Research shows that people tend to overweight small probabilities in description and underweight them in experience, thereby leading to a different pattern of choices between description and experience; a phenomenon known as the Description-Experience (DE) gap. However, little is known on how the addition of an intermediate option and contextual framing influences the DE gap and people’s search strategies. This paper tests the effects of an intermediate option and contextual framing on the DE gap and people’s search strategies, where problems require search for information before a consequential choice. In the first experiment, 120 participants made choice decisions across investment problems that differed in the absence or presence of an intermediate option. Results showed that adding an intermediate option did not reduce the DE gap on the maximizing option across a majority of problems. There were a large majority of choices for the intermediate option. Furthermore, there was an increase in switching between options due to the presence of the intermediate option. In the second experiment, 160 participants made choice decisions in problems like those presented in experiment 1; however, problems lacked the investment framing. Results replicated findings from the first experiment and showed a similar DE gap on the maximizing option in a majority of problems in both the absence and presence of the intermediate option. Again, there were a large majority of choices for the intermediate option. Also, there was an increase in switching between options due to the presence of the intermediate option. Meta-analyses revealed that the absence or presence of the intermediate option created certain differences in the strength of frequency and recency processes. Also, a single natural-mean heuristic model was able to account for the experimental results across both experiments. We discuss implications of our findings to consequential decisions made after information search.
This paper examines monthly and annual data to analyse predictability in the Indian monsoon rainfall. The periodic structure in the time series data is extracted using wavelets and the residual random part is separately modeled using artificial neural networks (ANN). Although wavelet and neural network based hybrid techniques have been widely applied in the recent years, the present approach has not been investigated so far. Our results show that the estimated periodic and random components comprise 30 and 15 %, respectively, variance of the total rainfall in case of annual data, whereas the model explains 93 % of variance in case of monthly data. It is shown that the prediction is more accurate when periodic and random parts are treated separately.
This paper presents a Multi-Layered MultiPattern Central Pattern Generator (CPG) that provides humanoid robots the ability to generate motor patterns in order to perform various upper body tasks (like: reaching and writing). This CPG has two control levels: 1) one for pattern formation (coordination); and 2) another for pattern generation (selection). A unique feature of this CPG is its ability to generate oscillatory, semi-oscillatory, and non-periodic patterns locally, simply through descending control. With a simple learning method the NAO humanoid robot was able to learn how to coordinate motor patterns at different joints in writing numbers from 0 to 9. With a neural-based structure, which separate between the coordination and the selection control levels, our approach is shown to be robust during the execution even with a noisy proprioception (sensory) feedback and also with noisy coordination (pattern formation descending control) signals.
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