Accurate Predictions Facilitate Robust Memory Encoding Independently From Stimulus Probability

We can use prior knowledge of temporal structure to make predictions about how an event will unfold, and this schematic knowledge has been shown to impact the way that event memories are encoded and later reconstructed. Existing paradigms for studying prediction, however, are largely unable to separate effects of prediction accuracy from effects of stimulus probability: likely outcomes are assumed to be predicted, while unlikely outcomes are assumed to cause prediction errors. Here we use a novel approach in which we can independently manipulate prediction success and stimulus probability, by using real-time eye-tracking when viewing moves in a board game. The moves can be consistent or inconsistent with a participant's predictions (assessed via fixation patterns) and can be also be likely or unlikely to be played by a strategic player. By decorrelating these two measures, we found that both probability and prediction accuracy boost memory through two separate mechanisms, leading to different eye-movement strategies at retrieval. Accurate prediction improved encoding precision, allowing participants to directly retrieve these moves without the use of schematic knowledge. Probable moves, on the other hand, led to improved memory through a retrieval-time strategy in which schematic knowledge was used to generate candidate moves for recognition. These results shed new light on the specific role of predictions in enhancing event memories, and provide a more realistic paradigm for studying schemas, learning, and decision making.

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Publication Details

Journal
Open Mind
Published
2025-01-01
DOI
https://doi.org/10.1162/opmi.a.14
Citations
1
Primary Topic
Memory and Neural Mechanisms
Type
article
Field-Weighted Citation Impact
1.43
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article

Accurate Predictions Facilitate Robust Memory Encoding Independently From Stimulus Probability

Christopher Baldassano, Jiawen Huang, Eleanor Furness, Morell-Jovan Kenmoe et al.
1 citations
Open Mind
Memory and Neural Mechanisms
1.43
article

Accurate Predictions Facilitate Robust Memory Encoding Independently From Stimulus Probability

Christopher Baldassano, Jiawen Huang, Eleanor Furness, Morell-Jovan Kenmoe, Yifang Liu, Ronak Elias, Hannah Tongxin Zeng
article en
1 citations

Abstract

We can use prior knowledge of temporal structure to make predictions about how an event will unfold, and this schematic knowledge has been shown to impact the way that event memories are encoded and later reconstructed. Existing paradigms for studying prediction, however, are largely unable to separate effects of prediction accuracy from effects of stimulus probability: likely outcomes are assumed to be predicted, while unlikely outcomes are assumed to cause prediction errors. Here we use a novel approach in which we can independently manipulate prediction success and stimulus probability, by using real-time eye-tracking when viewing moves in a board game. The moves can be consistent or inconsistent with a participant's predictions (assessed via fixation patterns) and can be also be likely or unlikely to be played by a strategic player. By decorrelating these two measures, we found that both probability and prediction accuracy boost memory through two separate mechanisms, leading to different eye-movement strategies at retrieval. Accurate prediction improved encoding precision, allowing participants to directly retrieve these moves without the use of schematic knowledge. Probable moves, on the other hand, led to improved memory through a retrieval-time strategy in which schematic knowledge was used to generate candidate moves for recognition. These results shed new light on the specific role of predictions in enhancing event memories, and provide a more realistic paradigm for studying schemas, learning, and decision making.

Open MindVol. 9
Columbia University (US)
Peace, Justice and strong institutions
Openalex Percentile: Top 16%
Memory and Neural Mechanisms
1.43
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Accurate Predictions Facilitate Robust Memory Encoding Independently From Stimulus Probability — Christopher Baldassano, Jiawen Huang, et al. · Open Mind (2025) | TGRS Research Map | TGRS