Pub Date : 2021-02-18DOI: 10.1142/9789814307789_0003
Erik D. Reichle
This chapter introduces formal models of cognition and explains how they are similar to verbal theories but use computer programs and mathematics to avoid the many limitations of human reasoning, thereby adding precision and rigor to their explanations. The chapter discusses Marr’s (1982) levels of analyses and how information-processing systems can be understood and described in terms of the task being performed, the representations and algorithms used to perform the task, and how the latter are implemented by physical systems. This then motivates discussion of three common approaches to modeling human cognition and behavior: process models, production-system models, and connectionist models. Each of these approaches is critiqued, with discussion of its merits and limitations. The three modeling approaches are then further illustrated by showing how each might be used to explain the finding that words can be identified more efficiently if they occur in predictable sentence contexts. The chapter closes with a discussion of how cognitive models are evaluated using their simplicity, theoretical scope, compatibility (e.g., with biology), and their capacity to generate novel predictions for guiding research.
{"title":"Formal Models","authors":"Erik D. Reichle","doi":"10.1142/9789814307789_0003","DOIUrl":"https://doi.org/10.1142/9789814307789_0003","url":null,"abstract":"This chapter introduces formal models of cognition and explains how they are similar to verbal theories but use computer programs and mathematics to avoid the many limitations of human reasoning, thereby adding precision and rigor to their explanations. The chapter discusses Marr’s (1982) levels of analyses and how information-processing systems can be understood and described in terms of the task being performed, the representations and algorithms used to perform the task, and how the latter are implemented by physical systems. This then motivates discussion of three common approaches to modeling human cognition and behavior: process models, production-system models, and connectionist models. Each of these approaches is critiqued, with discussion of its merits and limitations. The three modeling approaches are then further illustrated by showing how each might be used to explain the finding that words can be identified more efficiently if they occur in predictable sentence contexts. The chapter closes with a discussion of how cognitive models are evaluated using their simplicity, theoretical scope, compatibility (e.g., with biology), and their capacity to generate novel predictions for guiding research.","PeriodicalId":199937,"journal":{"name":"Computational Models of Reading","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130420185","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 : 2021-02-18DOI: 10.1093/oso/9780195370669.003.0007
Erik D. Reichle
This chapter opens with a discussion of the limitations of current models of reading, and moves on to the reasons why more comprehensive models of reading are necessary to advance our understanding of the mental, perceptual, and motoric processes that support reading. The chapter then provides a comparative analysis of the various approaches that have been adopted to model reading, and how the theoretical assumptions of models of word identification, sentence processing, discourse representation, and eye-movement control might be combined to build a more comprehensive model of reading in its entirety. The remainder of the chapter then describes one such model, Über-Reader, and a series of simulations to illustrate how the model explains word identification, sentence processing, the encoding and recall of discourse meaning, and the patterns of eye movements that are observed during reading. The final sections of the chapter then address both the limitations and possible future applications of the model.
{"title":"Synthesis","authors":"Erik D. Reichle","doi":"10.1093/oso/9780195370669.003.0007","DOIUrl":"https://doi.org/10.1093/oso/9780195370669.003.0007","url":null,"abstract":"This chapter opens with a discussion of the limitations of current models of reading, and moves on to the reasons why more comprehensive models of reading are necessary to advance our understanding of the mental, perceptual, and motoric processes that support reading. The chapter then provides a comparative analysis of the various approaches that have been adopted to model reading, and how the theoretical assumptions of models of word identification, sentence processing, discourse representation, and eye-movement control might be combined to build a more comprehensive model of reading in its entirety. The remainder of the chapter then describes one such model, Über-Reader, and a series of simulations to illustrate how the model explains word identification, sentence processing, the encoding and recall of discourse meaning, and the patterns of eye movements that are observed during reading. The final sections of the chapter then address both the limitations and possible future applications of the model.","PeriodicalId":199937,"journal":{"name":"Computational Models of Reading","volume":"219 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116187546","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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