autism

The Filter You Didn’t Choose

Posted on by Elise Johnson

This is the second post in a series leading up to the 16th annual International Forum on Consciousness, taking place in Madison this May, hosted by the BTC Institute, Promega and Usona Institute. The Forum gathers scientists, philosophers, and practitioners from dozens of different fields to investigate the nature of the mind. This year’s theme, “Unspoken Intelligence,” explores forms of perception and knowing that fall outside conventional cognition.

When she thought about a dog, she saw a dog, more specifically, every dog she had ever encountered, cycling through her mind like a card catalog with pictures attached. She assumed everyone did this. When she discovered they didn’t, that most people access something more like an abstract concept hovering somewhere between language and image, she was genuinely surprised. Temple Grandin had always known her mind didn’t work the way people expected. What she didn’t know, until she was an adult, was the specific shape of the difference.

Most of us know this story, or one like it. We understand that some minds filter experience differently, but the science on this doesn’t stop where the conversation usually does.

For most of its history, the field that mapped minds like Grandin’s looked at those that didn’t fit the available systems and concluded the minds were broken. (It didn’t ask whether the systems were.) More recently, the conversation has been reframing those minds not as deficient but as different.

For many people, that reframing has been transformative, changing how educators teach, how clinicians diagnose, and how workplaces are designed. We are now more familiar with alternative cognitive profiles such as autistic pattern recognition (like that experienced by Grandin), ADHD-associated divergent thinking, and the hyper-focused depth of what researchers call monotropic attention. These are not broken versions of normal cognition. They are different architectures, each with genuine capabilities that other minds aren’t built to produce.

The terms most commonly used to describe these differences, neurotypical and neurdivergent, are useful shorthand but they describe a binary the underlying biology doesn’t support. Cognitive traits distribute across a population the way most biological traits do. “Neurotypical” minds are simply closer to the statistical center. What we call “neurodivergent” can be better understood as the part of that population that differs visibly enough from the statistical center to make the variation impossible to ignore.

Continue reading “The Filter You Didn’t Choose”

The Simplex Things In Life: Utilizing Artificial Intelligence Models to Better Understand Autism

Posted on by Natalie Larsen

Autism Spectrum Disorder, or ASD, is nothing if not unique.

The way ASD manifests itself in people is unique; although it most often presents as some form of variable impairment in social interaction and communication, each individual has behaviors and habits that are as unique to them as snowflakes are to one another.

ASD has also proven itself to be a uniquely challenging disorder to study. In the past decade, de novo (new) mutations have been identified as key contributors to causality of ASD. However, the majority of these identified de novo mutations are located in protein-coding genes, which comprise only 1–2% of the entire human genome.

Up to this point, a majority of previous research has focused on identifying mutations located in the 20,000 identified genes in the protein-coding region, which would seem like a promising approach. Genes are the genetic blueprints for creating proteins, which control and perform crucial tasks in our bodies, such as fighting off infections, communicating between your organs, tissues, and cells as chemical messengers, and regulating your blood sugar levels. It seems like basic math: Genes + Mutations = Mutated Proteins. Mutated Proteins = Disrupted Protein Function.

However, it has been observed that all the known genes that are ASD-associated can explain only a minor fraction of new autism cases, and it is estimated that known de novo mutations in the protein-coding region contribute to not more than 30% of cases for individuals who have no family history of autism (better known as simplex ASD). This provides evidence to suggest mutations contributing to autism must additionally occur elsewhere in the genome. Continue reading “The Simplex Things In Life: Utilizing Artificial Intelligence Models to Better Understand Autism”