These reasoning and comparisons highlight the trade-offs and nuances involved in the explicit versus implicit dichotomy, and the importance of in-depth understanding.
There are abundant information flow and knowledge sources. Consider the effort of critical thinking as training your mind to seek reason, both explicit and implicit knowledge to reframe its approach to a conundrum in a certain way. There are several classic debates in various fields regarding the merits and drawbacks of explicit versus implicit approaches:
Explicit vs. Implicit Learning in Psychology: The relative effectiveness of explicit instruction versus implicit, discovery-based learning for different types of knowledge and skills, and the optimal balance between the two approaches. Explicit learning is about intentional, conscious learning of information, concepts, or skills through direct instruction and deliberate practice. Implicit learning is about unconscious, incidental acquisition of knowledge or skills through exposure and experience, without conscious effort.
Explicit vs. Implicit Memory in Cognitive Psychology: The distinct neural mechanisms underlying explicit and implicit memory, and their relative contributions to various cognitive functions and behaviors. Explicit memory is the conscious recollection of past experiences, facts, or events. Implicit memory is about the unconscious, unintentional influence of past experiences on current behavior or performance, without conscious recollection.
Explicit vs. Implicit Bias in Social Psychology: The relative impact of explicit and implicit biases on decision-making, social interactions, and real-world outcomes, and the most effective strategies for addressing both forms of bias. Explicit bias is about conscious, intentional attitudes or stereotypes that individuals hold and express openly. Implicit bias is about unconscious, unintentional attitudes or stereotypes that can influence perceptions, judgments, and behaviors without the individual's awareness.
Explicit vs. Implicit Knowledge Representation in AI: The strengths and limitations of explicit and implicit knowledge representation approaches, and the most effective ways to combine them for robust and flexible artificial intelligence systems. Explicit knowledge representation is about normal, structured, and symbolic representations of knowledge, such as rules, logical expressions, or ontologies. Implicit knowledge representation is about distributed, pattern-based representations of knowledge, such as those found in neural networks or deep learning models.
Explicit vs. Implicit Coordination in Organizational Behavior: The appropriate balance between explicit and implicit coordination strategies for effective organizational performance, and the factors that influence the viability and effectiveness of each approach. Explicit coordination is about formal, planned, and structured mechanisms for coordinating tasks, resources, and interdependencies within an organization, such as schedules, plans, and protocols. Implicit coordination is about informal, emergent, and spontaneous mechanisms for coordinating through shared understanding, mutual adjustment, and collective awareness.
These reasoning and comparisons highlight the trade-offs and nuances involved in the explicit versus implicit dichotomy, and the importance of understanding the unique characteristics, strengths, and limitations of each approach in various contexts.
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