I’d like you to know more about, however, is the degree to which our societies are attuned to non-autistic ways of working and being in the world. It’s not that (as often many people seem to assume) we’ve reached a nirvana where everyone can just do things at their own pace, but that people just don’t notice how they’re being given priority around the horizons that we set for default deadlines and planning, how often we change the accepted approach to a given form of practice, etc.

In my perfect world, I’d be able to focus days around specific forms of activity: maybe teaching classes on Monday and Tuesday, conducting research on Wednesday, holding meetings on Thursday morning and marking exams in the afternoon, etc. But that isn’t how things work out: my teaching activities are spread across the week and there are rules to enforce this kind of work scheduling. When I’ve requested a different approach, I’ve been told this would disadvantage other colleagues and is against “the rules”. My requests are characterised as special pleading or even suspicious. When I’ve asked for the terms of a meeting to be defined ahead of time, perhaps even collaboratively, colleagues can sometimes be defensive: “Why can’t we do this in a ‘relational’ way and just have a conversation? Why do you always need to email so much?”

The upside of this is that at the start of each day, I need to think my way through the number of interactions and tasks I’m going to be drawn into, and I try to consolidate for myself as much as possible. I calculate the people I need to work with who won’t be willing or able to accommodate a different pattern from their own. Those become the “triage” events that require special levels of energy and planning. Then I need to try and consolidate the rest of the time as best as I can, but it’s often the case that the triaging takes up *all* the time. The sad thing about this is that being flexible and accommodating of different patterns doesn’t just help autistic people like me – there are many other non-ND folx who need a bit of time to get “into gear” and might want to set up collaboratively designed ways of working to get around other invisible challenges.

If you’d like to learn more, I highly recommend:

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One of the key reasons I’m reluctant to share with others about being autistic relates to the way that communication by autistic people has been relentlessly pathologised. Even now, the key way that autism is defined in diagnostic manuals and social research primarily foregrounds, as one research article puts it is that: “autism manifests in communication difficulties, challenges with social interactions, and a restricted range of interests”. I don’t know a single autistic person who would foreground those things are the primary driver of their personal alterity and lived experience. They are challenges, but those aren’t the defining features of being autistic. But that’s the stereotype out there which is continually repeated by non-autistic researchers. This is foregrounded for those autists in Higher Education who declare a disability at work as we’re categorised in the following way by the Higher Education Statistics Agency: “A social/communication impairment such as Asperger’s syndrome / other autistic spectrum disorder.”

The upshot of this is that I have an abiding fear that when sharing about my neurodivergence with others, that person will subconsciously begin to find signs of disorder in every social interaction we have after that discovery. This has happened in the past and it’ll continue to happen in the future. And there are corrolaries which also make me wince, like when people speak really loudly or slowly to immigrants in spite of their clear English language proficiency. It’s very hard to surmount the challenges inherent in a relationship where someone is condescending because they have an implicit sense of personal superiority. And we all experience insecurity in ways that drives us to inhabit these spaces of superiority more often than we’d like to acknowledge.

So I’d like you to know about this worry I have.

But, there’s another piece in here that’s worth us considering. In the face of these odd diagnostic framings, I always want to ask: don’t we all have problems with social communication? Isn’t this a key part of being a living creature? Doesn’t every creature experience conflict as it occurs in any healthy relationship? There are whole fields of study, like philosophical hermeneutics, post-humanism and critical animal studies, which seek to confront the fascinating aspects of building understanding and the causes of misunderstanding in communication.

So rather than try to pretend you don’t notice when I’ve clearly missed your point, or I’ve read your reaction to something as more severe than you intended it to be, why not lean in to the awareness that you have trouble communicating sometimes too, that when you’re feeling tired and badgered by the world you might not have extra bandwidth for interpreting cues, mediating confusion or faciliting the process of bridging misunderstanding?

I’m fascinated by the ways that we hold culturally encoded double-standards around communication. In many cases, facilitating understanding by a listener or reader is taken to be a hallmark of skilled communication. This is undoubtedly the case, as I’ve learned from a lifetime of cross-cultural communication and teaching, which is often about troubleshooting how effectively you’ve been understood and learning to anticipate and surmount barriers. But if we’re being honest here, I think it’s worth acknowledging that being well-understood can also be a feature of having a homogenous social life and inhabiting hierarchies. It’s much more likely that, for most of us, we think of ourselves as easily understood and understanding people simply because we don’t spend that much time outside our comfort zone, staying within close-knit circles of people who share our experience, cultural background, social class, and particular competencies. There are forms of deference which are built into relationships where we are expected to mask misunderstanding and protect fragile egos.

What I really want to see is how a person performs when they’re thrown into a situation where they’re expected to communicate with people you don’t share much with. You can see this at work when people travel outside their home country for the first time, take an unexpected career transition, or move to a new place. Suddenly a person realises that their communication competencies do not arise from skills, experience and training, that those capabilities are more fragile than they’d expected and that there’s some hard work ahead. Moreover, when we are thrown into that kind of situation, we’re confronted with the sides of ourselves that emerge when we’re under stress: you may be impatient, sharp, slow to react, etc. and this compounds the embarrasment and difficulties of surmounting misunderstanding.

Some of the best teachers I’ve worked with are people who have placed themselves in situations of language and cultural diversity and developed forms of grace and patience for themselves and others which are the gateway to understanding. Some of the most skilled and empathetic communicators I know are neurodivergent people. Imagine how it might transform our organisations and families if we were more honest about how we’ve experienced breakdowns in communication, and more forensic about the aspects of our culture which drive us to conceal or hurry past misunderstanding in favour of quick and decisive action.

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Image of an hourglass half buried in the ground

In recent years, the British government has been pushing Universities to implement workload allocation models tracking staff time. This is at least notionally, about providing transparency and accountability around public spending around higher education. I fear, however, that it is more about promulgating a disengenuous model of “lazy academics” sitting around using government money and the need to control us more carefully. The origins and impact of this narrative as well as some helpful refutation from actual realiity are covered extensively in Peter Fleming’s Dark Academia (blog post book review from LSE linked here). But that is the reality that we’re under. And, truth be told, many academics have embraced these systems with open arms in hopes that they will proivide a utilitiarian tool for reducing their overwork and inequalities within the sector around workload. My observation so far is that they have increased and worsened these problems and privatised suffering by concealing it behind impersonal systems which can’t be confronted or held accountable. I’ll accept that there are likely exceptions to this and would be glad to hear if anyone has experienced systemic improvements in justice within their academic workplace as a result.

But this has led to a shift in the model by which academic workload is measured – from forms of work to time units. It used to be the case that we’d talk about sitting on a certain number of committees, teaching a certain number of modules, etc. but now all of these are converted into specific homogenised time units (“WAM Points”). I’ve worked in other sectors where workload is managed in this way so it’s nothing new to me, but I had thought for a moment that I’d escaped it, so have found this resurgence personally discouraging.

I’ve been thinking about this lately, in particular as I work in increasingly overt collaborations with other neurodivergent colleagues, and I’ve observed that this shift in workload management, surveillance and sanction has hit ND staff particularly hard. Given my research over the past decade has focussed on the philosophy and theology of time, I’ve hit upon some speculative conclusions I’d like to test out about time experience and this policy shift. In particular, I wonder whether neurodivergent people experience time in more variable and intense ways than non-ND.

Post-Taylorist scholars in business and organisational studies have begun to observe that time is not homogenous. And in really obvious ways our embodied experience of normal tasks is certainly not this way. Think of how you can sit a read a novel and the hours pass unnoticed, where in contrast you might find when completing an onerous task that the time passes with aching slowness. This is also the case with joyous work, however, as the bodily impacts of exercise are quite different from relaxation. Our hour of deeply pleasurable sprinting is still accounted differently in our bodies from an hour of walking. So too it must be at work: different kinds of activities have different levels of physiological demand on us. In some (limited) cases, workplace studies scholars (and even managers!) have built “recovery time” into specific kinds of tasks on the basis of this awareness. But it’s not just the tasks themselves, but also the “between times” and in other cases, scholars of work have noticed that “idle time” is a common and necessary feature of work providing padding around difficult tasks and opportunities for creative and non-linear thinking around problems. So too workplaces, especially in tech have emphasised unstructured time as part of a normal working week. It’s important to emphasise that for academics, at least in my experience, the block allocation we get for research time is NOT this kind of thing, as we spend most of the year being pressed for demands around production and that time is probably the most pressured of any I experience. Have a look at the ways that sabbaticals are handled now – we’re expected to write an extensive application detailing all the specific tasks we will complete and achievements we will attain, and are pressed relentlessly to report on this when that time has concluded to confirm that we have completed the list we have offered.

All of this things are true for any person who occupies a human body. But I think these things are far more intense for autistic people where flow and pace are far more intrinsic to executive function, working at tasks in a kind of self-generated sequence can be essential. I mention this a bit in a previous blog post where I talk about a “day in the life“. The tragic thing about this is that when they aren’t subject to trauma, coercion or control, when engaging their passions (like pretty much every academic I’ve ever met) autistic people will pursue tasks with unusual tenacity. So trying to account for our work in a mechanistic way is oppressive, but also unnecessary as we’re likely putting in long and strange hours to complete our work above and beyond, simply for “love of the game”.

This has some really concrete ramifications for workload management, however, as it foregrounds the ways that individual tasks can have quite different demands on people, especially in the case of neurodivergence. And these models deliberately disallow inflecting time burdens in different ways for different people. The expectation is usually that some things will be hard and others will be easier, but this really mobilises the ablist “superpower” narrative in unhelpful ways, e.g. if you are slowed down in one area, you must have a superpower to compensate for another area so you can “keep up”.

In a similar way, having recovery and buffer time is even more necessary, as we adapt to group work patterns which are unadapted and hostile. It was once the case that I could mask and conceal my own disabilities by offloading tasks that took me far longer, or were demanded in moments when I didn’t have energy or ability to complete them, into spare time. But increasingly our models exclude spare time as a general rule, require work on short notice and rapid deadlines, and I’ve found that there’s simply no place to put those things temporally.

The key point here is that if we can talk about and negotiate our shared workload together around tasks and abilities, things are quite different. But when we work with impersonal homogenised time, the guaranteed result will be oppressive for specific (perhaps all) people.

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In recent years, one of the joys of my work at the University has been to convene a regular tutorial / support group for neurodivergent students. As part of the process of unmasking and reflection, I’ve come to confront so much about my own past learning in University which was a (lonely and terrifying) struggle, from sensory overload, challenges processing and hearing lectures, processing information in different ways, and navigating frequent meltdowns and overload. Chatting with students (who have far more self-awareness at this stage in their University journey, but are still trying to navigate these challenges!) has been really meaningful. Our discussions have also been quite interesting from a research perspective, as we delve into points of pedagogical friction and dysfunction which are a regular part of their experience and to try and troubleshoot how we might adjust, confront, or repair those areas of exclusion where our curriculum doesn’t always map onto the diversity of our learners. We have found some things which are (or might be if we could find suitable allies around educational policy, which is sometimes a quest unto itself) easily fixed with small hacks, in other cases, it’s really just a matter of being able to speak aloud about challenges even when there’s nothing to be done. But then there are some issues where we identify a challenge which is much harder to pin down. It’s often the sort of thing where you might be tempted to dismiss it out of hand, but when 7 or 8 people all seem to have the same experiences, that cooroboration helps to identify something that requires further investigation.

I’ve been chasing one of these for a couple years now which runs something like this: some of our students experience really “spiky” performance in marks they receive for assessments. When I say spiky, I mean in the same semester they might get one of the highest marks we’ve ever awarded for a particular class, and at the same time they might be just above a failing mark in another. In some cases this happens because, when you’re navigating a learning environment which is constantly stressful and traumatic, energy levels can suddenly drop and executive function can evaporate. But even if we bracket out instances where this has been the case, there are other situations where learners submit a raft of essays, all composed with the same level of energy and deposited with the same level of confidence and the results are highly idiosyncratic and unexpected. This was very much the case for me as a learner: I managed to push my way through (obviously) higher education, but it was always by the skin of my teeth, fretting about that one module or essay that I’d nearly failed whilst getting superlative results in others. Let me emphasise, the phenomenon that I’m highlighting here isn’t a matter of ability suddenly flagging, or finding an area where I was lacking understanding or expertise. Sometimes I’d hand in an essay where I felt like I was saying something really important and meaningful, and the marker would return it with feedback indicating that they clearly didn’t understand what I was trying to do. This was (and is) usually framed as a failure to achieve proficiency in written communication. Because it’s always our fault when someone can’t understand us, right?

Since then I’ve learned about the double-empathy problem, originally developed by Damien Milton (original paper here). Researchers into autusim have consistently pursued this hypothetical frame – that breakdown in communication and understanding must lie within some pathology of the autistic person. This has been framed around “theory of mind” – the condescending, theoretically and empirically problematic suggestion that autistic people lack the ability to empathise or understand the mental states of other people, also sometimes called “mind blindness”. This much more comprehensive pathologisation of autistic lives can be confused with alexithymia (something I experience, as I relate here: A day in the life of neurodivergence) which is a much more specific condition, and which has been tied to both hyposensitivity (getting too little information from reading other people) and hypersensitivity (getting an overwhelming flood of information about the states of other people from microexpressions and body cues, which can be hard to parse when you’re under stress). It’s also the case that there is a very high co-incidence of trauma, at the levels of CPTSD for autistic people, which has effects (which can be addressed therapeautically) in impairing our ability to read other people (e.g. through the constant triggering of our threat perception and response). (some) Researchers have begun to be much more cautious about engaging with older theories around theory of mind, especially after they have begun to take into account trauma-informed approaches to experimental psychology. Getting back to Milton’s work, in setting aside the tendency to pathologise autistic people, Damien hypothesised that this lack of understanding, when it occurs, might happen to a much wider range of people. That breakdown in communication might arise from forms of cognitive difference, and seen in this way, might occur in BOTH directions. Milton wondered if it might be possible that autistic people might experience less communication breakdown when communicating with other autists and conversely if it might be possible to set up experiments which verified this was occurring. This research is just starting to coalesce as a field of study, but my reading of the literature (for an example, see Muskett et al 2009) is that this hypothesis has been confirmed and this requires substantial revision to psychological theories of autism.

The reason I bring up double-empathy is to ask, whether in the course of teaching and learning, we may have two-way breakdowns in communication, where written communication is the goal of the learning process. Is it possible that faculty (both autistic and allistic) are conveying prompts inviting students to write an essay which can be misunderstood when bridging neurological difference? And similarly, is it possible that students are writing essays which might be received quite differently, and even marked quite differently, when read by staff who are autistic or allistic? To be clear, as I’ve related elsewhere in blogposts, I think that the much heavier burden here is on allistic staff as autistic staff will have had a lifetime of training (sometimes on the level of conversion therapy) in interpreting and understanding communication across neurological difference. The especial challenge here is whether the opposite is true. I fear that in some cases it is not.

For now the advice that I give to students arises from my own experience: your learning process around written communication, especially where it will be largely evaluated by standards which exclude the salience of neurodivergence, will be spiky. You will be misunderstood, and there are few pathways to open up converastion with lecturers about this experience in practice unless you are willing to pathologise yourself (e.g. “I was under great stress and my writing suffered”). There are no mechanisms for faculty to assess their relative incompetence in understanding different forms of communication. And I see policy directions in higher education which are driven towards increasingly homogenous and binary assessments of written communication (good English v. bad English) which has implications for a wide variety of student and not just neurodivergent ones. I tell students that their learning journey is going to have a longer arc than they expect. I found that my own writing didn’t “click” with audiences consistently until I had time to synthesise the many many horizons I was trying to integrate (and this sense that one needs to integrate everything is a common experience for autistic learners). It wasn’t possible for me to compartmentalise in the ways that many other writers and learners do, which ensures a level of success in their early stages of education. It’s likely that I’ve also found an audience which has been developed over time, with readers who understand my broader project, have sympathy for and interest in it, and are able to “jump in” and understand what I’m trying to achieve.

The question I’m holding for right now is whether there are ways we can adjust our processes of teaching to adapt to a wider range of written communication styles, and celebrate the fact that learning journeys are often quite different. It’s possible that we cannot achieve this kind of adaptation without some radical reconfigurations. I’ve tried much of the fine-tuning approaches already in my own practice and with collegaues, and have not found much in the way of effects. I think that calls to abolish grades are probably a key part of the discussion we need to have around how we can more effectively configure the coaching relationship with student writers. The core issue here relates to neurodiversity on campuses – aside from box ticking and PR exercises, how far are we willing to go to craft pedagogy which embraces diversity and doesn’t punish it?

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Talk for UOB College of Arts & Law Mental Health Champions Network
14 November 2023

Background: Welcome! If you’ve come here for the first time, I’m very happy to have you. In case what you’ve read here or heard me share in a talk arouses your curiousity and you’d like to have a chat, I’ll just make a few brief requests of you here. As you’ll see, I’ve included quite a lot of links below amidst the text which I’ve vetted ahead of time. I’d be very grateful if you were able to take time to do a bit of reading and self-education before coming to me with questions. A good baseline might be to try and read at least two things I’ve referenced below before emailing or setting up a meeting and then we’ll even have something specific to open up a chat. Take note – I’ve also put up a variety of posts on this blog which reflect my stream-of-consciousness as I go about the work of academia in a variety of contexts over the past couple years. This is deliberate so that folks have a safe place for some learning-oriented voyeurism. I’ll emphasise that this is all exclusively my context and that it will not be even remotely similar to many other neurodivergent people, so take it all with a bit of a grain of salt. For one-to-one, I’d like to reserve my energy particularly for other ND folx and unpacking these details with friends whom I haven’t had a long disclosing conversation with yet. So if you’re an ally and we haven’t spent a lot of time together before, I’m delighted you’re here! But please do maybe start with reading and learning indirectly rather than going straight to the source. 🙂 I am very happy to offer training, reverse mentorship and one-to-one conversations, but I do need to balance my energy so I can’t respond to everything, so I may divert you to another colleague (we have a “speakers bureau” within the Staff ND group).

Thanks so much to everyone for coming today, and to the network for the invitation to share today. I’m mindful that there are many friends here from whom I’ve learned so much, and with whom I’ve collaborated on student support, research projects, and had arguments about University policy. I’m think it’s probably the same for my co-presenter. I’ve never spoken publicly about my autism and this is for a variety of reasons, some of which I’ll get into below and others which my co-presenter is going to share a bit about. But just to open for the record: I’m a late dx autistic adult who doesn’t tick the stereotypical “on the spectrum” boxes, and it has taken me years to come to terms with this part of my identity, develop a disability identity, and today marks a bit of a “coming out” which, if I’m being honest, feels more than a little terrifying. We have a non-official staff neurodiversity network that I’m a part of. Though there are many ND folks here, it is totally expected that, like me, most of you wouldn’t have chosen to disclose that as a disability or even to pursue diagnosis which can be hard to access and traumatic. But the ND staff network is a fantastic safe space, and if you’re looking for one, we’d very much welcome you with open arms!

So, with that preamble out of the way, I thought it might be helpful to share with you a “day in the life of Jeremy” with some behind-the-scenes access that you wouldn’t ordinarily have. I’ve been trained from birth in masking – the work of concealing my unique traits at all costs, so you probably wouldn’t notice any of this without some insider knowledge. You might relate to some parts of this story, but maybe not some others. I’d say that diversity is all around us, and we often conceal our own differences. But I’d probably want to resist the suggestion that people sometimes make when they feel solidarity that “we’re all a bit autistic.” As you’ll see, there are some pretty unique and sharp edges to my experience and while I love solidarity from allies, I think it’s important to grapple with your own divergence in careful ways that don’t appropriate the struggles that my co-presenter and I have had in our day to day.

I attended a training a few months ago. It was on a topic that I’m quite passionate about and interested in, so I was excited to do some learning alongside interesting and interested colleagues. This workshop was also a bit unique in that I’d only recently decided to start “coming out” or “unmasking,” that is, sharing about my autism with specific people in contexts where it seemed safe and inconsequential to do so (for more on this see Devon Price, 2022 below). For this event, I ticked the box on the registration form regarding accessibility needs and wrote out my request to the facilitator to send me information about the plan for the day ahead of the session. Autistic brains tend to work on the basis of focal attention and inertia, so some types of workshop structure can feel really hostile if someone is jumping around topics really quickly and not taking time to do deep dives. The flip side of this is that once I’ve started working on a particular project, I can sustain unusual levels of focus and concentration. When daily activities and thought experiments can’t be consolidated, I’ll need to prepare myself in advance so my discomfort doesn’t derail the activity and I can find some useful outlet for the torrent of thoughts that will result. Starting a bunch of small unrelated or non-premeditated tasks in succession can take a lot of extra emotional and mental work for me, to an extent that (as I’ve been learning) being forced to do this can make me unwell. So I’ve learned to take steps to mitigate that impact where I can. If I can get a “preview” of coming attractions for a session I can make some plans around parts that I might sit out on, or doing some advance preparation so that I can prepare mentally quickly if we’re switching around a lot. I also wrote on the form that I’d be arriving early to check out the accessibility of the room. My autistic body-mind is pretty sensitive to sensory inputs, so smells, sounds, lighting, and room configuration can make the difference between a super “cozy” session or meeting where I feel comfortable and engaged, able to bring myself fully, and a session where I’m having to navigate levels of stress bordering on panic and only able to bring little fragments of myself. The facilitator never responded to my request, which I later realised was the result of a clerical error that wasn’t their fault and they’d never seen my request. But I wasn’t worried as I knew from experience that processes of accommodation are often an afterthought, designed by people who don’t understand what they’re accommodating, and can break down as no one is monitoring their usefulness or the impacts of their implementation. I had planned to arrive early and figured I could have a quick private conversation ahead of the start time.

Because this is Britain, trains were late and cancelled, so I arrived a half hour late. This meant walking into a room full of people who stared at me, not in an unfriendly way, but actually many friends who offered smiles of welcome, as I struggled to find a seat. It was an uncomfortably hot summer day, so the organisers had set up two large fans at the front which were blowing loudly. Given that I’m really not able to filter out white noise, this was not ideal. Actually, to be honest, fans are one of my worst sounds. The lighting in the room was also harsh, so I also felt blasted by visual and auditory noise. I can also struggle to process auditory information (this is called CAPD), especially when I’m under stress, so I’ve begun to experiment with headphones and live closed capitioning in meetings. But this is not easy to do discretely, and this felt like an experience best begun “off the radar”.

The seating was configured in a semi-circle so that we were all facing each other and uncomfortably close. As a larger adult I barely had enough elbow room to avoid bumping into the person next to me, so as I sat I was uncomfortably aware of my own presence and the proximity of others. For reasons that neuropsychologists don’t fully understand, autistic people can sometimes find direct eye contact uncomfortable. In my case, facial expressions and body language are another kind of noise that I’m soaking up, and sometimes this is exhiliarating and other times it can be uncomfortable and overwhelming. This alterity is very useful in some cases as I will tend to intuit the emotional state of each individual in a room based on posture cues and micro-expressions I just can’t really filter out. (note for interested parties: I’ve put up a separate post on the blog for those who might be curious around how to adapt to support folx who are aversive to staring or can sometimes find it overwhelming).

Feeling already overwhelmed and uncomfortable, I tried to sit a row back from the circle but the facilitator asked me to change seats so that I could “join the community” and sit in the circle. I was too disoriented and tired to put up a fight and a quick mental cost-benefit analysis told me that I’d not be able to discretely signal my need for accommodation without uncomfortable disclosure in front of strangers. So I moved and tried to deflect attention with a comment about how I’d “not wanted to disturb the group” with my late arrival. Determined to make the most of this session, I settled back into my seat and tried to discretely review the materials we’d been given which included a community “agreement”. As I settled into my seat, I winced from pain in my neck and lower back. This flares up occasionally as I have several prolapsed discs. I haven’t ever experienced a traumatic injury, but I’ve learned that “the body keeps the score”, and have noticed that the many neurodivergent members of my extended family all have injuries and chronic conditions related to the sustained holding of bodily tension. This is common for autistic people, with very high levels of co-occurring medical conditions (for more on co-occuring conditions and the bodily impacts of stress and trauma, I’d recommend reading Price, 2022 or van der Kolk 2014 listed below).

This was proving to be a bad day. But just to be clear, I experience at least one of these every month, at an all school away-day, cross-cutting research workshops, in my own lectures, project budget meetings, etc. etc. Sometimes meltdowns coalesce into longer term burnout. This is pretty common for Autistic folks.

The facilitator made some pretty concerning claims about mental health in the session, which I took down verbatim as I was already taking notes for the session. In talking about the ways that it can be hard to overcome personality disorder, they observed at one point: “some people have an investment in being unwell” and a bit later in the morning in sharing unqualified observations about schizophrenia that: “we don’t know whether, just living around someone [with schizophrenia] can have an impact…” Actually, we do know that conflating risk factors with causality is generally a bad idea, I wanted to say, but I remained polite and quiet as I didn’t want to get flagged early as a non-compliant participant and knew that there would come a time when I’d need to release the flood of thoughts I was holding in. This process of holding back can often be really uncomfortable, even traumatic for a lot of communities who think in ways that are divergent, and is part of the trauma we hold. It’s also the reason that finding community with a group of fellow autists can be so important as it’s a space where we can “infodump” and quickly get into a flow of familiar conversation safely.

After two hours of grating noise, sharp light, bodily pain, and being assaulted by the intense emotions of 20 people feeling their ordinary feelings, something had to give. I was teetering on the edge of a panic attack.

Now, for some people, this might be a cause of major concern, but I experience meltdowns on a regular basis, sometimes multiple times in a single day. My experience of repeated challenges and persistent forms of acute stress and trauma have led me to develop a sophisticated suite of coping skills. Many people who have never experienced anxiety before can find their first experience of serious anxiety, however small it may be, to be completely disabling. Like many other neurodiverse persons, I eat small forms of anxiety for breakfast. Friends and family who are not aware that I am autistic have observed that I can be unusually calm under pressure and able to handle what seem like massive amounts of stress without slowing down.

By the time we’d gotten to the lunch break, I could barely concentrate on anything much less hold a coherent conversation, so I grabbed some food and took the elevator outside to see if I could find a quiet sensory environment to collect my thoughts and prepare for the afternoon. I also, at this point paused to judge whether this particular workshop was a safe space disclose a disability around neurodivergence. It’s rare for me to find signs that a facilitator or programme, even in the best of cases, shows signs of proactive disability awareness (I’ll be doing a later blog post on how to passively signal you are an ally, so check the blog in coming weeks for more on this), but I do still scan for signs every time I start some new social interaction.

Here’s how my scan had gone: The course pack which was about mental health made no references to neurodivergence at all, outside of some very oblique references to disability justice in a section on personality disorders, which was terse and tokenistic. The facilitator had handed out a set of “standards” for conduct indicating how they hoped we could do the session together, but again, I noticed that there wasn’t really any way to challenge these without being quite open and disclosive. The facilitator had mentioned that anyone could step out at any time if they needed to, but the room arrangement required any person who did this to interrupt the session by walking in front of the entire group. The facilitator also specified that taking a break needed to be less than 5 minutes, which in my experience is not enough time to recover from a meltdown or overload and certainly not enough of a help to justify a public exit from the room. I had a sense that there was a desire to make accommodations but not a lot of thought about how these might need to work based on lived experience.

So I spent the lunch break downing some more ibuprofen, practing deep breathing and walking outside with noise cancelling headphones on and found stress levels to be coming back down. When I returned to the room, I discretely changed my seating position to be a few rows back, out of the circle. To be clear: I understand the reasons for the circle. As a (masked autistic) workshop facilitator, I’ve frequently used this configuration for sessions based on the assumption that it can instill a sense of intimacy and connection across participants. Though I knew this was unlikely, I was desperately hopeful that the facilitator would register my repeated attempts to get out of the “limelight” for what they were and leave me alone. This was not to be the case. I was again asked to move. This time I’d come prepared with a deflection, so in as unconfrontational way as I could attempt, I quietly responded by saying “I think this is what I would prefer to do right now”. This was embarrasing, but manageable, as I’d been prepared for it. And as a note to the other facilitators here, if someone seems to register discomfort, rather than see it as defiance, it may be helpful to open up access for the whole group: “…it is quite hot in here, I wonder if anyone else if feeling like it might be helpful to anyone else if we shifted our seating a bit?” Based on my hyper-awareness, I’d already noticed at least one other person in that group who had also been attempting to shift seats, but was less willing than me to engage in confrontation. After I made this statement, the facilitator verbally registered their displeasure to the whole group regarding my decision. Then, thankfully, they moved on with the session. It was humiliating, but not unexpected. At the next break, I left as continued participation was untenable. As I walked to the train, I felt jarred by the whole experience, but it was not unfamiliar. What was new was that I’d made a choice to step away and tried to register some of my needs publicly. It was disempowering to have them rebuffed and shamed, but felt like a consolidation of something important to have done it.

This is my personal experience, and I’m reluctant to suggest this is a good template for understanding the experiences of other neurodivergent colleagues here at UOB. But it gives you at least a small sample of what it’s like being covertly autistic at UOB. On one hand, there are many features of being part of academic staff here which are a dream. I’m expected to pursue my passions with tenacity and speak about them to others. I’m allowed to work flexibly, conslidating tasks to some extent in ways that work around my cognitive preference for focal attention and flow.

However, I’m also aware that there are colleagues who work at this University whose specific research into autism which will result in forms of oppression for my people, designing programmes of eugenics and conversion therapy (e.g. ABA). I’m regularly subjected to policies and work relationships which invite forms of surveillance and control which cause dangerous levels of stress and trauma for me. Our environments here are rarely adapted for sensory sensitivities, and often technology we use and communication channels we set up are explicitly oriented around allistic preferences (you can read a bit more on the blog for some of my thoughts around digital accessiblity and oppression).

Different kinds of neurodivergence come in for different kinds of treatment, and I won’t presume to speak for my ADHD, bipolar, schizophrenic, or personality disorder labelled colleagues. Public attitudes towards some forms of neurodivergence are less hostile, particularly if there is a sense that uncomfortable forms of difference can be dampened by medication and that those persons aren’t terribly antisocial. But I think it’s fair to say that autistic people are often placed in the category of “uncomfortable” or even dangerous. I’d encourage you to look into the Cara Lisette’s #IAmNotDangerous campaign and campaigns highlighting the disproportionate levels of incarceration and punishment directed at autistic people if you’d like to learn more. In conversations I have at least every other week, a colleague or aquaintence associates a difficult or dangerous person with autism. This makes me aware that being “out” will leave me either (1) navigating forms of implicit bias or (2) responsible for educating colleagues before we can have a basis for conversation about how I am different from them. I enjoy teaching, so this really isn’t so bad, but I do get tired sometimes of having to put a lot of my already limited energy into deconstructing stereotypes.

Colleagues in Edinburgh did a study of autistic experiences with mental health support and they came to the conclusion that people…

> were being routinely denied access to mental health support purely for being autistic, as well as being disbelieved by mental health professionals when they were in distress, and finding services just inaccessible even when they were offered support. (https://medium.com/@sonnyhallett/counselling-for-different-ways-of-being-b89730c6ca2)

This really maps onto my experience here. Of the 20 or so times I’ve shared about my autism with colleagues, or of the many more times I’ve spoken more generically about distress in the workplace, some of those times I’ve been met with disbelief or (unwitting) condescension. I have found forms of support that are putatively offered for people like me on campus to be radically inaccessible, often fine-tuned to manage the workloads of those offering support and not foremost around the vulnerability of those they are intended for. But perhaps most of all, our support policies are often designed by people who do not have lived experience of disabilities. It’s a tricky balance we’ve discussed in the ND network between trying to bring experience into the foreground to enable more careful planning and help educate eager allies (we love and are grateful for you!), whilst being careful not to drain ourselves dry psychologically.

I’ve mentioned this whole being out in public thing is quite new to me. Thankfully there are some other braver colleagues who are ahead of me on this. But if anyone here wants to chat a bit more about things I’ve raised here, or draw some representation into a committee, I am happy to have a chat and offer my perspective. If I could make one small request, it might be that before you make this kind of request, that you re-read this presentation (I’ll make it available on my blog) and also read at least one of the resources I’ve included below. Then we’ll have some shared learning we can process and discuss together, side by side.

I don’t know what my story means for this group, and I’l be eager to hear from you about your own experiences and learn together as we explore this theme today. I’m delighted that people are coming together to speak passionately about mental health on our campus and open up conversations that have been stifled for way too long. And I can also see that while that conversation is mobilising, it’s also early days for the ways we speak about and carry awareness of mental wellbeing and especially neurodivergence.

 

Some useful references:

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If you’re going to be around a table, bring some options for people to fidget or an activity that uses your hands (and warn people / encourage others to do the same in advance) so there’s no need to sit around staring at one another:

  • you can do  figet toys for the entire group at the meeting (fidget toys are awesome for everyone!) or something like build legos.

Or consider changing format / venue:

  • take your meeting “on a walk,” and talk while you walk (medical research has found that ambulatory movement can be very helpful for anyone who is trying to think out a problem!)
  • find a place where it’s normal to sit side by side: some of my best conversations have been sitting next to someone enjoying a view on a bench or in side-by-side chairs drinking coffee

And generally:

  • create opportunities for people to indicate different needs or deflect without cost; one very easy one is to encourage people to have laptops with them so they can “look stuff up”. I’ll often have my laptop out while I’m listening. Somteimes I’ll use this for closed captioning audio so I can read what people are saying when it’s hard to hear. Sometimes, I’ll be chasing down ideas that are inspired by what we’re talking about in greater depth than the conversation format will allow. Sometimes I need to step away from focal attention, tuning out for 5 minutes so I can regroup and rejoin the conversation. I don’t usually lose track of the flow of conversation as I can monitor it passively rather than actively and this kind of switching from active to passive can make participation easier

But remember two things: (1) it’s ok. We’ve done this before, and this one meeting isn’t going to be the thing that ends us and (2) the worst case scenario is having someone single you out and try to plan was to meet your needs on the spot in public.

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So I think this might be a good summary of an ND preferences wrt/ digital systems. The author doesn’t claim to be autistic, but I certainly shout “amen” like every other line. Would love to know if others relate: https://catgirl.ai/log/comfy-software/

I also think that customisability is important because it’s often the only way that many of us can getting software accessibility. Eg by making it that way ourselves. So I’m a hacker because I like to play with digital tools, but am also starting to realize that I HAD to become a bit geeky or I would have been left behind in a zillion ways. Get to the front of the pack so you don’t get left behind…

The HackerNews comment thread for that article is also a hot and interesting mess – highlighting the ways that different ND flavours and generational cultures frame how we are allowed to speak. I found myself wondering if it was a suitable proxy for what we might find if at UOB we could pull back the curtain… c/w: discussions of depression, suicide, ablism, and generally insensitivity: https://news.ycombinator.com/item?id=33053144