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.</li>
	<li>For the clinical groups, recruit individuals who have been diagnosed with Alzheimer&#8217;s disease15 or mild cognitive impairment16,17. Recruit only individuals who are able to give informed consent, according to the clinician&#39;s judgment. For accurate comparisons, match the age range and mean of the clinical groups with that of the healthy older adult participants.</li>
	<li>Measure the severity of dementia, for example, using the Clinical Dementia Rating Scale18 (CDR, 0=no dementia, 0.5=very mild, 1=mild, 2=moderate, 3=severe). Exclude patients with severe dementia because the task may be too difficult for them. Do not include participants who seem unable to follow instructions, despite their severity rating.</li>
</ol>
2. Stimulus construction
<ol>
	<li>Select word stimuli to address specific research questions, for example, whether semantic or orthographic/phonological variables have a stronger influence on word recognition19 in different populations.</li>
	<li>Calculate from a corpus20 or retrieve from a database21 variables reflecting semantic, phonological, and orthographic characteristics of the stimuli so they can be used either as theoretically motivated predictors explaining word recognition reaction times or as control variables. Also, use participants&#8217; gender, age, and years of education as explanatory or control variables.</li>
	<li>In addition to the real words, build a set of matched pseudo-words. Pseudo-words resemble real words in that they conform to the language&#8217;s norms for placement of certain letters in certain word positions (phonotactics). In order to control for phonotactics, create pseudo-words, for example, by randomly recombining the first syllables from some words with the second syllables from other words. Remove any items that happened to produce a real word through this recombination and all the items that violate the phonotactics of the language.</li>
	<li>Match the pseudo-words with the target words in terms of the word length in letters and bigram frequency, which is the average number of times that all combinations of two subsequent letters occur in a text corpus. These variables have been shown to influence recognition speed. 
	NOTE: Manipulating the pseudo-word ratio (e.g., the number of real words relative to the number of pseudo-words) may lead to different results, with responses to the less probable stimuli being slower and less accurate22.</li>
	<li>Add a set of real-word fillers in order to decrease participant&#8217;s expectancy of the next stimulus belonging to a certain type (e.g., a certain inflectional class). Choose them, for instance, from different word categories (e.g., inflectional classes) than the ones used to construct stimuli according to the characteristics of interest.</li>
</ol>
3. Experimental design
<ol>
	<li>Present letter strings horizontally, one at a time, subtending a visual angle of about 5&#176;.</li>
	<li>Begin the experiment with a practice session that includes a small number of trials, with one word presented per trial (e.g., 15 words and 15 pseudo-words not included in the actual experiment). This is to familiarize the participant with the task and the response buttons. If the participant is not responding accurately (&#8216;yes&#8217; button for real words and &#8216;no&#8217; button for pseudo-words) during the practice trials, provide feedback and redo the practice session.</li>
	<li>Divide the experiment into blocks and give short breaks after the practice session and between the blocks. These breaks allow participants to rest their eyes and will reduce fatigue.</li>
	<li>Start each new block with a few filler items that will not be included in the analysis (e.g., common nouns such as dog, sister, year) because the first few trials of the block are sometimes ignored by participants with MCI or AD.</li>
	<li>Present the experimental items in a random order for each participant.</li>
	<li>Begin each trial with a fixation mark (e.g., a + sign) appearing in the center of the screen for 500 ms, followed by a blank screen for a fixed (e.g., 500 ms) or variable amount of time (e.g., 500-800 ms).</li>
	<li>Immediately after the blank screen, present a letter string (word or pseudo-word) for 1,500 ms or until the participant responds.</li>
	<li>After a response is made or after 1,500 ms from the onset of the word (whichever comes first), follow again with a blank screen until 3000 ms has passed from the beginning of the trial.</li>
	<li>Repeat this sequence until all of the items in the experiment have been presented. 
	NOTE: Times for the delay between the stimuli serve as an example. Changing them may affect the pattern of results.</li>
</ol>
4. Experimental procedure
<ol>
	<li>Place the participant in front of a computer monitor at a viewing distance of about 80 cm in a normally lit room.</li>
	<li>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 with their dominant hand (e.g., the index finger for real words and the middle finger for pseudo-words) or using the index finger of each hand. 
	NOTE: Participants try to optimize their performance in line with the instructions. Thus, their responses will be affected by stressing speed over accuracy or vice versa23.</li>
</ol>
5. Analyzing data with a mixed-effects model in R
NOTE: Many different statistical programs can be used to perform the analysis. This section describes steps for analyzing data in R24.
<ol>
	<li>Obtain the reaction time (RT) measured in milliseconds for each trial from the output file of the presentation program (e.g., E-Studio software).</li>
	<li>Install the packages&#160;lme428&#160;and&#160;lmerTest29. Attach packages with the function&#160;library&#160;or&#160;require.</li>
	<li>Import data into R by using, e.g., the&#160;read.table&#160;function.</li>
	<li>Check the need for transformation, e.g., with the boxcox function from the MASS package25, as the distribution of RT data is typically highly skewed. 
	&#62; library (MASS) 
	&#62; boxcox(RT ~ Expnanatory_variable, data = yourdata) 
	NOTE: The graph produced by the boxcox function shows a 95% confidence interval for the boxcox transformation parameter. Depending on the lambda values located within this interval, the needed transformation can be chosen, e.g., &#955;=&#8722;1 (inverse transformation), &#955;=0 (logarithmic transformation), &#955;=1/2 (square root transformation), and &#955;=1/3 (cube root transformation).
	<ol>
		<li>Transform the RT values using inverted transformed RTs (e.g., -1000/RT) or binary logarithms of RTs (e.g., log2(RT)) since these transformations tend to provide more normal-like distributions for reaction times in lexical decision experiments than raw RTs26.</li>
		<li>Alternatively, use statistical methods that do not rely on normal distributions and fit robust linear mixed-effects models and provide estimates on which outliers or other sources of contamination have little influence27.</li>
	</ol>
	</li>
	<li>Since reaction time analyses are typically conducted on accurate responses, exclude trials in which the participants&#8217; response was incorrect (a response of &#8220;no&#8221; to real words) as well as omissions.
	<ol>
		<li>Also, exclude responses to pseudo-words and fillers unless there are specific hypotheses about them.</li>
		<li>Exclude trials with response times faster than 300 ms because they typically indicate that the participant was too late responding to a previous stimulus or that he or she accidentally pressed the response button before reading the stimulus.</li>
	</ol>
	</li>
	<li>Build a basic linear mixed-effects model that identifies RT as the outcome measure and Subject, Item, and Trial as random effects. Note that variables whose values are randomly sampled from a larger set (population) of values are included as random effects and variables with a small number of levels or for which all levels are included in the data are fixed effects. Add the random effects in the form (1 | Subject) in order to estimate random intercepts for each of the random effects. 
	&#62; g1 = lmer (RT ~ (1 | Subject) + (1 | Item) + (1 | Trial), data = yourdata) 
	&#62; summary (g1)</li>
	<li>Add explanatory variables in a theoretically motivated order. For instance, add words&#8217; base frequency as a fixed effect. Some variables, such as base or surface frequency, have Zipfian distributions, so insert them in the model with a transformation that results in a more Gaussian distribution shape, e.g., logarithmic transformation. 
	&#62; g2 = lmer (RT ~ log(BaseFrequency + 1) + (1 | Subject) + (1 | Item) + (1 | Trial), data = yourdata) 
	&#62; summary (g2)</li>
	<li>Check with the Anova function if adding each predictor (e.g., BaseFrequency) significantly improved the predictive power of the model compared to a model without the predictor. 
	&#62; anova (g1, g2)
	<ol>
		<li>If there is no significant difference in the fit of the new model over the simpler model, prefer the simplest model with fewer predictors. Also, check the Akaike Information Criterion (AIC)30 of each model. AIC is a measure of how well statistical models fit a set of data according to maximum likelihood. Lower values indicate a better fit for the data31. 
		&#62; AIC (g1); AIC (g2)</li>
	</ol>
	</li>
	<li>Repeat steps 5.7. and 5.8. by adding other explanatory variables, e.g., some of those that are presented in Table 1, one by one in a theoretically motivated order and keeping only those that significantly improve the predictive power of the model. If variable stimulus onset asynchrony was used, include it as a fixed-effect variable in the model.</li>
	<li>Check for theoretically motivated interactions between predictors. For instance, add a term of interaction the log of Base Frequency by Age. 
	&#62; g3 = lmer (RT ~ log(BaseFrequency + 1) + Age + log(BaseFrequency + 1) : Age + (1 | Subject) + (1 | Item) + (1 | Trial), data = yourdata) 
	NOTE: It is possible that a predictor is significant as a term of interaction with another variable, but not significant as the main predictor. In this case, do not remove this predictor from the model (include it also as the main effect).</li>
	<li>Add by-participant random slopes32 for predictors by including &#8220;1 +&#8221; before the variable name, then &#8220;| Subject&#8221;, e.g., (1 + log(BaseFrequency&#160; + 1) | Subject), because participants&#8217; response times might be affected by words&#39; lexical characteristics in different ways. 
	NOTE: If there are many continuous predictors, allowing them all to have random slopes is unrealistic because random slope models require large amounts of data to accurately estimate variances and covariances33,34. In case the maximal model does not converge (in other words, successfully compute), simplify the model33. Alternatively, implement Bayesian versions of multilevel modeling35.</li>
	<li>Run the analysis for each participant group separately. Alternatively, run an analysis on all data, with group as a fixed-effect predictor, and then test for an interaction of group by significant predictors. 
	&#62; g4 = lmer (RT ~ log(BaseFrequency + 1) + Age + log(BaseFrequency + 1) : Age&#160; + Group + log(BaseFrequency + 1) : Group +&#160; (1 + log(BaseFrequency + 1) | Subject) +&#160; (1 | Item) + (1 | Trial), data = yourdata)</li>
	<li>In order to remove the influence of possible outliers, exclude data points with absolute standardized residuals exceeding, e.g., 2.5 standard deviations26, and re-fit the model with the new data (yourdata2). 
	&#62; yourdata2 = yourdata [abs(scale(resid(g4))) &#60; 2.5, ] 
	&#62; g5 = lmer (RT ~ log(BaseFrequency + 1) + Age + log(BaseFrequency + 1) : Age&#160; + Group + log(BaseFrequency + 1) : Group + (1 + log(BaseFrequency +1) | Subject) +&#160; (1 | Item) + (1 | Trial), data = yourdata2) 
	NOTE: Not all extreme data points are harmful for the model &#8211; only those that have excessive leverage over the model.</li>
	<li>In the case of exploratory (data-driven) analysis, use backward stepwise regression: include all variables in the initial analysis and then remove non-significant variables from the model in a step-by-step fashion. Alternatively, use the automatic procedure of eliminating non-significant predictors with the step function provided by the package lmerTest29. 
	&#62; step (g4)</li>
</ol>

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M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Inflected+words+in+production:+Evidence+for+a+morphologically+rich+lexicon.">Inflected words in production: Evidence for a morphologically rich lexicon.</a> The Quarterly Journal of Experimental Psychology. 69, 432-454 (2016).</li><li>Yarkoni, T., Balota, D., Yap, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Moving+beyond+Coltheart&#8217;s+N:+A+new+measure+of+orthographic+similarity.">Moving beyond Coltheart&#8217;s N: A new measure of orthographic similarity.</a> Psychonomic Bulletin &amp; Review. 15 (5), 971-979 (2008).</li><li>Cohen, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Recognition+and+retrieval+of+proper+names:+Age+differences+in+the+fan+effect.">Recognition and retrieval of proper names: Age differences in the fan effect.</a> European Journal of Cognitive Psychology. 2 (3), 193-204 (1990).</li><li>Kemmerer, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Cognitive neuroscience of language. , (2015).</li><li>Baayen, R. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Analyzing linguistic data: A practical introduction to statistics using R. , (2008).</li><li>Nikolaev, A., Lehtonen, M., Higby, E., Hyun, J., Ashaie, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+facilitatory+effect+of+rich+stem+allomorphy+but+not+inflectional+productivity+on+single-word+recognition.">A facilitatory effect of rich stem allomorphy but not inflectional productivity on single-word recognition.</a> Applied Psycholinguistics. 39, 1221-1238 (2018).</li><li>Nikolaev, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Behavioural+and+ERP+effects+of+paradigm+complexity+on+visual+word+recognition.">Behavioural and ERP effects of paradigm complexity on visual word recognition.</a> Language, Cognition and Neuroscience. 10, 1295-1310 (2014).</li></ol>

The authors have nothing to disclose.

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>
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			<td>PC with Windows and Keyboard</td>
			<td></td>
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			<td></td>
		</tr>
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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 ...

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9.0K Views.  University of Helsinki. 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.

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

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

The rubber hand illusion (RHI) is a popular experimental paradigm. Participants view touch on an artificial rubber hand while the participants' own hidden hand is touched. If the viewed and felt touches are given at the same time then this is sufficient to induce the compelling experience that the rubber hand is one's own hand. The RHI can be used to investigate exactly how the brain constructs distinct body representations for one's own body. Such representations are crucial for successful interactions with the external world. To obtain a subjective measure of the RHI, researchers typically ask participants to rate statements such as "I felt as if the rubber hand were my hand". Here we demonstrate how the crossmodal congruency task can be used to obtain an objective behavioral measure within this paradigm.
The variant of the crossmodal congruency task we employ involves the presentation of tactile targets and visual distractors. Targets and distractors are spatially congruent (i.e. same finger) on some trials and incongruent (i.e. different finger) on others. The difference in performance between incongruent and congruent trials - the crossmodal congruency effect (CCE) - indexes multisensory interactions. Importantly, the CCE is modulated both by viewing a hand as well as the synchrony of viewed and felt touch which are both crucial factors for the RHI.
The use of the crossmodal congruency task within the RHI paradigm has several advantages. It is a simple behavioral measure which can be repeated many times and which can be obtained during the illusion while participants view the artificial hand. Furthermore, this measure is not susceptible to observer and experimenter biases. The combination of the RHI paradigm with the crossmodal congruency task allows in particular for the investigation of multisensory processes which are critical for modulations of body representations as in the RHI.

We demonstrate how an objective measure can be employed in the widely employed rubber hand illusion paradigm. This measure is obtained by modifying the well-established crossmodal congruency task. This task allows the investigation of multisensory processes which are critical for modulations of body representations as in the rubber hand illusion.

The rubber hand illusion (RHI) is a popular experimental paradigm. Participants view touch on an artificial rubber hand while the participants' own hidden ...

Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks

A set of behavioral tasks for assessing perceptual and sensorimotor timing abilities in the general population (i.e., non-musicians) is presented here with the goal of uncovering rhythm disorders, such as beat deafness. Beat deafness is characterized by poor performance in perceiving durations in auditory rhythmic patterns or poor synchronization of movement with auditory rhythms (e.g., with musical beats). These tasks include the synchronization of finger tapping to the beat of simple and complex auditory stimuli and the detection of rhythmic irregularities (anisochrony detection task) embedded in the same stimuli. These tests, which are easy to administer, include an assessment of both perceptual and sensorimotor timing abilities under different conditions (e.g., beat rates and types of auditory material) and are based on the same auditory stimuli, ranging from a simple metronome to a complex musical excerpt. The analysis of synchronized tapping data is performed with circular statistics, which provide reliable measures of synchronization accuracy (e.g., the difference between the timing of the taps and the timing of the pacing stimuli) and consistency. Circular statistics on tapping data are particularly well-suited for detecting individual differences in the general population. Synchronized tapping and anisochrony detection are sensitive measures for identifying profiles of rhythm disorders and have been used with success to uncover cases of poor synchronization with spared perceptual timing. This systematic assessment of perceptual and sensorimotor timing can be extended to populations of patients with brain damage, neurodegenerative diseases (e.g., Parkinson&#8217;s disease), and developmental disorders (e.g., Attention Deficit Hyperactivity Disorder).

Behavioral tasks that allow for the assessment of perceptual and sensorimotor timing abilities in the general population (i.e., non-musicians) are presented. Synchronization of finger tapping to the beat of an auditory stimuli and detecting rhythmic irregularities provides a means of uncovering rhythm disorders.

A set of behavioral tasks for assessing perceptual and sensorimotor timing abilities in the general population (i.e., non-musicians) is presented here ...

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

As cognitive neuroscience methods develop, established experimental tasks are used with emerging brain imaging modalities. Here transferring a paradigm (the visual oddball task) with a long history of behavioral and electroencephalography (EEG) experiments to a functional magnetic resonance imaging (fMRI) experiment is considered. The aims of this paper are to briefly describe fMRI and when its use is appropriate in cognitive neuroscience; illustrate how task design can influence the results of an fMRI experiment, particularly when that task is borrowed from another imaging modality; explain the practical aspects of performing an fMRI experiment. It is demonstrated that manipulating the task demands in the visual oddball task results in different patterns of blood oxygen level dependent (BOLD) activation. The nature of the fMRI BOLD measure means that many brain regions are found to be active in a particular task. Determining the functions of these areas of activation is very much dependent on task design and analysis. The complex nature of many fMRI tasks means that the details of the task and its requirements need careful consideration when interpreting data. The data show that this is particularly important in those tasks relying on a motor response as well as cognitive elements and that covert and overt responses should be considered where possible. Furthermore, the data show that transferring an EEG paradigm to an fMRI experiment needs careful consideration and it cannot be assumed that the same paradigm will work equally well across imaging modalities. It is therefore recommended that the design of an fMRI study is pilot tested behaviorally to establish the effects of interest and then pilot tested in the fMRI environment to ensure appropriate design, implementation and analysis for the effects of interest.

Transferring a paradigm with a history of use in EEG experiments to an fMRI experiment is considered. It is demonstrated that manipulating the task demands in the visual oddball task resulted in different patterns of BOLD activation and illustrated how task design is crucial in fMRI experiments.

As cognitive neuroscience methods develop, established experimental tasks are used with emerging brain imaging modalities. Here transferring a paradigm ...

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

Cognitive impairment, particularly involving dysfunction of circuitry within the prefrontal cortex (PFC), represents a core feature of many neuropsychiatric and neurodevelopmental disorders, including depression, post-traumatic stress disorder, schizophrenia and autism spectrum disorder. Deficits in cognitive function also represent the most difficult symptom domain&#160;to successfully treat, as serotonin reuptake inhibitors and tricyclic antidepressants have only modest effects. Functional neuroimaging studies and postmortem analysis of human brain tissue implicate the PFC as being a primary region of dysregulation in patients with these disorders. However, preclinical behavioral assays used to assess these deficits in mouse models which can be readily manipulated genetically and could provide the basis for studies of new treatment avenues have been underutilized. Here we describe the adaptation of a behavioral assay, the attentional set shifting task (AST), to be performed in mice to assess prefrontal cortex mediated cognitive deficits. The neural circuits underlying behavior during the AST are highly conserved across humans, nonhuman primates and rodents, providing excellent face, construct and predictive validity.

The goal of this protocol is to perform a behavioral assay such as the attentional set shifting task (AST) to assess prefrontal cortex-mediated cognitive flexibility in mice.

Cognitive impairment, particularly involving dysfunction of circuitry within the prefrontal cortex (PFC), represents a core feature of many ...

Comparing the Frequency Effect Between the Lexical Decision and Naming Tasks in Chinese

In psycholinguistic research, the frequency effect can be one of the indicators for eligible experimental tasks that examine the nature of lexical access. Usually, only one of those tasks is chosen to examine lexical access in a study. Using two exemplar experiments, this paper introduces an approach to include both the lexical decision task and the naming task in a study. In the first experiment, the stimuli were Chinese characters with frequency and regularity manipulated. In the second experiment, the stimuli were switched to Chinese two-character words, in which the word frequency and the regularity of the leading character were manipulated. The logic of these two exemplar experiments was to explore some important issues such as the role of phonology on recognition by comparing the frequency effect between both the tasks. The results revealed different patterns of lexical access from those reported in the alphabetic systems. The results of Experiment 1 manifested a larger frequency effect in the naming task as compared to the LDT, when the stimuli were Chinese characters. And it is noteworthy that, in Experiment 1, when the stimuli were regular Chinese characters, the frequency effect observed in the naming task was roughly equivalent to that in the LDT. However, a smaller frequency effect was shown in the naming task as compared to the LDT, when the stimuli were switched to Chinese two-character words in Experiment 2. Taking advantage of the respective demands and characteristics in both tasks, researchers can obtain a more complete and precise picture of character/word recognition.

Researchers adopt both the lexical decision task and the naming task to investigate some important topics such as character/word recognition by comparing the frequency effect between these two tasks. This article introduces this approach through two exemplar experiments and elaborates on the underlying logic.

In psycholinguistic research, the frequency effect can be one of the indicators for eligible experimental tasks that examine the nature of lexical access. ...

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

This protocol describes the administration of the 4 Mountains Test (4MT), a short test of spatial memory, in which memory for the topographical layout of four mountains within a computer-generated landscape is tested using a delayed match-to-sample paradigm. Allocentric spatial memory is assessed by altering the viewpoint, colors and textures between the initially presented and target images.
Allocentric spatial memory is a key function of the hippocampus, one of the earliest brain regions to be affected in Alzheimer&#39;s disease (AD) and impairment of hippocampal function predates the onset of dementia. It was hypothesized that performance on the 4MT would aid the diagnosis of predementia AD, which manifests clinically as Mild Cognitive Impairment (MCI).
The 4MT was applied to patients with MCI, stratified further based on cerebrospinal fluid (CSF) AD biomarker status (10 MCI biomarker positive, 9 MCI biomarker negative), and with mild AD dementia, as well as healthy controls. Comparator tests included tests of episodic memory and attention widely accepted as sensitive measures of early AD. Behavioral data were correlated with quantitative MRI measures of the hippocampus, precuneus and posterior cingulate gyrus.
4MT scores were significantly different between the two MCI groups (p = 0.001), with a test score of &#8804;8/15 associated with 100% sensitivity and 78% specificity for the classification of MCI with positive AD biomarkers, i.e., predementia AD. 4MT test scores correlated with hippocampal volume (r = 0.42) and cortical thickness of the precuneus (r = 0.55).
In conclusion, the 4MT is effective in identifying the early stages of AD. The short duration, easy application and scoring, and favorable psychometric properties of the 4MT fulfil the need for a simple but accurate diagnostic test for predementia AD.

The 4 Mountains Test: A Short Test of Spatial Memory with High Sensitivity for the Diagnosis of Pre-dementia Alzheimer&#039;s Disease

This article describes the 4 Mountains Test (4MT), a hippocampus-dependent test of working allocentric spatial memory. The hippocampus is affected early in Alzheimer's disease (AD) and this article outlines the 4MT methodology and results of patient testing, which demonstrates the value of the 4MT in the diagnosis of pre-dementia AD.

This protocol describes the administration of the 4 Mountains Test (4MT), a short test of spatial memory, in which memory for the topographical layout of ...

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

This protocol describes a modified serial reaction time (SRT) task used to study implicit motor sequence learning. Unlike the classic SRT task that involves finger-pressing movements while sitting, the modified SRT task requires participants to step with both feet while maintaining a standing posture. This stepping task necessitates whole body actions that impose postural challenges. The foot-stepping task complements the classic SRT task in several ways. The foot-stepping SRT task is a better proxy for the daily activities that require ongoing postural control, and thus may help us better understand sequence learning in real-life situations. In addition, response time serves as an indicator of sequence learning in the classic SRT task, but it is unclear whether response time, reaction time (RT) representing mental process, or movement time (MT) reflecting the movement itself, is a key player in motor sequence learning. The foot-stepping SRT task allows researchers to disentangle response time into RT and MT, which may clarify how motor planning and movement execution are involved in sequence learning. Lastly, postural control and cognition are interactively related, but little is known about how postural control interacts with learning motor sequences. With a motion capture system, the movement of the whole body (e.g., the center of mass (COM)) can be recorded. Such measures allow us to reveal the dynamic processes underlying discrete responses measured by RT and MT, and may aid in elucidating the relationship between postural control and the explicit and implicit processes involved in sequence learning. Details of the experimental set-up, procedure, and data processing are described. The representative data are adopted from one of our previous studies. Results are related to response time, RT, and MT, as well as the relationship between the anticipatory postural response and the explicit processes involved in implicit motor sequence learning.

The &quot;Motor&quot; in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

We introduce the foot-stepping serial reaction time (SRT) task. This modified SRT task, complementing the classic SRT task that involves only finger-pressing movement, better approximates daily sequenced activities and allows researchers to study the dynamic processes underlying discrete response measures and disentangle the explicit process operating in implicit sequence learning.

This protocol describes a modified serial reaction time (SRT) task used to study implicit motor sequence learning. Unlike the classic SRT task that ...

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

Investigating the interactions among multiple participants is a challenge for researchers from various disciplines, including the decision sciences and spatial cognition. With a local area network and dedicated software platform, experimenters can efficiently monitor the behavior of the participants that are simultaneously immersed in a desktop virtual environment and digitalize the collected data. These capabilities allow for experimental designs in spatial cognition and navigation research that would be difficult (if not impossible) to conduct in the real world. Possible experimental variations include stress during an evacuation, cooperative and competitive search tasks, and other contextual factors that may influence emergent crowd behavior. However, such a laboratory requires maintenance and strict protocols for data collection in a controlled setting. While the external validity of laboratory studies with human participants is sometimes questioned, a number of recent papers suggest that the correspondence between real and virtual environments may be sufficient for studying social behavior in terms of trajectories, hesitations, and spatial decisions. In this article, we describe a method for conducting experiments on decision-making and navigation with up to 36 participants in a networked desktop virtual reality setup (i.e., the Decision Science Laboratory or DeSciL). This experiment protocol can be adapted and applied by other researchers in order to set up a networked desktop virtual reality laboratory.

This paper describes a method for conducting multi-user experiments on decision-making and navigation using a networked computer laboratory.

Investigating the interactions among multiple participants is a challenge for researchers from various disciplines, including the decision sciences and ...

The Unpredictable Chronic Mild Stress Protocol for Inducing Anhedonia in Mice

Depression is a highly prevalent and debilitating condition, only partially addressed by current pharmacotherapies. The lack of response to treatment by many patients prompts the need to develop new therapeutic alternatives and to better understand the etiology of the disorder. Pre-clinical models with translational merits are rudimentary for this task. Here we present a protocol for the unpredictable chronic mild stress (UCMS) method in mice. In this protocol, adolescent mice are chronically exposed to interchanging unpredictable mild stressors. Resembling the pathogenesis of depression in humans, stress exposure during the sensitive period of mice adolescence instigates a depressive-like phenotype evident in adulthood. UCMS can be used for screenings of antidepressants on the variety of depressive-like behaviors and neuromolecular indices. Among the more prominent tests to assess depressive-like behavior in rodents is the sucrose preference test (SPT), which reflects anhedonia (core symptom of depression). The SPT will also be presented in this protocol. The ability of UCMS to induce anhedonia, instigate long-term behavioral deficits and enable reversal of these deficits via chronic (but not acute) treatment with antidepressants strengthens the protocol&#39;s validity compared to other animal protocols for inducing depressive-like behaviors.

Here we present the unpredictable chronic mild stress protocol in mice. This protocol induces a long-term depressive-like phenotype and enables to assess the efficacy of putative antidepressants in reversing the behavioral and neuromolecular depressive-like deficits.

Depression is a highly prevalent and debilitating condition, only partially addressed by current pharmacotherapies. The lack of response to treatment by ...

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 MCI tests are time-consuming such that indiscriminate massive screening would not be cost-effective. Here, we describe a protocol that uses machine learning techniques to rapidly select candidates for further screening via a question-based MCI test. This minimizes the number of resources required for screening because only patients who are potentially MCI positive are tested further.
This methodology was applied in an initial MCI research study that formed the starting point for the design of a selective screening decision tree. The initial study collected many demographic and lifestyle variables as well as details about patient medications. The Short Portable Mental Status Questionnaire (SPMSQ) and the Mini-Mental State Examination (MMSE) were used to detect possible cases of MCI. Finally, we used this method to design an efficient process for classifying individuals at risk of MCI. This work also provides insights into lifestyle-related factors associated with MCI that could be leveraged in the prevention and early detection of MCI among elderly populations.

This methodology produces decision trees that target population groups more prone to suffering from mild cognitive impairment and are useful for cost-effective selective screening of the disease.

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