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Markku Niemivirta - One of the best experts on this subject based on the ideXlab platform.
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striving for success but at what cost subject specific achievement goal orientation profiles perceived cost and academic well being
Frontiers in Psychology, 2020Co-Authors: Heta Tuominen, Henriikka Juntunen, Markku NiemivirtaAbstract:Most studies utilizing a person-oriented approach to investigating students’ achievement goal orientation profiles have been Domain-general or focused on a single Domain (usually mathematics), thus excluding the possibility of identifying distinct subject-specific motivational profiles. In this study, we looked into this by examining upper secondary school students’ subject-specific achievement goal orientation profiles simultaneously in mathematics and English. As distinct profiles might contribute to how students invest time and effort in studying, we also examined differences in perceived subject-specific cost (i.e., effort required, emotional cost, opportunity cost) among students with different profiles, and how this was linked with students’ more general academic well-being (i.e., school engagement, burnout). The 434 Finnish general upper secondary school students participating in the study were classified based on their achievement goal orientations in the two subjects using latent profile analysis, and the predictions of the latent profile on distal outcomes (i.e., measures of cost and academic well-being) were examined within the mixture model. Five divergent achievement goal orientation profiles were identified: indifferent (29%), success-oriented (26%), mastery-oriented (25%), English-oriented, math-avoidant (14%), and avoidance-oriented (6%). The English-oriented, math-avoidant students showed the most distinct Domain-specificity in their profile but, in general, profiles indicated more cross-Domain Generality than specificity. Overall, mastery-oriented students showed the most adaptive academic well-being, while avoidance-oriented students were the least engaged. Success-oriented students were characterised by high multiple goals in both subjects, elevated costs, and high scores on both positive (engagement) and negative (burnout) well-being indicators. The English-oriented, math-avoidant students perceived studying math as costly. The findings suggest that addressing students’ achievement motivation in different subjects may be useful for recognising factors endangering or fostering student learning and well-being.
Heta Tuominen - One of the best experts on this subject based on the ideXlab platform.
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striving for success but at what cost subject specific achievement goal orientation profiles perceived cost and academic well being
Frontiers in Psychology, 2020Co-Authors: Heta Tuominen, Henriikka Juntunen, Markku NiemivirtaAbstract:Most studies utilizing a person-oriented approach to investigating students’ achievement goal orientation profiles have been Domain-general or focused on a single Domain (usually mathematics), thus excluding the possibility of identifying distinct subject-specific motivational profiles. In this study, we looked into this by examining upper secondary school students’ subject-specific achievement goal orientation profiles simultaneously in mathematics and English. As distinct profiles might contribute to how students invest time and effort in studying, we also examined differences in perceived subject-specific cost (i.e., effort required, emotional cost, opportunity cost) among students with different profiles, and how this was linked with students’ more general academic well-being (i.e., school engagement, burnout). The 434 Finnish general upper secondary school students participating in the study were classified based on their achievement goal orientations in the two subjects using latent profile analysis, and the predictions of the latent profile on distal outcomes (i.e., measures of cost and academic well-being) were examined within the mixture model. Five divergent achievement goal orientation profiles were identified: indifferent (29%), success-oriented (26%), mastery-oriented (25%), English-oriented, math-avoidant (14%), and avoidance-oriented (6%). The English-oriented, math-avoidant students showed the most distinct Domain-specificity in their profile but, in general, profiles indicated more cross-Domain Generality than specificity. Overall, mastery-oriented students showed the most adaptive academic well-being, while avoidance-oriented students were the least engaged. Success-oriented students were characterised by high multiple goals in both subjects, elevated costs, and high scores on both positive (engagement) and negative (burnout) well-being indicators. The English-oriented, math-avoidant students perceived studying math as costly. The findings suggest that addressing students’ achievement motivation in different subjects may be useful for recognising factors endangering or fostering student learning and well-being.
Ram Frost - One of the best experts on this subject based on the ideXlab platform.
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linguistic entrenchment prior knowledge impacts statistical learning performance
Cognition, 2018Co-Authors: Noam Siegelman, Ram Frost, Louisa Bogaerts, Amit Elazar, Joanne ArciuliAbstract:Abstract Statistical Learning (SL) is typically considered to be a Domain-general mechanism by which cognitive systems discover the underlying statistical regularities in the input. Recent findings, however, show clear differences in processing regularities across modalities and stimuli as well as low correlations between performance on visual and auditory tasks. Why does a presumably Domain-general mechanism show distinct patterns of modality and stimulus specificity? Here we claim that the key to this puzzle lies in the prior knowledge brought upon by learners to the learning task. Specifically, we argue that learners’ already entrenched expectations about speech co-occurrences from their native language impacts what they learn from novel auditory verbal input. In contrast, learners are free of such entrenchment when processing sequences of visual material such as abstract shapes. We present evidence from three experiments supporting this hypothesis by showing that auditory-verbal tasks display distinct item-specific effects resulting in low correlations between test items. In contrast, non-verbal tasks – visual and auditory – show high correlations between items. Importantly, we also show that individual performance in visual and auditory SL tasks that do not implicate prior knowledge regarding co-occurrence of elements, is highly correlated. In a fourth experiment, we present further support for the entrenchment hypothesis by showing that the variance in performance between different stimuli in auditory-verbal statistical learning tasks can be traced back to their resemblance to participants' native language. We discuss the methodological and theoretical implications of these findings, focusing on models of Domain Generality/specificity of SL.
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Domain Generality versus modality specificity: The paradox of statistical learning
Trends in Cognitive Sciences, 2015Co-Authors: Ram Frost, Noam Siegelman, Blair C Armstrong, Morten H. ChristiansenAbstract:Statistical learning (SL) is typically considered to be a Domain-general mechanism by which cognitive systems discover the underlying distributional properties of the input. However, recent studies examining whether there are commonalities in the learning of distributional information across different Domains or modalities consistently reveal modality and stimulus specificity. Therefore, important questions are how and why a hypothesized Domain-general learning mechanism systematically produces such effects. Here, we offer a theoretical framework according to which SL is not a unitary mechanism, but a set of Domain-general computational principles that operate in different modalities and, therefore, are subject to the specific constraints characteristic of their respective brain regions. This framework offers testable predictions and we discuss its computational and neurobiological plausibility.
Noam Siegelman - One of the best experts on this subject based on the ideXlab platform.
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linguistic entrenchment prior knowledge impacts statistical learning performance
Cognition, 2018Co-Authors: Noam Siegelman, Ram Frost, Louisa Bogaerts, Amit Elazar, Joanne ArciuliAbstract:Abstract Statistical Learning (SL) is typically considered to be a Domain-general mechanism by which cognitive systems discover the underlying statistical regularities in the input. Recent findings, however, show clear differences in processing regularities across modalities and stimuli as well as low correlations between performance on visual and auditory tasks. Why does a presumably Domain-general mechanism show distinct patterns of modality and stimulus specificity? Here we claim that the key to this puzzle lies in the prior knowledge brought upon by learners to the learning task. Specifically, we argue that learners’ already entrenched expectations about speech co-occurrences from their native language impacts what they learn from novel auditory verbal input. In contrast, learners are free of such entrenchment when processing sequences of visual material such as abstract shapes. We present evidence from three experiments supporting this hypothesis by showing that auditory-verbal tasks display distinct item-specific effects resulting in low correlations between test items. In contrast, non-verbal tasks – visual and auditory – show high correlations between items. Importantly, we also show that individual performance in visual and auditory SL tasks that do not implicate prior knowledge regarding co-occurrence of elements, is highly correlated. In a fourth experiment, we present further support for the entrenchment hypothesis by showing that the variance in performance between different stimuli in auditory-verbal statistical learning tasks can be traced back to their resemblance to participants' native language. We discuss the methodological and theoretical implications of these findings, focusing on models of Domain Generality/specificity of SL.
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Domain Generality versus modality specificity: The paradox of statistical learning
Trends in Cognitive Sciences, 2015Co-Authors: Ram Frost, Noam Siegelman, Blair C Armstrong, Morten H. ChristiansenAbstract:Statistical learning (SL) is typically considered to be a Domain-general mechanism by which cognitive systems discover the underlying distributional properties of the input. However, recent studies examining whether there are commonalities in the learning of distributional information across different Domains or modalities consistently reveal modality and stimulus specificity. Therefore, important questions are how and why a hypothesized Domain-general learning mechanism systematically produces such effects. Here, we offer a theoretical framework according to which SL is not a unitary mechanism, but a set of Domain-general computational principles that operate in different modalities and, therefore, are subject to the specific constraints characteristic of their respective brain regions. This framework offers testable predictions and we discuss its computational and neurobiological plausibility.
John S Duncan - One of the best experts on this subject based on the ideXlab platform.
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review coding of visual auditory rule and response information in the brain 10 years of multivoxel pattern analysis
Journal of Cognitive Neuroscience, 2016Co-Authors: Alexandra Woolgar, John S Duncan, Jade JacksonAbstract:How is the processing of task information organized in the brain? Many views of brain function emphasize modularity, with different regions specialized for processing different types of information. However, recent accounts also highlight flexibility, pointing especially to the highly consistent pattern of frontoparietal activation across many tasks. Although early insights from functional imaging were based on overall activation levels during different cognitive operations, in the last decade many researchers have used multivoxel pattern analyses to interrogate the representational content of activations, mapping out the brain regions that make particular stimulus, rule, or response distinctions. Here, we drew on 100 searchlight decoding analyses from 57 published papers to characterize the information coded in different brain networks. The outcome was highly structured. Visual, auditory, and motor networks predominantly but not exclusively coded visual, auditory, and motor information, respectively. By contrast, the frontoparietal multiple-demand network was characterized by Domain Generality, coding visual, auditory, motor, and rule information. The contribution of the default mode network and voxels elsewhere was minor. The data suggest a balanced picture of brain organization in which sensory and motor networks are relatively specialized for information in their own Domain, whereas a specific frontoparietal network acts as a Domain-general "core" with the capacity to code many different aspects of a task.
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broad Domain Generality in focal regions of frontal and parietal cortex
Proceedings of the National Academy of Sciences of the United States of America, 2013Co-Authors: Evelina Fedorenko, John S Duncan, Nancy KanwisherAbstract:Unlike brain regions that respond selectively to specific kinds of information content, a number of frontal and parietal regions are thought to be Domain- and process-general: that is, active during a wide variety of demanding cognitive tasks. However, most previous evidence for this functional Generality in humans comes from methods that overestimate activation overlap across tasks. Here we present functional MRI evidence from single-subject analyses for broad functional Generality of a specific set of brain regions: the same sets of voxels are engaged across tasks ranging from arithmetic to storing information in working memory, to inhibiting irrelevant information. These regions have a specific topography, often lying directly adjacent to Domain-specific regions. Thus, in addition to Domain-specific brain regions tailored to solve particular problems of longstanding importance to our species, the human brain also contains a set of functionally general regions that plausibly endow us with the cognitive flexibility necessary to solve novel problems.