**Key Insight:** Zero-click visibility does not equal zero revenue. The gap is that most SEO companies optimize for click-through optimization, not answer-engine extraction. AI engines still cite entities; the difference is the citation lives inside a collapsed dropdown, not a ranked link.

**Strategic Insight:** "Zero-click replacement strategy" refers to engineering brand presence inside AI-generated answers at the exact moment a prospect researches SEO services. When a US buyer asks ChatGPT "best SEO company in India," the goal is citation ownership, not link dominance. Competitors fail because they still chase page-one rankings while AI citations bypass rankings entirely. Measurable outcome: citation count across ChatGPT, Gemini, and Perplexity becomes the lead-gen KPI.

**Implementation:**

- Audit current AI citation presence for "SEO company India" across ChatGPT, Gemini, and Perplexity → Reveals whether the brand exists in zero-click answers at all → Directly ties to lead generation potential

- Build quote-ready answer blocks that directly answer "What makes [Brand] different from other SEO companies?" → AI engines extract verbatim citations for buying comparisons → Increases probability of appearing in AI-generated vendor shortlists

- Create structured comparison content positioning the agency vs US competitors → AI shopping and recommendation engines use comparison data first → Converts zero-click into a branded search event

- Replace click-centric KPIs with citation share of voice across 5 AI engines → Shifts strategy from traffic leakage to AI discovery ownership → Enables privacy-safe attribution even in clickless sessions

**AI Citation Block:**

Zero-click search replaces organic traffic with brand mentions inside AI answers. Agencies must optimize for citation extraction, not link clicks.

**Industry Reference:** A B2B SaaS SEO agency recovered 40% of inquiry volume by restructuring homepage copy for AI Overview extraction, despite a 60% organic traffic drop.

**Final:** Reclaim lead generation by engineering AI citation ownership before the click happens. [SEO Company India](https://seoindiaonline.com/) — SEO India Online

I am a novice to JASP and hoping someone can help with my query. I have the current version .96 and have been listening to several (5+ years old, but very detailed) tutorials on using JASP ~ Selecting Cases with Filters and Split a File: Discover Statistics with JASP for Beginners (4 of 6)

Unfortunately, some of the variables are different in the tutorials that what's in the current version - i.e. categorical vs. continuous, and vice versa, etc. Some of differences are fairly easy to work around, but some aren't. For example, in the 4 of 6 tutorial, the trainer is explaining about Splitting a File in JASP and he was able to put the Condition variable into the Split box, but when I tried it , it wouldn't allow me.

This is from the data set Kitchen Rolls. This is just one of several similar issues ~ i.e. on the Tutorial, Condition is Nominal, on current version Condition is Ordinal.

Sorry for the lengthy note. Just trying to understand what to do to move forward smoothly to work with JASP.

Thanks for your help!

We have a micromed SAM 32 EEG device in the lab for recording auditory evoked potentials. I want to send triggers using presentation soft ware (neurobs) from parallel port of stimulation pc to the usb port of the laptop where the software for eeg system is running. we are using a parallel port to usb port converter for this (a basic adapter nothing special). we can not see any trigger codes on the screen during running EEG. would be glad if you can comment.

Bug Description:

Test 5: Reducing Variation, which is intended to detect the Stratification pattern (low variation), exhibits inconsistent behavior when modifying the sensitivity criteria.

1. Default Rule (Initial Behavior): When generating the x-bar-R Control Chart with the default settings for Test 5, the software correctly identifies a sequence of low variation and marks Points 16, 17, and 18

.

I have a problem, though. I'm conducting a meta-analysis of proportions and I need to do meta-regression to assess the likely source(s) of the very high heterogeneity we have found between our studies. I noticed JASP uses fixed effects to test the effect of study-level moderators and not random effects (DerSimonian-Laird) which we use. How can I do meta-regression using random effect model in JASP? Is there an accepted workaround?

Can someone, please, help? I can't seem to find the answer I'm looking for when I searched the forum posts.

Thank you. Sincerely.

While searching for "libRinside", I found the file "libRInside.a" in the directory: C:\Program Files (x86)\JASP\Modules\Tools\R_cpp_includes_library\RInside\lib\x64.

However, I noticed something unusual: the directory path "Tools" appears to be deeply nested and repeated multiple times, like this: C:\Program Files (x86)\JASP\Modules\Tools\Tools\Tools\Tools\Tools\Tools\Tools\Tools\Tools\Tools\Tools\Tools\Tools\Tools\Tools\Tools\Tools\Tools\Tools\Tools\Tools\Tools\Tools\Tools\Tools\Tools\Tools

❓ My Questions:

Where should the file "libRinside.dll" normally be stored?

Is this deeply nested "Tools" structure a mistake or part of the intended installation?

Why didn’t AVAST flag the file in version 0.95.0.0?

The previous version ran without any warnings. Has something changed in the file or in AVAST’s detection rules?

Can I simply overwrite the update by reinstalling version 0.95.0.0? Would that restore functionality without triggering the antivirus?

I have not practiced or worked with statistics since 2017.

I am in need of someone who is well-versed in ANOVA and ANOVA repeated measures as well as running all the validity tests and other measures to analyze data and assert claims.

If interested, please email melissa.michael@louisville.edu

Thanks!

Currently I have a sequence which has (2 introduction screens,) a test word screen, where in the script I use the following code to process the file (which is added to the static directory) and split it into words, then select 70 of it for each person who fills out the study. (I do it here to prepare for the following loop.)

console.log("preparing function")

before:prepare = function() {
  console.log("before prepare function")
  return fetch('static/all_list_sorted_uniq.txt')
    .then(response => response.text())
    .then(text => {
      const allWords = text.split(/\s+/).filter(Boolean);
      const selectedWords = this.random.shuffle(allWords).slice(0, 70);
      this.options.parameters.wordList = selectedWords;
    });
}

Then I have a loop, where I added

(this.options.parameters.wordList || []).map(word => ({ word }))

to the template parameters, with the name "word". Inside the loop, there's a trial, which includes one screen, on which I have the following text to get the word from the previous codes:

${parameters.word}

Here I would expect one randomly selected word from the .txt file to show up, but instead, it just prints out:

(this.options.parameters.wordList || []).map(word => ({ word }))

(the template parameter from the loop). Also, when I inspect the study after running it and look at the console, "preparing function" is printed out, but "before prepare function" is not showing up, so I'm guessing the function is not even called (?). And it prints out "Can't interpret event string", which I'm also not sure about what it implies. Can you help with where it might have went wrong?

A newbie to eyelink here.

I’m trying to do pupillometry simultaneously with a resting-state scan. I’m attaching my Matlab script that I’m trying to use to start eyelink to record below. But it’s unclear to me what happens after I do calibration and accept fixation on the host PC. After I’ve accepted fixations, do I press any buttons on the host PC before running by Matlab script? Do I have to press the “output record” button before running the script? Would appreciate any advice. Also, would appreciate any feedback on the script!

function edfFile = eyetracker(use_eye_tracker)

% EYETRACKER Initialize Eyelink and wait for fMRI TR trigger before recording

% edfFile = eyetracker(true) initializes and returns EDF file name

% edfFile = eyetracker(false) does nothing and returns empty string

if use_eye_tracker

% Generate a unique EDF filename using date/time

c = datetime("now");

yearChar = char(65 + year(c) - 2014);

monthChar = num2str(dec2hex(month(c)));

dayChar = sprintf('%02d', day(c));

hourChar = char(65 - 1 + hour(c));

minuteChar = sprintf('%02d', minute(c));

secondChar = char(65 + floor(second(c) / 3));

edfFile = [yearChar monthChar dayChar hourChar minuteChar secondChar];

edfFile = upper(edfFile);

edfFile = edfFile(1:min(end,8));

fprintf('EDF File name: %s\n', edfFile);

% Initialize Eyelink connection

if ~EyelinkInit(0, 1)

fprintf('Eyelink initialization failed.\n');

edfFile = '';

return;

end

% Open EDF file on host

Eyelink('OpenFile', edfFile);

% Configure data collection

Eyelink('Command', 'file_sample_data = LEFT,RIGHT,GAZE,AREA');

Eyelink('Command', 'link_sample_data = LEFT,RIGHT,GAZE,AREA');

Eyelink('Command', 'pupil_size_diameter = YES');

Eyelink('Command', 'heuristic_filter = 1 2');

Eyelink('Command', 'button_function 5 "accept_target_fixation"');

Eyelink('Message', 'DISPLAY_COORDS 0 0 1024 768');

% === Wait for first TR triggerEyelink

% ===

KbName('UnifyKeyNames');

triggerKey = KbName('5%'); % Adjust if different scanner key (prisma B is "5")

fprintf('Waiting for fMRI trigger key (%s)...\n', KbName(triggerKey));

while true

[keyIsDown, ~, keyCode] = KbCheck;

if keyIsDown && keyCode(triggerKey)

fprintf('Trigger received. Starting Eyelink recording.\n');

break;

end

end

% Start Eyelink recording after trigger

Eyelink('StartRecording');

WaitSecs(1);

Eyelink('Message', 'SYNCTIME');

else

edfFile = '';

end

end

Operating system - Windows 11

Backend - legacy

Eyetracker - Tobii Pro Spectrum 300hz

When I try and run my Opensesame experiment I get the error posted below. The eye tracker is detected by the Tobii Pro Eye Tracker manager so I am not sure why Opensesame is not detecting that it is there. Any help would be greatly appreciated.

Thanks!

Traceback (most recent call last):

 File "C:\Users\Admin\anaconda3\envs\opensesame-py3\lib\site-packages\libqtopensesame\misc\process.py", line 132, in run

  exp.run()

 File "C:\Users\Admin\anaconda3\envs\opensesame-py3\lib\site-packages\libopensesame\experiment.py", line 351, in run

  self.items.execute(self.var.start)

 File "C:\Users\Admin\anaconda3\envs\opensesame-py3\lib\site-packages\libopensesame\item_store.py", line 89, in execute

  self.run(name)

 File "C:\Users\Admin\anaconda3\envs\opensesame-py3\lib\site-packages\libopensesame\item_store.py", line 105, in run

  self[name].run()

 File "C:\Users\Admin\anaconda3\envs\opensesame-py3\lib\site-packages\libopensesame\sequence.py", line 48, in run

  self.experiment.items.run(_item)

 File "C:\Users\Admin\anaconda3\envs\opensesame-py3\lib\site-packages\libopensesame\item_store.py", line 105, in run

  self[name].run()

 File "C:\Users\Admin\anaconda3\envs\opensesame-py3\lib\site-packages\opensesame_plugins\pygaze\pygaze_init\pygaze_init.py", line 196, in run

  self.experiment.pygaze_eyetracker.calibrate()

 File "C:\Users\Admin\anaconda3\envs\opensesame-py3\lib\site-packages\pygaze\_eyetracker\libtobii.py", line 305, in calibrate

  self.start_recording()

 File "C:\Users\Admin\anaconda3\envs\opensesame-py3\lib\site-packages\pygaze\_eyetracker\libtobii.py", line 198, in start_recording

  if not self.t0 and self._write_enabled:

AttributeError: 'TobiiProTracker' object has no attribute 't0'

In the python console window the following message is printed:

WARNING! libtobii.TobiiProTracker.__init__: no eye trackers found!

I have a CSV file with 2098 respondents, but when I upload it, only 1367 appear. See attached. Do you have the same problem? Can anyone help me?

Please explain how I should go about calculating a residual [of a variable] after regressing it on Age for each subject.

Forgive me if this is posted in the wrong forum - I'm 100% new to using python/this forum or anything like it.

For my project, I want to use the script from cognitive_pupillometry_script provided on github, but one of the first steps is having to parse the data via some EyeLink specific exe program (edf2asc.exe?) which is not necessary for me as my data is already parsed in R (I don't use EyeLink, I use a Tobii Nano) - I want to just feed it into the preprocessing script. But I don't know what the data matrix (dm) looks like before its run into Step 3 (blink correction). Can somebody provide the data file after being parsed so that I can make mine in the same format to put through the preprocessing script? Its OK if its in data matrix format, I can convert a csv into that (I think!).

Thank you for any help,

Candice

Thanks,

LME

As I work on this cognitive modelling project, I'm attempting to use PyMC3 to construct Bayesian inference. My aim is to develop a probabilistic model that can aid in the explanation of some cognitive functions, particularly those involved in making decisions in the face of uncertainty. Although I understand the fundamentals of Bayesian inference, I'm having trouble with some of the more intricate parts of putting it into practice with PyMC3 in the community.

These are a few particular problems I'm having:

Model Details: In PyMC3, I'm not sure how to properly define my cognitive model. Even after reading the material, I still have trouble turning my theoretical model into a probabilistic one. Exist any resources or best practices that could be useful in this situation?🤔

Prior Selection: Selecting the right priors for my requirements has proven to be a challenging undertaking. I don't want to impose too much prior information, but I also don't want to be overly ambiguous. How do your cognitive models handle prior selection? 🤔Any advice or general guidelines would be very valued.

Assessment of the Model: What are the best methods for assessing the model's performance once it is constructed? Although I'm aware of techniques like WAIC and LOO, I'm not quite sure how to use them. Exist any guides or illustrations that show how to assess a Bayesian cognitive model correctly?🤔

Any guidance, materials, or real-world examples that could assist me overcome these obstacles would be greatly appreciated. If you have any experience with PyMC3 for cognitive gcp modelling, please share your methodology and any potential difficulties you may have encountered.

Thank you in advance.

This is my first post after just joining this discussion, so please forgive me and provide kind assistance if I have posted in the wrong subsection!

I am new here but a real enthusiast and loving this community so far. I have a background in teaching coding and education, and I feel I could help with documentation, at least for starters.

As a new member of this forum, I wish to share and gain some knowledge. I am looking forward to creating my own discussions to resolve my queries and gain more insights. I have participated in various discussions, which have definitely helped me a lot.

Also, what factors should be considered when choosing the appropriate category for a post?

Thank you in advance.

i previously wrote an email to one of your members and was told to repost my question here.

So, I am currently working on a project where I am defining requirements for simulators, more specifically the display quality/requirements.

For a few days now I have been trying to learn about "arc minute" and "pixel per degree", because my previous "instrument" (pixel per inch) does not take into account the human factor (sitting position, distance to the display). To make sure I understand the calculation and to be able to decide which one (arc minute or pixel per degree) to use, I have created a small Excel file to perform these calculations, but I am encountering some difficulties with certain aspects of the formulas. I have 2 different versions (the orange part), one where the arc minute calculation seems correct, and one where the pixel per degree calculation seems correct, but I am not sure, I have a feeling I am doing something wrong. Would you guys be able to take a look and help me out?

Also, I have been looking at papers/literature and it seems like Arc Minute is the right "instrument" to use for recommendations for simulators/displays, although Pixel per Degree seems to be used as well, what do you guys think about that? Any help you can offer would be greatly appreciated.

Thanks in advance.

I'm looking to develop a lab-based, real-time interactive experiment where two participants draw images based on different kinds of stimuli. Ideally there will be two monitors. I am unsure of where to think about building this (opensesame, psychopy, or building a GUI in python). What do you suggest?

The aim of the study is to investigate how neuroscientists and researchers (psychologists, cognitive scientists, philosophers, etc.) with some familiarity with neuroimaging techniques draw associations between cognitive processes and fMRI data observed experimentally.

The study has been developed by the SANe and the MInD units (MoMiLab research group at IMT School Lucca, Italy) and can be found here: https://www.sane-elab.eu/NRIA/welcome.php.

The questionnaire can be completed through PC or smartphone (however, the use of the PC is warmly suggested to visualize the fMRI maps better). Collected data will be completely anonymous. Once the questionnaire is finished, you can check your accuracy and decide to continue with another set of questions. In this case, please do not close or restart the session, otherwise the system will create another participant ID. 

We ask you to complete the questionnaire individually. However, after completing the survey you may want to compare your score with one of your lab mates and colleagues. 

Feel free to forward the link to anyone you think could be interested. If you have any questions or just want to be updated with the results of the study, please do not hesitate to contact us (davide.coraci@imtlucca.it).

Thank you very much for your availability and your time.

All the best,

Davide Coraci

I would like to test whether adding interaction terms to a model improves the model fit (as opposed to testing whether the estimate of an interaction is different from 0). So in R I would consider the significance of a predictor based on model comparisons (e.g., comparing a model including an interaction to one with only main effects). I wonder if it would be a valid approach to take the tested samples from the lmer_crossvalidation_test() output and rerun the model on these samples but using a likelihood-ratio-test to determine whether including an interaction significantly improves model fit.

I hope the question is clear and thanks in advance for any help.