# brain: apropos of persistent exploratory predisposition, chaotic pigeons are helping redefine what we know about learning.

<< ️Pigeons seem to defy a century-old psychology law about how rewards and consequences help us learn. >>

<< ️New research suggests the birds themselves avoid stability in their decision-making, instead choosing to live “at the edge of chaos.” As model species for learning and behavior, these birds are helping researchers test a century-old law about how humans and other creatures learn. >>

<< ️When learning something new, people and animals alike tend to repeat behaviors that are rewarded. First proposed by Edward Thorndike in 1898, this principle is so well established in psychology that it's become known as the law of effect. But the law implies that beyond making a behavior more frequent, rewards also make it more consistent: reducing variability in the specific way behaviors are performed over time. >>

<< ️Edward A. Wasserman and his colleagues decided to put it to the test in pigeons—a species that has been integral to the study of learning at the university’s Comparative Cognition Laboratory for more than 50 years. And the study’s results, published in the Journal of Experimental Psychology: Animal Learning and Cognition, suggest these birds experience variability as the spice of life. >>

<< ️You could argue the birds are just utterly resistant to locking into anything stable. >> Edward A. Wasserman.

<< ️this paper leaves open many questions about the [neurological] mechanisms for future scientists to explore. >> Aaron Blaisdell.

K. R. Callaway, Sarah Lewin Frasier. Chaotic pigeons are helping redefine what we know about learning. SciAm. Jun 30, 2026. 

https://www.scientificamerican.com/article/chaotic-pigeons-are-helping-redefine-what-we-know-about-learning

Wasserman E.A., Orr O.R.P., Li S. Variability, stability, and the law of effect. J. of Exp. Psychol: Animal Learning and Cognition, 52(3), 129–138. Jul 2026. 

https://psycnet.apa.org/record/2027-47688-001

Also: brain, neuro, behav, curiosity, game, Nomads, in https://www.inkgmr.net/kwrds.html 

Keywords: brain, behavior, curiosity, games, Nomads.

# aibot: I think, therefore I hallucinate: minds, machines, and the art of being wrong.

<< This theoretical work examines 'hallucinations' in both human cognition and large language models, comparing how each system can produce perceptions or outputs that deviate from reality. Drawing on neuroscience and machine learning research, (AA) highlight the predictive processes that underlie human and artificial thought. >>

<< In humans, complex neural mechanisms interpret sensory information under uncertainty, sometimes filling in gaps and creating false perceptions. This inference occurs hierarchically: higher cortical levels send top-down predictions to lower-level regions, while mismatches (prediction errors) propagate upward to refine the model. LLMs, in contrast, rely on auto-regressive modeling of text and can generate erroneous statements in the absence of robust grounding. >>

<< Despite these different foundations - biological versus computational - the similarities in their predictive architectures help explain why hallucinations occur. (AA) propose that the propensity to generate incorrect or confabulated responses may be an inherent feature of advanced intelligence. In both humans and AI, adaptive predictive processes aim to make sense of incomplete information and anticipate future states, fostering creativity and flexibility, but also introducing the risk of errors. (Their) analysis illuminates how factors such as feedback, grounding, and error correction affect the likelihood of 'being wrong' in each system. (AA) suggest that mitigating AI hallucinations (e.g., through improved training, post-processing, or knowledge-grounding methods) may also shed light on human cognitive processes, revealing how error-prone predictions can be harnessed for innovation without compromising reliability. By exploring these converging and divergent mechanisms, the paper underscores the broader implications for advancing both AI reliability and scientific understanding of human thought. >>️

Sebastian Barros. I Think, Therefore I Hallucinate: Minds, Machines, and the Art of Being Wrong. arXiv: 2503.05806v1 [q-bio.NC]. 4 Mar 4, 2025.

https://arxiv.org/abs/2503.05806

Also: brain, curiosity, novelty, uncertainty, error, mistake, jazz, ai (artificial intell), in https://www.inkgmr.net/kwrds.html 

Keywords: brain, cognition, perceptions, curiosity, novelty, hallucinations, errors, prediction, prediction errors, error-prone predictions, AI, artificial intelligence, LLMs

# gst: curiosity-driven search for novel behaviors.

<< One of the first exciting things to do with a new physical system is to go exploring—to tune parameters and see what unexpected behaviors the system is capable of. >>️

AA << combine active and unsupervised learning for automated exploration of nonequilibrium systems with unknown order parameters. (They) iteratively use active learning based on current order parameters to expand the library of behaviors and relearn order parameters based on this expanded library. (They) demonstrate the utility of this approach in Kuramoto models of increasing complexity. In addition to reproducing known phases, (AA) reveal previously unknown behavior and related order parameters, and demonstrate how to align search with human intuition. >>️

Martin J. Falk, Finnegan D. Roach, et al. Curiosity-driven search for novel nonequilibrium behaviors. Phys. Rev. Research 6, 033052. Jul 11, 2024. 

https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033052  

Also: curiosity, behav, transition, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, curiosity, behav, transition