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Teleological thinking is driven by aberrant associations
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Teleological thought — the tendency to ascribe purpose to objects and events — is useful in some cases (encouraging explanation-seeking), but harmful in others (fueling delusions and conspiracy theories). What drives maladaptive teleological thinking? A fundamental distinction in how we learn causal relationships between events is whether it can be best explained via associations versus via propositional thought. Here, we propose that directly contrasting the contributions of these two pathways can elucidate where teleological thinking goes wrong. We modified a causal learning task such that we could encourage one pathway over another in different instances. Across experiments (total N=600), teleological tendencies were correlated with delusion-like ideas and uniquely explained by aberrant associative learning, but not by learning via propositional rules. Computational modeling suggested that the relationship between associative learning and teleological thinking can be explained by spurious prediction errors that imbue random events with more significance — providing a new understanding for how humans make meaning of lived events.
Title: Teleological thinking is driven by aberrant associations
Description:
Teleological thought — the tendency to ascribe purpose to objects and events — is useful in some cases (encouraging explanation-seeking), but harmful in others (fueling delusions and conspiracy theories).
What drives maladaptive teleological thinking? A fundamental distinction in how we learn causal relationships between events is whether it can be best explained via associations versus via propositional thought.
Here, we propose that directly contrasting the contributions of these two pathways can elucidate where teleological thinking goes wrong.
We modified a causal learning task such that we could encourage one pathway over another in different instances.
Across experiments (total N=600), teleological tendencies were correlated with delusion-like ideas and uniquely explained by aberrant associative learning, but not by learning via propositional rules.
Computational modeling suggested that the relationship between associative learning and teleological thinking can be explained by spurious prediction errors that imbue random events with more significance — providing a new understanding for how humans make meaning of lived events.
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