Adults with poor reading skills: How lexical knowledge interacts with scores on standardized reading comprehension tests

Journal of Memory and Language

2018

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When a word is read in a text, the aspects of its meanings that are encoded should be those relevant to the text and not those that are irrelevant. We tested whether older adults, college students, and adults with poor literacy skills accomplish contextually relevant encoding. Participants read short stories, which were followed by true/false test sentences. Among these were sentences that matched the relevant meaning of a word in a story and sentences that matched a different meaning. We measured the speed and accuracy of responses to the test sentences and used a decision model to separate the information that a reader encodes from the reader’s speed/accuracy tradeoff settings. We found that all three groups encoded meanings as contextually relevant. The findings illustrate how a decision-making model combined with tests of particular comprehension processes can lead to further understanding of reading skills.

Section: Methodsmentioning

confidence: 80%

Adults with poor reading skills, older adults, and college students: The meanings they understand during reading using a diffusion model analysis

McKoon

Journal of Memory and Language

2018

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“…Given findings from Chen and Li's (2014) meta-analysis, we were concerned that any significant or non-significant correlations that we found between non-symbolic task performance and mathematics achievement would be underpowered in our sample. One “proof of existence” example from the domain of reading (McKoon & Ratcliff, 2015) illustrates that in a large individual differences study with struggling adult readers, drift rates from a lexical decision task were correlated with standardized reading scores and IQ scores (McKoon & Ratcliff, 2016). Future research should involve the testing of many more children from each grade level to establish whether there are also significant relations among model parameters for symbolic and non-symbolic numerical decision tasks and standardized achievement/IQ scores in the domain of mathematics.…”

Section: Discussionmentioning

confidence: 99%

Individual differences in the components of children’s and adults’ information processing for simple symbolic and non-symbolic numeric decisions

Thompson

Journal of Experimental Child Psychology

McKoon

2016

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How do speed and accuracy trade off, and what components of information processing develop as children and adults make simple numeric comparisons? Data from symbolic and non-symbolic number tasks were collected from 19 first graders (M = 7.12 years), 26 second/third graders (M = 8.20 years), 27 fourth/fifth graders (M = 10.46 years), and 19 seventh/eighth graders (M =13.22). The non-symbolic task asked children to decide whether an array of asterisks had a larger or smaller number than 50, and the symbolic task asked whether a two-digit number was greater or less than 50. We used a diffusion model analysis (Ratcliff, 1978) to estimate components of processing in tasks from accuracy, correct and error response times, and response time distributions. Participants who were accurate on one task were accurate on the other, and participants who made fast decisions on one task made fast decisions on the other. Older participants extracted a higher quality of information from the stimulus arrays, were more willing to make a decision, and were faster at encoding, transforming the stimulus representation, and executing their responses. Individual participants' accuracy and RTs were uncorrelated. Drift rate and boundary settings were significantly related across tasks, but they were unrelated to each other. Accuracy was mainly determined by drift rate, and RT was mainly determined by boundary separation. We concluded that RT and accuracy operate largely independently.

“…They have been used to study individual differences in IQ, working memory, and reading measures [122-125], and to examine deficits in populations such as aphasics [166], older adults and children [13-14,123-124], children [167], low literacy adults [168], dyslexics [169], ADHD [170-171], schizophrenia [172], and in depressed and anxious individuals [116,173]. …”

Section: Figurementioning

confidence: 99%

Diffusion Decision Model: Current Issues and History

Trends in Cognitive Sciences

Smith

Brown

et al. 2016

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1,380

1,481

There is growing interest in diffusion models to represent the cognitive and neural processes of speeded decision making. Sequential-sampling models like the diffusion model have a long history in psychology. They view decision making as a process of noisy accumulation of evidence from a stimulus. The standard model assumes that evidence accumulates at a constant rate during the second or two it takes to make a decision. This process can be linked to the behaviors of populations of neurons and to theories of optimality. Diffusion models have been used successfully in a range of cognitive tasks and as psychometric tools in clinical research to examine individual differences. In this article, we relate the models to both earlier and more recent research in psychology.