| Information Processing |
Processes vast amounts of information rapidly and automatically, often without conscious awareness (From the first studies of the unconscious mind to consumer neuroscience: A systematic literature review, 2023) |
Processes large datasets quickly, extracting patterns and generating outputs without explicit programming for each task (Deep Learning, 2015) |
| Pattern Recognition |
Recognizes complex patterns in sensory input and past experiences, influencing behavior and decision-making (Analysis of Sources about the Unconscious Hypothesis of Freud, 2017) |
Excels at identifying patterns in training data, forming the basis for generating new content or making predictions (A Survey on Deep Learning in Medical Image Analysis, 2017) |
| Creativity |
Contributes to creative insights and problem-solving through unconscious incubation and associative processes (The Study of Cognitive Psychology in Conjunction with Artificial Intelligence, 2023) |
Generates novel combinations and ideas by recombining elements from training data in unexpected ways (e.g., GANs in art generation) (Generative Adversarial Networks, 2014) |
| Emotional Processing |
Processes emotional information rapidly, influencing mood and behavior before conscious awareness (Unconscious Branding: How Neuroscience Can Empower (and Inspire) Marketing, 2012) |
Can generate text or images with emotional content based on patterns in training data, but lacks genuine emotions (Language Models are Few-Shot Learners, 2020) |
| Memory Consolidation |
Plays a crucial role in memory consolidation during sleep, strengthening neural connections (The Role of Sleep in Memory Consolidation, 2001) |
Analogous processes in some AI systems involve memory consolidation and performance improvement (In search of dispersed memories: Generative diffusion models are associative memory networks, 2024) |
| Implicit Learning |
Acquires complex information without conscious awareness, as in procedural learning (Implicit Learning and Tacit Knowledge, 1994) |
Learns complex patterns and rules from data without explicit programming, similar to implicit learning in humans (Deep Learning for Natural Language Processing, 2018) |
| Bias and Heuristics |
Employs cognitive shortcuts and biases that can lead to systematic errors in judgment (Thinking, Fast and Slow, 2011) |
Can amplify biases present in training data, leading to skewed outputs or decision-making (Mind vs. Mouth: On Measuring Re-judge Inconsistency of Social Bias in Large Language Models, 2023) |
| Associative Networks |
Forms complex networks of associations between concepts, influencing thought and behavior (The associative basis of the creative process, 2010) |
Creates dense networks of associations between elements in training data, enabling complex pattern completion and generation tasks (Attention Is All You Need, 2017) |
| Parallel Processing |
Processes multiple streams of information simultaneously (Parallel Distributed Processing: Explorations in the Microstructure of Cognition, 1986)) |
Utilizes parallel processing architecture (e.g., neural networks) to handle multiple inputs and generate outputs (Next Generation of Neural Networks, 2021) |
| Intuition |
Generates rapid, automatic judgments based on unconscious processing of past experiences (Blink: The Power of Thinking Without Thinking, 2005) |
Produces quick outputs based on learned patterns, which can appear intuitive but lack genuine understanding (BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, 2019) |
| Priming Effects |
Unconscious exposure to stimuli influences subsequent behavior and cognition (Attention and Implicit Memory: Priming-Induced Benefits and Costs, 2016) |
Training on specific datasets can “prime” generative AI to produce biased or contextually influenced outputs (AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias, 2018) |
| Symbol Grounding |
Grounds abstract symbols in sensorimotor experiences and emotions (The Symbol Grounding Problem, 1990) |
Struggles with true symbol grounding, relying instead on statistical correlations in text or other data (Symbol Grounding Through Cumulative Learning, 2006) |
| Metaphorical Thinking |
Uses embodied metaphors to understand and reason about abstract concepts (Metaphors We Live By, 1980) |
Can generate and use metaphors based on learned patterns but lacks deep understanding of their embodied nature (Deep Learning-Based Knowledge Injection for Metaphor Detection, 2023) |
| Dream Generation |
Produces vivid, often bizarre narratives and imagery during REM sleep (The Interpretation of Dreams, 1900) |
Some generative models can produce dream-like, surreal content (Video generation models as world simulators, 2024) |
| Cognitive Dissonance |
Automatically attempts to reduce inconsistencies between beliefs and behaviors (A Theory of Cognitive Dissonance, 1957) |
MoE architectures can handle a wider range of inputs without ballooning model size, suggesting potential for resolving conflicts between different AI components by synthesizing expert opinions into a coherent whole (Optimizing Generative AI Networking, 2024). |
Typically speaking, the appearance of similarities should be explored for functional similarities. Even in the world of prompting, a user gets demonstrably better results (in some contexts) by acting as-if they’re talking to a person. We can explore the similarities between interpersonal interaction and AI-interaction for overlap in effective modalities without claiming they’re equivalent.
Who is “we”? The reality is that these grifters are constantly claiming equivalence. The term “AI” is itself a misleading equivalence. I would much rather not be part of that “we”.
“We” being anyone interested in scientific exploration, hence the papers linked in the post and comments.