Pub Date : 2020-10-21DOI: 10.1007/s42113-021-00106-1
Darren Haywood, Frank D. Baughman
{"title":"Multidimensionality in Executive Function Profiles in Schizophrenia: a Computational Approach Using the Wisconsin Card Sorting Task","authors":"Darren Haywood, Frank D. Baughman","doi":"10.1007/s42113-021-00106-1","DOIUrl":"https://doi.org/10.1007/s42113-021-00106-1","url":null,"abstract":"","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"6 1","pages":"381 - 394"},"PeriodicalIF":0.0,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84172563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The prediction of everyday human behavior is a central goal in the behavioral sciences. However, efforts in this direction have been limited, as (1) the behaviors studied in most surveys and experiments represent only a small fraction of all possible behaviors, and (2) it has been difficult to generalize data from existing studies to predict arbitrary behaviors, owing to the difficulty in adequately representing such behaviors. Our paper attempts to address each of these problems. First, by sampling frequent verb phrases in natural language and refining these through human coding, we compile a dataset of nearly 4000 common human behaviors. Second, we use distributed semantic models to obtain vector representations for our behaviors, and combine these with demographic and psychographic data, to build supervised, deep neural network models of behavioral propensities for a representative sample of the US population. Our best models achieve reasonable accuracy rates when predicting propensities for novel (out-of-sample) participants as well as novel behaviors, and offer new insights for modeling psychographic and demographic differences in behavior. This work is a first step towards building predictive theories of everyday behavior, and thus improving the generality and naturalism of research in the behavioral sciences.
{"title":"Representing and Predicting Everyday Behavior","authors":"M. Singh, Russell Richie, Sudeep Bhatia","doi":"10.31234/osf.io/kb53h","DOIUrl":"https://doi.org/10.31234/osf.io/kb53h","url":null,"abstract":"The prediction of everyday human behavior is a central goal in the behavioral sciences. However, efforts in this direction have been limited, as (1) the behaviors studied in most surveys and experiments represent only a small fraction of all possible behaviors, and (2) it has been difficult to generalize data from existing studies to predict arbitrary behaviors, owing to the difficulty in adequately representing such behaviors. Our paper attempts to address each of these problems. First, by sampling frequent verb phrases in natural language and refining these through human coding, we compile a dataset of nearly 4000 common human behaviors. Second, we use distributed semantic models to obtain vector representations for our behaviors, and combine these with demographic and psychographic data, to build supervised, deep neural network models of behavioral propensities for a representative sample of the US population. Our best models achieve reasonable accuracy rates when predicting propensities for novel (out-of-sample) participants as well as novel behaviors, and offer new insights for modeling psychographic and demographic differences in behavior. This work is a first step towards building predictive theories of everyday behavior, and thus improving the generality and naturalism of research in the behavioral sciences.","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"24 1","pages":"1-21"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85090632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-09DOI: 10.1007/s42113-020-00090-y
Aslı Kılıç, Jessica M. Fontaine, K. Malmberg, A. Criss
{"title":"The Moderating Role of Feedback on Forgetting in Item Recognition","authors":"Aslı Kılıç, Jessica M. Fontaine, K. Malmberg, A. Criss","doi":"10.1007/s42113-020-00090-y","DOIUrl":"https://doi.org/10.1007/s42113-020-00090-y","url":null,"abstract":"","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"233 1","pages":"178 - 190"},"PeriodicalIF":0.0,"publicationDate":"2020-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89709483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-12DOI: 10.1007/s42113-020-00088-6
Chara Tsoukala, M. Broersma, Antal van den Bosch, Stefan L. Frank
{"title":"Simulating Code-switching Using a Neural Network Model of Bilingual Sentence Production","authors":"Chara Tsoukala, M. Broersma, Antal van den Bosch, Stefan L. Frank","doi":"10.1007/s42113-020-00088-6","DOIUrl":"https://doi.org/10.1007/s42113-020-00088-6","url":null,"abstract":"","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"1 1","pages":"87 - 100"},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78428893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Maarten van der Velde, Florian Sense, J. Borst, H. van Rijn
An adaptive learning system offers a digital learning environment that adjusts itself to the individual learner and learning material. By refining its internal model of the learner and material over time, such a system continually improves its ability to present appropriate exercises that maximise learning gains. In many cases, there is an initial mismatch between the internal model and the learner’s actual performance on the presented items, causing a “cold start” during which the system is poorly adjusted to the situation. In this study, we implemented several strategies for mitigating this cold start problem in an adaptive fact learning system and experimentally tested their effect on learning performance. The strategies included predicting difficulty for individual learner-fact pairs, individual learners, individual facts, and the set of facts as a whole. We found that cold start mitigation improved learning outcomes, provided that there was sufficient variability in the difficulty of the study material. Informed individualised predictions allowed the system to schedule learners’ study time more effectively, leading to an increase in response accuracy during the learning session as well as improved retention of the studied items afterwards. Our findings show that addressing the cold start problem in adaptive learning systems can have a real impact on learning outcomes. We expect this to be particularly valuable in real-world educational settings with large individual differences between learners and highly diverse materials.
{"title":"Alleviating the Cold Start Problem in Adaptive Learning using Data-Driven Difficulty Estimates","authors":"Maarten van der Velde, Florian Sense, J. Borst, H. van Rijn","doi":"10.31234/osf.io/hf2vw","DOIUrl":"https://doi.org/10.31234/osf.io/hf2vw","url":null,"abstract":"An adaptive learning system offers a digital learning environment that adjusts itself to the individual learner and learning material. By refining its internal model of the learner and material over time, such a system continually improves its ability to present appropriate exercises that maximise learning gains. In many cases, there is an initial mismatch between the internal model and the learner’s actual performance on the presented items, causing a “cold start” during which the system is poorly adjusted to the situation. In this study, we implemented several strategies for mitigating this cold start problem in an adaptive fact learning system and experimentally tested their effect on learning performance. The strategies included predicting difficulty for individual learner-fact pairs, individual learners, individual facts, and the set of facts as a whole. We found that cold start mitigation improved learning outcomes, provided that there was sufficient variability in the difficulty of the study material. Informed individualised predictions allowed the system to schedule learners’ study time more effectively, leading to an increase in response accuracy during the learning session as well as improved retention of the studied items afterwards. Our findings show that addressing the cold start problem in adaptive learning systems can have a real impact on learning outcomes. We expect this to be particularly valuable in real-world educational settings with large individual differences between learners and highly diverse materials.","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"10 1","pages":"231-249"},"PeriodicalIF":0.0,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91147069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-26DOI: 10.1007/s42113-020-00085-9
M. Lee, Karyssa A. Courey
{"title":"Modeling Optimal Stopping in Changing Environments: a Case Study in Mate Selection","authors":"M. Lee, Karyssa A. Courey","doi":"10.1007/s42113-020-00085-9","DOIUrl":"https://doi.org/10.1007/s42113-020-00085-9","url":null,"abstract":"","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"33 1","pages":"1 - 17"},"PeriodicalIF":0.0,"publicationDate":"2020-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76103356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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