#meta-cognition

What is the corelation between intelligence and consciousness?

Yes, there is a correlation between intelligence and consciousness, but it is weaker and more complicated than people usually assume. The two capacities overlap in some functions of the brain, yet they are not the same thing and they do not increase together in a simple linear way. To see this clearly, it helps to separate the terms.

Intelligence usually refers to the ability to process information such as solving problems, recognizing patterns, learning quickly, planning actions, and manipulating abstract concepts more efficiently. Psychologists typically measure it through reasoning tasks, working memory capacity, and pattern recognition.

Consciousness, in contrast, refers to the presence of subjective awareness and self-representation. In philosophy of mind this is often discussed as the system’s ability to generate a model of itself within its own processing. This idea is strongly associated with the work of Thomas Metzinger, who argues that the brain creates a transparent self-model that makes experiences appear as happening to a subject.

Because both intelligence and consciousness rely on advanced neural processing, they share some cognitive infrastructure. For example, high working memory capacity and complex internal modeling tend to support both reflective thought and sophisticated reasoning. This is why more intelligent individuals often show greater meta-cognition which we understand as the ability to think about their own thinking.

But correlation does not mean identity. A system can be intelligent without being highly conscious. Many artificial systems demonstrate complex problem-solving but show no evidence of subjective awareness. Even in humans there are forms of intelligence that operate mostly unconsciously. Skilled musicians, athletes, or mathematicians often perform complex tasks automatically without reflective awareness during execution.

The reverse is also true. Conscious awareness does not guarantee high intelligence. A person can clearly experience thoughts, emotions, and perceptions while still having limited reasoning ability. The more interesting relationship appears when intelligence becomes reflective. At that point the orgnism begins to analyze its own models, beliefs, and motivations. This creates meta-awareness. Here the two capacities begin to interact more strongly.

Philosophers such as Arthur Schopenhauer and later Emil Cioran pointed out that when intelligence turns inward by examining existence, mortality, and the limits of meaning it can amplify conscious awareness of problems that simpler minds never articulate. That is why reflective intelligence sometimes correlates with existential dissatisfaction. However, the key mechanism is not intelligence itself. The mechanism is recursive modeling by the brain representing its own processes and evaluating them. Intelligence simply provides the computational capacity for that recursion.

So the real relationship can be summarized in structural terms. Basic consciousness can exist without high intelligence. High intelligence can operate partly outside consciousness. But when intelligence is used to model the self and the world recursively, consciousness becomes more complex and often more unsettling.

For the first time in history, an entire generation is growing up with artificial intelligence as a constant presence. AI tools help children write, learn, search, create, and solve problems—often faster and more conversationally than any previous technology. While debates about AI tend to focus on ethics, jobs, or regulation, a quieter and equally important question is emerging: how does growing up with AI shape how people think?

The cognitive impact of AI isn’t inherently positive or negative. Like previous technologies—calculators, the internet, smartphones—it changes how thinking happens. The difference is that AI doesn’t just provide information; it actively participates in cognition.

From Information Access to Cognitive Partnership

Earlier digital tools expanded access to knowledge. AI tools go a step further by offloading cognitive effort. They summarize, generate, recommend, and reason alongside users. For young people, this means fewer barriers between a question and an answer—but also fewer moments of productive struggle.

This shift changes learning dynamics. Instead of memorizing or synthesizing information manually, users increasingly focus on:

Asking the right questions

Evaluating outputs

Iterating ideas collaboratively

In many ways, AI encourages meta-cognition—thinking about thinking. But it can also reduce opportunities to build foundational skills if used passively.

Attention, Depth, and Mental Endurance

One concern among educators and cognitive scientists is whether constant AI assistance weakens attention span and deep focus. When answers arrive instantly, the brain has fewer incentives to wrestle with complexity.

Over time, this may affect:

Mental endurance for long-form problem solving

Tolerance for ambiguity or delayed understanding

Willingness to explore ideas without immediate payoff

However, this outcome isn’t inevitable. When AI is used as a scaffold rather than a shortcut, it can actually extend attention by reducing cognitive overload and allowing learners to engage with more complex material earlier.

Creativity: Diminished or Amplified?

AI’s ability to generate text, images, music, and code raises fears that creativity will atrophy. Why imagine something when a machine can produce it instantly?

Yet creativity is not just generation—it’s selection, direction, and judgment. Young users growing up with AI often act more like creative directors than creators in the traditional sense. They experiment rapidly, remix ideas, and explore possibilities that would have been inaccessible before.

The cognitive shift here is subtle:

Less emphasis on raw production

More emphasis on taste, intent, and refinement

The risk lies not in using AI to create, but in accepting outputs uncritically without reflection or ownership.

Problem-Solving and Dependency Risk

One of the biggest cognitive trade-offs is dependency. When AI tools solve problems step-by-step, users may internalize fewer problem-solving frameworks themselves.

This can affect:

Transfer of knowledge to new contexts

Independent reasoning under pressure

Confidence in one’s own thinking

At the same time, AI can expose users to better problem-solving models than they might encounter otherwise—if they are taught to analyze and question those models rather than follow them blindly.

How AI Shapes Identity and Self-Efficacy

Growing up with AI also influences how people perceive their own intelligence. If a machine always has the answer, it’s easy to feel less capable—or conversely, to feel empowered by extended capability.

The key cognitive factor is agency. When users see AI as a tool they control, self-efficacy increases. When they see it as an authority, confidence can erode.

Education and parenting play a critical role here. Framing AI as a collaborator—not a replacement for thinking—helps preserve a healthy sense of intellectual ownership.

Learning to Think With AI, Not Instead of It

The most important cognitive skill emerging in the AI era is not memorization or speed—it’s discernment. Knowing when to trust AI, when to question it, and when to think independently will define intellectual maturity.

This includes:

Evaluating accuracy and bias

Understanding limitations and uncertainty

Integrating AI output with human judgment

In this sense, AI literacy becomes cognitive literacy.

Final Thoughts

Growing up with AI tools will undoubtedly reshape cognition—but reshaping is not the same as diminishing. The impact depends less on the technology itself and more on how it’s integrated into learning, culture, and daily life.

If AI replaces thinking, cognitive depth may suffer. If it augments curiosity, reflection, and exploration, it may help raise a generation capable of tackling more complex problems than ever before.

The future of human cognition won’t be defined by whether we use AI—but by whether we learn to think with intention in its presence.

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Someone cut me off while going rather fast on the road to work the other day and it made me angry.

I feel guilty about being angry, because I remember a story where a man was being driven to the hospital by a friend, but the highway was backed up so his friend drove on the shoulder, but everyone else was so upset that they blocked off the shoulder and the guy ended up dying, so maybe this person that cut me off had an emergency and regardless it didn't really affect me.

I feel frustrated by being guilty, because it was rather dangerous for someone to be speeding regardless, and they could have caused an even greater emergency if indeed they were in one.

I feel sad because I can imagine my friend dying next to me and not being able to think totally rationally and desperately doing anything to save them.

I feel resolved to prepare myself mentally for such an occasion so that I don't act recklessly, and spread awareness of such situations so other people don't act rashly out of anger or worry either.

. . .

So I had never heard of "meta-emotions" until just now. I have heard of meta-cognition and have obsessed over it to some degree, thinking about existence and the nature of thinking, logic, mathematics, and what it means for us to have the capacity for any of these. However, this now seems rather trivial in the face of "meta-emotions".

That may seem counter-intuitive, and indeed it hurts me to even say it, but I mean it. I have long avoided my emotions because I always come to a sort of dead-end paralysis where I know I should be acting but I feel too constricted by fear and sadness that I dissociate from the situation entirely. I found comfort in rational thought and logical science as I could make sense of such things, but here's the problem: those emotions never went away, they just got buried.

These unresolved emotions have caused numerous problems over the years to where I effectively feel lobotomized from how little I am able to feel about a situation or connect with friends or close family. I don't respond to messages, my finances diminish, and I flunked out of college. I just...couldn't muster enough energy to care.

But that always confused me, because there are times when I feel like I am experiencing emotions so intense that I would explode if I moved a single muscle or muttered a single word, so I knew there were emotions there. So how do I reconcile all this? If I feel, I hit a dead end. If I hit a dead end, I repress. If I repress, I stop caring. If I stop caring, I explode with painful feelings. What do I do?

Well it turned out there's an interesting little trick that my partner just taught me: "what do you feel about those feelings?" With that simple phrase, my partner prompted me over and over with each new set of feelings, until finally I found the will to act and reached out to a friend I hadn't in years!

Abstract

The study investigated “Meta-cognition as a determinant of productive learning among out-of-school emerging adults engaged in mechanic work in Buea Municipality”. The specific objective is; To ascertain the extent to which executive functioning impacts productive learning among out-of-school emerging adults engaged in mechanic work in Buea municipality. Methodologically, the research design used was experimental design and the type of design quasi-experimental research design and exploratory research design with the aid of a pretest and posttest. The population of the study was made up of all apprentices between the ages 15 to 25 in mechanic garages, and trainers. The target population were 26 apprentices and 4 trainers and the accessible population was 12 apprentices and 4 trainers. The study took place in Buea in Cameroon in two mechanic garages.  The instruments used for data collection were questionnaire, observational checklist and interview guide. The procedure for data collection was through observing and training the expert on psycho-pedagogy skills who later trained the apprentice and they were later tested. Pretest and posttest were carried out. The study was carried out in two mechanic workshops. Each of the garages had experimental and control groups. The sampling technique was purposive sampling technique. Quantitative data were entered using EpiData Version 3.1 (EpiData Association, Odense Denmark, 2008) and analyzed using the Statistical Package for Social Sciences (SPSS) Standard version, Release 21.0 (IBM Inc. 2012). Data collected from the field were subjected to both descriptive and inferential statistics. For the descriptive data, frequency distribution tables and charts were used to present and describe the data obtained. Cohen’s d was used to compare assert significant difference of the inferential statistics. The findings were as follows; executive function should constantly be applied in mechanic garages by the trainers so that apprentice can gain knowledge, aptitude, competencies and become productive.

Keywords: Metacognition; executive functioning; productive learning; out of school emerging adults; mechanic work.

Introduction

Metacognition entails that learners reflect with accuracy on their cognition [1]. Metacognition also requires planning, self-regulation of both cognition and affective or motivational states and allocation and other intellectual resources, executive functioning forms part of the construct. Carlson & Moses (2001) argue that executive functioning may be a prerequisite skill for the development of metacognition. Schraw & Moshman [1] posit that metacognitive development proceeds as follows; cognitive knowledge appears first with learners reflecting on their accuracy of the cognition an consolidation of this skills, ability to regulate next with dramatic improvement in monitoring regulation and evaluation in the form of planning and finally, construction of metacognitive theories, these theories allow for the integration of cognitive knowledge and cognitive regulation, as learners construct their own theories, come to reflect on their own thinking and learning. Productive learning involves bringing in skills, strategies that makes learning fruitful among out of school emerging adults from the ages 15 to 25.

Objective of the Study

1.1. General Objective

1.       To determine the extent to which meta-cognition predicts productive learning among out-of-school emerging adults engaged in mechanic work in Buea Municipality

1.2. Specific Objectives of the Study

Specifically, this study is intended:

1.       To investigate the extent to which executive functioning impacts productive learning among out-of-school emerging adults engaged in mechanic work in Buea municipality

Background to the Study

Metacognition is being aware of one’s own cognitive structure and learning characteristics. According Flavell [2] metacognition is a system which organizes information, experiences, objectives and strategies. Metacognition refers also to thinking about thinking, generally covers various skills that are inter-related to thinking and learning which are critical thinking, reflective thinking, problem solving, executive functioning, and making of decisions in the process of problem solving. Furthermore, the main indicator of this study is executive functioning as an indicator of metacognition   which consist of those capacities that enable a person to engage successfully in independent, purposive self-serving behaviour. The Executive functioning asks questions how and whether a person goes about doing something. The executive function is thus conceptualized as having four components; violation, planning, purposive action and effective performance which each involves a distinctive set of activity related behaviour. According to Tchombe [3], cognitive learning takes place through mutual reciprocity. This is determined via participation which is oriented by cultural belief about knowledge, parent-child expectations and aspirations.

Therefore, the skills and competencies learners employ for meaning making through shared activities have enormous impact on how learners make decisions which is held by understanding of the dynamics of their cognitive developmental sequence. Productive learning here is skill learning, learning through experience that solve real life problems. Emerging adults are young people between the ages 15 to 25 who are out of school and engaged in mechanic work as apprentices with their experts. According to Lo-oh (2009) the implication in the lifecourse is evidenced in how young people conceive and define adult status today. According to him, in the African sub region in general and Cameroon in particular, the transition to adulthood is an arduous task characterized by several challenges.

Statement of the Problem

The problematic in this study is productive learning. The researcher observed that most apprentices in the mechanic workshop turn out to be less skilful in solving problems in cars when cars have breakdowns especially when the expert is not around, especially thinking out of the box to resolve problems in breakdowns like removing the engine, and other complicated mechanic repairs. Literature Review

Metacognition

Research activity in metacognition began with John Flavell who is considered to be the father of the field. Metacognition is a concept that has been refer to a variety of epistemological processes. Metacognition essentially means cognition about cognition; that is thoughts about thoughts, knowledge about knowledge.  So, if metacognition involves perceiving remembering and so forth, then metacognition involves thinking about one’s own perception, understanding, remembrance.

Executive Functioning

Executive functioning includes responsible processes, for directing focus, managing and integrating cognitive functions related to everyday life tasks as well as new and complex problems. Executive functioning as used in this study describes a set of mental processes that helps connect past experience with present action through critical thinking. It involves thinking out of the box in order to solve a problem. Executive function is used in when the following activities are being performed that is planning, organizing, strategizing and paying attention to and remembering details.

Productive Learning

sees productive learning as learning through human development.

Productive learning is learning on the basis of productive activity in social serious situation, learning on the basis of experience by being able to achieve something important in one’s environment. Productive learning involves active and inquiry learning or integrating creativity, competencies, fostering learners’ ability to think critically and learn autonomously based on constructivism in order that learners produce creative things based on problem solving method.

The Concept of Mechanic Work

Mechanic work is a trade craftsmanship. Mechanic work involves application of specific knowledge in the design, selection, construction, operation and maintenance of automobiles. Mechanic work is geared to test, diagnose, service and completely maintain faults relating to the conventional automobile assembly like vehicles of different brands. Theoretical review

Vygotsky Socio-Cultural Theory (1896-1934)

Vygotsky believed that individual development could not be understood without reference to the social and cultural context within which such development is embedded. He states that using activity mediators, the human being is able to modify the environment and this is her way of interacting with nature. Hence, Zone of Proximal Development is actually the gap between actual competence level (what problem level a learner is able to independently solve), and the potential development level (what problem level could she solve with guidance from a tutor). It supports a representation of intellectual development based on continuity.

Transformative Learning Theory by Mezirow (1978)

According to Mezirow [4] transformative learning is the process of making meaning from our experiences through reflection, critical reflection and critical self-reflection. Meaning according to Mezirow means making sense of the day to dayness of our experiences. He eventually names this process perspective transformation to reflect change within the core and central meaning structures. Perspectives are made up of sets of beliefs, values and assumptions that we have acquired through our life’s experiences.

Method

1.1. Research Design

The researcher used a triangulation approach, where the researcher used both qualitative and quantitative research methods to collect data.  An experimental design was used in this study supported with an exploratory sequential design. The type of experimental design was a quasi-experimental design which was chosen to identify an intervention effect using an experimental group and control group to ascertain treatment effects on experimental group, through pre-test and post-test. The procedures employed tests causal effect (XY) and test causal hypothesis. Starting by ensuring through a pre-test that the two groups are comparable and with a post-test repeating the pre-test including researcher constructed test to ensure change in design.

1.2. Instrument for Data Collection

The following methods were used to gather information from the correspondence. A questionnaire of 5 items per objective were conducted, an observational checklist and an interview guide was also designed that had statements from the following measures, coaching, scaffolding, executive functioning and productive learning measured the following; aptitude, mastery experience, attitude, discipline, knowledge, skills and competency development as seen in the appendices. A lesson note was prepared for mechanic work for the intervention with the used of the experiential learning as a teaching method.

1.3. Findings

Research hypothesis three: There is no significant relationship between executive functioning and productive learning among out-of-school emerging adults engaged in mechanic work in Buea Municipality. (Cohen’s d ): If the theoretical effect size is smaller than the calculated one, we then reject the hypothesis that the means are not significantly different at 90% power and at 95% CL with cohort sample 3 and a total sample size 6 as it is the case in our study context. As for the total score in executive functioning for mechanic in the experimental group, the mean at pretest was 9.5 and rose to 11.5 at posttest and this increase was significant (negative Cohen’s d). In fact, the theoretical effect size is smaller than the calculated one, we then reject the hypothesis that the means are not different. This therefore implies that there was a significant progression from pre-test to post-test.The significant improvement in productive learning score was as the result of improvement in executive functioning because such improvement was not obtained in the control group where no significant improvement was realized in coaching from pretest to posttest.

The hypothesis here stated is then accepted.

Executive Functioning and Productive Learning among Out-Of-School Emerging Adults Engaged In Mechanic Work

There was no significant difference between the two workshops for both masters and mechanics. As for the total score in executive functioning for mechanic in the experimental group, the mean at pretest was average and rose to at posttest and this increase was significant. This therefore implies that there was a significant progression from pre-test to post-test. This matches with Flavell [5] metacognitive theory who referred to metacognition theory as thinking about your own thinking. The root “meta” means “beyond”, so the term refers to “beyond thinking” [6-15].

Conclusion

To conclude, thinking out of the box enable out of school emerging adults to reflect, think critically and work positively towards a positive growth mind, that makes them gain skills, aptitude, knowledge, competencies, and are able to repair cars and engaged in car maintenance to solve car breakdowns [15-23].

More information regarding this Article visit: OAJBGSR

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Clearly the computing substrate i have grown up uses algorithm on this priority. 1.recursion 2.pattern matching(probably a side-effect of strong memory) 3.Deductive Reasoning 4.Inductive Reasoning

Note Inductive Reasoning at the last?? that may be the reason, i haven’t developed many products so far.. I should include probabilistic Reasoning, but not sure where that comes in…

What do you do when you realize you have a chronic condition/problem in letting go? (i.e: when you realize that you have a tendency to never leave the past alone and an OCD to recall what your masters’ thesis guide would have said/actually said, what your ex-startup boss would have said/said, what your ex-colleagues said/would have said..) You sigh, accept it and learn to manage it. You learn…