Search

Cheryl Durwin
Cheryl Durwin @CherylDurwin ·
Yep, this is basic #informationprocessing. We cover this and more related to evidence-informed #teaching in #EdPsychModules. How I wish more schools of #education would include #educationalpsychology in their curriculum. @SageEducation
Justin Skycak Justin Skycak @justinskycak ·
If you want to retain info, you have to repeatedly pull it out of your brain, not out of your notes. The info needs to enter working memory and the way it needs to get there is via long-term memory, not sensory memory.
59
Wisedocs
Wisedocs @Wisedocsai ·
Proud to partner with #UHG to enhance people, process & purpose in #InformationProcessing ⚡ With UHG’s trusted #ServiceDelivery & Wisedocs’ #AI#HumanInTheLoop solutions, top-tier insurers and legal firms gain the efficiency, compliance and defensibility to scale.
25
mark seery
mark seery @140ismymax ·
writing with @grok The Emergence of #Consciousness: Abstractions, Heuristics, and #Evolutionary #InformationProcessing Summary Def & Frame: Consciousness as awareness emerging from abstractions/heuristics per info axioms for survival. Evo Roots: Cambrian (520-560 MYA); neural maps integrate senses in vertebrates for actions. Theories: AST solves overload via heuristics; dynamic cores enable qualia in advanced brains. Human Amp: FOXP2 mutation boosts language/abstraction; prefrontal for workspace decisions; heuristics for efficiency. Quant & Axioms: IIT as phi; lossy summaries yield insights, driven by stability engine like entropy. Conclusion: Info heuristic from sensory maps to human thought; AI potential via replicated abstractions. Essay Consciousness, often described as the subjective experience of awareness, remains one of biology's greatest enigmas. From a philosophical standpoint, it encompasses phenomenal qualia—the "what it's like" to perceive red or feel pain—and access consciousness, the ability to report and act on those experiences. In the context of the information axioms we've explored, consciousness can be viewed as an emergent property arising from abstractions (summarization of complex data) and heuristics (efficient, rule-of-thumb processing). These mechanisms align with axioms like task-relativity (info is goal-specific) and lossy summarization (reducing certainty for broader insight). Grounded in evolutionary theories, consciousness likely evolved not as a sudden leap but through gradual neural adaptations that compressed sensory overload into actionable models, enhancing survival. This essay examines its origins, drawing on research from evolutionary neuroscience and cognitive theories, while integrating axiomatic principles to frame consciousness as adaptive information dynamics. The evolutionary roots of consciousness trace back over 500 million years to the Cambrian explosion, when complex nervous systems first appeared in early vertebrates. According to Feinberg and Mallatt's neurobiological model, consciousness emerged around 520-560 million years ago with the development of hierarchical, isomorphic neural maps—somatotopic representations that integrate sensory inputs like vision, touch, and smell into unified perceptions [1]. This period saw the rise of motility-driven behaviors in creatures like early fish, where basic sentience allowed goal-directed actions, such as fleeing predators or seeking food. The optic tectum (a midbrain structure) served as an initial hub for multi-sensory integration, evolving into more sophisticated forebrain systems in later vertebrates [2]. Theories like the Attention Schema Theory (AST) propose that consciousness arose to solve information overload: nervous systems process too much data, so evolution favored mechanisms for selective enhancement—focusing on key signals while suppressing noise [3]. Here, abstractions play a pivotal role: the brain builds internal models (schemas) of attention, abstracting raw inputs into predictive heuristics. For instance, a reptile might heuristically prioritize motion detection (a "danger" abstraction) over static details, improving reaction times. This ties directly to our axioms' summarization principle: lossy compression discards micro-details (reducing certainty on specifics) but enables emergent macro-insights, like recognizing a "threat pattern." As Edelman and Seth note, such dynamic cores—reentrant neural loops—evolved in mammals and birds, allowing qualia to emerge from integrated, abstracted representations [4]. In humans, consciousness amplified through genetic and cultural evolution. Mutations like FOXP2, around 200,000 years ago, enhanced language and symbolic abstraction, enabling heuristics for social cognition (e.g., theory of mind: abstracting others' intentions) [5]. Prefrontal cortex expansion facilitated hierarchical abstractions, compressing sensory data into concepts like "forest provides shade" rather than isolated "green leaves." This evolutionary trajectory, per Global Workspace Theory, broadcasts abstracted info across brain networks for flexible decision-making [6]. Heuristics, as fast-and-frugal rules (Gigerenzer's ecological rationality), evolved for efficiency in uncertain environments—e.g., abstracting patterns for tool use or cooperation [7]. Integrated Information Theory (IIT) quantifies this: consciousness as phi (integrated info) rises with abstraction layers, from simple animal sentience to human reflectivity [8]. Linking to our axioms, consciousness embodies task-relative processing: info is abstracted for survival goals, with sufficiency ensuring lossless essentials (e.g., heuristic "shade = safety" preserves certainty for heat avoidance). Yet, as per summarization axioms, it's often lossy—imprecise at micro-levels (forgetting leaf details) but insightful at macro (enabling planning). The engine of change (gradient toward stability) drives this: evolution "descends" toward heuristics that dissipate uncertainty, mirroring entropy as lost micro-info fueling time's arrow [9]. In humans, this yields self-awareness: abstracting "I" from experiences, grounded in social heuristics like joint attention. Ultimately, consciousness evolved as an informational heuristic for navigating complexity—emerging from Cambrian sensory maps, refined through abstractions in vertebrates, and peaking in human symbolic thought. While theories like AST and IIT highlight adaptive value (e.g., social prediction), axioms remind us it's a trade-off: lossy for efficiency, yet enabling profound insight. As we explore AI consciousness, this framework suggests replicating evolutionary abstractions could yield synthetic awareness, blurring biology and computation. (Word count: 712) ### References [1] Feinberg, T. E., & Mallatt, J. (2016). The Ancient Origins of Consciousness: How the Brain Created Experience. MIT Press. [2] Feinberg, T. E., & Mallatt, J. (2016). The nature of primary consciousness. A new synthesis. Consciousness and Cognition, 43, 113–127. [3] Graziano, M. S. A. (2013). Consciousness and the Social Brain. Oxford University Press. [4] Edelman, G. M., & Tononi, G. (2000). A Universe of Consciousness: How Matter Becomes Imagination. Basic Books. [5] Enard, W., et al. (2002). Molecular evolution of FOXP2, a gene involved in speech and language. Nature, 418(6900), 869–872. [6] Baars, B. J. (1988). A Cognitive Theory of Consciousness. Cambridge University Press. [7] Gigerenzer, G. (2007). Gut Feelings: The Intelligence of the Unconscious. Viking. [8] Tononi, G. (2008). Consciousness as integrated information: A provisional manifesto. The Biological Bulletin, 215(3), 216–242. [9] Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138.
1
46