Parts of this work have been supported by the German Research Foundation (DFG) via grants 1515/1-2 and 1515/6-1 to Ingrid Scharlau.

NOTE: Some steps in this protocol can be accomplished using custom software provided (along with installation instructions) at http://groups.upb.de/viat/TVATOJ. In the protocol, this collection of programs and scripts is referred to as &#8220;TVATOJ&#8221;.
1. Selection of Stimulus Material
<ol>
	<li>Select stimuli according to the research question. 
	NOTE: In general, two targets are shown at different locations on the screen. Stimuli that have been used with the present method include, for example, shapes, digits, letters, singletons in pop-out displays, and objects in natural images. The latter three types were used in this protocol. 
	NOTE: Several different stimulus types are included in the TOJ plugin (&#34;psylab_toj_stimulus&#34; provided with TVATOJ) for the experiment builder OpenSesame12.</li>
	<li>When creating new stimulus types, make sure that the properties of interest have to be encoded for the judgment by making them important for the task or select stimuli where the properties of interest are encoded automatically (e.g., singletons in pop-out displays).</li>
</ol>
2. Power Estimation and Planning
<ol>
	<li>Perform a Bayesian power analysis by simulating data sets with the selected model, planned design (SOA distribution and repetitions), sample sizes, and hypothesized parameters. Estimate whether it is likely to reach the research goal (for instance, a certain difference in the parameters). If the power is not sufficient, alter the design by adding or shifting SOAs or repetitions and repeat the analysis.
	<ol>
		<li>To use the provided TVATOJ software, open and edit the script &#34;exp1-power.R&#34;. Follow the comments in the file to adjust it for the specific analysis. For general information on Bayesian power estimation refer to Kruschke13.</li>
	</ol>
	</li>
</ol>
3. Specification or Programming of the Experiment
<ol>
	<li>Use an experiment builder or psychophysical presentation library to implement the experiment.
	<ol>
		<li>To use the OpenSesame TOJ plugin provided in TVATOJ, drag the &#34;psylab_toj_stimulus&#34; plugin into a trial presentation loop. Alternatively, open the &#34;simple-toj.osexp&#34; example experiment in OpenSesame.</li>
		<li>Select the desired stimulus type from the dropdown menu &#34;Stimulus type&#34; in the psylab_toj_stimulus configuration. Follow the instructions in TVATOJ for adding new stimulus types if required.</li>
	</ol>
	</li>
	<li>Specify the trials as described in the following steps.
	<ol>
		<li>For every experimental condition, create trials with the planned SOAs. When using the psylab_toj_stimulus plugin and OpenSesame, add all varied factors as variables to the trial loop (e.g. &#34;SOA&#34;).</li>
		<li>Add rows to the table to realize all factor combinations (e.g., seven SOAs, from -100 to 100 msec, crossed with the experimental conditions &#34;attention&#34; and &#34;neutral&#34;). Adjust the loop&#39;s &#34;Repeat&#34; attribute to create sufficient repetitions (see protocol step 2 for determining the distribution and repetition of SOAs). 
		NOTE: Typically, at most 800 trials can be presented within one hour. If more repetitions are needed, consider splitting the experiment into several sessions. Make sure that the &#34;Order&#34; attribute of the loop is set to &#34;Random&#34; before running the experiment.</li>
		<li>In the psylab_toj_stimulus plugin configuration, add placeholders (e.g. &#34;[SOA]&#34;) for the varied factors in the respective fields. Enter constant values in the fields of factors that are not varied. 
		NOTE: Before running the experiment, make sure that accurate timing is guaranteed. If appropriate timing behavior of newer monitors was not verified, use CRT monitors and synchronize with the vertical retrace signal12.</li>
	</ol>
	</li>
</ol>
4. Experimental Procedure
<ol>
	<li>Welcoming and briefing of the participants
	<ol>
		<li>Welcome the participants and inform them about the general form of the experiment (computer-based perception experiment). Inform the participants about the prospective duration of the experiment. Obtain the participants&#39; informed consent to participate in the experiment.</li>
		<li>Ensure that the participants show normal or corrected-to-normal vision (optimally by conducting short vision tests). Some deficits, such as color blindness, may be tolerable if they do not interfere with the research question for the particular type of stimulus material.</li>
		<li>Provide a quiet booth where the experiment is conducted. Adjust the chair, chin rest, keyboard position, and so on, to ensure optimal viewing and response conditions for the experiment.</li>
		<li>Make the participants aware that the experiment requires attention and mental focus and can be fatiguing. Ask them to take short breaks when required. It is, however, equally important not to perform these simple tasks under strong attentional strain. Tell the participants that it is okay to make some errors.</li>
	</ol>
	</li>
	<li>Instruction and warm-up
	<ol>
		<li>Present onscreen instructions for the task, detailing the presentation sequence and response collection procedure. Inform the participants that the task is to report the order in which the targets arrived, and that this will be difficult in some trials. Ask the participants to report their first impression when they cannot tell the order for certain, and let them guess if they have no such impression at all. 
		NOTE: In the binary TOJs used here, there is no option to indicate the perception of simultaneity. To avoid excessive guessing, do not point out the presence of trials with simultaneously presented targets explicitly. Let these simply be difficult trials with the instructions outlined above.</li>
		<li>To avoid eye movements during the trials, ask participants to fixate a mark which is shown in the center of the screen. Ask them to rest their head on a chin rest.</li>
		<li>Ask the participants to take short breaks if necessary. Let them know when breaks are allowed and when they must be avoided (e.g., during the target presentation and before the response).</li>
		<li>Include a short training in which participants can get used to the task. To this end, present a random subset of the experimental trials (see protocol step 3.2). 
		NOTE: Because the task itself is rather simple, ten to twenty trials are usually sufficient. It can be advantageous to increase the participants&#39; confidence in their performance in this task. This can be done by slowing down the presentation and providing feedback.</li>
		<li>Obtain the participants&#39; confirmation that they have understood the task (let them explain it) and that they have no further questions.</li>
	</ol>
	</li>
	<li>Running the main experiment
	<ol>
		<li>Let the experimental software start with the presentation of the main trials. Leave the booth for the main experiment.</li>
	</ol>
	</li>
</ol>
5. Model-based Analysis of the TOJ Data
<ol>
	<li>Convert the raw data files into counts of &#34;probe first&#34; judgments for every SOA. For instance, run the script &#34;os2toj.py&#34; provided with TVATOJ.</li>
	<li>Run the Bayesian estimation procedure to estimate the main parameters wp and C, the derived ones vp and vr and the differences of the parameters. For this purpose, run the script &#34;run-evaluation.R&#34; after editing it according to the instructions provided in the file.</li>
	<li>When the sampling has finished, the differences of interest for the research questions can be assessed. Examples can be found in the following section.</li>
</ol>

<ol>
	<li>Posner, M. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Orienting+of+attention.">Orienting of attention.</a> Quarterly Journal of Experimental Psychology. 32 (1), 3-25 (1980).</li><li>Van der Heijden, A., Wolters, G., Groep, J., Hagenaar, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-letter+recognition+accuracy+benefits+from+advance+cuing+of+location.">Single-letter recognition accuracy benefits from advance cuing of location.</a> Perception &amp; Psychophysics. 42 (5), 503-509 (1987).</li><li>Bundesen, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+theory+of+visual+attention.">A theory of visual attention.</a> Psychological Review. 97 (4), 523-547 (1990).</li><li>Bublak, P., 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=Staged+decline+of+visual+processing+capacity+in+mild+cognitive+impairment+and+Alzheimer's+disease.">Staged decline of visual processing capacity in mild cognitive impairment and Alzheimer's disease.</a> Neurobiology of Aging. 32 (7), 1219-1230 (2011).</li><li>Petersen, A., Kyllingsb&#230;k, S., Bundesen, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measuring+and+modeling+attentional+dwell+time.">Measuring and modeling attentional dwell time.</a> Psychonomic Bulletin &amp; Review. 19 (6), 1029-1046 (2012).</li><li>Vroomen, J., Keetels, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Perception+of+intersensory+synchrony:+A+tutorial+review.">Perception of intersensory synchrony: A tutorial review.</a> Attention, Perception, &amp; Psychophysics. 72 (4), 871-884 (2010).</li><li>T&#252;nnermann, J., Petersen, A., Scharlau, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Does+attention+speed+up+processing?+Decreases+and+increases+of+processing+rates+in+visual+prior+entry.">Does attention speed up processing? Decreases and increases of processing rates in visual prior entry.</a> Journal of Vision. 15 (3), 1-27 (2015).</li><li>Kr&#252;ger, A., T&#252;nnermann, J., Scharlau, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fast+and+conspicuous?+Quantifying+salience+with+the+Theory+of+Visual+Attention.">Fast and conspicuous? Quantifying salience with the Theory of Visual Attention.</a> Advances in Cognitive Psychology. 12 (1), 20 (2016).</li><li>Bundesen, C., Habekost, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Principles of Visual Attention: Linking Mind and Brain. , (2008).</li><li>Plummer, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=JAGS:+A+program+for+analysis+of+Bayesian+graphical+models+using+Gibbs+sampling.">JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling.</a> Proceedings of the 3rd international workshop on distributed statistical computing. , 124-125 (2003).</li><li>Kruschke, J. K., Vanpaemel, W., Busemeyer, J., Townsend, J., Wang, Z. J., Eidels, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bayesian+estimation+in+hierarchical+models.">Bayesian estimation in hierarchical models.</a> The Oxford Handbook of Computational and Mathematical Psychology. , 279-299 (2015).</li><li>Math&#244;t, S., Schreij, D., Theeuwes, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=OpenSesame:+An+open-source,+graphical+experiment+builder+for+the+social+sciences.">OpenSesame: An open-source, graphical experiment builder for the social sciences.</a> Behavior Research Methods. 44 (2), 314-324 (2012).</li><li>Kruschke, J. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. , (2015).</li><li>Rensink, R. A., O'Regan, J. K., Clark, J. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=To+see+or+not+to+see:+The+need+for+attention+to+perceive+changes+in+scenes.">To see or not to see: The need for attention to perceive changes in scenes.</a> Psychological Science. 8 (5), 368-373 (1997).</li><li>T&#252;nnermann, J., Kr&#252;ger, N., Mertsching, B., Mustafa, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Affordance+estimation+enhances+artificial+visual+attention:+Evidence+from+a+change-blindness+study.">Affordance estimation enhances artificial visual attention: Evidence from a change-blindness study.</a> Cognitive Computation. 7 (5), 525-538 (2015).</li><li>Shore, D. I., Klein, R. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+effects+of+scene+inversion+on+change+blindness.">The effects of scene inversion on change blindness.</a> The Journal of General Psychology. 127 (1), 27-43 (2000).</li><li>Scharlau, I., Neumann, O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Temporal+parameters+and+time+course+of+perceptual+latency+priming.">Temporal parameters and time course of perceptual latency priming.</a> Acta Psychologica. 113 (2), 185-203 (2003).</li><li>Schneider, K. A., Bavelier, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Components+of+visual+prior+entry.">Components of visual prior entry.</a> Cognitive Psychology. 47 (4), 333-366 (2003).</li><li>Scharlau, I., Neumann, O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Perceptual+latency+priming+by+masked+and+unmasked+stimuli:+Evidence+for+an+attentional+interpretation.">Perceptual latency priming by masked and unmasked stimuli: Evidence for an attentional interpretation.</a> Psychological Research. 67 (3), 184-196 (2003).</li><li>Shore, D. I., Spence, C., Klein, R. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Visual+prior+entry.">Visual prior entry.</a> Psychological Science. 12 (3), 205-212 (2001).</li><li>Alcal&#225;-Quintana, R., Garc&#237;a-P&#233;rez, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fitting+model-based+psychometric+functions+to+simultaneity+and+temporal-order+judgment+data:+MATLAB+and+R+routines.">Fitting model-based psychometric functions to simultaneity and temporal-order judgment data: MATLAB and R routines.</a> Behavior Research Methods. 45 (4), 972-998 (2013).</li><li>Hoffman, M. D., Gelman, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+No-U-turn+sampler:+adaptively+setting+path+lengths+in+Hamiltonian+Monte+Carlo.">The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo.</a> Journal of Machine Learning Research. 15 (1), 1593-1623 (2014).</li><li>Lee, M. D., Wagenmakers, E. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Bayesian cognitive modeling: A practical course. , (2014).</li><li>Vangkilde, S., Bundesen, C., Coull, J. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prompt+but+inefficient:+Nicotine+differentially+modulates+discrete+components+of+attention.">Prompt but inefficient: Nicotine differentially modulates discrete components of attention.</a> Psychopharmacology. 218 (4), 667-680 (2011).</li><li>T&#252;nnermann, J., Scharlau, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Peripheral+Visual+Cues:+Their+Fate+in+Processing+and+Effects+on+Attention+and+Temporal-order.">Peripheral Visual Cues: Their Fate in Processing and Effects on Attention and Temporal-order.</a> Front. Psychol. 7 (1442), (2016).</li></ol>

The authors have nothing to disclose.

The protocol in this article describes how to conduct simple TOJs and fit the data with models based on fundamental stimulus encoding. Three experiments demonstrated how the results can be evaluated in a hierarchical Bayesian estimation framework to assess the influence of attention in highly different stimulus material. Salience in pop-out displays led to increased attentional weights. Also, increased weights were estimated for action space objects in natural images. However, due to the persisting advantage when spatial...

How attention is distributed in space and time is one of the most important factors in human visual perception. Objects that capture attention because of their conspicuity or importance are typically processed faster and with higher accuracy. In behavioral research, such performance benefits have been demonstrated in a variety of experimental paradigms. For instance, allocating attention to the target location speeds up the reaction in probe detection tasks1. Similarly, the accuracy of reporting letters is improved by attention2. Such findings prove that attention enhances processing, but they remain hopelessly mute about how this enhancement is established.
The present paper shows that low-level mechanisms behind attentional advantages can be assessed by measuring the processing speed of individual stimuli in a model-based framework that relates the measurements to fine-grained components of attention. With such a model, the overall processing capacity and its distribution among the stimuli can be inferred from processing speed measurements.
Bundesen&#39;s Theory of Visual Attention (TVA)3 provides a suitable model for this endeavor. It is typically applied to data from letter report tasks. In the following, the fundamentals of TVA are explained and it is shown how they can be extended to model temporal-order judgment (TOJ) data obtained with (almost) arbitrary stimuli. This novel method provides estimates of processing speed and resource distribution which can be readily interpreted. The protocol in this article explains how to plan and conduct such experiments and details how the data can be analyzed.
As mentioned above, the usual paradigm in TVA-based modeling and estimation of attention parameters is the letter report task. Participants report the identities of a set of letters which is briefly flashed and typically masked after a varying delay. Among other parameters, the rate at which visual elements are encoded into visual short-term memory can be estimated. The method has been successfully applied to questions in fundamental and clinical research. For instance, Bublak and colleagues4 assessed which attentional parameters are affected in different stages of age-related cognitive deficits. In fundamental attention research, Petersen, Kyllingsb&#230;k, and Bundesen5 used TVA to model the attentional dwell time effect, the observer&#39;s difficulty in perceiving the second of two targets at certain time intervals. A major drawback of the letter report paradigm is that it requires sufficiently overlearned and maskable stimuli. This requirement limits the method to letters and digits. Other stimuli would require heavy training of participants.
The TOJ paradigm requires neither specific stimuli nor masking. It can be used with any kind of stimuli for which the order of appearance can be judged. This extends the stimulus range to pretty much everything that could be of interest, including direct cross-modal comparisons6.
Investigating attention with TOJs is based on the phenomenon of attentional prior entry which is a measure of how much earlier an attended stimulus is perceived compared to an unattended one. Unfortunately, the usual method for analyzing TOJ data, fitting observer performance psychometric functions (such as cumulative Gaussian or logistic functions), cannot distinguish whether attention increases the processing rate of the attended stimulus or if it decreases the rate of the unattended stimulus7. This ambiguity is a major problem because the question whether the perception of a stimulus is truly enhanced or if it benefits because of the withdrawal of resources from a competing stimulus is a question of both fundamental and practical relevance. For instance, for the design of human-machine interfaces it is highly relevant to know if increasing the prominence of one element works at the expense of another one.
The TOJ task usually proceeds as follows: A fixation mark is presented for a brief delay, typically a randomly drawn interval shorter than a second. Then, the first target is presented, followed after a variable stimulus onset asynchrony (SOA) by the second target. At negative SOAs, the probe, the attended stimulus, is shown first. At positive SOAs, the reference, the unattended stimulus, leads. At an SOA of zero, both targets are shown simultaneously.
Typically, presenting the target refers to switching the stimulus on. Under certain conditions, however, other temporal events, such as a flicker of an already present target or offsets are used8.
In TOJs, responses are collected in an unspeeded manner, usually by keys mapped to the stimulus identities and presentation orders (e.g., if stimuli are squares and diamonds, one key indicates &#34;square first&#34; and another one &#34;diamond first&#34;). Importantly, for the evaluation, these judgments must be converted to &#34;probe first&#34; (or &#34;reference first&#34;) judgments.
In the present work, a combination of the processing model of TVA and the TOJ experimental paradigm is used to eliminate the problems in either individual domain. With this method, readily interpretable speed parameters can be estimated for almost arbitrary visual stimuli, enabling to infer how the observer&#39;s attention is allocated to competing visual elements.
The model is based on TVA&#39;s equations for the processing of individual stimuli, which will be shortly explained in the following. The probability that one stimulus is encoded into visual short-term memory before the other is interpreted as the probability of judging this stimulus as appearing first. The individual encoding durations are exponentially distributed9:
<img alt="Equation 1" src="/files/ftp_upload/54856/54856eq1.jpg" /> (1)
The maximum ineffective exposure duration t0 is a threshold before which nothing is encoded at all. According to TVA, the rate vx,i at which object x is encoded as member of a perceptual category i (such as color or a shape) is given by the rate equation,
<img alt="Equation 2" src="/files/ftp_upload/54856/54856eq2.jpg" />&#160;. (2)
The strength of the sensory evidence that x belongs to category i is expressed in &#951;x,i, and &#946;i is a decision bias for categorizing stimuli as members of category i. This is multiplied by attentional weights. Individual attentional weights wx are divided by the attentional weights of all objects in the visual field. Hence, the relative attentional weight is calculated as
<img alt="Equation 3" src="/files/ftp_upload/54856/54856eq3.jpg" />&#160; (3)
where R represents all categories and &#951;x,i represents the sensory evidence that object x belongs to category j. The value &#960;j is called pertinence of category j and reflects a bias to make categorizations in j. The overall processing capacity C is the sum of all processing rates for all stimuli and categorizations. For a more detailed description of TVA, refer to Bundesen and Habekost&#39;s book9.
In our novel method, Equation 1, which describes the encoding of individual stimuli, is transformed into a model of TOJs. Assuming that selection biases and report categories are constant within an experimental task, the processing rates vp and vr of the two target stimuli probe (p) and reference (r) depend on C and the attentional weights in the form vp= C &#183; wp and vr = C &#183; wr, respectively. The new TOJ model expresses the success probability Pp1st that a participant judges the probe stimulus to be first as a function of the SOA and the processing rates. It can be formalized as follows:
<img alt="Equation 4" src="/files/ftp_upload/54856/54856eq4.jpg" /> (4)
A more detailed description of how this equation is derived from the basic TVA equations is described by T&#252;nnermann, Petersen, and Scharlau7.
For the sake of simplicity, the parameter t0 is omitted in the model in Equation 1. According to the original TVA, t0 should be identical for both targets in the TOJ task, and, therefore, it cancels out. However, this assumption may sometimes be violated (see section Discussion).
For fitting this equation to TOJ data, a hierarchical Bayesian estimation scheme11 is suggested. This approach allows to estimate the attentional weights wp and wr of the probe and reference stimuli and the overall processing rate C. These parameters, the resulting uptake rates vp and vr, and attention-induced differences between them, can be assessed on the subject and group levels along with estimated uncertainties. The hierarchical model is illustrated in Figure 1. During the planning stage for an experiment, convenient Bayesian power analysis can be conducted.
The following protocol describes how to plan, execute and analyze TOJ experiments from which processing speed parameters and attentional weights for visual stimuli can be obtained. The protocol assumes that the researcher is interested in how an attentional manipulation influences the processing speeds of some targets of interest.
<img alt="Figure 1" src="/files/ftp_upload/54856/54856fig1.jpg" />
Figure 1: Graphical model used in the Bayesian estimation procedure. Circles indicate estimated distributions; double circles indicate deterministic nodes. Squares indicate data. The relations are given on the right side of the figure. The nodes outside the rounded frames (&#8220;plates&#8221;) represent mean and dispersion estimates of TVA parameters (see Introduction) on the group level. In the &#8220;j Subjects&#8221; plate, it can be seen how attentional weights (w) are combined with the overall processing rates (C) to from stimulus processing rates (v) on the subject level. Plate &#8220;i SOAs&#8221; shows how these TVA parameters are then transformed (via the function Pp1st described in the Introduction) into the success probability (&#952;) for the binomially distributed responses at each SOA. Therefore, the &#952; together with the repetitions of the SOA (n) describe the data points (y). For more details on notation and interpretation of graphical models, refer to Lee and Wagenmakers23. Note that for the sake of clarity, the nodes that represent differences of parameters have been omitted. These deterministic parameters are indicated in the figures of the experimental results instead. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/54856/54856fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

<table border="0" cellpadding="0" cellspacing="0" width="336">
	<tbody>
		<tr height="17">
			<td height="17" style="height:17px;width:336px;">Personal Computer</td>
		</tr>
		<tr height="17">
			<td height="17" style="height:17px;">(Open Source) Experimentation and evaluation software</td>
		</tr>
	</tbody>
</table>

measuring attention and visual processing speed by model-based analysis of temporal-order judgments

This protocol describes how to conduct temporal-order experiments to measure visual processing speed and the attentional resource distribution. The proposed method is based on a new and synergistic combination of three components: the temporal-order judgments (TOJ) paradigm, Bundesen&#39;s Theory of Visual Attention (TVA), and a hierarchical Bayesian estimation framework. The method provides readily interpretable parameters, which are supported by the theoretical and neurophysiological underpinnings of TVA. Using TOJs, TVA-based estimates can be obtained for a broad range of stimuli, whereas traditional paradigms used with TVA are mainly limited to letters and digits. Finally, the meaningful parameters of the proposed model allow for the establishment of a hierarchical Bayesian model. Such a statistical model allows assessing results in one coherent analysis both on the subject and the group level.
To demonstrate the feasibility and versatility of this new approach, three experiments are reported with attention manipulations in synthetic pop-out displays, natural images, and a cued letter-report paradigm.

In the following, results obtained with the proposed method are reported. Three experiments measured the influence of different attentional manipulations with three highly different types of stimulus material. The stimuli are simple line segments in pop-out patterns, action space objects in natural images, and cued letter targets.
Experiment 1: Salience in pop-out displays 
Experiment 1 aimed at measuring the influence of v...

Watch this Scientific Journal Video about Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments at JoVE.com

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Temporal-order judgments can be used to estimate processing speed parameters and attentional weights and thereby to infer the mechanisms of attentional processing. This methodology can be applied to a wide range of visual stimuli and works with many attention manipulations.

This protocol describes how to conduct temporal-order experiments to measure visual processing speed and the attentional resource distribution. The ...

measuring-attention-visual-processing-speed-model-based-analysis

Research

JoVE Journal

Behavior

9.8K Views.  Paderborn University. Temporal-order judgments can be used to estimate processing speed parameters and attentional weights and thereby to infer the mechanisms of attentional processing. This methodology can be applied to a wide range of visual stimuli and works with many attention manipulations.

Video: Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

The 5-Choice Serial Reaction Time Task: A Task of Attention and Impulse Control for Rodents

This protocol describes the 5-choice serial reaction time task, which is an operant based task used to study attention and impulse control in rodents. Test day challenges, modifications to the standard task, can be used to systematically tax the neural systems controlling either attention or impulse control. Importantly, these challenges have consistent effects on behavior across laboratories in intact animals and can reveal either enhancements or deficits in cognitive function that are not apparent when rats are only tested on the standard task. The variety of behavioral measures that are collected can be used to determine if other factors (i.e., sedation, motivation deficits, locomotor impairments) are contributing to changes in performance. The versatility of the 5CSRTT is further enhanced because it is amenable to combination with pharmacological, molecular, and genetic techniques.

This protocol describes the 5-choice serial reaction time task, which is an operant based task used to study attention and impulse control in rodents. Test day challenges, which are modifications of the standard task, increase flexibility of the task and can be combined with other manipulations to more fully characterize behavior.

This protocol describes the 5-choice serial reaction time task, which is an operant based task used to study attention and impulse control in rodents. ...

Topographical Estimation of Visual Population Receptive Fields by fMRI

Visual cortex is retinotopically organized so that neighboring populations of cells map to neighboring parts of the visual field. Functional magnetic resonance imaging allows us to estimate voxel-based population receptive fields (pRF), i.e., the part of the visual field that activates the cells within each voxel. Prior, direct, pRF estimation methods1 suffer from certain limitations: 1) the pRF model is chosen a-priori and may not fully capture the actual pRF shape, and 2) pRF centers are prone to mislocalization near the border of the stimulus space. Here a new topographical pRF estimation method2 is proposed that largely circumvents these limitations. A linear model is used to predict the Blood Oxygen Level-Dependent (BOLD) signal by convolving the linear response of the pRF to the visual stimulus with the canonical hemodynamic response function. PRF topography is represented as a weight vector whose components represent the strength of the aggregate response of voxel neurons to stimuli presented at different visual field locations. The resulting linear equations can be solved for the pRF weight vector using ridge regression3, yielding the pRF topography. A pRF model that is matched to the estimated topography can then be chosen post-hoc, thereby improving the estimates of pRF parameters such as pRF-center location, pRF orientation, size, etc. Having the pRF topography available also allows the visual verification of pRF parameter estimates allowing the extraction of various pRF properties without having to make a-priori assumptions about the pRF structure. This approach promises to be particularly useful for investigating the pRF organization of patients with disorders of the visual system.

It is important to obtain unbiased estimates of visual population receptive fields (pRFs) by functional magnetic resonance imaging. We use mild regularization constraints to estimate pRF topography without a-priori assumptions about pRF shape, allowing us to choose specific pRF models post-hoc. This is particularly advantageous in subjects with visual-pathway lesions.

Visual cortex is retinotopically organized so that neighboring populations of cells map to neighboring parts of the visual field. Functional magnetic ...

Methods to Test Visual Attention Online

Online data collection methods have particular appeal to behavioral scientists because they offer the promise of much larger and much more representative data samples than can typically be collected on college campuses. However, before such methods can be widely adopted, a number of technological challenges must be overcome &#8211; in particular in experiments where tight control over stimulus properties is necessary. Here we present methods for collecting performance data on two tests of visual attention. Both tests require control over the visual angle of the stimuli (which in turn requires knowledge of the viewing distance, monitor size, screen resolution, etc.) and the timing of the stimuli (as the tests involve either briefly flashed stimuli or stimuli that move at specific rates). Data collected on these tests from over 1,700 online participants were consistent with data collected in laboratory-based versions of the exact same tests. These results suggest that with proper care, timing/stimulus size dependent tasks can be deployed in web-based settings.

To replicate laboratory settings, online data collection methods for visual tasks require tight control over stimulus presentation. We outline methods for the use of a web application to collect performance data on two tests of visual attention.

Online data collection methods have particular appeal to behavioral scientists because they offer the promise of much larger and much more representative ...

Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control

Attention control is the ability to selectively attend to some sensory signals while ignoring others. This ability is thought to involve two processes: enhancement of sensory signals that are to be attended and the attenuation of sensory signals that are to be ignored. The overall strength of attentional modulation is often measured by comparing the amplitude of a sensory neural response to an external input when attended versus when ignored. This method is robust for detecting attentional modulation, but precludes the ability to assess the separate dynamics of attending and ignoring processes. Here, we describe methodology to measure independently the neurophysiological signals of attending and ignoring using the intermodal attention task (IMAT). This task, when combined with electroencephalography, isolates neurophysiological sensory responses in auditory and visual modalities, when either attending or ignoring, with respect to a passive control. As a result, independent dynamics of attending and of a ignoring can be assessed in either modality. Our results using this task indicate that the timing and cortical sources of attending and ignoring effects differ, as do their contributions to the attention modulation effect, pointing to unique neural trajectories and demonstrating sample utility of measuring them separately.

Attention control comprises enhancement of target signals and attenuation of distractor signals. We describe an approach to measure separately but concurrently, the neurophysiology of attending and ignoring in sustained intermodal attention, utilizing a passive control condition during which neither process is continuously engaged.

Attention control is the ability to selectively attend to some sensory signals while ignoring others. This ability is thought to involve two processes: ...

A Method to Quantify Visual Information Processing in Children Using Eye Tracking

Visual problems that occur early in life can have major impact on a child's development. Without verbal communication and only based on observational methods, it is difficult to make a quantitative assessment of a child's visual problems. This limits accurate diagnostics in children under the age of 4 years and in children with intellectual disabilities. Here we describe a quantitative method that overcomes these problems. The method uses a remote eye tracker and a four choice preferential looking paradigm to measure eye movement responses to different visual stimuli. The child sits without head support in front of a monitor with integrated infrared cameras. In one of four monitor quadrants a visual stimulus is presented. Each stimulus has a specific visual modality with respect to the background, e.g., form, motion, contrast or color. From the reflexive eye movement responses to these specific visual modalities, output parameters such as reaction times, fixation accuracy and fixation duration are calculated to quantify a child's viewing behavior. With this approach, the quality of visual information processing can be assessed without the use of communication. By comparing results with reference values obtained in typically developing children from 0-12 years, the method provides a characterization of visual information processing in visually impaired children. The quantitative information provided by this method can be advantageous for the field of clinical visual assessment and rehabilitation in multiple ways. The parameter values provide a good basis to: (i) characterize early visual capacities and consequently to enable early interventions; (ii) compare risk groups and follow visual development over time; and (iii), construct an individual visual profile for each child.

A method is described to quantify the quality of visual information processing based on reflexive eye movements in response to specific visual modalities. Reaction times and fixation output parameters are used to characterize visual performance in children with and without visual impairments from 6 months of age.

Visual problems that occur early in life can have major impact on a child's development. Without verbal communication and only based on observational ...

Intracortical Inhibition Within the Primary Motor Cortex Can Be Modulated by Changing the Focus of Attention

It is well recognized that an external focus (EF) compared with an internal focus (IF) of attention improves motor learning and performance. Studies have indicated benefits in accuracy, balance, force production, jumping performance, movement speed, oxygen consumption, and fatiguing task. Although behavioral outcomes of using an EF strategy are well explored, the underlying neural mechanisms remain unknown. A recent TMS study compared the activity of the primary motor cortex (M1) between an EF and an IF. More precisely, this study showed that, when adopting an EF, the activity of intracortical inhibitory circuits is enhanced.
On the behavioral level, the present protocol tests the influence of attentional foci on the time to task failure (TTF) when performing submaximal contractions of the first dorsal interosseous (FDI). Additionally, the current paper describes two TMS protocols to assess the influence of attentional conditions on the activity of cortical inhibitory circuits within the M1. Thus, the present article describes how to use single-pulse TMS at intensities below the motor threshold (subTMS) and paired-pulse TMS, inducing short-interval intracortical inhibition (SICI) when applied to the M1. As these methods are assumed to reflect the responsiveness of GABAergic inhibitory neurons, without being affected by spinal reflex circuitries, they are well suited to measuring the activity of intracortical inhibitory circuits within the M1.
The results show that directing attention externally improves motor performance, as participants were able to prolong the time to task failure. Moreover, the results were accompanied by a larger subTMS-induced electromyography suppression and SICI when adopting an EF compared to an IF. As the level of cortical inhibition within the M1 was previously demonstrated to influence motor performance, the enhanced inhibition with an EF might contribute to the better movement efficiency observed in the behavioral task, indicated by a prolonged TTF with an EF.

Using two different transcranial magnetic stimulation (TMS) protocols, this manuscript describes how to measure and compare cortical inhibition within the primary motor cortex when adopting different attentional foci.

It is well recognized that an external focus (EF) compared with an internal focus (IF) of attention improves motor learning and performance. Studies have ...

Central and Divided Visual Field Presentation of Emotional Images to Measure Hemispheric Differences in Motivated Attention

Two dominant theories on lateralized processing of emotional information exist in the literature. One theory posits that unpleasant emotions are processed by right frontal regions, while pleasant emotions are processed by left frontal regions. The other theory posits that the right hemisphere is more specialized for the processing of emotional information overall, particularly in posterior regions.
Assessing the different roles of the cerebral hemispheres in processing emotional information can be difficult without the use of neuroimaging methodologies, which are not accessible or affordable to all scientists. Divided visual field presentation of stimuli can allow for the investigation of lateralized processing of information without the use of neuroimaging technology. 
This study compared central versus divided visual field presentations of emotional images to assess differences in motivated attention between the two hemispheres. The late positive potential (LPP) was recorded using electroencephalography (EEG) and event-related potentials (ERPs) methodologies to assess motivated attention. Future work will pair this paradigm with a more active behavioral task to explore the behavioral impacts on the attentional differences found.

This study compared central versus divided visual field presentations of emotional images to assess differences in motivated attention between the two hemispheres. The late positive potential (LPP) was recorded using electroencephalography (EEG) and event-related potentials (ERPs) methodologies to assess motivated attention.

Two dominant theories on lateralized processing of emotional information exist in the literature. One theory posits that unpleasant emotions are processed ...

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

In a typical eye tracking study using the visual world paradigm, participants' eye movements to objects or pictures in the visual workspace are recorded via an eye tracker as the participant produces or comprehends a spoken language describing the concurrent visual world. This paradigm has high versatility, as it can be used in a wide range of populations, including those who cannot read and/or who cannot overtly give their behavioral responses, such as preliterate children, elderly adults, and patients. More importantly, the paradigm is extremely sensitive to fine grained manipulations of the speech signal, and it can be used to study the online processing of most topics in language comprehension at multiple levels, such as the fine grained acoustic phonetic features, the properties of words, and the linguistic structures. The protocol described in this article illustrates how a typical visual world eye tracking study is conducted, with an example showing how the online processing of some semantically complex statements can be explored with the visual world paradigm.

The visual world paradigm monitors participants' eye movements in the visual workspace as they are listening to or speaking a spoken language. This paradigm can be used to investigate the online processing of a wide range of psycholinguistic questions, including semantically complex statements, such as disjunctive statements.

In a typical eye tracking study using the visual world paradigm, participants' eye movements to objects or pictures in the visual workspace are recorded ...

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Young children's visual environments are dynamic, changing moment-by-moment as children physically and visually explore spaces and objects and interact with people around them. Head-mounted eye tracking offers a unique opportunity to capture children's dynamic egocentric views and how they allocate visual attention within those views. This protocol provides guiding principles and practical recommendations for researchers using head-mounted eye trackers in both laboratory and more naturalistic settings. Head-mounted eye tracking complements other experimental methods by enhancing opportunities for data collection in more ecologically valid contexts through increased portability and freedom of head and body movements compared to screen-based eye tracking. This protocol can also be integrated with other technologies, such as motion tracking and heart-rate monitoring, to provide a high-density multimodal dataset for examining natural behavior, learning, and development than previously possible. This paper illustrates the types of data generated from head-mounted eye tracking in a study designed to investigate visual attention in one natural context for toddlers: free-flowing toy play with a parent. Successful use of this protocol will allow researchers to collect data that can be used to answer questions not only about visual attention, but also about a broad range of other perceptual, cognitive, and social skills and their development.

Young children do not passively observe the world, but rather actively explore and engage with their environment. This protocol provides guiding principles and practical recommendations for using head-mounted eye trackers to record infants' and toddlers' dynamic visual environments and visual attention in the context of natural behavior.

Young children's visual environments are dynamic, changing moment-by-moment as children physically and visually explore spaces and objects and interact ...

Spotlighting Customers' Visual Attention at the Stock, Shelf and Store Levels with the 3S Model

Several models of the in-store search process exist in the fields of retailing, marketing, and consumer-based research. The present article presents a new conceptualization of this search process, which captures customers&#8217; visual attention at three distinct levels of analysis: Stock, Shelf, and Store. We refer to this conceptualization as the 3S Model and illustrate its usefulness through three eye-tracking studies, one from each level of analysis. Our experimental examples, which range from manipulating certain stimuli on a single product (e.g., the placement of textual and pictorial packaging elements) to manipulating the entire shopping trip for customers during their stay in a store (e.g., through more or less specific shopping tasks), highlight the broad applicability of this alternative approach for understanding customers&#8217; in-store search behavior. Thus, our model can be seen as a helpful tool for researchers interested in how to conduct experimental eye-tracking studies that shed light on the perceptual processes preceding product choices and purchase decisions. The 3S Model is equally suitable in controlled lab conditions and under ecologically valid settings in the real retail environment. Furthermore, it can be used from the micro level, with a focus on the meaningful metrics on a particular product, through the intermediate level, with the emphasis on the area surrounding products in shelves and other in-store spaces, all the way to the macro level, examining customers&#8217; navigational paths throughout a store as a function of their shopping tasks, cognitive capacity, or ability to acquire in-store information.

This article presents a new conceptualization of the in-store search process, the 3S Model, which captures customers&#8217; visual attention at three distinct levels of analysis: Stock, Shelf, and Store. We illustrate the usefulness of our conceptualization through three eye-tracking studies, one from each level of analysis in the 3S Model.

Several models of the in-store search process exist in the fields of retailing, marketing, and consumer-based research. The present article presents a new ...