Name: lexical decision task for studying written word recognition in adults with and without dementia or mild cognitive impairment
Uploaded: 2019-06-25T14:30:00
Description: Watch this Scientific Journal Video about Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment at JoVE.com

We thank Minna Lehtonen, Tuomo H&#228;nninen, Merja Hallikainen, and Hilkka Soininen for their contribution to the data collection and processing reported here. The data collection was supported by VPH Dementia Research enabled by EU, Grant agreement No. 601055.

The protocol follows the guidelines of the Ethics Committee of the Hospital District of Northern Savo (IRB00006251).
1. Participant screening
<ol>
	<li>Recruit younger and older adults who have&#160;normal or corrected-to-normal vision and are native speakers of the language tested unless the study addresses specific research questions regarding second language acquisition.</li>
	<li>For healthy control groups, exclude participants who have a history of neurological or psychiatric disorders....

<ol>
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By using a simple language task that does not require language production, the present study investigated the impact of various lexical variables on word recognition in neurologically healthy younger and older adults, as well as in people with Alzheimer&#8217;s disease or Mild Cognitive Impairment. The age range used for recruiting &#8220;older adults&#8221; might depend on the specific research interests; however, the range for the healthy elderly group should match as closely as possible the age range and distribution ...

Words are stored in the mental lexicon in a highly interconnected network. The connections between words may reflect shared properties, such as semantic similarity (e.g., dog and cat), form similarity (dog and fog), or frequent co-occurrence in common language use (e.g., dog and leash). Cognitive theories of language, such as usage-based theory1, argue that every encounter of a word by a language user has an effect on the word&#8217;s mental representation. According to Exemplar Theory, a word&#8217;s representation consists of many exemplars, which are built up from individual tokens of language use and which represent the variability that exists for a given category. The frequency of use2 impacts representations in memory by contributing to the strength of an exemplar1.
Word recognition speed can reveal the characteristics of the mental lexicon. A commonly used experimental paradigm for measuring the speed of word recognition is the lexical decision task. In this task, participants are presented with letter strings on a monitor, one at a time. They are instructed to decide as quickly as possible whether the letter string on the screen is a real word or not by pressing the corresponding button.
By examining reaction times for real words, researchers can address a number of important questions about language processing. For example, identifying which factors make recognition faster can test hypotheses about the structure of the mental lexicon and reveal its architecture. Moreover, comparisons of performance across different groups of participants can help us understand the influence of various types of language experience, or, in the case of aging or neurodegenerative diseases (e.g., Alzheimer&#8217;s disease), the role of cognitive decline.
Some factors (e.g., the frequency of use) exhibit greater influence on word recognition than other factors (e.g., word length). With advancing age, the way people recognize written words might change3,4. Younger adults tend to rely heavily on semantic (meaning-based) aspects of a word, such as how many compounds (e.g., bulldog) or derived words (e.g., doggy) share aspects of both form and meaning with the target word (in this case, dog). Word recognition for older adults appears to be more influenced by form-based aspects, such as the frequency that two subsequent letters co-occur in the language (e.g., the letter combination st occurs more often in English words than the combination sk).
To determine the factors that influence the word recognition speed across different groups, the researcher can manipulate certain variables in the stimulus set and then test the power of these variables to predict word recognition speed. For example, to test whether word recognition is driven by semantic or form-based factors, the stimulus set should include variables that reflect the degree of connectivity of a word to its semantic neighbors in the mental lexicon or its connectivity to other words that share part of its form.
This method was used in the current study to investigate whether word recognition speed is influenced by different factors in younger and older adults and in individuals with Alzheimer&#8217;s disease (AD) or mild cognitive impairment (MCI)3. The method described here is based on visual word recognition but can be adapted to the auditory modality. However, some variables that are significant predictors of reaction times in a typical visual lexical decision experiment might not predict response latencies in an auditory lexical decision or may have the opposite effect. For example, the phonological neighborhood has the opposite effect across these two modalities5: &#160;words with larger phonological neighborhoods exhibit a facilitatory effect on visual word recognition but result in longer response latencies in auditory lexical decision6.
Word-finding difficulties in older adults7 have been generally attributed to difficulty accessing the phonological word form rather than a breakdown of the semantic representation8. However, AD research has primarily focused on semantic declines9,10,11,12,13,14. It is important to disentangle how semantic and orthographic factors influence the recognition of written words in aging with and without cognitive decline. The influence of form-related factors is more pronounced in older than in younger adults, and it remains significant in people with MCI or AD3. Thus, this methodology can help us uncover features of the mental lexicon across different populations and identify changes in the lexicon&#8217;s organization with age and neuropathology. One concern when testing patients with neuropathology is that they may have difficulties accessing task-related knowledge. However, the lexical decision task is a simple task with no burden on working memory or other complex cognitive skills that many patients exhibit problems with. It has been considered appropriate for AD and MCI populations.

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			<td>E-Prime</td>
			<td>Psychology Software Tools</td>
			<td>version 2.0.10.356.</td>
			<td></td>
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			<td>PC with Windows and Keyboard</td>
			<td></td>
			<td></td>
			<td></td>
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			<td>R</td>
			<td>R Foundation for Statistical Computing</td>
			<td>R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.</td>
			<td></td>
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lexical decision task for studying written word recognition in adults with and without dementia or mild cognitive impairment

Older adults are slower at recognizing visual objects than younger adults. The same is true for recognizing that a letter string is a real word. People with Alzheimer&#39;s disease (AD) or Mild Cognitive Impairment (MCI) demonstrate even longer responses in written word recognition than elderly controls. Despite the general tendency towards slower recognition in aging and neurocognitive disorders, certain characteristics of words influence word recognition speed regardless of age or neuropathology (e.g., a word&#8217;s frequency of use). We present here a protocol for examining the influence of lexical characteristics on word recognition response times in a simple lexical decision experiment administered to younger and older adults and people with MCI or AD. In this experiment, participants are asked to decide as quickly and accurately as possible whether a given letter string is an actual word or not. We also describe mixed-effects models and principal components analysis that can be used to detect the influence of different types of lexical variables or individual characteristics of participants on word recognition speed.

Table 1 shows a list of variables that were obtained from three different sources (a corpus, a dictionary, and pilot testing of test items) that are included in the analysis as fixed-effect predictors. Many of these variables have been previously reported to affect word recognition speed.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Corpus:</td>
			<td></td>
		</tr>
		<tr>
			<td>Base frequency</td>
			<td>the numbe...

Watch this Scientific Journal Video about Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment at JoVE.com

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

This article describes how to implement a simple lexical decision experiment to assess written word recognition in neurologically healthy participants and in individuals with dementia and cognitive decline. We also provide a detailed description of reaction time analysis using principal components analysis (PCA) and mixed-effects modeling.

Older adults are slower at recognizing visual objects than younger adults. The same is true for recognizing that a letter string is a real word. People ...

The lexical decision task is used to measure word recognition speed. This task can reveal characteristics of the mental lexicon and how the lexicon changes with aging and with neurodegenerative disorders. Many language tasks require the coordination of language and other aspects of cognition.The lexical decision task does not rely heavily on other cognitive abilities, which may be compromised in some populations, such as patients with dementia. Helping to demonstrate the procedure will be Dalia Garcia, our lab manager. Place the participant in front of a computer monitor at a viewing distance of about 80 centimeters in a normally lit room.How was the ride? It was good. Press the left button if the word is a real word.Instruct the participant to decide as quickly and accurately as possible whether the letter string on the screen is a real word or not by pressing one of two corresponding buttons. Start the experiment with a practice session that includes a small number of trials with one word presented horizontally per trial subtending a visual angle of about five degrees. Divide the experiment into blocks and give short breaks after the practice session and between the blocks to allow participants to rest their eyes and reduce fatigue.Next, start each new block with a few filler items of common nouns, such as dog, sister, or year, that will not be included in the analysis. Present the items in a random order. Begin each experiment trial with a fixation mark appearing in the center of the screen for 500 milliseconds followed by a blank screen for another 500 milliseconds.Immediately after the blank screen, present a letter string for 1, 500 milliseconds or until the participant responds. Finally, after a response is made, follow again with a blank screen until 3, 000 milliseconds has passed from the beginning of the trial. Begin by opening the output file of the presentation program and obtain the reaction time, measured in milliseconds, for each trial.Import data into R by using, for example, the read. table function. Install the packages lme4 and lmerTest.Attach packages with the function library or require. Check the need for transformation using the boxcox function as the distribution of reaction time data is typically highly skewed. Transform the reaction time values using inverted transformed reaction times or binary logarithms of reaction times since these transformations tend to provide more normal-like distributions for reaction times in lexical decision experiments than raw reaction time values.Next, exclude pseudo word and filler trials as well as incorrect responses and omissions. Exclude trials with response times faster than 300 milliseconds because they typically indicate that the participant was too late responding to a previous stimulus. Next, build a basic linear mixed effects model that identifies reaction times as the outcome measure and subject item and trial as random effects.Add the random effects in order to estimate random intercepts for each of the random effects. Add explanatory variables in a theoretically-motivated order. For instance, add words-based frequency as a fixed effect.Insert variables, such as base or surface frequency, into the model with a transformation that results in a more Gaussian distribution shape. Check with the anova function if adding each predictor significantly improved the predictive power of the model compared to a model without the predictor. If there is no significant difference in the fit of the new model over the simpler model, choose the simplest model with fewer predictors.Then, check the Akaike information criterion of each model using the AIC function. Lower values indicate a better fit for the data. Next, check for theoretically-motivated interactions between predictors.For instance, add a term of interaction, the log of base frequency by age. Then add by participant random slopes for predictors by including one plus before the variable name, then a vertical bar, then subject, because participants'reaction times might be affected by their individual characteristics or by words'lexical characteristics in different ways. Run the analysis for each participant group separately or run an analysis on all data with group as a fixed effect predictor and then test for an interaction of group by significant predictors.In order to remove the influence of possible outliers, exclude data points with absolute standardized residuals exceeding 2.5 standard deviations and refit the model with the new data. Finally, in the case of exploratory data-driven analysis, use a backwards stepwise regression. Include all variables in the initial analysis and then remove non-significant variables from the model in a step-by-step fashion.These results indicate how word recognition speed might be different for younger adults and older adults. Only two principle components, PC1 and PC4, were significant in the young adults. Three components were significant predictors in the models for elderly controls, individuals with mild cognitive impairment, and individuals with Alzheimer's disease.This third component, PC2, is interpreted as reflecting the influence of form-based aspects of a word on word recognition speed. Further, one interesting difference between the three elderly groups emerged. Education significantly predicted speed of word recognition for elderly controls and individuals with mild cognitive impairment, but not for individuals with Alzheimer's disease.This methodology can be applied to other types of questions about the mental lexicon and to other populations, such as multilinguals and people with aphasia.

Research

JoVE Journal

Behavior

The Crossmodal Congruency Task as a Means to Obtain an Objective Behavioral Measure in the Rubber Hand Illusion Paradigm

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Transferring Cognitive Tasks Between Brain Imaging Modalities: Implications for Task Design and Results Interpretation in fMRI Studies

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The Attentional Set Shifting Task: A Measure of Cognitive Flexibility in Mice

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The 4 Mountains Test: A Short Test of Spatial Memory with High Sensitivity for the Diagnosis of Pre-dementia Alzheimer's Disease

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The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

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A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

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The Unpredictable Chronic Mild Stress Protocol for Inducing Anhedonia in Mice

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Mild cognitive impairment (MCI) is the first sign of dementia among elderly populations and its early detection is crucial in our aging societies. Common ...