Monday, February 26, 2024

The "enjunkification" of our online lives

I want to pass on two articles I've poured over several times, that describe the increasing "complexification" or "enjunkification" of our online lives. The first is "The Year Millennials Aged Out of the Internet" by Millenial writer Max Reed. Here are some clips from the article. 

Something is changing about the internet, and I am not the only person to have noticed. Everywhere I turned online this year, someone was mourning: Amazon is “making itself worse” (as New York magazine moaned); Google Search is a “bloated and overmonetized” tragedy (as The Atlantic lamented); “social media is doomed to die,” (as the tech news website The Verge proclaimed); even TikTok is becoming enjunkified (to bowdlerize an inventive coinage of the sci-fi writer Cory Doctorow, republished in Wired). But the main complaint I have heard was put best, and most bluntly, in The New Yorker: “The Internet Isn’t Fun Anymore.”

The heaviest users and most engaged American audience on the internet are no longer millennials but our successors in Generation Z. If the internet is no longer fun for millennials, it may simply be because it’s not our internet anymore. It belongs to zoomers now...zoomers, and the adolescents in Generation Alpha nipping at their generational heels, still seem to be having plenty of fun online. Even if I find it all inscrutable and a bit irritating, the creative expression and exuberant sociality that made the internet so fun to me a decade ago are booming among 20-somethings on TikTok, Instagram, Discord, Twitch and even X.

...even if you’re jealous of zoomers and their Discord chats and TikTok memes, consider that the combined inevitability of enjunkification and cognitive decline means that their internet will die, too, and Generation Alpha will have its own era of inscrutable memes and alienating influencers. And then the zoomers can join millennials in feeling what boomers have felt for decades: annoyed and uncomfortable at the computer.

The second article I mention is Jon Caramanica's:  "Have We Reached the End of TikTok’s Infinite Scroll?" Again, a few clips:

The app once offered seemingly endless chances to be charmed by music, dances, personalities and products. But in only a few short years, its promise of kismet is evaporating...increasingly in recent months, scrolling the feed has come to resemble fumbling in the junk drawer: navigating a collection of abandoned desires, who-put-that-here fluff and things that take up awkward space in a way that blocks access to what you’re actually looking for.
This has happened before, of course — the moment when Twitter turned from good-faith salon to sinister outrage derby, or when Instagram, and its army of influencers, learned to homogenize joy and beauty...the malaise that has begun to suffuse TikTok feels systemic, market-driven and also potentially existential, suggesting the end of a flourishing era and the precipice of a wasteland period.
It’s an unfortunate result of the confluence of a few crucial factors. Most glaring is the arrival of TikTok’s shopping platform, which has turned even small creators into spokespeople and the for-you page of recommendations into an unruly bazaar...The effect of seeing all of these quasi-ads — QVC in your pocket — is soul-deadening...The speed and volume of the shift has been startling. Over time, Instagram became glutted with sponsored content and buy links, but its shopping interface never derailed the overall experience of the app. TikTok Shop has done that in just a few months, spoiling a tremendous amount of good will in the process.


 

 

Friday, February 23, 2024

Using caffeine to induce flow states

I pass on this link to an article in Neuroscience & Biobehavioral Reviews (open access) and show the Highlights and Abstract of the article below.  One of the coauthors, Steven Kotler, who is executive director at the "Flow Research Collective" was mentioned in my previous 2019 Mind Blog post "A Schism in Flow-land? Flow Genome Project vs. Flow Research Collective."  It was the last in a series of critical posts that started in 2017. While I agree from my personal experience that caffeine (as well other common stimulants) can induce more immersion in and focus on a task, I find find the text, which has a bloated unselective bibliography, to be mind-numbing gibble-gabble, just as were the writing efforts I was reviewing in 2017-2019,  However,  the authors do offer a recitation that some will find useful of "psychological and biological effects of caffeine that, conceptually, enhance flow" - whatever that means.

Highlights

-Caffeine promotes motivation (‘wanting’) and lowers effort aversion, thus facilitating flow.
-Caffeine boosts flow by increasing parasympathetic high frequency heart rate variability.
-Striatal endocannabinoid modulation by caffeine improves stress tolerance and flow.
-Chronic caffeine alters network activity, resulting in greater alertness and flow.
-Caffeine re-wires the dopamine reward system in ADHD for better attention and flow.

 Abstract

Flow is an intrinsically rewarding state characterised by positive affect and total task absorption. Because cognitive and physical performance are optimal in flow, chemical means to facilitate this state are appealing. Caffeine, a non-selective adenosine receptor antagonist, has been emphasized as a potential flow-inducer. Thus, we review the psychological and biological effects of caffeine that, conceptually, enhance flow. Caffeine may facilitate flow through various effects, including: i) upregulation of dopamine D1/D2 receptor affinity in reward-associated brain areas, leading to greater energetic arousal and ‘wanting’; ii) protection of dopaminergic neurons; iii) increases in norepinephrine release and alertness, which offset sleep-deprivation and hypoarousal; iv) heightening of parasympathetic high frequency heart rate variability, resulting in improved cortical stress appraisal, v) modification of striatal endocannabinoid-CB1 receptor-signalling, leading to enhanced stress tolerance; and vi) changes in brain network activity in favour of executive function and flow. We also discuss the application of caffeine to treat attention deficit hyperactivity disorder and caveats. We hope to inspire studies assessing the use of caffeine to induce flow.

Wednesday, February 21, 2024

AI makes our humanity matter more than ever.

I want to pass on this link to an NYTimes Opinion Guest essay by Aneesh Raman, a work force expert at LinkedIn,  and

Minouche Shafik, who is now the president of Columbia University, said: “In the past, jobs were about muscles. Now they’re about brains, but in the future, they’ll be about the heart.”

The knowledge economy that we have lived in for decades emerged out of a goods economy that we lived in for millenniums, fueled by agriculture and manufacturing. Today the knowledge economy is giving way to a relationship economy, in which people skills and social abilities are going to become even more core to success than ever before. That possibility is not just cause for new thinking when it comes to work force training. It is also cause for greater imagination when it comes to what is possible for us as humans not simply as individuals and organizations but as a species.

Monday, February 19, 2024

Comparing how generative AI and living organisms generate meaning suggests future direction for AI development

I want to pass on this open source opinion article in Trends in Cognitive Sciences by Karl Friston, Andy Clark, and other prominent figures who study generative models of sentient behavior in living organisms.  (They suggest a future direction for AI development that is very similar to the vision described in the previous MindBlog post, which described a recent article by Venkatesh Rao.) Here are the highlights and abstract of the article.

Highlights

  • Generative artificial intelligence (AI) systems, such as large language models (LLMs), have achieved remarkable performance in various tasks such as text and image generation.
  • We discuss the foundations of generative AI systems by comparing them with our current understanding of living organisms, when seen as active inference systems.
  • Both generative AI and active inference are based on generative models, but they acquire and use them in fundamentally different ways.
  • Living organisms and active inference agents learn their generative models by engaging in purposive interactions with the environment and by predicting these interactions. This provides them with a core understanding and a sense of mattering, upon which their subsequent knowledge is grounded.
  • Future generative AI systems might follow the same (biomimetic) approach – and learn the affordances implicit in embodied engagement with the world before – or instead of – being trained passively.

Abstract

Prominent accounts of sentient behavior depict brains as generative models of organismic interaction with the world, evincing intriguing similarities with current advances in generative artificial intelligence (AI). However, because they contend with the control of purposive, life-sustaining sensorimotor interactions, the generative models of living organisms are inextricably anchored to the body and world. Unlike the passive models learned by generative AI systems, they must capture and control the sensory consequences of action. This allows embodied agents to intervene upon their worlds in ways that constantly put their best models to the test, thus providing a solid bedrock that is – we argue – essential to the development of genuine understanding. We review the resulting implications and consider future directions for generative AI.

Saturday, February 17, 2024

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

I pass on the PDF of an article from the Gemini Team at Google. Here's the abstract describing a "working memory" system vastly greater than our own, that can hold 10 million tokens  (a 'token' is roughly 0.75 words):

In this report, we present the latest model of the Gemini family, Gemini 1.5 Pro, a highly compute-efficient multimodal mixture-of-experts model capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio. Gemini 1.5 Pro achieves near-perfect recall on long-context retrieval tasks across modalities, improves the state-of-the-art in long-document QA, long-video QA and long-context ASR, and matches or surpasses Gemini 1.0 Ultra’s state-of-the-art performance across a broad set of benchmarks. Studying the limits of Gemini 1.5 Pro’s long-context ability, we find continued improvement in next-token prediction and near-perfect retrieval (>99%) up to at least 10M tokens, a generational leap over existing models such as Claude 2.1 (200k) and GPT-4 Turbo (128k). Finally, we highlight surprising new capabilities of large language models at the frontier; when given a grammar manual for Kalamang, a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person learning from the same content.

Friday, February 16, 2024

An agent-based vision for scaling modern AI - Why current efforts are misguided.

I pass on my edited clips from Venkatesh Rao’s most recent newsletter - substantially shortening its length and inserting a few definitions of techo-nerd-speak acronyms he uses in brackets [  ].  He suggests interesting analogies between the future evolution of Ai and the evolutionary course taken by biological organisms:

…specific understandings of embodiment, boundary intelligence, temporality, and personhood, and their engineering implications, taken together, point to an agent-based vision of how to scale AI that I’ve started calling Massed Muddler Intelligence or MMI, that doesn’t look much like anything I’ve heard discussed.


…right now there’s only one option: monolithic scaling. Larger and larger models trained on larger and larger piles of compute and data…monolithic scaling is doomed. It is headed towards technical failure at a certain scale we are fast approaching


What sort of AI, in an engineering sense, should we attempt to build, in the same sense as one might ask, how should we attempt to build 2,500 foot skyscrapers? With brick and mortar or reinforced concrete? The answer is clearly reinforced concrete. Brick and mortar construction simply does not scale to those heights


…If we build AI datacenters that are 10x or 100x the scale of todays and train GPT-style models on them …problems of data movement and memory management at scale that are already cripplingly hard will become insurmountable…current monolithic approaches to scaling AI are the equivalent of brick-and-mortar construction and fundamentally doomed…We need the equivalent of a reinforced concrete beam for AI…A distributed agent-based vision of modern AI is the scaling solution we need.

Scaling Precedents from Biology

There’s a precedent here in biology. Biological intelligence scales better with more agent-like organisms. For example: humans build organizations that are smarter than any individual, if you measure by complexity of outcomes, and also smarter than the scaling achieved by less agentic eusocial organisms…ants, bees, and sheep cannot build complex planet-scale civilizations. It takes much more sophisticated agent-like units to do that.

Agents are AIs that can make up independent intentions and pursue them in the real world, in real time, in a society of similarly capable agents (ie in a condition of mutualism), without being prompted. They don’t sit around outside of time, reacting to “prompts” with oracular authority…as in sociobiology, sustainably scalable AI agents will necessarily have the ability to govern and influence other agents (human or AI) in turn, through the same symmetric mechanisms that are used to govern and influence them…If you want to scale AI sustainably, governance and influence cannot be one way street from some privileged agents (humans) to other less privileged agents (AIs)….

If you want complexity and scaling, you cannot govern and influence a sophisticated agent without opening yourself up to being governed and influenced back. The reasoning here is similar to why liberal democracies generally scale human intelligence far better than autocracies. The MMI vision I’m going to outline could be considered “liberal democracy for mixed human-AI agent systems.” Rather than the autocratic idea of “alignment” associated with “AGI,” MMIs will call for something like the emergent mutualist harmony that characterizes functional liberal democracies. You don’t need an “alignment” theory. You need social contract theory.

The Road to Muddledom

Agents, and the distributed multiagent systems (MAS) that represent the corresponding scaling model, obviously aren’t a new idea in AI…MAS were often built as light architectural extensions of early object-oriented non-AI systems… none of this machinery works or is even particularly relevant for the problem of scaling modern AI, where the core source of computational intelligence is a large-X-model with fundamentally inscrutable input-output behavior. This is a new, oozy kind of intelligence we are building with for the first time. ..We’re in new regimes, dealing with fundamentally new building materials and aiming for new scales (orders of magnitude larger than anything imagined in the 1990s).

Muddling Doctrines

How do you build muddler agents? I don’t have a blueprint obviously, but here are four loose architectural doctrines, based on the four heterodoxies I noted at the start of this essay (see links there): embodiment, boundary intelligence, temporality, and personhood.

Embodiment matters: The physical form factor AI takes is highly relevant to to its nature, behavior, and scaling potential.

Boundary intelligence matters. Past a threshold, intelligence is a function of the management of boundaries across which data flows, not the sophistication of the interiors where it is processed.

Temporality matters: The kind of time experienced by an AI matters for how it can scale sustainably.

Personhood matters: The attributes of an AI that enable humans and AIs to relate to each other as persons (I-you), rather than things (I-it), are necessary elements to being able to construct coherent scalably composable agents at all.


The first three principles require that AI computation involve real atoms, live in real time, and deal with the second law of thermodynamics

The fourth heterodoxy turns personhood …into a load-bearing architectural element in getting to scaled AI via muddler agents. You cannot have scaled AI without agency, and you cannot have a scalable sort of agency without personhood.

As we go up the scale of biological complexity, we get much programmable and flexible forms of communication and coordination. … we can start to distinguish individuals by their stable “personalities” (informationally, the identifiable signature of personhood). We go from army ants marching in death spirals to murmurations of starlings to formations of geese to wolf packs maneuvering tactically in pincer movements… to humans whose most sophisticated coordination patterns are so complex merely deciphering them stresses our intelligence to the limit.

Biology doesn’t scale to larger animals by making very large unicellular creatures. Instead it shifts to a multi-cellular strategy. Then it goes further: from simple reproduction of “mass produced” cells to specialized cells forming differentiated structures (tissues) via ontogeny (and later, in some mammals, through neoteny). Agents that scale well have to be complex and variegated agents internally, to achieve highly expressive and varied behaviors externally. But they must also present simplified facades — personas — to each other to enable the scaling and coordination.

Setting aside questions of philosophy (identity, consciousness),  personhood is a scaling strategy. Personhood is the behavioral equivalent of a cell. “Persons” are stable behavioral units that can compose in “multicellular” ways because they communicate differently than simpler agents with weak or non-existent personal boundaries, and low-agency organisms like plants and insects.

When we form and perform “personas,” we offer a harder interface around our squishy interior psyches that composes well with the interfaces of other persons for scaling purposes. A personhood performance is something like a composability API [application programmers interface] for intelligence scaling.

Beyond Training Determinism

…Right now AIs experience most of their “time” during training, and then effectively enter a kind of stasis. …They requiring versioned “updates” to get caught up again…GPT4 can’t simply grow or evolve its way to GPT5 by living life and learning from it. It needs to go through the human-assisted birth/death (or regeneration perhaps) singularity of a whole new training effort. And it’s not obvious how to automate this bottleneck in either a Darwinian or Lamarckian way.

…For all their power, modern AIs are still not able to live in real time and keep up with reality without human assistance outside of extremely controlled and stable environments…As far as temporality is concerned, we are in a “training determinism” regime that is very un-agentic and corresponds to genetic determinism in biology.What makes agents agents is that they live in real time, in a feedback loop with external reality unfolding at its actual pace of evolution.

Muddling Through vs. Godding Through

Lindblom’s paper identifies two patterns of agentic behavior, “root” (or rational-comprehensive) and “branch” (or successive limited comparisons), and argues that in complicated messy circumstances requiring coordinated action at scale, the way actually effective humans operate is the branch method, which looks like “muddling through” but gradually gets there, where the root method fails entirely. Complex here is things humans typically do in larger groups, like designing and implementing complex governance policies or undertaking complex engineering projects. The threshold for “complex” is roughly where explicit coordination protocols become necessary scaffolding. This often coincides with the threshold where reality gets too big to hold in one human head.

The root method attempts to fight limitations with brute, monolithic force. It aims to absorb all the relevant information regarding the circumstances a priori (analogous to training determinism), and discover the globally optimal solution through “rational” and “comprehensive” thinking. If the branch method is “muddling through,” we might say that the root, or rational-comprehensive approach, is an attempt to “god through.”…Lindblom’s thesis is basically that muddling through eats godding through for lunch.

To put it much more bluntly: Godding through doesn’t work at all beyond small scales and it’s not because the brains are too small. Reasoning backwards from complex goals in the context of an existing complex system evolving in real time doesn’t work. You have to discover forwards (not reason forwards) by muddling.

..in thinking about humans, it is obvious that Lindblom was right…Even where godding through apparently prevails through brute force up to some scale, the costs are very high, and often those who pay the costs don’t survive to complain…Fear of Big Blundering Gods is the essential worry of traditional AI safety theology, but as I’ve been arguing since 2012 (see Hacking the Non-Disposable Planet), this is not an issue because these BBGs will collapse under their own weight long before they get big enough for such collapses to be exceptionally, existentially dangerous.

This worry is similar to the worry that a 2,500 foot brick-and-mortar building might collapse and kill everybody in the city…It’s not a problem because you can’t build a brick-and-mortar building to that height. You need reinforced concrete. And that gets you into entirely different sorts of safety concerns.

Protocols for Massed Muddling

How do you go from individual agents (AI or human) muddling through to masses of them muddling through together? What are the protocols of massed muddling? These are also the protocols of AI scaling towards MMIs (Massed Muddler Intelligences)

When you put a lot of them together using a mix of hard coordination protocols (including virtual-economic ones) and softer cultural protocols, you get a massed muddler intelligence, or MMI. Market economies and liberal democracies are loose, low-bandwidth examples of MMIs that use humans and mostly non-AI computers to scale muddler intelligence. The challenge now is to build far denser, higher bandwidth ones using modern AI agents.

I suspect at the scales we are talking about, we will have something that looks more like a market economy than like the internal command-economy structure of the human body. Both feature a lot of hierarchical structure and differentiation, but the former is much less planned, and more a result of emergent patterns of agglomeration around environmental circumstances (think how the large metros that anchor the global economy form around the natural geography of the planet, rather than how major organ systems of the human body are put together).

While I suspect MMIs will partly emerge via choreographed ontogenic roadmaps from a clump of “stem cells” (is that perhaps what LxMs [large language models] are??), the way market economies emerge from nationalist industrial policies, overall the emergent intelligences will be masses of muddling rather than coherent artificial leviathans. Scaling “plans” will help launch, but not determine the nature of MMIs or their internal operating protocols at scale. Just like tax breaks and tariffs might help launch a market economy but not determine the sophistication of the economy that emerges or the transactional patterns that coordinate it. This also answers the regulation question: Regulating modern AI MMIs will look like economic regulation, not technology regulation.

How the agentic nature of the individual muddler agent building block is preserved and protected is the critical piece of the puzzle, just as individual economic rights (such as property rights, contracting regimes) are the critical piece in the design of “free” markets.

Muddling produces a shell of behavioral uncertainty around what a muddler agent will do, and how it will react to new information, that creates an outward pressure on the compressive forces created by the dense aggregation required for scaling. This is something like the electron degeneracy pressure that resists the collapse of stars under their own gravity. Or how the individualist streak in even the most dedicated communist human resists the collapse of even the most powerful cults into pure hive minds. Or how exit/voice dynamics resist the compression forces of unaccountable organizational management.

…the fundamental intentional tendency of individual agents, on which all other tendencies, autonomous or not, socially influencable or not, rest…[is]  body envelope integrity.

…This is a familiar concern for biological organisms. Defending against your body being violently penetrated is probably the foundation of our entire personality. It’s the foundation of our personal safety priorities — don’t get stabbed, shot, bitten, clawed or raped. All politics and economics is an extension of envelope integrity preservation instincts. For example, strictures against theft (especially identity theft) are about protecting the body envelope integrity of your economic body. Habeas corpus is the bedrock of modern political systems for a reason. Your physical body is your political body…if you don’t have body envelope integrity you have nothing.

This is easiest to appreciate in one very visceral and vivid form of MMIs: distributed robot systems. Robots, like biological organisms, have an actual physical body envelope (though unlike biological organisms they can have high-bandwidth near-field telepathy). They must preserve the integrity of that envelope as a first order of business … But robot MMIs are not the only possible form factor. We can think of purely software agents that live in an AI datacenter, and maintain boundaries and personhood envelopes that are primarily informational rather than physical. The same fundamental drive applies. The integrity of the (virtual) body envelope is the first concern.

This is why embodiment is an axiomatic concern. The nature of the integrity problem depends on the nature of the embodiment. A robot can run away from danger. A software muddler agent in a shared memory space within a large datacenter must rely on memory protection, encryption, and other non-spatial affordances of computing environments.

Personhood is the emergent result of successfully solving the body-envelope-integrity problem over time, allowing an agent to present a coherent and hard mask model to other agents even in unpredictable environments. This is not about putting a smiley-faced RLHF [Reinforcement Learning from Human Feedback]. mask on a shoggoth interior to superficially “align” it. This is about offering a predictable API for other agents to reliably interface with, so scaled structures in time and social space don’t collapse.  [They have] hardness - the property or quality that allows agents with soft and squishy interiors to offer hard and unyielding interfaces to other agents, allowing for coordination at scale.

…We can go back to the analogy to reinforced concrete. MMIs are fundamentally built out of composite materials that combine the constituent simple materials in very deliberate ways to achieve particular properties. Reinforced concrete achieves this by combining rebar and cement in particular geometries. The result is a flexible language of differentiated forms (not just cuboidal beams) with a defined grammar.

MMIs will achieve this by combining embodiment, boundary management, temporality, and personhood elements in very deliberate ways, to create a similar language of differentiated forms that interact with a defined grammar.

And then we can have a whole new culture war about whether that’s a good thing.

Wednesday, February 14, 2024

How long has humanity been at war with itself?

I would like to point MindBlog readers to an article by Deborah Barsky with the title of this post. The following clip provides relevant links to the Human Bridges project of the Independent Media Institute. 

Deborah Barsky is a writing fellow for the Human Bridges project of the Independent Media Institute, a researcher at the Catalan Institute of Human Paleoecology and Social Evolution, and an associate professor at the Rovira i Virgili University in Tarragona, Spain, with the Open University of Catalonia (UOC). She is the author of Human Prehistory: Exploring the Past to Understand the Future (Cambridge University Press, 2022).

Monday, February 12, 2024

The Art of Doing Nothing

Deep into the Juniper/Cedar pollen allergy season in Austin TX, I'm being frustrated that I have so little energy to do things. It's as if my batteries can not muster more than a 10% charge. I try to tell myself that it's OK to 'just be", to do nothing, and have not been very successful at this.  So, I enjoyed  stumbling upon a  recent Guardian article, "The art of doing nothing: have the Dutch found the answer to burnout culture?," whose URL I pass on to MindBlog readers.  It describes the concept of 'niken,' or the Dutch art of doing nothing. It has  has ameliorated my concern over how little I have been getting done, and references a 2019 NYTimes article, "The Case for Doing Nothing" that went viral when it was first published.  Letting go of always finding problems that need to be solved let's one face  a question posed by the meditation guru Loch Kelly: "What is there when there are no problems to be solved?" Variants of this question have been addressed in a thread of numerous MindBlog posts that I have now largely drawn to a close.

Friday, February 09, 2024

Bodily maps of musical sensations across cultures

Interesting work from Putkinen et al. (open source):  

Significance

Music is inherently linked with the body. Here, we investigated how music's emotional and structural aspects influence bodily sensations and whether these sensations are consistent across cultures. Bodily sensations evoked by music varied depending on its emotional qualities, and the music-induced bodily sensations and emotions were consistent across the tested cultures. Musical features also influenced the emotional experiences and bodily sensations consistently across cultures. These findings show that bodily feelings contribute to the elicitation and differentiation of music-induced emotions and suggest similar embodiment of music-induced emotions in geographically distant cultures. Music-induced emotions may transcend cultural boundaries due to cross-culturally shared links between musical features, bodily sensations, and emotions.
Abstract
Emotions, bodily sensations and movement are integral parts of musical experiences. Yet, it remains unknown i) whether emotional connotations and structural features of music elicit discrete bodily sensations and ii) whether these sensations are culturally consistent. We addressed these questions in a cross-cultural study with Western (European and North American, n = 903) and East Asian (Chinese, n = 1035). We precented participants with silhouettes of human bodies and asked them to indicate the bodily regions whose activity they felt changing while listening to Western and Asian musical pieces with varying emotional and acoustic qualities. The resulting bodily sensation maps (BSMs) varied as a function of the emotional qualities of the songs, particularly in the limb, chest, and head regions. Music-induced emotions and corresponding BSMs were replicable across Western and East Asian subjects. The BSMs clustered similarly across cultures, and cluster structures were similar for BSMs and self-reports of emotional experience. The acoustic and structural features of music were consistently associated with the emotion ratings and music-induced bodily sensations across cultures. These results highlight the importance of subjective bodily experience in music-induced emotions and demonstrate consistent associations between musical features, music-induced emotions, and bodily sensations across distant cultures.

Wednesday, February 07, 2024

Historical Myths as Culturally Evolved Technologies for Coalitional Recruitment

I pass on to MindBlog readers the abstract of a recent Behavioral and Brain Science article by Sijilmassi et al. titled "‘Our Roots Run Deep’: Historical Myths as Culturally Evolved Technologies for Coalitional Recruitment."  Motivated readers can obtain a PDF of the article from me. 

One of the most remarkable manifestations of social cohesion in large-scale entities is the belief in a shared, distinct and ancestral past. Human communities around the world take pride in their ancestral roots, commemorate their long history of shared experiences, and celebrate the distinctiveness of their historical trajectory. Why do humans put so much effort into celebrating a long-gone past? Integrating insights from evolutionary psychology, social psychology, evolutionary anthropology, political science, cultural history and political economy, we show that the cultural success of historical myths is driven by a specific adaptive challenge for humans: the need to recruit coalitional support to engage in large scale collective action and prevail in conflicts. By showcasing a long history of cooperation and shared experiences, these myths serve as super-stimuli, activating specific features of social cognition and drawing attention to cues of fitness interdependence. In this account, historical myths can spread within a population without requiring group-level selection, as long as individuals have a vested interest in their propagation and strong psychological motivations to create them. Finally, this framework explains, not only the design-features of historical myths, but also important patterns in their cross-cultural prevalence, inter-individual distribution, and particular content.

Monday, February 05, 2024

Functional human brain tissue produced by layering different neuronal types with 3D bioprinting

A very important advance by Su-Chun Zhang and collaborators at the University of Wisconsin that moves studies of nerve cells connecting in nutrient dishes from two to three dimensions:  

Highlights

  • Functional human neural tissues assembled by 3D bioprinting
  • Neural circuits formed between defined neural subtypes
  • Functional connections established between cortical-striatal tissues
  • Printed tissues for modeling neural network impairment

Summary

Probing how human neural networks operate is hindered by the lack of reliable human neural tissues amenable to the dynamic functional assessment of neural circuits. We developed a 3D bioprinting platform to assemble tissues with defined human neural cell types in a desired dimension using a commercial bioprinter. The printed neuronal progenitors differentiate into neurons and form functional neural circuits within and between tissue layers with specificity within weeks, evidenced by the cortical-to-striatal projection, spontaneous synaptic currents, and synaptic response to neuronal excitation. Printed astrocyte progenitors develop into mature astrocytes with elaborated processes and form functional neuron-astrocyte networks, indicated by calcium flux and glutamate uptake in response to neuronal excitation under physiological and pathological conditions. These designed human neural tissues will likely be useful for understanding the wiring of human neural networks, modeling pathological processes, and serving as platforms for drug testing.
 

 


Friday, February 02, 2024

Towards a Metaphysics of Worlds

I have a splitting headache from having just watched a 27 minute long YouTube rapid fire lecture by Venkatesh Rao, given last November at the Autonomous Worlds Assembly in Istanbul (part of DevConnect, a major Ethereum ecosystem event).  His latest newsletter “Towards a Metaphysics of Worlds” gives adds some notes and context, and gives a link  to its slides. As Rao notes:

“This may seem like a glimpse into a very obscure and nerdy subculture for many (most?) of you, but I think something very important and interesting is brewing in this scene and more people should know about it.”

I would suggest that you to skip the YouTube lecture and cherry pick your way through his slides.  Some are very simple and quite striking, clearly presenting interesting ideas about the epistomology, ontology, and definitions of worlds.  Here is Slide 11, where what Rao means by "Worlds" is made more clear: