EDTech Innovation Hub’s Post

As a Korean American, this article on the bilingual advantage in delaying Alzheimer's onset resonates deeply with me. I've noticed that constantly using two languages fosters cognitive flexibility and problem-solving skills crucial in caregiving contexts. For those who are still learning, intermittent exposure to a second language later in life can still confer cognitive benefits. It is never too late to start learning a different language, and engaging your brain through this neural network can strengthen the connection between language, cognition, and overall well-being! #bilingual #careyaya #alzheimers https://lnkd.in/gNzy-Ark

ai.plainenglish.io: In the rapidly evolving field of artificial intelligence, developmental psychologists are exploring the potential of ChatGPT, a powerful language model developed by OpenAI, to enhance their research and understanding of human development. ChatGPT, based on the GPT-3.5 architecture, can simulate conversations, generate hypotheses, aid in language development studies, explore cognitive processes, evaluate emotional intelligence, and be integrated into interactive learning tools for educational purposes. While offering exciting opportunities, researchers must consider limitations and ethical considerations, such as bias and privacy issues. ChatGPT presents a versatile and powerful tool to complement traditional research methods, providing fresh insights and innovative avenues for the study of human development. As technology advances, the collaboration between developmental psychology and artificial intelligence is likely to yield even more exciting discoveries, pushing the boundaries of our understanding of human growth and behavior.

#Repost @emotionallycoaching Autobiographical memory refers to our ability to remember events and details from our lives. In neurodivergence, there can be unique challenges and differences in how neurodivergent individuals experience and recall autobiographical memories. One common characteristic among neurodivergent individuals conditions is atypical cognitive processing, which can impact aspects of autobiographical memory. For instance, people may exhibit strengths in detail-oriented memory but struggle with remembering the big picture or the context of events. This can lead to difficulty in creating a coherent narrative of their life story. There may also be differences in working memory and attention regulation, which can affect the encoding and retrieval of autobiographical memories. This may result in a fragmented or inconsistent recollection of past events, making it challenging to form a cohesive understanding of one’s own life experiences. Differences in language processing may also impact autobiographical memory due to difficulties in encoding verbal information. This can affect the ability to recall specific details or events heavily reliant on language-based cues. In addition to these cognitive factors, emotional regulation differences commonly seen in neurodivergent individuals can also influence autobiographical memory. For example, heightened emotional responses to specific memories can lead to either over-emphasis or avoidance of specific past events in memory recall. Further, sensory sensitivities and perceptual differences in neurodivergent individuals can also shape how autobiographical memories are stored and retrieved. For instance, someone with sensory processing differences may have vivid sensory-based memories that play a significant role in their recollection of past events. Despite these challenges, it is essential to recognise that neurodiversity also brings unique strengths and perspectives to autobiographical memory. There may be exceptional attention to detail or unique creativity which helps to shape autobiographical memory in meaningful ways for that person. #autobiographicalmemory #timelinecoaching #neurodivergent

💡 AI Outperforms Human Psychologists in Social Intelligence Test A recent study published in Frontiers in Psychology compared the social intelligence of advanced AI language models like ChatGPT-4, Google Bard, and Bing to human psychologists. 📄"The Human participants were a sample of male psychologists in the Kingdom of Saudi Arabia with one of two levels of education (Bachelor’s and doctoral students) at King Khalid University during 2023–2024. The study sample consisted of 180 participants, including 72 bachelor’s students and 108 doctoral students in counseling psychological program." "We conducted a single evaluation for each AI model on August 1, 2023 of its SI performance using the Social Intelligence Scale (Sufyan, 1998). In each evaluation, we provided AI the same 64 standard SI scenarios. A link to the questionnaire was sent to human participants via e-mail. While the large linguistic models of AI were asked to answer the scale items individually and their answers were collected in a separate external file by directing a question to the AI models to choose the appropriate answer from the alternative points for each item in the scale." 📊 Key findings: 1️⃣ ChatGPT-4 outperformed all participating psychologists on the Social Intelligence Scale. 2️⃣ Bing surpassed more than half of the psychologists, especially those at the bachelor's level. 3️⃣ Google Bard's performance was comparable to bachelor's level psychologists but significantly lower than doctoral students. 💬 Does this surprise you? I didn't expect AI would beat humans at social intelligence. What areas of intelligence do you expect Humans to outcompete AI in over the longer term? 📚 References/further reading Sufyan, N. S., Fadhel, F. H., Alkhathami, S. S., & Mukhadi, J. Y. A. (2024). Artificial intelligence and social intelligence: preliminary comparison study between AI models and psychologists. Frontiers in Psychology, 15. Follow MIT FutureTech if you are interested in technological trends and their social implications. #AI #technology #economics

*Integrating Neural Coordination and Ideological Influences: A Linguistic Approach to Understanding the Interplay of Mind and Brain Functions.* **Executive Summary:** This study explores the intricate relationship between cognitive processes, brain neural networks, and ideological influences, drawing a metaphorical parallel with linear functions of magnetism. Emphasizing the coordination of mind and brain, it delves into how linguistic cooperation in language contributes to the comprehension of food-related concepts. **Connection of Ideas in Linguistics of Cooperation in Language to Understand Food:** Examining linguistic cooperation in language involves understanding how shared meanings and cultural nuances influence cognitive processes related to food. Words, expressions, and shared narratives create a cognitive framework that influences perceptions, preferences, and behaviors toward food, shedding light on the interplay between language, ideology, and neural coordination. **Merits:** 1. *Holistic Understanding:* The approach allows for a comprehensive understanding of how language, ideologies, and neural processes synergize in shaping cognitive functions. 2. *Cultural Insights:* Linguistic analysis provides insights into cultural influences on perceptions of food, enhancing cross-cultural understanding. 3. *Interdisciplinary Insight:* Bridging linguistics, neuroscience, and cognitive psychology offers a multifaceted perspective on the mind-brain relationship. **Demerits:** 1. *Simplification Concerns:* The linear functions analogy may oversimplify the complexity of neural and cognitive processes. 2. *Subjectivity:* Ideologies and linguistic expressions can vary widely, introducing subjectivity in interpretation and analysis. 3. *Ethical Considerations:* Exploration of ideologies should be approached with sensitivity to avoid reinforcing stereotypes or biases. **Measure:** A balanced and nuanced approach is crucial, incorporating advanced neuroscientific methodologies, linguistic analyses, and interdisciplinary collaboration. Regular validation of findings through diverse cultural contexts and critical reflection on potential biases will contribute to the reliability and applicability of the research.

**Title of the Topic:** *Integrating Neural Coordination and Ideological Influences: A Linguistic Approach to Understanding the Interplay of Mind and Brain Functions.* **Executive Summary:** This study explores the intricate relationship between cognitive processes, brain neural networks, and ideological influences, drawing a metaphorical parallel with linear functions of magnetism. Emphasizing the coordination of mind and brain, it delves into how linguistic cooperation in language contributes to the comprehension of food-related concepts. **Connection of Ideas in Linguistics of Cooperation in Language to Understand Food:** Examining linguistic cooperation in language involves understanding how shared meanings and cultural nuances influence cognitive processes related to food. Words, expressions, and shared narratives create a cognitive framework that influences perceptions, preferences, and behaviors toward food, shedding light on the interplay between language, ideology, and neural coordination. **Merits:** 1. *Holistic Understanding:* The approach allows for a comprehensive understanding of how language, ideologies, and neural processes synergize in shaping cognitive functions. 2. *Cultural Insights:* Linguistic analysis provides insights into cultural influences on perceptions of food, enhancing cross-cultural understanding. 3. *Interdisciplinary Insight:* Bridging linguistics, neuroscience, and cognitive psychology offers a multifaceted perspective on the mind-brain relationship. **Demerits:** 1. *Simplification Concerns:* The linear functions analogy may oversimplify the complexity of neural and cognitive processes. 2. *Subjectivity:* Ideologies and linguistic expressions can vary widely, introducing subjectivity in interpretation and analysis. 3. *Ethical Considerations:* Exploration of ideologies should be approached with sensitivity to avoid reinforcing stereotypes or biases. **Measure:** A balanced and nuanced approach is crucial, incorporating advanced neuroscientific methodologies, linguistic analyses, and interdisciplinary collaboration. Regular validation of findings through diverse cultural contexts and critical reflection on potential biases will contribute to the reliability and applicability of the research.

Does AI outperform humans in understanding human psychology? After coming across the thought-provoking post by Peter Slattery, PhD, I decided to run my own little test with a scientifically backed test from Clearer Thinking (link below). Results: GPT4 outperformed: 57% of psychology experts outperformed: 91% of non-expert study participants Luckily, it's the dimension where I am still slightly better. My results: You outperformed: 72% of psychology experts You outperformed: 95% of non-expert study participants Conclusion - Yes, even GPT4 knows humans better than most experts. Test: https://lnkd.in/gd73U3D2

Navigating the world of anxiety can often feel like a solitary path, but my professional experience has shown me how shared these experiences are among many of us. Anxiety isn’t just a personal struggle; it’s a pattern of protection that has gone into overdrive. In my own battle and through helping others, I’ve learned that what often looks like a mental illness is, in many cases, a series of learned responses—responses that can be unlearned. Through innovative approaches like Rapid Transformational Therapy (RTT), and Neuro linguistic programming (NLP)we can rewire these protective instincts to serve us, not hinder us. If you’re seeking a way to transform your fight-or-flight response from a source of constant anxiety to a foundation of strength, I’m here to guide you. My approach doesn’t just manage symptoms—it gets to the heart of the cause, offering lasting change and a return to self. Are you ready to reshape your response to anxiety and reclaim control? Contact me to explore how RTT can redefine your well-being. #LeadersInHealing #RTTTherapist #MentalHealthAwareness #AnxietyRecovery #ProfessionalGrowth #BehavioralChange #Neuroplasticity #RTT #WellnessInLeadership #InnovativeTherapies #HealthAndWellbeing #rttworks #nlppractitioner #nlpcoaching

We often hear about the concerns with AI, but let's switch gears and chat about the cool, positive ways AI is making a difference. Like, have you heard about how it’s helping kids with dyslexia? It's pretty awesome to see tech tackling long-standing challenges and truly making life better. I’m all in on using smart solutions for tough problems. What’s your take on these innovative AI applications? https://lnkd.in/dm68QP7f #AI #GenAI #LearningDevelopment

Excited to announce I've just been named a Hilldale Research Fellow by the University of Wisconsin-Madison for 2024-2025! Grateful for the funding to continue my research with Dr. Gary Lupyan in cognitive psychology, exploring causal reasoning in Large Language Models (LLMs) and what this can tell us about causal models in human cognition. While some argue that LLMs merely "parrot" back learned associations from the training data with no grasp of the underlying information being represented, we propose that the degree to which LLMs and humans converge on higher-order causal reasoning tasks poses a dilemma for proponents of the "causal parrots" theory: either it is possible that a more complex "internal model" isn't necessary for explaining causal reasoning, or some semblance of a human-like internal model can arise through association-based learning from language alone. You can read the abstract for the original grant proposal, "Something Has to Give: Implications of Large Language Models for Human Understanding" below. "If a person answers a question correctly, how can we tell if the answer reflects an underlying understanding or if they are merely parroting something they’ve heard or read? Typically, understanding is defined as the ability to generalize beyond surface-level observations. Causal knowledge in particular implies a robust level of understanding, as it requires us to represent relationships beyond mere associations. Alarm clocks and waking up are associated, but waking up does not cause the alarm to sound. What then are we to make of the fact that Large Language Models such as chatGPT frequently make correct causal attributions? Does this mean it has learned some internal model of the phenomenon, or is it possible that its response is based on a superficial pattern of associations? If it’s the latter, can we be sure people are not doing the same thing?  We propose a discovery  procedure for eliciting causal statements that are then evaluated by people and LLMs to search for misalignments: statements that people agree on and LLMs do not, helping to understand the limit of understanding that is achievable by LLMs."