ABSTRACT Differences in the learnability of linguistic patterns may be crucial in decidingamong a... more ABSTRACT Differences in the learnability of linguistic patterns may be crucial in decidingamong alternative learningmodels. This paper compares theability of Englishspeakers (Experiment1) and Portuguese speakers(Experiment2) to learn two complex rhythm ...
... derived. With respect to sentence verification tasks (Bott & Noveck, 2004; Noveck & P... more ... derived. With respect to sentence verification tasks (Bott & Noveck, 2004; Noveck & Posada, 2003; Bott, Bailey, & Grodner, 2012), it seems likely that part of the ... phenomena: phonetic categorization (Spivey, Grosjean, & Knoblich, 2005), restricted ...
Scalar implicatures are inferences that arise when a weak expression is used instead of a stronge... more Scalar implicatures are inferences that arise when a weak expression is used instead of a stronger alternative. For example, when a speaker says, “Some of the children are in the classroom,” she often implies that not all of them are. Recent processing studies of scalar implicatures have argued that generating an implicature carries a cost. In this study we investigated
Journal of Experimental Psychology: Learning, Memory, and Cognition, 2009
Naïve observers typically perceive some groupings for a set of stimuli as more intuitive than oth... more Naïve observers typically perceive some groupings for a set of stimuli as more intuitive than others. The problem of predicting category intuitiveness has been historically considered the remit of models of unsupervised categorization. In contrast, this article develops a measure of category intuitiveness from one of the most widely supported models of supervised categorization, the generalized context model (GCM). Considering different category assignments for a set of instances, the authors asked how well the GCM can predict the classification of each instance on the basis of all the other instances. The category assignment that results in the smallest prediction error is interpreted as the most intuitive for the GCM-the authors refer to this way of applying the GCM as "unsupervised GCM." The authors systematically compared predictions of category intuitiveness from the unsupervised GCM and two models of unsupervised categorization: the simplicity model and the rational model. The unsupervised GCM compared favorably with the simplicity model and the rational model. This success of the unsupervised GCM illustrates that the distinction between supervised and unsupervised categorization may need to be reconsidered. However, no model emerged as clearly superior, indicating that there is more work to be done in understanding and modeling category intuitiveness.
Artificial grammar learning (AGL) is an experimental paradigm that has been used extensively inco... more Artificial grammar learning (AGL) is an experimental paradigm that has been used extensively incognitive research for many years to study implicit learning, associative learning, and generalization on the basis of either similarity or rules. Without computer assistance, it is virtually impossible to generate appropriate grammatical training stimuli along with grammatical or nongrammatical test stimuli that control relevant psychological variables. We present the first flexible, fully automated software for selecting AGL stimuli. The software allows users to specify a grammar of interest, and to manipulate characteristics of training and test sequences, and their relationship to each other. The user therefore has direct control over stimulus features that may influence learning and generalization in AGL tasks. The software, AGL StimSelect, enables researchers to develop AGL designs that would not be feasible without automatic stimulus selection. It is implemented in MATLAB.
ABSTRACT Differences in the learnability of linguistic patterns may be crucial in decidingamong a... more ABSTRACT Differences in the learnability of linguistic patterns may be crucial in decidingamong alternative learningmodels. This paper compares theability of Englishspeakers (Experiment1) and Portuguese speakers(Experiment2) to learn two complex rhythm ...
... derived. With respect to sentence verification tasks (Bott & Noveck, 2004; Noveck & P... more ... derived. With respect to sentence verification tasks (Bott & Noveck, 2004; Noveck & Posada, 2003; Bott, Bailey, & Grodner, 2012), it seems likely that part of the ... phenomena: phonetic categorization (Spivey, Grosjean, & Knoblich, 2005), restricted ...
Scalar implicatures are inferences that arise when a weak expression is used instead of a stronge... more Scalar implicatures are inferences that arise when a weak expression is used instead of a stronger alternative. For example, when a speaker says, “Some of the children are in the classroom,” she often implies that not all of them are. Recent processing studies of scalar implicatures have argued that generating an implicature carries a cost. In this study we investigated
Journal of Experimental Psychology: Learning, Memory, and Cognition, 2009
Naïve observers typically perceive some groupings for a set of stimuli as more intuitive than oth... more Naïve observers typically perceive some groupings for a set of stimuli as more intuitive than others. The problem of predicting category intuitiveness has been historically considered the remit of models of unsupervised categorization. In contrast, this article develops a measure of category intuitiveness from one of the most widely supported models of supervised categorization, the generalized context model (GCM). Considering different category assignments for a set of instances, the authors asked how well the GCM can predict the classification of each instance on the basis of all the other instances. The category assignment that results in the smallest prediction error is interpreted as the most intuitive for the GCM-the authors refer to this way of applying the GCM as "unsupervised GCM." The authors systematically compared predictions of category intuitiveness from the unsupervised GCM and two models of unsupervised categorization: the simplicity model and the rational model. The unsupervised GCM compared favorably with the simplicity model and the rational model. This success of the unsupervised GCM illustrates that the distinction between supervised and unsupervised categorization may need to be reconsidered. However, no model emerged as clearly superior, indicating that there is more work to be done in understanding and modeling category intuitiveness.
Artificial grammar learning (AGL) is an experimental paradigm that has been used extensively inco... more Artificial grammar learning (AGL) is an experimental paradigm that has been used extensively incognitive research for many years to study implicit learning, associative learning, and generalization on the basis of either similarity or rules. Without computer assistance, it is virtually impossible to generate appropriate grammatical training stimuli along with grammatical or nongrammatical test stimuli that control relevant psychological variables. We present the first flexible, fully automated software for selecting AGL stimuli. The software allows users to specify a grammar of interest, and to manipulate characteristics of training and test sequences, and their relationship to each other. The user therefore has direct control over stimulus features that may influence learning and generalization in AGL tasks. The software, AGL StimSelect, enables researchers to develop AGL designs that would not be feasible without automatic stimulus selection. It is implemented in MATLAB.
Uploads
Papers by Todd Bailey