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      Excessive teleological thinking is driven by aberrant associations and not by failure of reasoning

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          Summary

          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 excessive and maladaptive teleological thinking? In causal learning, there is a fundamental distinction between associative learning versus learning via propositional mechanisms. Here, we propose that directly contrasting the contributions of these two pathways can elucidate the roots of excess teleology. We modified a causal learning task such that we could encourage associative versus propositional mechanisms in different instances. Across three 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 excessive 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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          Highlights

          • People spuriously believe that events happen for a reason, but we do not know why

          • Kamin blocking can reveal the causal learning roots of excessive teleological thought

          • We need to distinguish between blocking via associations vs. propositional reasoning

          • Spurious teleological thinking correlates with associative, not propositional blocking

          Abstract

          Health sciences; Human activity in medical context; Association analysis

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          Most cited references59

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          A Causal Link Between Prediction Errors, Dopamine Neurons and Learning

          Situations where rewards are unexpectedly obtained or withheld represent opportunities for new learning. Often, this learning includes identifying cues that predict reward availability. Unexpected rewards strongly activate midbrain dopamine neurons. This phasic signal is proposed to support learning about antecedent cues by signaling discrepancies between actual and expected outcomes, termed a reward prediction error. However, it is unknown whether dopamine neuron prediction error signaling and cue-reward learning are causally linked. To test this hypothesis, we manipulated dopamine neuron activity in rats in two behavioral procedures, associative blocking and extinction, that illustrate the essential function of prediction errors in learning. We observed that optogenetic activation of dopamine neurons concurrent with reward delivery, mimicking a prediction error, was sufficient to cause long-lasting increases in cue-elicited reward-seeking behavior. Our findings establish a causal role for temporally-precise dopamine neuron signaling in cue-reward learning, bridging a critical gap between experimental evidence and influential theoretical frameworks.
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            A model for Pavlovian learning: variations in the effectiveness of conditioned but not of unconditioned stimuli.

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              The propositional nature of human associative learning.

              The past 50 years have seen an accumulation of evidence suggesting that associative learning depends on high-level cognitive processes that give rise to propositional knowledge. Yet, many learning theorists maintain a belief in a learning mechanism in which links between mental representations are formed automatically. We characterize and highlight the differences between the propositional and link approaches, and review the relevant empirical evidence. We conclude that learning is the consequence of propositional reasoning processes that cooperate with the unconscious processes involved in memory retrieval and perception. We argue that this new conceptual framework allows many of the important recent advances in associative learning research to be retained, but recast in a model that provides a firmer foundation for both immediate application and future research.
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                Author and article information

                Contributors
                Journal
                iScience
                iScience
                iScience
                Elsevier
                2589-0042
                15 August 2023
                15 September 2023
                15 August 2023
                : 26
                : 9
                : 107643
                Affiliations
                [1 ]Yale University, New Haven, CT 06520, USA
                Author notes
                []Corresponding author philip.corlett@ 123456yale.edu
                [2]

                Lead contact

                Article
                S2589-0042(23)01720-0 107643
                10.1016/j.isci.2023.107643
                10495659
                37705957
                64f6d3b7-a91f-4e65-b2ab-7e042d59267f
                © 2023 The Author(s)

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 19 June 2023
                : 31 July 2023
                : 11 August 2023
                Categories
                Article

                health sciences,human activity in medical context,association analysis

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