Seed topic: Critical thinking and cognitive training.
Critical thinking is a set of cognitive skills used to interpret information, evaluate evidence, reason logically, and make decisions under uncertainty. In clinical and behavioral science, these abilities rely on coordinated activity across the prefrontal cortex, anterior cingulate cortex, and networks supporting working memory, attention control, and cognitive flexibility. When individuals repeatedly engage in demanding tasks that require planning, error monitoring, and hypothesis testing, they strengthen executive functions (EF). EF includes inhibitory control (suppressing irrelevant impulses), working memory (holding and manipulating information), cognitive flexibility (shifting strategies), and goal management (planning and prioritizing steps). These mechanisms are not merely abstract virtues; they are measurable constructs linked to adaptive functioning and mental health outcomes.
Automation—especially advanced systems capable of completing tasks end-to-end—can reduce the need for certain cognitive operations. Agentic AI can generate outputs, execute routines, and optimize workflows. However, the reduced demand for EF activities may affect training-dependent capacities. Neurocognitively, many cognitive skills show experience-dependent plasticity. Repeated cognitive effort can reinforce neural pathways and improve efficiency of information processing. Conversely, if a person no longer performs tasks that engage working memory, sustained attention, or problem decomposition, the individual may experience a functional decline in those domains. This does not imply that people will become “less human,” but it does suggest that cognitive capability can be influenced by the distribution of cognitive loads across daily life.
From a mental health perspective, EF deficits are associated with a range of conditions. Reduced working memory capacity and poor inhibitory control are common in attention-deficit/hyperactivity disorder. Inefficient cognitive reappraisal and rigidity contribute to anxiety disorders and depressive disorders, where negative interpretations and rumination can become habitual. In post-traumatic stress disorder, attention and threat processing biases can impair flexible thinking. Even in typical development, insufficient opportunities to practice decision-making and error correction can weaken resilience and coping strategies. Therefore, replacing cognitive labor with automation raises a public health question: will people lose opportunities to practice EF skills that support emotional regulation and adaptive behavior?
Cognitive skill training involves deliberate effort: defining goals, generating strategies, checking outcomes, and revising approaches based on feedback. In neuropsychological terms, this repeatedly recruits top-down control processes, supports learning through error signals, and encourages metacognition—awareness of one’s own thinking. Metacognition is crucial for identifying when one’s reasoning is flawed, when information is missing, and when confidence should be calibrated. Without such practice, individuals may become over-reliant on external systems, reducing independent verification. Over-reliance can also interact with psychosocial factors: if a person feels their agency has decreased, motivation and self-efficacy may decline, increasing risk for demoralization, learned helplessness, or maladaptive avoidance.
A practical framework is to view AI as a tool that can both support and preserve cognition, depending on how it is integrated. For example, using AI to draft alternatives is not the same as outsourcing judgment. Maintaining engagement requires active tasks such as: (1) specifying requirements and constraints; (2) evaluating outputs for accuracy, bias, and relevance; (3) selecting among options using evidence-based criteria; (4) stress-testing assumptions and edge cases; and (5) reflecting on mistakes and learning from them. These steps preserve the cognitive loop of planning, monitoring, and revision.
Educational and workplace interventions can operationalize this principle. Cognitive apprenticeship models emphasize guided practice where novices learn strategies from mentors, then gradually assume responsibility. Similarly, “productive struggle” encourages engagement with appropriately challenging problems that stretch working memory and reasoning without overwhelming the learner. In clinical contexts, cognitive-behavioral therapy (CBT) provides a close analogue: patients learn to identify thought patterns, challenge them, and replace them with more accurate appraisals. CBT’s structure trains cognitive reappraisal and reduces rumination—skills that overlap with general critical thinking.
The healthiest approach is not to reject AI, but to ensure it does not become a substitute for cognitive agency. Individuals can retain relevance by deliberately choosing roles that demand reasoning, curiosity, and reflective practice—especially tasks that require uncertainty management, ethical judgment, and human-centered context. As automation expands, sustaining cognitive vitality may require intentional “cognitive exercise”: practicing reasoning, verifying information, and engaging in learning activities that demand active participation.
Source: [@_jimwong_ via LinkedIn/X post]
Jim Wong: Innovate. Be creative. Do the thinking. Continue to improve always. Never cease your life journey. If we are to stop n just simply assign all majority of our tasks to AI, is our existence still relevant to the world? Agentic AI is powerful. It can take over our tasks n perform them better than us. But it’s never the same us honing our critical n cognitive skills through actual training n thinking. linkedin.com/posts/jimjwong_…. #breaking
— @_jimwong_ May 1, 2026
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