
74 segments available
Jay McClelland is a cognitive scientist at Stanford. Please support this podcast by checking out our sponsors: - Paperspace: https://gradient.run/lex to get $15 credit - Skiff: https://skiff.org/lex to get early access - Uprising Food: https://uprisingfood.com/lex to get $10 off 1st starter bundle - Four Sigmatic: https://foursigmatic.com/lex and use code LexPod to get up to 60% off - Onnit: https://lexfridman.com/onnit to get up to 10% off EPISODE LINKS: Jay's Website: https://stanford.edu/~jlmcc/ PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ Full episodes playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4 Clips playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOeciFP3CBCIEElOJeitOr41 OUTLINE: 0:00 - Introduction 0:43 - Beauty in neural networks 5:02 - Darwin and evolution 10:47 - The origin of intelligence 17:29 - Explorations in cognition 23:33 - Learning representations by back-propagating errors 29:58 - Dave Rumelhart and cognitive modeling 43:01 - Connectionism 1:05:54 - Geoffrey Hinton 1:07:49 - Learning in a neural network 1:24:42 - Mathematics & reality 1:31:50 - Modeling intelligence 1:42:28 - Noam Chomsky and linguistic cognition 1:56:49 - Advice for young people 2:07:56 - Psychiatry and exploring the mind 2:20:35 - Legacy 2:26:24 - Meaning of life SOCIAL: - Twitter: https://twitter.com/lexfridman - LinkedIn: https://www.linkedin.com/in/lexfridman - Facebook: https://www.facebook.com/lexfridman - Instagram: https://www.instagram.com/lexfridman - Medium: https://medium.com/@lexfridman - Reddit: https://reddit.com/r/lexfridman - Support on Patreon: https://www.patreon.com/lexfridman
Jay McClelland discusses the profound connection between neural networks and the mysteries of thought. He reflects on the evolution of cognitive psychology and the importance of understanding the biological basis of cognition, challenging the notion that the nervous system is peripheral to the study of the mind.
"the following is a conversation with jay mcclelland a cognitive scientist at stanford and one of the seminal figures in the history of artificial intelligence and specifically neural networks having w..."
McClelland elaborates on Descartes' mechanistic view of animal behavior and the separation of mind and body. He argues for a biological understanding of cognition, emphasizing that the study of neural networks can bring us closer to understanding the human mind.
"you are one of the seminal figures in the history of neural networks at the intersection of uh cognitive psychology and computer science what do you have over the decades emerged as the most beautiful..."
In this segment, McClelland draws parallels between Darwin's struggle to understand evolution and the complexities of cognition. He discusses how Darwin's insights into evolution challenge traditional views and highlight the continuity of species, suggesting that humans are a product of nature.
"neural networks is how they allow us to link biology with the mysteries of thought and um you know in the when i was first entering the field myself in the late 60s early 70s cognitive psychology had ..."
McClelland reflects on the challenges of comprehending evolution over human timescales. He discusses the concept of punctuated equilibrium and how significant changes in mental abilities can occur, drawing on Piaget's theories of child development to illustrate these transitions.
"and i didn't agree with that i i always felt oh look i'm i'm a physical being i from dust to dust you know ashes to ashes and somehow i emerged from that um so that's really interesting so there was a..."
This segment explores the leap from basic biological processes to the emergence of human intelligence. McClelland questions how a single genetic mutation could lead to language and advanced cognitive abilities, emphasizing the importance of social engagement in this evolution.
"fundamentals of the human mind yeah i used to think um where i used to talk about the idea of awakening from the cartesian dream so descartes you know thought about these things right he he was walkin..."
McClelland discusses the role of language in facilitating collective intelligence among humans. He posits that language is not just a standalone ability but is intertwined with social structures and cognitive development, allowing for a richer understanding of the world.
"that allowed the physical contact with the stone to cause water to flow in various directions which caused water to flow under the statue and move the statue and he used this as the beginnings of a th..."
In this segment, McClelland examines how evolutionary biology and cognitive development intersect. He highlights the significance of understanding the biological underpinnings of cognition and how these insights can inform our understanding of the mind.
"cause action so he had a mechanistic theory of animal behavior and he thought that the human had this animal body but that some divine something else had to have come down and been placed in him to gi..."
McClelland reflects on his journey in cognitive science and the development of neural networks. He shares memorable moments from his early career, emphasizing the collaborative spirit of exploration in the field and the impact of foundational works on his understanding of cognition.
"things that you could directly measure when you stimulated neurons and stuff like that and um the study of cognition was something that you know was tied in with abstract computer algorithms and thing..."
Jay McClelland reflects on his early experiences with neural networks and the collaborative spirit of cognitive science in the 1980s. He shares memorable moments of discovery and the excitement of exploring ideas with pioneers like David Rumelhart and Geoffrey Hinton, emphasizing the beauty and potential of neural networks in understanding cognition.
"to be pretty continuous so let me uh let me step back to neural networks for for another brief minute you wrote parallel distributed processing books that explored ideas of neural networks in the 1980..."
McClelland discusses his journey in cognitive science during the mid-70s, highlighting a pivotal moment when he realized the limitations of traditional cognitive models. He recounts his encounter with James Anderson's work on neural network models, which bridged the gap between the mind and the brain, igniting his passion for understanding cognition through neural networks.
"early days i'm going to start sort of with my own process in the mid 70s and then into the late 70s when i met jeff hinson and he came to san diego and we were all together in my time in graduate scho..."
Jay McClelland describes the impact of Dave Rumelhart's book 'Explorations in Cognition' on the cognitive science community. He reflects on the collaborative atmosphere it fostered and the playful exploration of ideas that characterized the field during that time, setting the stage for significant advancements in understanding cognition.
"rummelhart had written a book together with another man named don norman and the book was called explorations in cognition and it was a series of chapters exploring interesting questions about cogniti..."
In this segment, McClelland shares his realization of the potential of neural networks to model cognitive processes. He recounts a transformative moment when he understood that thinking about the mind in terms of neural networks could help answer complex questions about cognition, highlighting the excitement and possibilities that emerged from this perspective.
"i was also you know still trying to get from the neurons to the to the cognition and i realized at one point i i got this opportunity to go to a conference where i heard a talk by a man named james an..."
McClelland discusses the emergence of neural network models in cognitive science, mentioning key figures like Jim Anderson and Steve Grossberg. He highlights the significance of their contributions and the excitement surrounding the development of neural networks as a means to understand cognitive processes, setting the stage for future advancements.
"me this was a bridge between the mind and the brain and i just like stuck and i i remember i was walking across campus one day in 1977 and i almost felt like saint paul on the road to damascus i said ..."
Jay McClelland reflects on the 'Parallel Models of Associative Memory' conference organized by Jeff Hinton and Jim Anderson. He discusses how this event brought together thinkers in the field and resonated with Dave Rumelhart's ideas, marking a significant moment in the evolution of cognitive modeling and neural networks.
"his phd dissertation showed up uh in an applicant pool to a postdoctoral training program that dave and don the two men i mentioned before remember heart and norman were administering and rommelhardt ..."
In this segment, McClelland explains the foundational concepts of neural networks and their relevance to machine learning. He discusses the significance of parallel computation in neural networks, emphasizing how each neuron acts as an independent computational unit, contributing to the overall processing of information in a way that mirrors biological systems.
"to kind of really resonate with some of rommel hart's um own thinking some of his reasons for wanting something other than the kinds of computation he'd been doing so far so let me talk about ronald h..."
McClelland elaborates on the revolutionary nature of thinking about computation through the lens of neural networks. He contrasts traditional sequential computing with the parallel processing capabilities of neural networks, highlighting how this shift in perspective has profound implications for understanding cognition and developing artificial intelligence.
"and uh the word parallel is really interesting so it's it's almost like synonymous from a computational perspective what how you thought at the time about neural networks that is parallel computation ..."
In this segment, McClelland discusses the structure of convolutional neural networks (CNNs) and their biological parallels. He explains how these networks process information through multiple layers, drawing comparisons to the human brain's structure and function, and illustrating the potential of neural networks to replicate cognitive processes.
"little computer in and of itself so the idea is that each you know our brains have oh look you know a hundred or hundreds almost a billion of these little neurons right um and they're all capable of d..."
Jay McClelland describes how convolutional neural networks transform raw pixel data into meaningful classifications, akin to human perception. He emphasizes the parallel processing capabilities of these networks and how they contribute to our understanding of cognition, showcasing the exciting advancements in artificial intelligence.
"running on a single computer that's right you're saying wait a minute what what why don't we take a really dumb very simple computer and just have a lot of them interconnected together and they're all..."
In this segment, McClelland discusses the emergence of cognitive abilities from simple neural network structures. He highlights how the integration of multiple constraints within these networks can lead to complex cognitive functions, emphasizing the potential for neural networks to model understanding and cognition.
"capabilities of deep learning and all these kinds of things but if i could just play this out a little bit a a convolutional neural network or a cnn which you know many people may have heard of is a s..."
McClelland reflects on the future of cognitive modeling and the ongoing questions surrounding the capabilities of deep learning. He emphasizes the importance of understanding how cognitive processes can emerge from neural networks, setting the stage for future research and exploration in the field.
"right um and then we think okay at the bottom level there's an array of things that are like the photoreceptors in there in the eye they respond to the amount of light of a certain wavelength at a cer..."
Jay McClelland discusses the complexities of cognitive processes, particularly how different concepts and representations link together in the mind. He reflects on the limitations of traditional AI models in capturing these intricate relationships and the need for a more integrated approach to understanding cognition.
"and get her money so she can buy herself an ice cream it's a huge amount of inference that has to happen to get those things to link up with each other and and he was interested in how the hell that c..."
McClelland introduces the concept of interactive models of reading, emphasizing how every level of analysis in reading—from pixels to letters to words—interacts with and influences each other. This model highlights the complexity of cognitive processing and the importance of understanding these interactions.
"of good old-fashioned ai wasn't giving him the answers to these questions yeah and by the way that's called good old-fashioned ai now it was called that well it was it was beginning to be called that ..."
In this segment, McClelland elaborates on the hierarchical nature of cognitive processing, where lower-level features contribute to higher-level understanding. He discusses how this structure allows for the emergence of comprehension and the fluidity of thought in cognitive systems.
"so he wrote a paper that just really first time i read it i said oh well you know yeah but is this important but after a while it just got under my skin and it was called an interactive model of readi..."
McClelland explains the role of specialized 'experts' in cognitive models, where different processing units evaluate and update hypotheses about language and meaning. This segment illustrates the collaborative nature of cognitive processing and how various levels of expertise contribute to understanding.
"have these little tiny uh elements that represent each of the pixels of each of the letters and then other ones that represent the line segments in them and other ones that represent the letters and o..."
This segment contrasts traditional AI approaches with neural network models, highlighting how the latter can replace expert systems with neuron-like processing units. McClelland discusses the implications of this shift for understanding cognition and the potential of neural networks to address complex cognitive questions.
"a completely interactive bi-directional parallel distributed process that is somehow because of the abstractions is hierarchical so like yeah so there's different layers of responsibilities different ..."
McClelland reflects on the concept of emergence in cognitive science, arguing for the reality of higher-level cognitive phenomena that arise from lower-level processes. He uses the analogy of sand dunes to illustrate how complex cognitive structures can emerge from simple interactions.
"so there so what ended up happening was that remote heart and i got together and we created a model called the interactive activation model of letter perception which is takes these little pixel level..."
In this segment, McClelland discusses the fluid nature of knowledge representation in cognitive systems, emphasizing that while knowledge may appear structured, it is dynamic and influenced by various factors. He explores the implications of this fluidity for understanding human cognition.
"the algorithmic side the optimization side those are all details like when you first start the idea that you can get far with this kind of way of thinking that in itself is a profound idea so do you l..."
McClelland delves into the complexities of visual recognition and how cognitive systems identify objects. He discusses the challenges of articulating the processes behind recognition and the importance of understanding the underlying mechanisms that contribute to this cognitive ability.
"um but it's not there there's the word time isn't written anywhere inside the bottle it's only written there in the picture we drew of the model to say that's the unit for the word time right yeah and..."
This segment explores the relationship between connectionism and human cognition, with McClelland discussing how connectionist models can capture aspects of cognitive processes. He reflects on the ongoing quest to understand the depth of human knowledge through the lens of connectionism.
"open my eyes and say oh that's lex or um oh you know there's my own dog and i recognize my dog which is a member of the same species as many other dogs but i know this one because of some slightly uni..."
McClelland contrasts the concepts of emergence and mechanism in artificial intelligence, discussing the philosophical implications of each. He emphasizes the importance of recognizing the emergent properties of cognition while also understanding the underlying mechanisms that facilitate these processes.
"you cannot you don't read the contents of the connections the connections only cause outputs to occur based on inputs yeah it's it's and for us that like final layer or some particular layer is very i..."
In this concluding segment, McClelland reflects on the nature of thought and understanding, proposing that cognitive processes are not merely mechanical but involve emergent properties. He discusses the implications of this perspective for the future of cognitive science and artificial intelligence.
"how do you square those two like do you think the connections can contain the depth of human knowledge and the depth of what uh dave romohart was thinking about of understanding well uh that remains t..."
Jay McClelland discusses the relationship between connectionist models and the emergence of cognition. He reflects on how simple systems, like cellular automata, can lead to complex behaviors that resemble life, suggesting that reality itself may be emergent. This segment explores the philosophical implications of emergent properties in cognitive science.
"into the cognitive it's like okay so if the under if the substrate is parallel distributed connectionist um then it doesn't mean that the contents of thought isn't you know like abstract and symbolic ..."
In this segment, McClelland emphasizes the importance of recognizing the 'magic' of emergent phenomena in cognition. He contrasts the views of eliminative materialism with the appreciation of the richness that emerges from cognitive processes. The discussion touches on philosophical perspectives from figures like Plato and Fellini, highlighting the depth of understanding that can arise from accepting the mystery of cognition.
"start looking very quickly like organisms that you forget that the forget how the actual thing operates they start looking like they're moving around they're eating each other some of them are generat..."
Jay McClelland shares poignant memories of his colleague Dave Rumelhart, who passed away due to a progressive neurological condition. He reflects on Rumelhart's contributions to cognitive science and the impact of his illness on their work. This segment highlights the emotional weight of losing a brilliant mind and the complexities of cognitive decline.
"that's so you know we won't try to figure out what it is we'll just accept it as given that that that occurs and um you know but he was still on to the magic of it yeah yeah we won't try to really rea..."
McClelland explains semantic dementia, a condition that affects the ability to understand meaning. He describes how patients progressively lose the ability to recognize and relate concepts, illustrating this with examples of patients' experiences. This segment delves into the implications of semantic dementia for understanding cognition and the nature of meaning.
"progressively more and more affected so i'm going to talk about the disorder and not about remember heart for a second okay sure the disorder is something my colleagues and collaborators have chosen t..."
In this segment, McClelland discusses the multifaceted nature of cognition, particularly in the context of cognitive impairments. He reflects on the gradual disintegration of cognitive abilities and the partial competencies that may remain. This exploration emphasizes the richness of human cognition and the emotional journey of witnessing cognitive decline.
"texture of distributed representation in a very nice way i've always felt but at the same time it was extremely poignant because this is exactly the condition that romal heart was undergoing and there..."
Jay McClelland details the development of the backpropagation algorithm in neural networks, highlighting its significance in learning processes. He recounts the collaboration with Dave Rumelhart and Geoffrey Hinton, discussing how their insights transformed the understanding of neural network learning. This segment provides a technical overview of backpropagation and its impact on cognitive modeling.
"is a celebration yeah yeah yeah and but just to say something more about the scientists and and the back propagation idea that you mentioned um so in in 1982 hinton had been there as a postdoc and org..."
Jay McClelland reflects on his interactions with Geoffrey Hinton, a pivotal figure in AI. He shares insights on Hinton's innovative ideas, including early concepts of transformers and recursive computation in neural networks, emphasizing Hinton's profound impact on cognitive science and machine learning.
"change their weights from the input and that's why it's called back problems yeah but so it came from hinton having introduced the concept of you know define your objective function figure out how to ..."
McClelland discusses the creative thinking of Geoffrey Hinton, particularly his approach to explaining complex concepts in AI without relying heavily on equations. He highlights Hinton's use of visual metaphors to convey ideas about deep learning, showcasing the importance of intuitive understanding in scientific discourse.
"in in in the full space of ideas here at the intersection of computation and cognition well so um jeff has said many things to me that had a profound impact on my thinking um and he's written several ..."
In this segment, McClelland explores the Boltzmann machine, a concept introduced by Hinton that connects logic with probabilistic models in AI. He discusses its significance in understanding cognition and how it represents a blend of theoretical physics and computer science, illustrating the interdisciplinary nature of AI research.
"sort of the idea that um when you when you call a subroutine you need to save the state that you had when you called it so you can get back to where you were when you're finished with the subroutine a..."
Jay McClelland emphasizes the importance of intuitive understanding in AI research, contrasting it with traditional mathematical approaches. He shares how Hinton's unique style of thinking and teaching has influenced his own understanding of cognitive processes and the development of computational intelligence.
"is that he doesn't write too many equations and people tell stories like oh in in the hints and lab meetings you don't get up at the board and write equations like you do in everybody else's machine l..."
McClelland discusses the concept of computational intelligence, reflecting on how AI can potentially surpass human cognitive capabilities. He emphasizes the excitement surrounding deep learning and its implications for understanding intelligence beyond human limitations, highlighting the ongoing advancements in the field.
"yeah the there's certain people like that here's an example some kind of weird mix of uh visual and intuitive and all those kinds of things feynman is another example different style thinking but very..."
In this segment, McClelland delves into the nature of mathematical cognition, referencing a paper that critiques the narrow view of mathematics as mere symbol manipulation. He argues for a broader understanding of mathematics as a tool for exploring idealized worlds and its relevance to real-world applications.
"early to mid 80s on something called the boltzmann machine was his way of connecting with that boolean tradition and bringing it into the more continuous probabilistic graded constraint satisfaction r..."
McClelland articulates his view of mathematics as a set of tools for exploring idealized worlds with precise relationships. He illustrates how mathematical concepts, such as triangles and congruence, have practical applications in the real world, emphasizing the importance of these ideas in various fields.
"some of these things well you're right with the bullying lineage and the the dream of computer science is uh somehow i mean i certainly think of humans this way that humans are one particular manifest..."
In this concluding segment, McClelland discusses the fundamental role of numbers in human society, using the example of counting sheep to illustrate the importance of numerical precision in everyday life. He reflects on how mathematics enables commerce, contracts, and the establishment of records, highlighting its foundational significance in human interactions.
"um you know not limited in the ways that we are by our own biology perhaps allowing us to scale the very mechanisms of human intelligence just increase its power through scale yes and and i think that..."
This segment focuses on how intuitive insights can lead to breakthroughs in mathematical thinking. McClelland discusses the importance of engaging with formal systems to foster intuitive discovery, drawing parallels with historical figures like Newton and Einstein who experienced flashes of insight that transformed their fields.
"about uh the properties and relations among uh sets of idealized objects and um uh you know the the mathematical notation system that we unfortunately focus way too much on is um just our way of expre..."
McClelland explores the potential of neural networks to generate creative outputs, likening their processes to human intuition. He discusses how these systems can synthesize novel ideas from vast amounts of training data, suggesting that they may reflect a form of intuitive reasoning akin to that of human mathematicians.
"the insights that human mathematicians have had is a combination of the kind of the intuitive kind of connectionist like knowledge that makes it so that something is just like obviously true so that y..."
In this segment, McClelland draws parallels between human chess players and neural networks, discussing how both utilize intuition to evaluate positions without exhaustive calculations. He highlights the moments of genius that can emerge from both human and machine reasoning in complex scenarios like chess.
"i came across this quotation from i'll replace while i was um walking in the in the woods with my wife in a state park in northern california uh late last summer and what it said on the bench was it i..."
McClelland emphasizes the potential for neural networks to create novel ideas, reflecting on the insights gained from systems like AlphaZero in chess. He discusses how these systems can exhibit moments of brilliance that resemble human creativity, suggesting a hopeful future for AI in generating innovative concepts.
"you know the ability of somebody like hinton or newton or einstein or romal heart or poincare to um archimedes is another example right so suddenly a flash of insight occurs it's it's like the constel..."
This segment delves into the historical development of formal systems in mathematics, tracing back to ancient philosophers. McClelland discusses how these systems have shaped human thought and the ability to engage in abstract reasoning, highlighting the significance of figures like Euclid in establishing foundational concepts.
"and so i feel like the the kinds of things that we're beginning to see um deep learning systems do of their own accord kind of gives me this feeling of of um i don't know hope or encouragement that ul..."
McClelland discusses the importance of immersion in formal systems for developing abstract thinking. He draws parallels between language acquisition and mathematical cognition, suggesting that both require deep engagement with structured systems to foster intuitive understanding.
"um and there's there's kind of like a sense that they've somehow synthesized something like novel out of the you know all of the particulars of all of the billions and billions of experiences that wen..."
In this segment, McClelland addresses the concept of the expert blind spot, where experts struggle to communicate their knowledge to non-experts. He discusses how this phenomenon can hinder understanding and emphasizes the need for awareness of one's own intuitive processes in teaching and sharing knowledge.
"know if you find them as captivating is you know on the deep mind side with alpha zero if you study chess the kind of solutions that has come up in terms of chess it is it there's novel ideas there it..."
McClelland explores the tension between intuitive knowledge and formal reasoning in cognitive science. He discusses how formal training can shape one's understanding of concepts, potentially distancing individuals from natural cognitive processes and limiting their ability to introspect.
"calculation which is the search taking a particular set of steps down the line to see how they unroll but there there is moments of genius in those systems too so that's another hopeful illustration t..."
This segment reflects on the legacy of philosophical thought in shaping modern cognitive science. McClelland discusses how early philosophers laid the groundwork for formal systems of thought, influencing contemporary understanding of cognition and the development of academic disciplines.
"here is one part of what i like to emphasize about mathematical cognition at least is that philosophers and logicians going back three or even a little more than 3 000 years ago began to develop these..."
McClelland examines the nature of systematic thought and its role in human cognition. He contrasts the structured thinking of formally trained individuals with the more intuitive processes of the general population, suggesting that immersion in academic disciplines can shape cognitive engagement.
"the kind of the touch point of a of a coherent document that sort of laid out this idea of an actual formal system within which these objects were characterized and the um the system of uh inference t..."
In this segment, McClelland discusses how enculturation influences cognitive processes, particularly in formal academic settings. He emphasizes the importance of understanding how cultural and educational backgrounds shape one's approach to reasoning and knowledge acquisition.
"uh in in experience thinking in that way that you know we now begin to think of our understanding of language as being right so we immerse ourselves in in a particular language in a particular world o..."
McClelland explores the relationship between language and cognition, particularly in the context of Chomsky's theories. He discusses how linguistic training can affect intuitive understanding and the implications for cognitive science as a whole.
"think that um systematic thought is the essential characteristic of the human mind as opposed to a derived and an acquired characteristic that results from acculturation in a certain mode that's been ..."
In this concluding segment, McClelland reflects on the limitations of intuitive knowledge in formal disciplines. He discusses the challenges faced by experts in conveying their understanding and the importance of recognizing the role of intuition in cognitive processes.
"it is important to draw that line but then to come back and look at it again and see some of the subtleties and interesting aspects of the difference so if we think about chomsky himself he was born i..."
Jay McClelland discusses the concept of 'beginner's mind' and the 'expert blind spot,' emphasizing how experts may struggle to communicate their knowledge to non-experts. He highlights the challenge of recognizing what is self-evident to them and how this can limit their understanding of cognition and mathematical thinking.
"the product of this sort of immersion and enculturation uh that is what i believe so and that's limiting it's it's something to be aware of does that limit you from uh having a good model of some of c..."
In this segment, McClelland reflects on the historical belief that natural numbers are innate to human cognition, contrasting it with the modern understanding that they are a cultural construction. He discusses how cognitive scientists have shifted their views on core knowledge and the implications for understanding human cognition.
"i think you're you're right i think that's a great way of characterizing it and um i also think that um it's related to um the concept of beginner's mind uh and um another concept called the expert bl..."
McClelland explores the idea that certain mathematical concepts may seem obvious to experts, leading to a lack of explicit teaching. He illustrates this with examples from education, emphasizing the importance of recognizing the implicit knowledge that experts possess and the challenges faced by students in grasping these concepts.
"you know the basic fundamentals of discrete quantities being countable and innumerable and you know indefinite in number um was was not something that had to be discovered um but he was wrong it turns..."
This segment delves into the difficulty of introspection and understanding one's cognitive limitations. McClelland discusses how individuals may struggle to recognize changes in their cognitive abilities over time and the importance of stepping back to gain a broader perspective on their understanding.
"who still exist who you know who don't have those systems so so this is just an example to me and where you know a certain mode of thinking about language itself or a certain mode of thinking about ge..."
McClelland introduces the concept of post-hoc rationalizations, explaining how individuals often misinterpret the reasons behind their behaviors. He references research by Nisbet and Wilson, highlighting the cultural influences on our understanding of our own actions and the limitations of self-awareness.
"i'm i although i don't want to dismiss biological individual differences completely i i find it much more interesting to think about the possibility that um you know it was that difference in the dinn..."
In this segment, McClelland shares valuable advice for young people navigating their careers. He emphasizes the importance of finding intrinsic motivation and celebrating personal discoveries, recounting his own journey through college and the pivotal moments that shaped his path.
"um actually part of the causal process that caused that behavior to occur or even valid observations of the set of constraints that led to the outcome but they are post-hoc rationalizations that we ca..."
McClelland reflects on his academic journey, discussing how he stumbled upon his passion for psychology amidst societal upheaval. He emphasizes the significance of nurturing intrinsic motivation and the role of personal experiences in shaping one's career path, drawing parallels to the experiences of his siblings.
"would you give to young people today in high school and college about um how to have a career or how to have a life they can be proud of finding the thing that you are intrinsically motivated to engag..."
McClelland shares his early aspirations in psychiatry and his disillusionment with the field's focus on medication over understanding the human mind. He contrasts his initial dreams of exploring psychological concepts with the reality of a biochemically driven discipline, leading him to pivot towards computer science and cognitive psychology.
"like for me personally i've always been excited about love and friendship between humans and just like the actual experience of it since i was a child just observing people around me and also been exc..."
In this segment, McClelland reflects on the shift in psychiatric practices towards a more biological approach, particularly during his time on the National Advisory Mental Health Council. He critiques the neglect of behavioral psychology and the lack of progress in treating mental illness, emphasizing the need for a more holistic understanding of mental health.
"for me i was a little bit disillusioned because of how much prescription medication and biochemistry is involved in the discipline of psychiatry as opposed to the dream of the the freud like use the m..."
McClelland discusses the evolving role of cognitive psychology in understanding the human mind, contrasting it with traditional psychiatry. He highlights how cognitive psychologists can engage in philosophical inquiries about the mind while psychiatrists have become more focused on medical treatments, losing their role as deep thinkers.
"well there's some aspect to and sorry to romanticize the whole philosophical conversation about the human mind but to me psychiatrists for time held the flag of we're the deep thinkers in the same way..."
Reflecting on his career, McClelland shares how the awareness of mortality motivated him to pursue more challenging questions in cognitive science. He discusses his transition to studying mathematical cognition, emphasizing the importance of focusing on significant and interesting questions as he approaches the later stages of his career.
"the i admire the richard feynman ability to do great low-level mathematics and physics and the high-level philosophy yeah i think it was uh frohm and young more than freud that was sort of initially k..."
In this segment, McClelland contemplates his legacy as a cognitive scientist. He expresses a desire to be remembered for fostering collaborative opportunities in science and for taking unconventional paths in research, inspired by the joy of crystallizing ideas into scientific progress.
"speaking which you happen to be a human being who is unfortunately not immortal that seems to be a fundamental part of the human condition that this riot ends do you think about the fact that you're g..."
McClelland shares a personal anecdote about his early experiences in research, highlighting the tension between empirical work and theoretical exploration. He reflects on the importance of theorizing in science and how he navigated the challenges of being labeled a non-theorist, ultimately emphasizing the value of integrating theory with experimental findings.
"interesting and important ones for sure what do you hope your legacy is you've done some incredible work in your life as a man as a scientist when the aliens and the human civilization is long gone an..."
In this segment, McClelland discusses the impact of labels in academia and how they can limit one's potential. He encourages listeners to embrace their evolving identities and to continue exploring new areas of research, regardless of how they may be categorized by others.
"labeled as a non-theorist right by this uh and um i could have like succumbed to that and said okay well i guess my job is to just go on and do experiments right but but uh that's not what i wanted to..."
McClelland explores the concept of meaning-making, suggesting that individuals create their own meaning through personal experiences and interactions. He emphasizes the role of historical and cultural influences in shaping our understanding of meaning, and how this process is an emergent result of human experience.
"um hold you back well let me ask the big question um when you look at into you said it started with colombia trying to observe these humans and they're doing weird stuff and you want to know why are t..."
In the concluding thoughts, McClelland reflects on the beauty of the stories humans tell about themselves and the emergent processes that shape these narratives. He expresses hope for humanity's future, envisioning a time when we will explore beyond Earth and continue to create and share meaningful stories.
"stories on top of each other and eventually we'll colonize hopefully other planets other solar systems other galaxies and will tell even better stories but all starts uh here on earth jay year speakin..."