When high working memory capacity is and is not beneficial for predicting nonlinear processes
Predicting the development of dynamic processes is vital in many areas of life. Previous findings are inconclusive as to whether higher working memory capacity (WMC) is always associated with using more accurate prediction strategies, or whether higher WMC can also be associated with using overly co...
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| Main Authors: | , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
2017
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| In: |
Memory & cognition
Year: 2016, Volume: 45, Issue: 3, Pages: 404-412 |
| ISSN: | 1532-5946 |
| DOI: | 10.3758/s13421-016-0665-0 |
| Online Access: | Verlag, kostenfrei, Volltext: http://dx.doi.org/10.3758/s13421-016-0665-0 Verlag, kostenfrei, Volltext: https://link.springer.com/article/10.3758/s13421-016-0665-0 
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| Author Notes: | Helen Fischer, Daniel Holt |
| Summary: | Predicting the development of dynamic processes is vital in many areas of life. Previous findings are inconclusive as to whether higher working memory capacity (WMC) is always associated with using more accurate prediction strategies, or whether higher WMC can also be associated with using overly complex strategies that do not improve accuracy. In this study, participants predicted a range of systematically varied nonlinear processes based on exponential functions where prediction accuracy could or could not be enhanced using well-calibrated rules. Results indicate that higher WMC participants seem to rely more on well-calibrated strategies, leading to more accurate predictions for processes with highly nonlinear trajectories in the prediction region. Predictions of lower WMC participants, in contrast, point toward an increased use of simple exemplar-based prediction strategies, which perform just as well as more complex strategies when the prediction region is approximately linear. These results imply that with respect to predicting dynamic processes, working memory capacity limits are not generally a strength or a weakness, but that this depends on the process to be predicted. |
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| Item Description: | Published online: October 27, 2016 Gesehen am 13.04.2017 |
| Physical Description: | Online Resource |
| ISSN: | 1532-5946 |
| DOI: | 10.3758/s13421-016-0665-0 |