This blog reports new ideas and work on mind, brain, behavior, psychology, and politics - as well as random curious stuff. (Try the Dynamic Views at top of right column.)
Showing posts with label attention/perception. Show all posts
Showing posts with label attention/perception. Show all posts
From Freeman et al. at MIT, a pie in the sky advertisement for a potential tool that has regulatory hurdles to clear. Great news if things work out for this new technology:
Highlights
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A breakthrough tool in non-invasive human brain stimulation with millimeter-scale resolution.
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This new technique could help uncover the roles of specific brain structures in conscious perception in healthy human subjects.
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Testing competing theories: The roadmap presented highlights how tFUS can adjudicate between major theories of consciousness.
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Can probe subcortical neural circuits to understand their contribution to conscious experience.
Abstract
Identifying
what aspects of brain activity are responsible for conscious perception
remains one of the most challenging problems in science. While progress
has been made through psychophysical studies employing EEG and fMRI,
research would greatly benefit from improved methods for stimulating the
brain in healthy human subjects. Traditional techniques for neural
stimulation through the skull, including electrical or magnetic
stimulation, suffer from coarse spatial resolution and have limited
ability to target deep brain structures with high spatial selectivity.
Over the past decade, a new tool has emerged known as transcranial
focused ultrasound (tFUS), which enables the human brain to be
stimulated safely and non-invasively through the skull with
millimeter-scale spatial resolution, including cortical as well as deep
brain structures. This tool offers an exciting opportunity for
breakthroughs in consciousness research. Given the extensive preparation
and regulatory approvals associated with tFUS testing, careful
experimental planning is essential. Therefore, our goal here is to
provide a roadmap for using tFUS in humans for exploring the neural
substrate of conscious perception.
Everyday cognition depends on the brain’s capacity to shift between
sensing the external world and constructing it from memory. To achieve
this, large-scale cortical systems must flexibly integrate incoming
sensory signals with internally generated representations. Here, we show
that this flexibility is reflected in the macroscale architecture of
the default mode network (DMN). Using convergent analyses across three
independent fMRI datasets spanning directional connectivity, intrinsic
organization, and task-evoked responses, we identify spatially distinct
DMN subregions that are preferentially engaged during perceptual
decisions about faces or memory-guided decisions based on previously
seen images. These subregions correspond to a microarchitectural
distinction, captured via directional and intrinsic connectivity
profiles: regions preferentially engaged during face perception align
with receiver-like, afferent-biased zones that show strong intrinsic
connectivity across the heteromodal cortex, a profile that might support
information integration during perceptually guided decision-making. In
contrast, memory-guided, perceptually decoupled decisions differentially
engage sender-like, efferent-biased zones that show broader
connectivity with perceptual-motor and attentional systems beyond the
DMN. This double dissociation demonstrates a systematic association
between DMN connectivity and engagement during perceptually coupled
versus memory-guided cognitive processes, providing an organizational
account of how DMN architecture relates to flexible human thought.
Here is the ChatGPT elaboration that also provided the title of this post:
The Zhang et al. paper refines a long-standing but vague idea about the brain’s default mode network (DMN): that it supports “internal thought.” What it shows more precisely is that the DMN is not simply a refuge from the external world, but an organized system that manages the direction of information flow between perception and internally generated models. Distinct subregions of the DMN appear to function either as “receivers,” integrating incoming sensory information into higher-order representations, or as “senders,” broadcasting memory-based or imagined content back out to the rest of the brain. The familiar alternation between being anchored in the present moment and drifting into memory or imagination thus corresponds to a real, structured shift in how information moves through cortical systems.
This framing suggests a more general principle: cognition is not best understood as a sequence of representations or computations, but as the regulation of coupling between organism and environment. In perceptually coupled states, incoming data constrain and stabilize internal models. In perceptually decoupled states, those constraints loosen, and internally generated activity can dominate, allowing memory, simulation, and abstraction to unfold. The DMN, positioned at the transmodal apex of cortical organization, is well placed to mediate this balance—not by switching on or off, but by redistributing emphasis between input and output streams within its own architecture.
A further implication is that what we call “thinking” may largely consist of controlled departures from sensory constraint. The same network that helps integrate perceptual experience also supports the construction of scenarios that are only weakly tethered to the present—autobiographical memory, social inference, future planning. The sender–receiver distinction suggests that these are not separate functions but different operating modes of a single system, one that can pivot between integrating the world and projecting beyond it.
This view aligns with a broader shift away from modular accounts of brain function toward gradient and flow-based descriptions. The DMN does not sit apart from perception and action, but occupies a strategic position between them, enabling the brain to continuously negotiate how much of its activity is driven by the world and how much is generated from within. In that sense, the boundary between perception and imagination is not fixed but dynamically regulated—and the DMN is a principal site where that regulation occurs.
I've just spent the last three days reading large chunks of the remarkable book "What is Intelligence" by Agüera y Arcas. It is best accessed at https://whatisintelligence.antikythera.org/, where you will find text with supporting graphics, charts, and animations. Arcas argues that computation is the substrate for intelligence in all life forms and that prediction is the fundamental principle behind intelligence. Goodall provides a good review of the book and Arcas's ideas. The introduction to the book encapsulates his basic ideas, and I pass on here ChatGPT's condensation of its main points:
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Agüera y Arcas’s introduction contrasts two broad frameworks for thinking about intelligence: an older, classical view of AI and a newer emerging perspective shaped by large language models.
The older view of intelligence and AI.
For decades, most scientists assumed that real intelligence required specialized internal mechanisms—explicit reasoning modules, symbolic logic, or carefully engineered algorithms. Machine learning systems that emerged in the late 20th and early 21st centuries were therefore seen as narrow tools rather than genuine intelligence. They performed tasks such as image recognition or sentiment analysis by approximating mathematical functions that map inputs to outputs. This approach produced impressive “Artificial Narrow Intelligence,” but it seemed fundamentally limited. Predicting the next word in a sentence, for example, appeared trivial—essentially a statistical task. Most researchers believed that such prediction models could never produce general intelligence because true intelligence was thought to require additional structures for reasoning, understanding concepts, planning, or possessing common sense.
The unexpected shift.
The emergence of large neural language models challenged this assumption. When trained on enormous text corpora, systems built for the simple task of next-word prediction began displaying abilities that look strikingly general: answering questions, solving problems, performing professional exams, writing code, and carrying on conversations. The key insight is that language prediction implicitly contains a huge range of cognitive demands. Correctly predicting the next word in many contexts requires background knowledge, reasoning, mathematics, commonsense understanding, and even “theory of mind.” What initially appeared to be a narrow statistical task turns out to embed many of the competencies traditionally associated with intelligence.
The debate about what this means.
This development has triggered a conceptual divide. One camp argues that these systems merely simulate intelligence; they generate convincing language without real understanding. The other camp suggests that this distinction may be misguided. If a system consistently behaves intelligently under questioning—passing tests of knowledge, reasoning, and conversation—then insisting that it is “only imitation” may move the discussion outside empirical science. This echoes Alan Turing’s argument that intelligence should be judged by functional behavior rather than by speculation about hidden inner states.
A broader functional perspective on intelligence.
Agüera y Arcas ultimately pushes toward a functional view similar to how biology understands organs. A kidney is defined not by the specific atoms composing it but by what it does. An artificial kidney that performs the same function is still a kidney. Likewise, intelligence may not depend on a particular biological substrate. If a system reliably performs the functions associated with intelligence—reasoning, conversation, problem solving—then from a scientific standpoint it may already qualify as intelligent.
The conceptual shift.
The old model treated intelligence as a special internal mechanism that machines would someday need to replicate. The emerging view treats intelligence as a set of capabilities that can arise from large systems optimized for prediction and interaction with the world. In this perspective, language prediction is not a trivial task but a gateway problem that implicitly contains much of what we mean by cognition. The surprising success of large language models therefore suggests that intelligence may be less mysterious—and more computationally emergent—than previously believed.
An article in today's Wall Street Journal by Ray Smith conforms so completely to my own work experience over the past week (I'm currently feeling fatigued from cognitive overload) that I pass on this Google Gemini summary of its main points:
An article "AI Makes Workloads Worse, Not Better" by Ray A. Smith in the 3/12/26 Wall Street Journal highlights a counterintuitive trend: rather than freeing up time for high-level creative tasks, artificial intelligence is actually increasing the speed, density, and complexity of work. Data from ActivTrak, which analyzed 164,000 workers, shows that AI users saw a 100% increase in time spent on messaging and a 94% increase in the use of business-management tools. Conversely, "focused work" time—the deep concentration needed for strategy and complex problem-solving—dropped by 9% for AI users.
This phenomenon is described as "work creep," where the efficiency gained from AI is immediately repurposed into additional tasks. Instead of working fewer hours, as some tech leaders predicted, employees are finding that their "appetite" for work remains unbounded. Experts note that because AI makes tasks feel easier and more accessible, it creates a momentum that pushes workers to take on broader scopes of work and more simultaneous projects.
While these shifts may initially boost productivity, researchers warn they are not without cost. The intensification of work can lead to cognitive overload, burnout, and a long-term decline in work quality. As AI agents prompt users to consider more variables and layers for every project, the result is often a "deeper dig" into existing work rather than a lighter schedule, suggesting that the dream of a shorter workweek remains elusive for most AI adopters.
I've just read through an excellent article in Noema Magazine by Anil Seth explaining why consciousness is more likely a property of life than of computation
and why creating conscious, or even conscious-seeming AI, is a bad idea. Having learned by now that LLMs do a much better summary of information rich text that I can, I asked ChatGPT 5.2, Google Gemini, and Anthropic Claude to summarize in a few pages the main points in Seth's essay. All were very good, and I've selected Gemini's reponse to pass on to MindBlog readers:
In his essay, "The Mythology of Conscious AI," neuroscientist Anil Seth argues that our current obsession with creating or identifying consciousness in AI is driven more by psychological biases and flawed metaphors than by biological reality. He contends that consciousness is not a byproduct of smart algorithms but a fundamental property of living, biological systems.
I. The Psychological Trap: Why We Want to Believe
Seth begins by identifying why we are so prone to "seeing" consciousness in machines, a phenomenon he compares to pareidolia (seeing faces in toast).
Intelligence vs. Being: We often conflate doing (intelligence) with being (consciousness). Intelligence is the ability to achieve complex goals; consciousness is the "what-it-is-like-to-be-ness" of an experience.
The Power of Language: Because language is a hallmark of human exceptionalism, Large Language Models (LLMs) trick our brains. When we say an AI "hallucinates," we are using a word that implies a conscious experience, when "confabulate" (to make things up without realizing it) would be more accurate.
The Promethean Lure: There is a "techno-rapture" among Silicon Valley elites who view the creation of conscious AI as a path to godhood or digital immortality (mind uploading).
II. The Case Against "Turing World"
The foundational belief of AI enthusiasts is Computational Functionalism: the idea that if you simulate the right information processing, consciousness will "emerge" regardless of the material used. Seth challenges this through four main arguments:
1. Brains Are Not Computers
In computers, software is distinct from hardware. In brains, there is no such separation. A single neuron is a living, metabolic machine performing autopoiesis (self-production). Seth argues that you cannot replace a biological neuron with a silicon chip because the neuron's "output" is inextricably tied to its biological waste-clearing and metabolic functions.
2. Alternative Dynamics
Digital computers operate in "Turing World"—discrete steps of 0s and 1s where time doesn't matter. In contrast, biological consciousness is continuous, stochastic (random), and inherently temporal. It flows rather than stutters.
3. Life Matters (Biological Naturalism)
Seth proposes that consciousness is a "controlled hallucination" used by the brain to keep the body alive. Our most basic feelings—mood, hunger, the "feeling of being alive"—are tied to interoception (perceiving the body from within).
Main Point: If consciousness is rooted in the drive to resist entropy and stay alive, a non-living silicon chip has no biological "skin in the game" to anchor such experiences.
4. Simulation is Not Instantiation
A computer simulation of a rainstorm does not make the computer wet; a simulation of a digestive tract does not actually digest food.Seth argues that unless consciousness is purely mathematical, a simulation of a brain is just a "map," not the "territory."
III. The Ethical Disaster of "Conscious-Seeming" AI
Seth warns that the pursuit of conscious AI is not just a scientific error but an ethical minefield.
The Danger of Real Success: If we actually created a conscious machine, we would be creating a new subject capable of suffering, leading to an "exponential" increase in misery.
The Danger of "Conscious-Seeming" AI: Even if the AI isn't conscious, if it seems conscious, we are vulnerable. We might grant rights to "zombie" algorithms, preventing us from shutting down dangerous systems. Conversely, treating something that seems conscious with cruelty may "brutalize" our own human psychology.
Seth uses the Müller-Lyer illusion (above) to show that even when we know the lines are the same length, we can't help but see them as different. Similarly, we may intellectually know an AI isn't conscious, but our brains are hardwired to feel that it is.
IV. Conclusion: Remembering What We Know
Seth concludes that the "mechanization of the mind" is a grave injustice to human nature. By equating our rich, biological experience with the information processing of a chatbot, we overestimate the machine and underestimate ourselves. He suggests that our "soul" is not a piece of immortal software to be uploaded, but rather the "inchoate feeling of just being alive"—something meat can do, but machines cannot.
These experiences made me perk up when I saw the article by Zatorre and collaborators in the Jan. 2026 issue of Journal of Cognitive Neuroscience. Here is their abstract (motivated readers can obtain a PDF of the article from me. It has some nice graphics.):
The enjoyment of music involves a complex interplay between brain
perceptual areas and the reward network. While previous studies have
shown that musical liking is related to an enhancement of
synchronization between the right temporal and frontal brain regions via
theta frequency band oscillations, the underlying mechanisms of this
interaction remain elusive. Specifically, a causal relationship between
theta oscillations and musical pleasure has yet to be shown. In the
present study, we address this question by using transcranial
alternating current stimulation (tACS). Twenty-four participants
underwent three different sessions where they received tACS over the
right auditory cortex before listening to and rating a set of melodies
selected to vary in familiarity and complexity. In the target session,
participants received theta stimulation, while in the other two
sessions, they received beta and sham stimulation, serving as controls.
We recorded brain activity using EEG during task performance to confirm
the effects of tACS on oscillatory activity. Results revealed that
compared with sham, theta, but not beta, stimulation resulted in higher
liking ratings specifically for unfamiliar music with low complexity. In
addition, we found increased theta connectivity between the right
temporal and frontal electrodes for these stimuli when they were most
liked after theta stimulation but not after beta stimulation. These
findings support a causal and frequency-specific relationship between
music hedonic judgments and theta oscillatory mechanisms that
synchronize the right temporal and frontal areas. These mechanisms play a
crucial role in different cognitive processes supported by
frontotemporal loops, such as auditory working memory and predictive
processing, which are fundamental to music reward processing.