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Associative Memory and Belief Formation

Benjamin Enke, Frederik Schwerter and Florian Zimmermann

No 7916, CESifo Working Paper Series from CESifo

Abstract: This paper experimentally studies the role of associative memory for belief formation. Real-world information signals are often embedded in memorable contexts. Thus, today’s news, and the contexts they are embedded in, may cue the selective retrieval of similar past news and hence contribute to the widely documented pattern of expectation overreaction. Based on a stylized version of models of associative memory in the literature, we develop a simple and tightly controlled experimental setup in which participants observe sequences of news about the stock market value of hypothetical companies. Here, identical types of news are associated with identical stories and images. In this setup, participants’ expectations strongly overreact to recent news. We successfully verify the model’s predictions about how the magnitude of overreaction should depend on the history of news. For example, once today’s news are associated with the stories and images of previous opposite news, expectations systematically underreact. By exogenously manipulating the scope for imperfect and associative recall in our setup, we further provide direct causal evidence for the role of memory in belief formation and overreaction. Finally, we use our experimental data to estimate the model parameters that govern the strength of imperfect and associative recall over different time horizons.

Keywords: beliefs; expectations; memory; bounded rationality (search for similar items in EconPapers)
JEL-codes: D01 (search for similar items in EconPapers)
Date: 2019
New Economics Papers: this item is included in nep-exp, nep-hpe and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Related works:
Working Paper: Associative Memory and Belief Formation (2020) Downloads
Working Paper: Associative Memory and Belief Formation (2020) Downloads
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