Causation

Correlating

The function of the Correlating (connecting) system is to seek patterns in our perceptions and cognitions. It looks for signs of order and structure in information. Then we use the Grouping system to categorise these relationships.

This system works to identify:

• temporal sequences — events that consistently occur in a particular order (categories: before, after, concurrent)

• spatial arrangements — objects or concepts that frequently appear together in physical or conceptual space (categories: in front/behind, above/below, beside, close/distant, etc.)

• coupled phenomena — things that seem to change in tandem

In business-as-usual mode, it uses pattern-detection mechanisms to scan incoming information for regularities that match patterns predicted by the active meaning schema. This helps us quickly identify familiar patterns and potential causal relationships without having to analyse every detail of our experience.

If we encounter unexpected correlations or fail to find expected patterns, our Correlation system may engage more conscious processes to investigate potential new patterns or to re-evaluate our understanding of existing ones.

Correlating discrepancies

Discrepancies originating in the Correlating sub-system could result from:

• Illusory correlation — perceiving significant relationships between things that occur by chance or are unrelated (see also apophenia)

• Clustering illusion — mistaking the clusters and 'streaks' that tend to occur in random phenomena for significant patterns, based on the erroneous assumption that 'random' equals evenly distributed or equally likely

• Cherry picking — filtering information so that you see patterns that you are looking for

• Associative priming — where a piece  of information activates an existing relationship so that you are on the look out for the connected information

• Selection bias — drawing correlation conclusions from non-representative samples of information (i.e. you either miss patterns because you're not looking at the whole picture or you see patterns that would vanish if you looked at the whole picture)

Extrapolating

The function of the Extrapolating (explaining) system is to form causal models and heuristics that allow us to construct explanations of the patterns we perceive in phenomena and to predict future phenomena.

This system builds on the patterns identified by the Correlating system and the values and categories of the Comparison system to:

• generate causal explanations for observed correlations

• predict future events based on identified patterns

• create mental models that represent how systems work

• develop rules of thumb (heuristics) for navigating complex domains

• test hypothetical scenarios through mental simulation

The Extrapolation system can be thought of as creating 'if-then' propositions. These can be explanatory/descriptive ('if the current situation is Y, then X must have preceded it') or predictive/directive ('if X happens, then Y will follow').

In business-as-usual mode, it applies established causal models from active meaning schemas to explain current experiences and anticipate what might happen next. This predictive capability is crucial for effective planning and decision-making.

When predictions fail or explanations prove inadequate, the Extrapolating system may engage in more conscious analysis to revise our causal models or develop new explanatory frameworks.

Extrapolating discrepancies

Discrepancies linked to the Extrapolating sub-system could result from:

• Causal fallacies — mistaking correlation for causation or assuming direct causation when mechanisms are more complex (see attribution bias)

• Over-generalisation — extending causal explanations beyond the contexts in which they were validated

• Narrative fallacy — the human tendency to prefer simple and coherent explanatory stories that are easy to understand and feel like 'common sense' but which arise from our unwillingness to accept ambiguity, complexity or randomness (see argument from incredulity and just-world hypothesis)

• Hindsight bias — constructing post-hoc explanations that seem obvious after the fact but weren't predictable beforehand