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}
Pub Date : 2020-06-01Epub Date: 2019-08-14DOI: 10.1007/s42113-019-00047-w
Adam W Broitman, Michael J Kahana, M Karl Healey
Longitudinal designs must deal with the confound between increasing age and increasing task experience (i.e., retest effects). Most existing methods for disentangling these factors rely on large sample sizes and are impractical for smaller scale projects. Here, we show that a measurement burst design combined with a model of retest effects can be used to study age-related change with modest sample sizes. A combined model of age-related change and retest-related effects was developed. In a simulation experiment, we show that with sample sizes as small as n = 8, the model can reliably detect age effects of the size reported in the longitudinal literature while avoiding false positives when there is no age effect. We applied the model to data from a measurement burst study in which eight subjects completed a burst of seven sessions of free recall every year for five years. Six additional subjects completed a burst only in years 1 and 5. They should, therefore, have smaller retest effects but equal age effects. The raw data suggested slight improvement in memory over five years. However, applying the model to the yearly-testing group revealed that a substantial positive retest effect was obscuring stability in memory performance. Supporting this finding, the control group showed a smaller retest effect but an equal age effect. Measurement burst designs combined with models of retest effects allow researchers to employ longitudinal designs in areas where previously only cross-sectional designs were feasible.
{"title":"Modeling Retest Effects in a Longitudinal Measurement Burst Study of Memory.","authors":"Adam W Broitman, Michael J Kahana, M Karl Healey","doi":"10.1007/s42113-019-00047-w","DOIUrl":"https://doi.org/10.1007/s42113-019-00047-w","url":null,"abstract":"<p><p>Longitudinal designs must deal with the confound between increasing age and increasing task experience (i.e., retest effects). Most existing methods for disentangling these factors rely on large sample sizes and are impractical for smaller scale projects. Here, we show that a measurement burst design combined with a model of retest effects can be used to study age-related change with modest sample sizes. A combined model of age-related change and retest-related effects was developed. In a simulation experiment, we show that with sample sizes as small as <i>n</i> = 8, the model can reliably detect age effects of the size reported in the longitudinal literature while avoiding false positives when there is no age effect. We applied the model to data from a measurement burst study in which eight subjects completed a burst of seven sessions of free recall every year for five years. Six additional subjects completed a burst only in years 1 and 5. They should, therefore, have smaller retest effects but equal age effects. The raw data suggested slight improvement in memory over five years. However, applying the model to the yearly-testing group revealed that a substantial positive retest effect was obscuring stability in memory performance. Supporting this finding, the control group showed a smaller retest effect but an equal age effect. Measurement burst designs combined with models of retest effects allow researchers to employ longitudinal designs in areas where previously only cross-sectional designs were feasible.</p>","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"3 2","pages":"200-207"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s42113-019-00047-w","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38680054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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