In this installment of the psychology of human creativity, I delve into the disturbing aspects of "malevolent creativity." Warning: it's not for the faint of heart.

Hi everyone , i am currently studying bsc life science ,first yr student minor in philosophy. I am very interested in psychology ,neurosci , philosophy and theology. So i searched it using several AI tools and google and found out cognitive sci is the most optimal route for me.

I wanted real world guidance that is it possible to shift from life sci to cognitive sci for my masters?

What are some of the best institutions for it? And how to prepare for them

Thank you.

The Role of Experience in Intelligence and Problem-Solving

I think experience plays a much larger role in intelligence than we typically acknowledge. Although intelligence and experience are not the same thing, I believe that many of the differences we observe in cognitive performance between children and adults can be explained, to a significant extent, by the accumulation of knowledge, familiarity, and mental frameworks over time.

Consider the difference between a ten-year-old child and an adult. The adult has had substantially more time to accumulate knowledge, encounter different situations, recognize patterns, and develop strategies for solving problems. Even when faced with something unfamiliar, the adult may have an advantage because they can draw upon previous experiences that are relevant to the problem. Consequently, what appears to be a difference in raw intelligence may sometimes be a difference in how much useful experience each person can apply.

This becomes particularly relevant when we consider reasoning tests. Although such tests attempt to measure cognitive abilities, they cannot completely separate reasoning from prior experience. For example, consider the sequence 1, 3, 5. Someone might immediately recognize that each number increases by two. However, this response does not necessarily demonstrate an ability to reason entirely independently of experience. The person may already be familiar with numerical sequences and recognize that these problems commonly involve identifying a mathematical pattern.

In other words, even when a problem appears unfamiliar, its underlying structure may resemble something the person has encountered before. Experience can help someone recognize what kind of problem they are dealing with, which strategies are likely to work, and what possibilities deserve consideration. This does not mean that the person is merely recalling an answer. Rather, their previous experiences may help them reason more effectively about something new.

More importantly, people do not simply accumulate isolated pieces of information. They develop mental frameworks that allow them to organize knowledge, recognize relationships, and apply what they have learned in different situations.

For example, someone who encounters the sequence 1, 3, 5 might initially learn that the numbers increase by two. Over time, they may generalize this understanding into a broader framework involving constant differences between consecutive numbers. They can then apply that framework to sequences with different starting numbers or different increments. Eventually, they may connect this knowledge to other mathematical concepts, allowing them to approach increasingly complex problems.

The important point is that experience can produce more than familiarity with individual problems. It can create interconnected frameworks that make it easier to understand and solve problems that have never been encountered in exactly the same form.

We can observe a similar process when learning geometry. When a teacher introduces the concept of a line, a student does not necessarily construct an entirely new concept from nothing. Instead, the student interprets the explanation through existing knowledge, visual experience, and previously learned ideas. Likewise, once someone understands that a triangle has three sides, they can use that established framework to recognize why a four-sided shape does not qualify as a triangle under the standard definition.

These examples illustrate a broader principle: learning is not an entirely independent process in which we generate every idea from scratch. New information is interpreted through what we already know. Our previous experiences influence which connections we recognize, which explanations make sense to us, and how effectively we can apply new knowledge.

When we encounter something unfamiliar, we often search for the closest familiar concept that might help us understand it. We then adapt that concept to the situation, determine where the similarities and differences lie, and refine our understanding accordingly. This process resembles analogical reasoning and the transfer of learning.

I believe this helps explain why experience can make people increasingly effective at solving problems. An adult may not have encountered a particular problem before, but they may have encountered many of the individual components that make up that problem. Their advantage may therefore come not only from possessing more information, but also from having a more developed network of mental frameworks through which they can interpret and connect new information.

This creates a reinforcing cycle. Experience helps us develop mental frameworks; those frameworks make it easier to learn from subsequent experiences; and each new learning experience can further refine or expand the frameworks we already possess. As these connections accumulate, we may become increasingly capable of recognizing patterns and adapting existing knowledge to unfamiliar situations.

However, I would distinguish this explanation from the claim that intelligence is simply another word for experience. Intelligence involves capacities related to reasoning, learning, adapting, and processing information, whereas experience supplies much of the knowledge and context through which those capacities operate. A person might have extensive experience in one field but struggle in another, while someone with less experience might learn a new concept remarkably quickly. Experience and cognitive ability therefore influence one another without being identical.

I also recognize that age-related differences in cognitive performance may involve changes in working memory, processing speed, attention, and other cognitive abilities. Consequently, I would not claim that experience explains every difference between children and adults. My argument is that experience and the development of mental frameworks may account for a substantial part of those differences, and that we should be careful not to attribute every performance gap to an abstract measure of intelligence without considering the role of prior learning.

Ultimately, I believe we should distinguish between a person's underlying capacity to reason and the accumulated frameworks that allow them to use that capacity effectively. Someone who solves a problem quickly may not necessarily possess a greater raw capacity for reasoning than someone who struggles with it. They may instead have more relevant experience, better-developed strategies, or a stronger ability to recognize the problem's underlying structure.

The central question, then, is not whether intelligence and experience are the same thing. They clearly are not. It is how much of what we interpret as superior intelligence actually reflects the accumulated effects of experience, the development of mental frameworks, and the ability to transfer existing knowledge to unfamiliar situations.

I'm interested in the relationship between the heart and brain in human thought and decision-making. I understand that current neuroscience identifies the brain as central to these processes, but I would like to understand how signals originating in the heart influence cognition, emotions, and conscious experience.

What evidence distinguishes the influence of cardiac signals on mental processes from the neural mechanisms responsible for generating thoughts and decisions? Are there any significant unanswered questions in this area?

The setup, from Arkes and Blumer (1985): you've bought a $100 ski trip to Michigan. Weeks later you buy a $50 one to Wisconsin, which you think will be more fun. Then you notice they're on the same weekend, and neither ticket can be returned.

Both are already paid for. The only question left is which weekend you'd enjoy more. 33 of 61 students still picked Michigan.

The second experiment used real money. The Ohio University Theater sold season tickets at random prices: $15, $13 or $8. Over the first five plays, full-price buyers used 4.11 tickets each. The discount groups used 3.32 and 3.29. By the last five plays, the difference had gone.

I put these together with Levitt's coin-flip study (20,000+ people; the ones the coin told to make a change were happier six months later) in one page. It also has a quiz on when past costs do count: https://sheets.works/data-viz/sunk-cost-fallacy

Happy to be corrected. The theatre figures are from page 128 of the paper

Who Actually Observes Reality?

When we open our eyes, we have the impression that we see the world as it really is. Objects have specific shapes, colors, and positions. We recognize faces, interpret movement, and distinguish our own bodies from the surrounding environment. Everything appears self-evident because these processes occur automatically.

Yet the brain does not receive a ready-made image of reality. It receives signals generated by sensory receptors and must reconstruct their most probable causes using memory, previous experience, and predictions about upcoming events.

This approach forms the foundation of predictive processing theories, developed in part within Karl Friston’s free-energy principle [1]. In this framework, perception, learning, and action are interconnected processes through which an organism updates its model of the environment and reduces discrepancies between predictions and incoming information.

Perception is therefore a continuous process of constructing and updating representations of reality. Its effectiveness depends on cooperation among multiple neural mechanisms that must simultaneously identify events, predict their consequences, and organize the organism’s responses.

An additional problem emerges, however. To act effectively, the system must identify not only its surroundings but also its own state and position relative to the environment. It must distinguish the consequences of its actions from external events, preserve continuity across previous experiences, and relate predictions to the capabilities of its own body.

In traditional psychological and philosophical language, these functions have often been associated with the concept of the ego, frequently understood as the center of conscious experience.

An alternative interpretation is possible: the ego may be a dynamically constructed frame of reference emerging from the coordination of predictive, memory-related, sensory, and adaptive processes.

Such a hypothesis allows us to investigate the ego as an organizational function of a biological system, whose mechanisms and limitations can be examined experimentally.

More

How do you personally catch yourself when falling into the Dunning-Kruger trap? Are there specific mental checks or habits you use to stay objective about your actual skill level?

I read article on how during rest our brains still learn, as well as other sources stating the same. In this article participants spent only 10 seconds resting but is there other research that looks into whether if longer rest would be better? If so how long? Does it depends on subject of learning?

In this talk, Michael Levin explores neuroscience beyond neurons, proposing that intelligence is not limited to neural systems but is a fundamental property of life at various scales. He argues that even non-neural cells, such as those involved in morphogenesis (how organisms develop their body shapes), exhibit collective problem-solving and cognitive behaviors.

Key concepts include:

• Cognition beyond brains: Levin posits that cells, tissues, and organisms can navigate "morphospace" (the space of possible body shapes) using bioelectric signaling, similar to how brains use electrical activity for neural computation (0:08 - 0:15).
• Morphogenesis as behavior: He demonstrates that embryos and cells use bioelectric patterns to "decide" how to build structures, and that these patterns can be manipulated to create novel anatomies or repair damage (0:30 - 0:34).
• Unconventional cognitive agents: The talk highlights experimental work with Xenobots—living machines made from frog cells—which demonstrate the ability to navigate, remember experiences, and exhibit behaviors despite having no nervous system (0:49 - 0:53).
• Implications for intelligence: By studying these "unconventional minds," Levin suggests we can better understand the nature of intelligence, agency, and how biological systems store information and make decisions (0:58 - 1:00).