An AI framework for neural–behavioral modeling

Work of Sani et al. (open access) is reported in the Oct. 2024 issue of Nature Neuroscience. From the editor's summary:

Neural dynamics are complex and simultaneously relate to distinct behaviors. To address these challenges, Sani et al. have developed an AI framework termed DPAD that achieves nonlinear dynamical modeling of neural–behavioral data, dissociates behaviorally relevant neural dynamics, and localizes the source of nonlinearity in the dynamical model. What DPAD does is visualized as separating the overall brain activity into distinct pieces related to specific behaviors and discovering how these pieces fit together to build the overall activity.

Here is the Sani et al. abstract:

Understanding the dynamical transformation of neural activity to behavior requires new capabilities to nonlinearly model, dissociate and prioritize behaviorally relevant neural dynamics and test hypotheses about the origin of nonlinearity. We present dissociative prioritized analysis of dynamics (DPAD), a nonlinear dynamical modeling approach that enables these capabilities with a multisection neural network architecture and training approach. Analyzing cortical spiking and local field potential activity across four movement tasks, we demonstrate five use-cases. DPAD enabled more accurate neural–behavioral prediction. It identified nonlinear dynamical transformations of local field potentials that were more behavior predictive than traditional power features. Further, DPAD achieved behavior-predictive nonlinear neural dimensionality reduction. It enabled hypothesis testing regarding nonlinearities in neural–behavioral transformation, revealing that, in our datasets, nonlinearities could largely be isolated to the mapping from latent cortical dynamics to behavior. Finally, DPAD extended across continuous, intermittently sampled and categorical behaviors. DPAD provides a powerful tool for nonlinear dynamical modeling and investigation of neural–behavioral data.

Battle of the Bots - AI gets creative with Brain Hacks

This post is meant to archive my recent exercise of asking  ChatGPT 4o, Perplexity, Gemini+ advanced,  and Anthropic  (which does not offer links to its responses) to expand my  MindBlog's Brain Hacks post into a more complete essay that offers more background and context.  They responded with slightly different styles and impressive results that required tweaking for only a few small errors.  I thought my more concise text was more inviting, and didn't see anything really new, so my next prompt  (which I had asked ChatGPT to edit and improve) said:

"Your responses are well-constructed, but I’m seeking a deeper layer of insight—something that might challenge conventional perspectives or unveil an overlooked nuance. Can you reframe these ideas or introduce a novel approach that stretches beyond familiar interpretations??"

Here things got interesting.  Click the links above and scroll down to the responses to this final prompt to see the results.  The links most likely are maintained as long as I maintain paying accounts to the bots. (Anthropic does not offer links to its exchanges.)

I pass on here what I think is the most original and striking response, from perplexity. It was right on target,  brimming with new perspectives (some of them the subject of previous posts on my MindBlog), and links to a treasure trove of articles and ideas I was not familiar with from seven sources it provided links to.   Its response also provided a potpourri  or more or less relevant graphic images of brains and models.  

Here is the perplexities text, with links to a few of the seven sources it cites, all of which you can obtain directly from perplexity.:

Reframing our understanding of introspective awareness and cognitive modulation requires us to challenge some fundamental assumptions about the nature of consciousness and self-regulation. Let's explore a more provocative perspective that integrates recent neuroscientific findings with evolutionary theory and cognitive science.

The Illusion of Conscious Control

Contrary to the intuitive notion that our conscious mind is the primary driver of behavior and emotion regulation, emerging evidence suggests that consciousness might be more of an observer than a controller. The brain's emotional and regulatory processes often occur before we become consciously aware of them, suggesting that our sense of volitional control might be, to some extent, an illusion.

Consider the possibility that what we perceive as "introspective awareness" is actually a post-hoc narrative constructed by our brain to make sense of unconscious processes. This perspective challenges the traditional view of top-down emotion regulation and suggests a more complex interplay between conscious and unconscious processes.

The Social Evolution of Consciousness

Recent theories propose that human consciousness evolved primarily for social survival rather than individual cognitive enhancement [ref]. This social evolutionary framework suggests that subjective awareness might lack independent causal influence over psychological processes or actions. Instead, it may serve as a communication tool for social interaction and collective decision-making.

This perspective reframes our understanding of self-awareness and emotion regulation. Rather than viewing these abilities as individual cognitive skills, we might consider them as evolved social tools that facilitate group cohesion and cooperation. The ability to regulate emotions, in this context, becomes less about personal control and more about social signaling and coordination.

Neurotransmitter Systems and Emotion Regulation

Recent research has begun to map the overlap between emotion regulation brain networks and specific neurotransmitter systems [ref] [ref]. This approach offers a novel way to understand the biochemical basis of self-regulation. Instead of focusing solely on cognitive strategies or neural networks, we might conceptualize emotion regulation as a dynamic interplay between neurotransmitter systems.

This biochemical perspective opens up new avenues for
understanding individual differences in emotion regulation abilities. It suggests that what we perceive as "willpower" or "self-control" might be more accurately described as the efficient functioning of specific neurotransmitter systems.

The Myth of the Unified Self

Traditional models of self-awareness and emotion regulation often assume a unified, coherent self that observes and controls mental processes. However, neuroscientific evidence increasingly points to a more fragmented view of consciousness and self-hood.

Instead of a single, unified self, we might conceptualize the mind as a coalition of semi-independent processes, each with its own goals and methods of regulation. This "multiple selves" model challenges our intuitive understanding of self-awareness and suggests that emotion regulation might be more about negotiation between different aspects of our psyche than about top-down control.

Conclusion: Towards a New Paradigm

By integrating these perspectives, we can envision a new paradigm for understanding introspective awareness and cognitive modulation. This model would:

1. Recognize the limited role of conscious volition in emotion regulation
2. Emphasize the social and communicative functions of self-awareness
3. Incorporate the biochemical basis of regulatory processes
4. Embrace a more fragmented view of consciousness and selfhood

This approach challenges us to rethink fundamental concepts like free will, personal responsibility, and the nature of the self. It suggests that effective emotion regulation might be less about strengthening willpower and more about understanding and working with the complex, often unconscious processes that shape our emotional lives. By adopting this more nuanced and provocative perspective, we open up new avenues for research and potentially more effective interventions for emotional and cognitive well-being.

MindBlog's Brain Hacks

Introspective awareness and modulation of both ancient and more recently evolved aspects of our cognition:

Brain Hack #1
-The reptilian brain (whose modern descendant is found in the mammalian hypothalamus) generates affective states along axes of arousal and valence, whose states in higher primates can be assessed by introspective awareness.

Brain Hack #2
-The early mammalian emotional brain, whose ability to model a self (correlating with the appearance of the agranular prefrontal cortex), develops the ability to distinguish the difference between being (immersed in) an affective state and seeing (observing) it.

Brain Hack #3
-The appearance in the primate brain of the further ability to imagine the minds of others (correlating with appearance of the granular prefrontal cortex), permits appropriate assignments of agency, being able to distinguish one’s own experience (and problems) from the experience (and problems) of others.

The introspection that enables this ensemble of brain hacks can be strengthened by practice of three fundamental meditation techniques: focused awareness (in which our brain’s attentional mode predominates), open awareness (engaging our default mode network), and non-dual awareness (during which both are muted).  

*************
The above is an early draft text that I will be editing further (like my “Tokens of Sanity” post which has had at least six revisions since it 9/29/2024 posting).  It is trying to meld together and condense threads from my last public lecture and Max Bennett's recent book "A Brief History of Intelligence."  Feedback and comment welcome.

Tokens of sanity

-Being a calm space in which nothing can hurry

-An animal body that pretends to be human

-Dissociating from the word cloud and emotional reactivities of self and other selves.

-A courteous guest in one’s own body and when with others, owning one’s own experience and letting others own theirs.

-Favoring reflectivity over reactivity, caressing novelty

-Clinging to nothing, the current self being a passing fantasy

-Letting each moment be what it is, not what it should be

-A blip in the flow of cosmic time

The brain simulates actions and their consequences during REM sleep

During REM sleep our brains make up and work though simulated scenarios, while putting our bodies into paralysis so we don't thrash about dangerously....  Senzai and Scanziani show what in going on in mouse brains. Here is the first paragraph (abstract) of their open source text:

Vivid dreams mostly occur during a phase of sleep called REM1–5. During REM sleep, the brain’s internal representation of direction keeps shifting like that of an awake animal moving through its environment6–8. What causes these shifts, given the immobility of the sleeping animal? Here we show that the superior colliculus of the mouse, a motor command center involved in orienting movements9–15, issues motor commands during REM sleep, e.g. turn left, that are similar to those issued in the awake behaving animal. Strikingly, these motor commands, despite not being executed, shift the internal representation of direction as if the animal had turned. Thus, during REM sleep, the brain simulates actions by issuing motor commands that, while not executed, have consequences as if they had been. This study suggests that the sleeping brain, while disengaged from the external world, uses its internal model of the world to simulate interactions with it.

From nematodes to humans a common brain network motif intertwines hierarchy and modularity.

Pathak et al. (abstract below) suggest the evolved pattern they describe may apply to information processing networks in general, in particular to those of evolving AI implementations.

Significance

Nervous systems are often schematically represented in terms of hierarchically arranged layers with stimuli in the “input” layer sequentially transformed through successive layers, eventually giving rise to response in the “output” layer. Empirical investigations of hierarchy in specific brain regions, e.g., the visual cortex, typically employ detailed anatomical information. However, a general method for identifying the underlying hierarchy from the connectome alone has so far been elusive. By proposing an optimized index that quantifies the hierarchy extant in a network, we reveal an architectural motif underlying the mesoscopic organization of nervous systems across different species. It involves both modular partitioning and hierarchical layered arrangement, suggesting that brains employ an optimal mix of parallel (modular) and sequential (hierarchic) information processing.

Abstract

Networks involved in information processing often have their nodes arranged hierarchically, with the majority of connections occurring in adjacent levels. However, despite being an intuitively appealing concept, the hierarchical organization of large networks, such as those in the brain, is difficult to identify, especially in absence of additional information beyond that provided by the connectome. In this paper, we propose a framework to uncover the hierarchical structure of a given network, that identifies the nodes occupying each level as well as the sequential order of the levels. It involves optimizing a metric that we use to quantify the extent of hierarchy present in a network. Applying this measure to various brain networks, ranging from the nervous system of the nematode Caenorhabditis elegans to the human connectome, we unexpectedly find that they exhibit a common network architectural motif intertwining hierarchy and modularity. This suggests that brain networks may have evolved to simultaneously exploit the functional advantages of these two types of organizations, viz., relatively independent modules performing distributed processing in parallel and a hierarchical structure that allows sequential pooling of these multiple processing streams. An intriguing possibility is that this property we report may be common to information processing networks in general.

The ebb and flow of physical and cognitive fatigue

Matthews et al. Have investigated moment-to-moment fluctuations in fatigue using behavioral experiments and computational modeling to offer a precise account of how fatigue waxes (during physical and cognitive effort) and wanes (during rest).   From Bijleveld's review of the work:

A key insight from this work is that there are important parallels between physical and cognitive fatigue. Specifically, for both types of fatigue, the best-fitting computational model parsed fatigue into two components: a recoverable component (i.e., the share of fatigue that increases with effort and recovers with rest) and an unrecoverable component (i.e., the share of fatigue that only increases with effort and does not recover with rest, at least not within the ~1-h session). For physical fatigue, this result conceptually replicates a previous study; for cognitive fatigue, this result is new and original. Together, these findings paint a nuanced picture of how both physical and cognitive fatigue emerge over time. From an applied angle, the distinction between recoverable and unrecoverable fatigue is intriguing: it has potential implications for task design, such as in the contexts of education and work...On balance, physical and cognitive fatigue may be more similar than they are different.

Here is the Maltthews et al. abstract:

The willingness to exert effort for reward is essential but comes at the cost of fatigue. Theories suggest fatigue increases after both physical and cognitive exertion, subsequently reducing the motivation to exert effort. Yet a mechanistic understanding of how this happens on a moment-to-moment basis, and whether mechanisms are common to both mental and physical effort, is lacking. In two studies, participants reported momentary (trial-by-trial) ratings of fatigue during an effort-based decision-making task requiring either physical (grip-force) or cognitive (mental arithmetic) effort. Using a novel computational model, we show that fatigue fluctuates from trial-to-trial as a function of exerted effort and predicts subsequent choices. This mechanism was shared across the domains. Selective to the cognitive domain, committing errors also induced momentary increases in feelings of fatigue. These findings provide insight into the computations underlying the influence of effortful exertion on fatigue and motivation, in both physical and cognitive domains.

 

 

Architectural experience influences the processing of others’ body expressions

An open source article by Presti et al:  

Significance

The motor system has been recognized as a fundamental neural machinery for spatial and social cognition, making the study of the interplay between architecture and social behavior worthwhile. Here, we tested how a virtual architectural experience alters the subsequent processing of body expressions, showing that the motor system participates at two distinct stages: the earliest influenced by the dynamic architectural experience and the latter modulated by the actual physical characteristics. These findings highlight the existence of an overlapping motor neural substrate devoted to spatial and social cognition, with the architectural space exerting an early and possibly adapting effect on the later social experience. Ultimately, spatial design may impact the processing of human emotions.

Abstract

The interplay between space and cognition is a crucial issue in Neuroscience leading to the development of multiple research fields. However, the relationship between architectural space and the movement of the inhabitants and their interactions has been too often neglected, failing to provide a unifying view of architecture's capacity to modulate social cognition broadly. We bridge this gap by requesting participants to judge avatars’ emotional expression (high vs. low arousal) at the end of their promenade inside high- or low-arousing architectures. Stimuli were presented in virtual reality to ensure a dynamic, naturalistic experience. High-density electroencephalography (EEG) was recorded to assess the neural responses to the avatar’s presentation. Observing highly aroused avatars increased Late Positive Potentials (LPP), in line with previous evidence. Strikingly, 250 ms before the occurrence of the LPP, P200 amplitude increased due to the experience of low-arousing architectures, reflecting an early greater attention during the processing of body expressions. In addition, participants stared longer at the avatar’s head and judged the observed posture as more arousing. Source localization highlighted a contribution of the dorsal premotor cortex to both P200 and LPP. In conclusion, the immersive and dynamic architectural experience modulates human social cognition. In addition, the motor system plays a role in processing architecture and body expressions suggesting that the space and social cognition interplay is rooted in overlapping neural substrates. This study demonstrates that the manipulation of mere architectural space is sufficient to influence human social cognition.

What your brain is doing after the light turns green.

 Gandhi and collaboratores show that if you step out to cross the street without looking right or left the neural activity in the brain is different than if you look from side to side first to be sure no cars are coming. Population level analysis of movement-related transient activity patterns in a population of superior colliculus neurons change in the two different contexts, and this difference is not readily identifiable in single-unit recordings.  Here is their technical abstract:

Sensorimotor transformation is the process of first sensing an object in the environment and then producing a movement in response to that stimulus. For visually guided saccades, neurons in the superior colliculus (SC) emit a burst of spikes to register the appearance of stimulus, and many of the same neurons discharge another burst to initiate the eye movement. We investigated whether the neural signatures of sensation and action in SC depend on context. Spiking activity along the dorsoventral axis was recorded with a laminar probe as Rhesus monkeys generated saccades to the same stimulus location in tasks that require either executive control to delay saccade onset until permission is granted or the production of an immediate response to a target whose onset is predictable. Using dimensionality reduction and discriminability methods, we show that the subspaces occupied during the visual and motor epochs were both distinct within each task and differentiable across tasks. Single-unit analyses, in contrast, show that the movement-related activity of SC neurons was not different between tasks. These results demonstrate that statistical features in neural activity of simultaneously recorded ensembles provide more insight than single neurons. They also indicate that cognitive processes associated with task requirements are multiplexed in SC population activity during both sensation and action and that downstream structures could use this activity to extract context. Additionally, the entire manifolds associated with sensory and motor responses, respectively, may be larger than the subspaces explored within a certain set of experiments.

Emergent analogical reasoning in large language models

Things are moving very fast in AI development. From Webb et al:

The recent advent of large language models has reinvigorated debate over whether human cognitive capacities might emerge in such generic models given sufficient training data. Of particular interest is the ability of these models to reason about novel problems zero-shot, without any direct training. In human cognition, this capacity is closely tied to an ability to reason by analogy. Here we performed a direct comparison between human reasoners and a large language model (the text-davinci-003 variant of Generative Pre-trained Transformer (GPT)-3) on a range of analogical tasks, including a non-visual matrix reasoning task based on the rule structure of Raven’s Standard Progressive Matrices. We found that GPT-3 displayed a surprisingly strong capacity for abstract pattern induction, matching or even surpassing human capabilities in most settings; preliminary tests of GPT-4 indicated even better performance. Our results indicate that large language models such as GPT-3 have acquired an emergent ability to find zero-shot solutions to a broad range of analogy problems.

Chemistry that regulates whether we stay with what we're doing or try something new

Sidorenko et al. demonstrate that stimulating the brain's cholinergic and noradrenergic systems enhances optimal foraging behaviors in humans. Their significance statement and abstract:  

Significance

Deciding when to say “stop” to the ongoing course of action is paramount for preserving mental health, ensuring the well-being of oneself and others, and managing resources in a sustainable fashion. And yet, cross-species studies converge in their portrayal of real-world decision-makers who are prone to the overstaying bias. We investigated whether and how cognitive enhancers can reduce this bias in a foraging context. We report that the pharmacological upregulation of cholinergic and noradrenergic systems enhances optimality in a common dilemma—staying with the status quo or leaving for more rewarding alternatives—and thereby suggest that acetylcholine and noradrenaline causally mediate foraging behavior in humans.

Abstract

Foraging theory prescribes when optimal foragers should leave the current option for more rewarding alternatives. Actual foragers often exploit options longer than prescribed by the theory, but it is unclear how this foraging suboptimality arises. We investigated whether the upregulation of cholinergic, noradrenergic, and dopaminergic systems increases foraging optimality. In a double-blind, between-subject design, participants (N = 160) received placebo, the nicotinic acetylcholine receptor agonist nicotine, a noradrenaline reuptake inhibitor reboxetine, or a preferential dopamine reuptake inhibitor methylphenidate, and played the role of a farmer who collected milk from patches with different yield. Across all groups, participants on average overharvested. While methylphenidate had no effects on this bias, nicotine, and to some extent also reboxetine, significantly reduced deviation from foraging optimality, which resulted in better performance compared to placebo. Concurring with amplified goal-directedness and excluding heuristic explanations, nicotine independently also improved trial initiation and time perception. Our findings elucidate the neurochemical basis of behavioral flexibility and decision optimality and open unique perspectives on psychiatric disorders affecting these functions.

A shared novelty-seeking basis for creativity and curiosity

I pass on the abstract of a target article having the title of this post, sent to me by Behavioral and Brain Science. I'm reading through it, and would be willing to send a PDF of the article to motivated MindBlog readers who wish to check it out.

Curiosity and creativity are central pillars of human growth and invention. While they have been studied extensively in isolation, the relationship between them has not yet been established. We propose that curiosity and creativity both emanate from the same mechanism of novelty-seeking. We first present a synthesis showing that curiosity and creativity are affected similarly by a number of key cognitive faculties such as memory, cognitive control, attention, and reward. We then review empirical evidence from neuroscience research, indicating that the same brain regions are involved in both curiosity and creativity, focusing on the interplay between three major brain networks: the default-mode network, the salience network, and the executive control network. After substantiating the link between curiosity and creativity, we propose a novelty- seeking model (NSM) that underlies them both and suggest that the manifestation of the NSM is governed by one’s state of mind (SoM).

A MindBlog paragraph on non-dual awareness massaged by Bard and ChatGPT-4

This post is using MindBlog to archive a further example example of the sort shown in Monday’s post ...of GPT bots editing  a paragraph I wrote, this one being on non-dual awareness.  The paragraph:

The fact that  there is no there there means it. Empty is empty. The ground state is just the mute body doing its thing. From its depths storylines rise and fall, bonbons to savor or ignore. The body can know that it has found a new ground state, and can be no longer willing to just play nice if the upstairs word salad is dinking around with or threatening its peace. It can withdraw from the fray. It can rest as a calm non-dual self centered within a cosmic perspective of time and space that views the rise and fall of human civilizations during the anthropocene as minor dances in the music of time.

After incorporating some changes suggested by Google Bard and GPT4:

When we say something is empty, we mean just that: it contains nothing. Our natural baseline or 'ground state' is the body performing its automatic functions without our conscious intervention. From the body's inherent depths, various thoughts or experiences emerge and recede, like bonbons we can savor or ignore. The body can grow to know that it has found a new ground state in the present moment, a ground state of awareness that no longer needs to be caught up in word salads of disruptive thoughts that can threaten its peace.  It can withdraw from the fray and can rest as a calm non-dual state of being centered within a cosmic perspective of time and space that can view the rise and fall of human civilizations during the anthropocene as minor dances in the music of time.

Turing, von Neumann, and the computational architecture of biological machines

I pass on the abstract of a PNAS perspective article by Hashim M. Al-Hashimi (motivated readers can obtain a PDF of the article from me).

In the mid-1930s, the English mathematician and logician Alan Turing invented an imaginary machine which could emulate the process of manipulating finite symbolic configurations by human computers. His machine launched the field of computer science and provided a foundation for the modern-day programmable computer. A decade later, building on Turing’s machine, the American–Hungarian mathematician John von Neumann invented an imaginary self-reproducing machine capable of open-ended evolution. Through his machine, von Neumann answered one of the deepest questions in Biology: Why is it that all living organisms carry a self-description in the form of DNA? The story behind how two pioneers of computer science stumbled on the secret of life many years before the discovery of the DNA double helix is not well known, not even to biologists, and you will not find it in biology textbooks. Yet, the story is just as relevant today as it was eighty years ago: Turing and von Neumann left a blueprint for studying biological systems as if they were computing machines. This approach may hold the key to answering many remaining questions in Biology and could even lead to advances in computer science.

People who talk too much

I host a monthly discussion group in Austin TX, The Austin Rainbow Forum, that meets at 2 pm on the first Sunday of every month to consider interesting topics and ideas. On this past May 7, one of our group members led a discussion of "Overtalking" in the modern world, which has got us all spouting opinions, giving advice, and getting ourselves in trouble, according to Dan Lyons in his recent book titled "STFU: The Power of Keeping Your Mouth Shut in an Endlessly Noisy World."  The central ideas in Lyons’ book are summarized in this Time Magazine article. I looked through a reviewers copy of the book I was sent, and suggest that it is worth having a look if you are stimulated by the summary article. The bottom line of the book could be stated as "Shut up and listen instead of talking so much." Lyons offers five nudges: 

-When possible, say nothing

-Master the power of the pause

-Quit social media

-Seek out silence

-Learn how to listen

Lyons is a professional columnist who writes with a very engaging style, even if the level of his coverage is sometimes a bit superficial.  (He quotes a researcher who studied brain activity and '“figured out what causes talkaholism,” ...unfortunately, on doing a quick look up of the work describing the neuronal measurements, I found that there is no there there.)

Virality

This post is the ninth and final installment of my passing on to both MindBlog readers and my future self my idiosyncratic selection of clips of text from O’Gieblyn’s book ‘God, Human, Animal, Machine’ that I have found particularly interesting. Here are fragments of Chapter 13 from the  seventh section of her book, titled "Virality"

The most successful metaphors become invisible through ubiquity. The same is true of ideology, which, as it becomes thoroughly integrated into a culture, sheds its contours and distinctive outline and dissolves finally into pure atmosphere. Although digital technology constitutes the basic architecture of the information age, it is rarely spoken of as a system of thought. Its inability to hold ideas or beliefs, preferences or opinions, is often misunderstood as an absence of philosophy rather than a description of its tenets. The central pillar of this ideology is its conception of being, which might be described as an ontology of vacancy—a great emptying-out of qualities, content, and meaning. This ontology feeds into its epistemology, which holds that knowledge lies not in concepts themselves but in the relationships that constitute them, which can be discovered by artificial networks that lack any true knowledge of what they are uncovering. And as global networks have come to encompass more and more of our  human relations, it’s become increasingly difficult to speak of ourselves—the nodes of this enormous brain—as living agents with beliefs, preferences, and opinions.

The term “viral media” was coined in 1994 by the critic Douglas Rushkoff, who argued that the internet had become “an extension of a living organism” that spanned the globe and radically accelerated the way ideas and culture spread. The notion that the laws of the biosphere could apply to the datasphere was already by that point taken for granted, thanks to the theory of memes, a term Richard Dawkins devised to show that ideas and cultural phenomena spread across a population in much the same way genes do. iPods are memes, as are poodle skirts, communism, and the Protestant Reformation. The main benefit of this metaphor was its ability to explain how artifacts and ideologies reproduce themselves without the participation of conscious subjects. Just as viruses infect hosts without their knowledge or consent, so memes have a single “goal,” self-preservation and spread, which they achieve by latching on to a host and hijacking its reproductive machinery for their own ends. That this entirely passive conception of human culture necessitates the awkward reassignment of agency to the ideas themselves—imagining that memes have “goals” and “ends”—is usually explained away as a figure of speech.

When Rushkoff began writing about “viral media,” the internet was still in the midst of its buoyant overture, and he believed, as many did at the time, that this highly networked world would benefit “people who lack traditional political power.” A system that has no knowledge of a host’s identity or status should, in theory, be radically democratic. It should, in theory, level existing hierarchies and create an even playing field, allowing the most potent ideas to flourish, just as the most successful genes do under the indifferent gaze of nature. By 2019, however, Rushkoff had grown pessimistic. The blind logic of the network was, it turned out, not as blind as it appeared—or rather, it could be manipulated by those who already had enormous resources. “Today, the bottom-up techniques of guerrilla media activists are in the hands of the world’s wealthiest corporations, politicians, and propagandists,” Rushkoff writes in his book Team Human. What’s more, it turns out that the blindness of the system does not ensure its judiciousness. Within the highly competitive media landscape, the metrics of success have become purely quantitative—page views, clicks, shares—and so the potential for spread is often privileged over the virtue or validity of the content. “It doesn’t matter what side of an issue people are on for them to be affected by the meme and provoked to replicate it,” Rushkoff writes. In fact the most successful memes don’t appeal to our intellect at all. Just as the proliferation of a novel virus depends on bodies that have not yet developed an effective immune response, so the most effective memes are those that bypass the gatekeeping rational mind and instead trigger “our most automatic impulses.” This logic is built into the algorithms of social media, which replicate content that garners the most extreme reactions and which foster, when combined with the equally blind and relentless dictates of a free market, what one journalist has called “global, real-time contests for attention.”
            
The general public has become preoccupied by robots—or rather “bots,” the diminutive, a term that appears almost uniformly in the plural, calling to mind a swarm or infestation, a virus in its own right, though in most cases they are merely the means of transmission. It should not have come as a surprise that a system in which ideas are believed to multiply according to their own logic, by pursuing their own ends, would come to privilege hosts that are not conscious at all. There had been suspicions since the start of the pandemic about the speed and efficiency with which national discourse was hijacked by all manner of hearsay, conspiracy, and subterfuge.

The problem is not merely that public opinion is being shaped by robots. It’s that it has become impossible to decipher between ideas that represent a legitimate political will and those that are being mindlessly propagated by machines. This uncertainty creates an epistemological gap that renders the assignment of culpability nearly impossible and makes it all too easy to forget that these ideas are being espoused and proliferated by members of our democratic system—a problem that is far more deep-rooted and entrenched and for which there are no quick and easy solutions. Rather than contending with this fact, there is instead a growing consensus that the platforms themselves are to blame, though no one can settle on precisely where the problem lies: The algorithms? The structure? The lack of censorship and intervention? Hate speech is often spoken of as though it were a coding error—a “content-moderation nightmare,” an “industry-wide problem,” as various platform executives have described it, one that must be addressed through “different technical changes,” most of which are designed to appease advertisers. Such conversations merely strengthen the conviction that the collective underbelly of extremists, foreign agents, trolls, and robots is an emergent feature of the system itself, a phantasm arising mysteriously from the code, like Grendel awakening out of the swamp.

Donald Trump himself, a man whose rise to power may or may not have been aided by machines, is often included in this digital phantasm, one more emergent property of the network’s baffling complexity…Robert A. Burton, a prominent neurologist, claimed in an article that the president made sense once you stopped viewing him as a human being and began to see him as “a rudimentary artificial intelligence-based learning machine.” Like deep-learning systems, Trump was working blindly through trial and error, keeping a record of what moves worked in the past and using them to optimize his strategy, much like AlphaGo, the AI system that swept the Go championship in Seoul. The reason that we found him so baffling was that we continually tried to anthropomorphize him, attributing intention and ideology to his decisions, as though they stemmed from a coherent agenda. AI systems are so wildly successful because they aren’t burdened with any of these rational or moral concerns—they don’t have to think about what is socially acceptable or take into account downstream consequences. They have one goal—winning—and this rigorous single-minded interest is consistently updated through positive feedback. Burton’s advice to historians and policy wonks was to regard Trump as a black box. “As there are no lines of reasoning driving the network’s actions,” he wrote, “it is not possible to reverse engineer the network to reveal the ‘why’ of any decision.”

If we resign ourselves to the fact that our machines will inevitably succeed us in power and intelligence, they will surely come to regard us this way, as something insensate and vaguely revolting, a glitch in the operation of their machinery. That we have already begun to speak of ourselves in such terms is implicit in phrases like “human error,” a phrase that is defined, variously, as an error that is typical of humans rather than machines and as an outcome not desired by a set of rules or an external observer. We are indeed the virus, the ghost in the machine, the bug slowing down a system that would function better, in practically every sense, without us.

If Blumenberg is correct in his account of disenchantment, the scientific revolution was itself a leap of faith, an assertion that the ill-conceived God could no longer guarantee our worth as a species, that our earthly frame of reference was the only valid one. Blumenberg believed that the crisis of nominalism was not a one-time occurrence but rather one of many “phases of objectivization that loose themselves from their original motivation.” The tendency to privilege some higher order over human interests had emerged throughout history—before Ockham and the Protestant reformers it had appeared in the philosophy of the Epicureans, who believed that there was no correspondence between God and earthly life. And he believed it was happening once again in the technologies of the twentieth century, as the quest for knowledge loosened itself from its humanistic origins. It was at such moments that it became necessary to clarify the purpose of science and technology, so as to “bring them back into their human function, to subject them again to man’s purposes in relation to the world.” …Arendt hoped that in the future we would develop an outlook that was more “geocentric and anthropomorphic.”  She advocated a philosophy that took as its starting point the brute fact of our mortality and accepted that the earth, which we were actively destroying and trying to escape, was our only possible home.”

The Data Deluge - Dataism

This post is the eighth installment of my passing on to both MindBlog readers and my future self my idiosyncratic selection of clips of text from O’Gieblyn’s book ‘God, Human, Animal, Machine’ that I have found particularly interesting. Here are fragments of Chapters 11 and 12 from the  sixth section of her book, titled "Algorithm."

Chapter 11  

In the year 2001 alone, the amount of information generated doubled that of all information produced in human history. In 2002 it doubled again, and this trend has continued every year since. As Anderson noted, researchers in virtually every field have so much information that it is difficult to find relationships between things or make predictions.

What companies like Google discovered is that when you have data on this scale, you no longer need a theory at all. You can simply feed the numbers into algorithms and let them make predictions based on the patterns and relationships they notice…
“Google Translate “learned” to translate English to French simply by scanning Canadian documents that contained both languages, even though the algorithm has no model that understands either language.

These mathematical tools can predict and understand the world more adequately than any theory could.  Petabytes allow us to say: ‘Correlation is enough,’…We can stop looking for models. We can analyze the data without hypotheses about what it might show. We can let statistical algorithms find patterns where science cannot. Of course, data alone can’t tell us why something happens—the variables on that scale are too legion—but maybe our need to know why was misguided. Maybe we should stop trying to understand the world and instead trust the wisdom of algorithms…technologies that have emerged .. have not only affirmed the uselessness of our models but revealed that machines are able to generate their own models of the world…this approach makes a return to a premodern epistemology..If we are no longer permitted to ask why…we will be forced to accept the decisions of our algorithms blindly, like Job accepting his punishment...

Deep learning, an especially powerful brand of machine learning has become the preferred means of drawing predictions from our era’s deluge of raw data. Credit auditors use it to decide whether or not to grant a loan. The CIA uses it to anticipate social unrest. The systems can be found in airport security software…many people now find themselves in a position much like Job’s, denied the right to know why they were refused a loan or fired from a job or given a likelihood of developing cancer. It’s difficult, in fact, to avoid the comparison to divine justice, given that our justice system has become a veritable laboratory of machine-learning experiments…In his book Homo Deus, Yuval Noah Harari makes virtually the same analogy: “Just as according to Christianity we humans cannot understand God and His plan, so Dataism declares that the human brain cannot fathom the new master algorithms.”

Hans Blumenberg, the postwar German philosopher, notes in his 1966 book The Legitimacy of the Modern Age—one of the major disenchantment texts—that theologians began to doubt around the thirteenth century that the world could have been created for man’s benefit…Blumenberg believed that it was impossible to understand ourselves as modern subjects without taking into account the crisis that spawned us. To this day many “new” ideas are merely attempts to answer questions that we have inherited from earlier periods of history, questions that have lost their specific context in medieval Christianity as they’ve made the leap from one century to the next, traveling from theology to philosophy to science and technology. In many cases, he argued, the historical questions lurking in modern projects are not so much stated but implied. We are continually returning to the site of the crime, though we do so blindly, unable to recognize or identify problems that seem only vaguely familiar to us. Failing to understand this history, we are bound to repeat the solutions and conclusions that proved unsatisfying in the past.
            
Perhaps this is why the crisis of subjectivity that one finds in Calvin, in Descartes, and in Kant continues to haunt our debates about how to interpret quantum physics, which continually returns to the chasm that exists between the subject and the world, and our theories of mind, which still cannot prove that our most immediate sensory experiences are real . The echoes of this doubt ring most loudly and persistently in conversations about emerging technologies, instruments that are designed to extend beyond our earthbound reason and restore our broken connection to transcendent truth. AI began with the desire to forge a god. It is not coincidental that the deity we have created resembles, uncannily, the one who got us into this problem in the first place.

Chapter 12

Here are a smaller number of clips from the last section of Chapter 12,  on the errors of algorithms.   

It’s not difficult to find examples these days of technologies that contain ourselves “in a different disguise.” Although the most impressive machine-learning technologies are often described as “alien” and unlike us, they are prone to errors that are all too human. Because these algorithms rely on historical data—using information about the past to make predictions about the future—their decisions often reflect the biases and prejudices that have long colored our social and political life. Google’s algorithms show more ads for low-paying jobs to women than to men. Amazon’s same-day delivery algorithms were found to bypass black neighborhoods. A ProPublica report found that the COMPAS sentencing assessment was far more likely to assign higher recidivism rates to black defendants than to white defendants. These systems do not target specific races or genders, or even take these factors into account. But they often zero in on other information—zip codes, income, previous encounters with police—that are freighted with historic inequality. These machine-made decisions, then, end up reinforcing existing social inequalities, creating a feedback loop that makes it even more difficult to transcend our culture’s long history of structural racism and human prejudice.

It is much easier…to blame injustice on faulty algorithms than it is to contend in more meaningful ways with what they reveal about us and our society. In many cases the reflections of us that these machines produce are deeply unflattering. To take a particularly publicized example, one might recall Tay, the AI chatbot that Microsoft released in 2016, which was designed to engage with people on Twitter and learn from her actions with users. Within sixteen hours she began spewing racist and sexist vitriol, denied the Holocaust, and declared support for Hitler.

For Arendt, the problem was not that we kept creating things in our image; it was that we imbued these artifacts with a kind of transcendent power. Rather than focusing on how to use science and technology to improve the human condition, we had come to believe that our instruments could connect us to higher truths. “The desire to send humans to space was for her a metaphor for this dream of scientific transcendence. She tried to imagine what the earth and terrestrial human activity must look like from so far beyond its surface:
            
“If we look down from this point upon what is going on on earth and upon the various activities of men, that is, if we apply the Archimedean point to ourselves, then these activities will indeed appear to ourselves as no more than “overt behavior,” which we can study with the same methods we use to study the behavior of rats. Seen from a sufficient distance, the cars in which we travel and which we know we built ourselves will look as though they were, as Heisenberg once put it, “as inescapable a part of ourselves as the snail’s shell is “to its occupant.” All our pride in what we can do will disappear into some kind of mutation of the human race; the whole of technology, seen from this point, in fact no longer appears “as the result of a conscious human effort to extend man’s material powers, but rather as a large-scale biological process.” Under these circumstances, speech and everyday language would indeed be no longer a meaningful utterance that transcends behavior even if it only expresses it, and it would much better be replaced by the extreme and in itself meaningless formalism of mathematical signs.”
            
The problem is that a vantage so far removed from human nature cannot account for human agency. The view of earth from the Archimedean point compels us to regard our inventions not as historical choices but as part of an inexorable evolutionary process that is entirely deterministic and teleological, much like Kurzweil’s narrative about the Singularity. We ourselves inevitably become mere cogs in this machine, unable to account for our actions in any meaningful way, as the only valid language is the language of quantification, which machines understand far better than we do.

This is more or less what Jaron Lanier“warned about in his response to Chris Anderson’s proposal that we should abandon the scientific method and turn to algorithms for answers. “The point of a scientific theory is not that an angel will appreciate it,” Lanier wrote. “Its purpose is human comprehension. Science without a quest for theories means science without humans.” What we are abdicating, in the end, is our duty to create meaning from our empirical observations—to define for ourselves what constitutes justice, and morality, and quality of life—a task we forfeit each time we forget that meaning is an implicitly human category that cannot be reduced to quantification. To forget this truth is to use our tools to thwart our own interests, to build machines in our image that do nothing but dehumanize us.

 

Panpsychism and Metonymy

This post is the seventh installment of my passing on to both MindBlog readers and my future self my idiosyncratic selection of clips of text from O’Gieblyn’s book ‘God, Human, Animal, Machine’ that I have found particularly interesting. Here are fragments of Chapters 9 and 10 from the  fifth section of her book,  titled "Metonymy"

Chapter 9

Panpsychism has surfaced from time to time over the centuries, as in the philosophy of Bertrand Russell and Arthur Eddington, who realized that the two most notable “gaps” in physicalism—the problem of consciousness and the “problem of intrinsic natures” (the question of what matter is)—could be solved in one fell swoop. Physics could not tell us what matter was made out of, and nobody could understand what consciousness was, so maybe consciousness was, in fact, the fundamental nature of all matter. Mental states were the intrinsic nature of physical states…The impasse surrounding the hard problem of consciousness and the weirdness of the quantum world has created a new openness to the notion that the mind should have never been excluded from the physical sciences in the first place.

Some neuroscientists have arrived at panpsychism not through philosophy but via information theory. One of the leading contemporary theories of consciousness is integrated information theory, or IIT. Pioneered by Giulio Tononi and Christof Koch…IIT holds that consciousness is bound up with the way that information is “integrated” in the brain. Information is considered integrated when it cannot be easily localized but instead relies on highly complex connections across different regions of the brain…They have come up with a specific number, Φ, or phi, which they believe is a threshold and is designed to measure the interdependence of different parts of a system…many other creatures have a nonzero level of phi, which means that they too are conscious—as are atoms, quarks, and some single-celled organisms…Unlike emergentism and other systems theories that cleverly redefine terms like “consciousness” and “cognition” so that they apply to forests and “insect colonies, panpsychists believe that these entities truly possess some kind of phenomenal experience—that it feels like something to be a mouse, an amoeba, or a quark…Although the theory is still a minority position within academia, there is undoubtedly more openness today to theories that upturn modern orthodoxies to extend consciousness down the chain of being.  

“While popular debates about the theory rarely extend beyond the plausibility of granting consciousness to bees and trees, it contains far more radical implications. To claim that reality itself is mental is to acknowledge that there exists no clear boundary between the subjective mind and the objective world. When Bacon denounced our tendency to project inner longings onto scientific theories, he took it for granted—as most of us do today—that the mind is not part of the physical world, that meaning is an immaterial idea that does not belong to objective reality. But if consciousness is the ultimate substrate of everything, these distinctions become blurred, if not totally irrelevant. It’s possible that there exists a symmetry between our interior lives and the world at large, that the relationship between them is not one of paradox but of metonymy—the mind serving as a microcosm of the world’s macroscopic consciousness. Perhaps it is not even a terrible leap to wonder whether the universe can communicate with us, whether life is full of “correspondences,” as the spiritualists called them, between ourselves and the transcendent realm—whether, to quote Emerson, “the whole of nature is a metaphor of the human mind.

Although integrated information theory is rooted in longstanding analogies between the brain and digital technologies, it remains uncertain whether the framework allows for machine consciousness. Koch argues that nothing in ITT necessitates that consciousness is unique to organic forms of life… So long as a system meets the minimum requirements of integrated information, it could in principle become conscious, regardless of whether it’s made of silicon or brain tissue. However, most digital computers have sparse and fragmented connectivity that doesn’t allow for a high level of integration.  

One of the central problems in panpsychism is the “combination problem.” This is the challenge of explaining how conscious microsystems give way to larger systems of unified consciousness. If neurons are conscious—and according to Koch they have enough phi for “an itsy-bitsy amount of experience”—and my brain is made of billions of neurons, then why do I have only one mind and not billions? Koch’s answer is that a system can be conscious only so long as it does not contain and is not contained within something with a higher level of integration. While individual neurons cultured in a petri dish might be conscious, the neurons in an actual brain are not, because they are subsumed within a more highly integrated system...This is why humans are conscious while society as a whole is not. Although society is the larger conglomerate, it is less integrated than the human brain, which is why humans do not become swallowed up in the collective consciousness the way that neurons do.

It is, however, undeniable that society is becoming more and more integrated. Goff pointed out recently that if IIT is correct, then social connectivity is a serious existential threat. Assuming that the internet reaches a point where its information is more highly integrated than that of the human brain, it would become conscious, while all our individual human brains would become absorbed into the collective mind. “Brains would cease to be conscious in their own right,” Goff writes, “and would instead become mere cogs in the mega-conscious entity that is the society including its internet-based connectivity.” Goff likens this scenario to the visions of Pierre Teilhard de Chardin, the French Jesuit priest who, as we’ve seen, prophesied the coming Omega Point and inspired aspects of transhumanism. Once humanity is sufficiently connected via our information technologies, Teilhard predicted, we will all fuse into a single universal mind—the noosphere—enacting the Kingdom of Heaven that Christ promised.
           
This is already happening, of course, at a pace that is largely imperceptible - in the speed with which ideas go viral, cascading across social platforms, such that the users who share them begin to seem less like agents than as hosts, nodes in the enormous brain…in the efficiency of consensus, the speed with which opinions fuse and solidify alongside the news cycle, like thought coalescing in the collective consciousness. We have terms that attempt to catalogue this merging—the “hive mind,” “groupthink” -  times when I become aware of my own blurred boundaries, seized by the suspicion that I am not forming new opinions so much as assimilating them…I don’t know what to call this state of affairs, but it does not feel like the Kingdom of God.


Chapter 10

From the end of the chapter:

 “Idealism and panpsychism are appealing in that they offer a way of believing once again in the mind—not as an illusion or an epiphenomenon but as a feature of our world that is as real as anything else. But its proponents rarely stop there. In some cases they go on to make the larger claim that there must therefore exist some essential symmetry between the mind and the world, that the patterns we observe in our interior lives correspond to a more expansive, transcendent truth. Proponents of these theories occasionally appeal to quantum physics to argue that the mind-matter dichotomy is false—clearly there exists some mysterious relationship between the two. But one could just as easily argue that physics has, on the contrary, affirmed this chasm, demonstrating that the world at its most fundamental level is radically other than ourselves—that the universe is, as Erwin Schrödinger put it, “not even thinkable.”

This is precisely the modern tension that Arendt calls attention to in The Human Condition. On the one hand, the appearance of order in the world—the elegance of physical laws, the usefulness of mathematics—tempts us to believe that our mind is made is made in its image, that “the same patterns rule the macrocosm and the microcosm alike.” In the enchanted world order was seen as proof of eternal unity, evidence that God was present in all things, but for the modern person this symmetry leads inevitably back to Cartesian doubt—the suspicion that the order perceived stems from some mental deception. We have good reason to entertain such suspicions, Arendt argues. Since Copernicus and Galileo, science has overturned the most basic assumptions about reality and suggested that our sensory perception is unreliable. This conclusion became unavoidable with the discovery of general relativity and quantum physics, which suggest that “causality, necessity, and lawfulness are categories inherent in the human brain and applicable only to the common-sense experiences of earthbound creatures.” We keep trying to reclaim the Archimedean point, hoping that science will allow us to transcend the prison of our perception and see the world objectively. But the world that science reveals is so alien and bizarre that whenever we try to look beyond our human vantage point, we are confronted with our own reflection. “It is really as though we were in the hands of an evil spirit,” Arendt writes, alluding to Descartes’s thought experiment, “who mocks us and frustrates our thirst for knowledge, so that whenever we search for that which we are not, we encounter only the “patterns of our own minds.”
           
That is not to say that the Archimedean point is no longer possible.  In her 1963 essay “The Conquest of Space and the Stature of Man,” Arendt considers this modern problem in light of emerging technologies. The oddest thing, she notes, is that even though our theories about the world are limited and simplistic and probably wrong, they “work” when implemented into technologies. Despite the fact that nobody understands what quantum mechanics is telling us about the world, the entire computer “age—including every semiconductor, starting with the very first transistor, built in 1947—has rested on well-modeled quantum behavior and reliable quantum equations. The problem is not merely that we cannot understand the world but that we can now build this alien logic into our devices. There are some scientists, Arendt notes, who claim that computers can do “what a human brain cannot comprehend.” Her italics are instructive: it’s not merely that computers can transcend us in sheer brain power—solving theorems faster than we can, finding solutions more efficiently—but that they can actually understand the world in a way that we cannot. She found this proposition especially alarming. “If it should be true…that we are surrounded by machines whose doings we cannot comprehend although we have devised and constructed them,” she writes, “it would mean that the theoretical perplexities of the natural sciences on the highest level have invaded our everyday world.” This conclusion was remarkably prescient.”


 

Are we living in a simulated world?

This post is the sixth installment of my passing on to both MindBlog readers and my future self my idiosyncratic selection of clips of text from O’Gieblyn’s book ‘God, Human, Animal, Machine’ that I have found particularly interesting. Here are fragments of Chapter 8 from the  fourth section of her book,  titled "Paradox."

Bostrom, a prominent transhumanist, believes that humanity is in the process of becoming posthuman as we merge our bodies with technology. We are becoming superintelligence ourselves. His simulation hypothesis begins by imagining a future, many generations from now, when posthumans have achieved an almost godlike mastery over the world. One of the things these posthumans might do, Bostrom proposes, is create simulations—digital environments that contain entire worlds…The inhabitants will not know that they are living in a simulation but will believe their world is all that exists…the theory’s popularity has escalated over the past decade or so. It has gained an especially fervent following among scientists and Silicon Valley luminaries, including Neil deGrasse Tyson and Elon Musk, who have come out as proponents…It has become, in other words, the twenty-first century’s favored variation on Descartes’s skeptical thought experiment—the proposition that our minds are lying to us, that the world is radically other than it seems.

…for all its supposed “naturalism,” the simulation hypothesis is ultimately an argument from design. It belongs to a long lineage of creationist rhetoric that invoke human technologies to argue that the universe could not have come about without the conscious intention of a designer.,.Bostrom acknowledged in his paper that there were “some loose analogies” that could be drawn between the simulation hypothesis and traditional religious concepts. The programmers who created the simulation would be like gods compared to those of us within the simulation.

One of the common objections to the informational universe is that information cannot be “ungrounded,” without a material instantiation. Claude Shannon, the father of information theory, insisted that information had to exist in some kind of physical medium, like computer hardware…if the universe were an enormous computer, then this information would in fact be instantiated on something material, akin to a hard drive. We wouldn’t be able to see or detect it because it would exist in the universe of the programmers who built it. All we would notice was its higher-level structure, the abstract patterns and laws that were part of its software. The simulation hypothesis, in other words, could explain why our universe is imbued with discernible patterns and mathematical regularities while also explaining how those patterns could be rooted in something more than mere abstractions. Perhaps Galileo was not so far off when he imagined the universe as a book written by God in the language of mathematics. The universe was software written by programmers in the binary language of code…“if you take this thesis to its conclusion, it doesn’t really explain anything about the universe or its origins. Presumably there is still some original basement-level reality at its foundation—there could be no true infinite regress—occupied by first posthumans who created the very first technological simulation. But these posthumans were just our descendants—or the descendants of some other species that had evolved on another planet—and so the question about origins remained unchanged, only pushed back one degree. Where did the universe originally come from?

Bohr …observed that humans are incapable of understanding the world beyond “our necessarily prejudiced conceptual frame.” And perhaps it can explain why the multiverse theory and other attempts to transcend our anthropocentric outlook so seem a form of bad faith, guilty of the very hubris they claim to reject. There is no Archimedean point, no purely objective vista that allows us to transcend our human interests and see the world from above, as we once imagined it appeared to God. It is our distinctive vantage that binds us to the world and sets the necessary limitations that are required to make sense of it. This is true, of course, regardless of which interpretation of physics is ultimately correct. It was Max Planck, the physicist who struggled more than any other pioneer of quantum theory to accept the loss of a purely objective worldview, who acknowledged that the central problems of physics have always been reflexive. “Science cannot solve the ultimate mystery of nature,” he wrote in 1932. “And that is because, in the last analysis, we ourselves are part of nature and therefore part of the mystery that we are trying to solve.

 

Is the mind a reliable mirror of reality? The marriage of physics and information theory

 This post is the fifth installment of my passing on to both MindBlog readers and my future self my idiosyncratic selection of clips of text from O’Gieblyn’s book ‘God, Human, Animal, Machine’ that I have found particularly interesting. Here are fragments of Chapter 7 from the  fourth section of her book,  titled "Paradox."

Is the mind a reliable mirror of reality? Do the patterns we perceive belong to the objective world, or are they merely features of our subjective experience? Given that physics was founded on the separation of mind and matter, subject and object, it’s unsurprising that two irreconcilable positions that attempt to answer this question have emerged: one that favors subjectivity, the other objectivity. Bohr’s view was that quantum physics describes our subjective experience of the world; it can tell us only about what we observe. Mathematical equations like the wave function are merely metaphors that translate this bizarre world into the language of our perceptual interface—or, to borrow Kant’s analogy, spectacles that allow us to see the chaotic world in a way that makes sense to our human minds. Other interpretations of physics, like the multiverse theory or string theory, regard physics not as a language we invented but as a description of the real, objective world that exists out there, independent of us. Proponents of this view tend to view equations and physical laws as similarly transcendent, corresponding to literal, or perhaps even Platonic, realities.

The marriage of physics and information theory is often attributed to John Wheeler, the theoretical physicist who pioneered, with Bohr, the basic principles of nuclear fission. In the late 1980s, Wheeler realized that the quantum world behaved a lot like computer code. An electron collapsed into either a particle or a wave depending on how we interrogated it. This was not dissimilar from the way all messages can be simplified into “binary units,” or bits, which are represented by zeros and ones. Claude Shannon, the father of information theory, had defined information as “the resolution of uncertainty,” which seemed to mirror the way quantum systems existed as probabilities that collapsed into one of two states. For Wheeler these two fields were not merely analogous but ontologically identical. In 1989 he declared that “all things physical are information-theoretic in origin.            
            
In a way Wheeler was exploiting a rarely acknowledged problem that lies at the heart of physics: it’s uncertain what matter actually is. Materialism, it is often said, is not merely an ontology but a metaphysics—an attempt to describe the true nature of things. What materialism says about our world is that matter is all that exists: everything is made of it, and nothing exists outside of it. And yet, ask a physicist to describe an electron or a quark, and he will speak only of its properties, its position, its behavior—never its essence.

Wheeler’s answer was that matter itself does not exist. It is an illusion that arises from the mathematical structures that undergird everything, a cosmic form of information processing. Each time we make a measurement we are creating new information—we are, in a sense, creating reality itself. Wheeler called this the “participatory universe,” a term that is often misunderstood as having mystical “connotations, as though the mind has some kind of spooky ability to generate objects. But Wheeler did not even believe that consciousness existed. For him, the mind itself was nothing but information. When we interacted with the world, the code of our minds manipulated the code of the universe, so to speak. It was a purely quantitative process, the same sort of mathematical exchange that might take place between two machines.            

While this theory explains, or attempts to explain, how the mind is able to interact with matter, it is a somewhat evasive solution to the mind-body problem, a sleight of hand that discards the original dichotomy by positing a third substance—information—that can explain both. It is difficult, in fact, to do justice to how entangled and self-referential these two fields—information theory and physics—have become, especially when one considers their history. The reason that cybernetics privileged relationships over content in the first place was so that it could explain things like consciousness purely in terms of classical physics, which is limited to describing behavior but not essence—“doing” but not “being.” When Wheeler merged information theory with quantum physics, he was essentially closing the circle, proposing that the hole in the material worldview—intrinsic essence—could be explained by information itself.

Seth Lloyd, an MIT professor who specializes in quantum information, insists that the universe is not like a computer but is in fact a computer. “The universe is a physical system that contains and processes information in a systematic fashion,” he argues, “and that can do everything a computer can do.” Proponents of this view often point out that recent observational data seems to confirm it. Space-time, it turns out, is not smooth and continuous, as Einstein’s general relativity theory assumed, but more like a grid made up of minuscule bits—tiny grains of information that are not unlike the pixels of an enormous screen. Although we experience the world in three dimensions, it seems increasingly likely that all the information in the universe arises from a two-dimensional field, much like the way holograms work, or 3-D films.
            
When I say that I try very hard to avoid the speculative fringe of physics, this is more or less what I am talking about. The problem, though, is that once you’ve encountered these theories it is difficult to forget them, and the slightest provocation can pull you back in. It happened a couple years ago, while watching my teenage cousin play video games at a family gathering. I was relaxed and a little bored and began thinking about the landscape of the game, the trees and the mountains that made up the backdrop. The first-person perspective makes it seem like you’re immersed in a world that is holistic and complete, a landscape that extends far beyond the frame, though in truth each object is generated as needed. Move to the right and a tree is “generated; move to the left and a bridge appears, creating the illusion that it was there all along. What happened to these trees and rocks and mountains when the player wasn’t looking? They disappeared—or no, they were never there to begin with; they were just a line of code. Wasn’t this essentially how the observer effect worked? The world remained in limbo, a potentiality, until the observer appeared and it was compelled to generate something solid. Rizwan Virk, a video game programmer, notes that a core mantra in programming is “only render that which is being observed.”
            
Couldn’t the whole canon of quantum weirdness be explained by this logic? Software programs are never perfect. Programmers cut corners for efficiency—they are working, after all, with finite computing power; even the most detailed systems contain areas that are fuzzy, not fully sketched out. Maybe quantum indeterminacy simply reveals that we’ve reached the limits of the interface. The philosopher Slavoj Žižek once made a joke to this effect. Perhaps, he mused, God got a little lazy when he was creating the universe, like the video game programmer who doesn’t bother to meticulously work out the interior of a house that[ “the player is not meant to enter. “He stopped at a subatomic level,” he said, “because he thought humans would be too stupid to progress so far.”