diff --git a/data+analysis/01-pilots-1ab-choose-explain/02-data-exploration.html b/data+analysis/01-pilots-1ab-choose-explain/02-data-exploration.html index 8a83f17..8e5a9c7 100644 --- a/data+analysis/01-pilots-1ab-choose-explain/02-data-exploration.html +++ b/data+analysis/01-pilots-1ab-choose-explain/02-data-exploration.html @@ -2,7 +2,7 @@ - + @@ -18,9 +18,9 @@ ul.task-list{list-style: none;} ul.task-list li input[type="checkbox"] { width: 0.8em; -margin: 0 0.8em 0.2em -1.6em; -vertical-align: middle; +margin: 0 0.8em 0.2em -1em; vertical-align: middle; } + pre > code.sourceCode { white-space: pre; position: relative; } pre > code.sourceCode > span { display: inline-block; line-height: 1.25; } pre > code.sourceCode > span:empty { height: 1.2em; } @@ -47,53 +47,23 @@ -khtml-user-select: none; -moz-user-select: none; -ms-user-select: none; user-select: none; padding: 0 4px; width: 4em; -color: #aaaaaa; } -pre.numberSource { margin-left: 3em; border-left: 1px solid #aaaaaa; padding-left: 4px; } +pre.numberSource { margin-left: 3em; padding-left: 4px; } div.sourceCode { } @media screen { pre > code.sourceCode > span > a:first-child::before { text-decoration: underline; } } -code span.al { color: #ff0000; font-weight: bold; } -code span.an { color: #60a0b0; font-weight: bold; font-style: italic; } -code span.at { color: #7d9029; } -code span.bn { color: #40a070; } -code span.bu { color: #008000; } -code span.cf { color: #007020; font-weight: bold; } -code span.ch { color: #4070a0; } -code span.cn { color: #880000; } -code span.co { color: #60a0b0; font-style: italic; } -code span.cv { color: #60a0b0; font-weight: bold; font-style: italic; } -code span.do { color: #ba2121; font-style: italic; } -code span.dt { color: #902000; } -code span.dv { color: #40a070; } -code span.er { color: #ff0000; font-weight: bold; } -code span.ex { } -code span.fl { color: #40a070; } -code span.fu { color: #06287e; } -code span.im { color: #008000; font-weight: bold; } -code span.in { color: #60a0b0; font-weight: bold; font-style: italic; } -code span.kw { color: #007020; font-weight: bold; } -code span.op { color: #666666; } -code span.ot { color: #007020; } -code span.pp { color: #bc7a00; } -code span.sc { color: #4070a0; } -code span.ss { color: #bb6688; } -code span.st { color: #4070a0; } -code span.va { color: #19177c; } -code span.vs { color: #4070a0; } -code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } +!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return b}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),r=n.n(e);function c(t){try{return document.execCommand(t)}catch(t){return}}var a=function(t){t=r()(t);return c("cut"),t};function o(t,e){var n,o,t=(n=t,o="rtl"===document.documentElement.getAttribute("dir"),(t=document.createElement("textarea")).style.fontSize="12pt",t.style.border="0",t.style.padding="0",t.style.margin="0",t.style.position="absolute",t.style[o?"right":"left"]="-9999px",o=window.pageYOffset||document.documentElement.scrollTop,t.style.top="".concat(o,"px"),t.setAttribute("readonly",""),t.value=n,t);return e.container.appendChild(t),e=r()(t),c("copy"),t.remove(),e}var f=function(t){var e=1 - + - + @@ -2612,6 +3020,7 @@

Causal Implicature - Pilot data analysis

+ @@ -2667,8 +3076,8 @@

Study details

-

-

Figure 1: Screenshot from a trial in condition ‘association’ for Pilot 1a.

+

+
Figure 1: Screenshot from a trial in condition ‘association’ for Pilot 1a.
@@ -2685,8 +3094,8 @@

Study details

-

-

Figure 2: Screenshot from a trial in condition ‘association’ for Pilot 1b. Notice the additional sentence after the critical sentence in bold.

+

+
Figure 2: Screenshot from a trial in condition ‘association’ for Pilot 1b. Notice the additional sentence after the critical sentence in bold.
@@ -2702,16 +3111,16 @@

Results

-

-

(a) Pilot 1a

+

+
(a) Pilot 1a
-

-

(b) Pilot 1b

+

+
(b) Pilot 1b
@@ -2801,7 +3210,7 @@

Results

knitr::kable(evaluate_fit_joint(fit_joint))
- +
@@ -2907,7 +3316,7 @@

Results

knitr::kable(evaluate_fit(fit1))
-
+
@@ -2964,7 +3373,7 @@

Results

knitr::kable(evaluate_fit(fit2))
-
+
@@ -3035,7 +3444,7 @@

knitr::kable(justifications)
-

+
@@ -3163,7 +3572,7 @@

3927

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4214

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4213

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4128

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4184

- + @@ -5641,7 +6050,7 @@

4142

- + @@ -5902,9 +6311,23 @@

{ + const doc = window.document; + const targetCell = annoteEl.getAttribute("data-target-cell"); + const targetAnnotation = annoteEl.getAttribute("data-target-annotation"); + const annoteSpan = window.document.querySelector(selectorForAnnotation(targetCell, targetAnnotation)); + const lines = annoteSpan.getAttribute("data-code-lines").split(","); + const lineIds = lines.map((line) => { + return targetCell + "-" + line; + }) + let top = null; + let height = null; + let parent = null; + if (lineIds.length > 0) { + //compute the position of the single el (top and bottom and make a div) + const el = window.document.getElementById(lineIds[0]); + top = el.offsetTop; + height = el.offsetHeight; + parent = el.parentElement.parentElement; + if (lineIds.length > 1) { + const lastEl = window.document.getElementById(lineIds[lineIds.length - 1]); + const bottom = lastEl.offsetTop + lastEl.offsetHeight; + height = bottom - top; + } + if (top !== null && height !== null && parent !== null) { + // cook up a div (if necessary) and position it + let div = window.document.getElementById("code-annotation-line-highlight"); + if (div === null) { + div = window.document.createElement("div"); + div.setAttribute("id", "code-annotation-line-highlight"); + div.style.position = 'absolute'; + parent.appendChild(div); + } + div.style.top = top - 2 + "px"; + div.style.height = height + 4 + "px"; + let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter"); + if (gutterDiv === null) { + gutterDiv = window.document.createElement("div"); + gutterDiv.setAttribute("id", "code-annotation-line-highlight-gutter"); + gutterDiv.style.position = 'absolute'; + const codeCell = window.document.getElementById(targetCell); + const gutter = codeCell.querySelector('.code-annotation-gutter'); + gutter.appendChild(gutterDiv); + } + gutterDiv.style.top = top - 2 + "px"; + gutterDiv.style.height = height + 4 + "px"; + } + selectedAnnoteEl = annoteEl; + } + }; + const unselectCodeLines = () => { + const elementsIds = ["code-annotation-line-highlight", "code-annotation-line-highlight-gutter"]; + elementsIds.forEach((elId) => { + const div = window.document.getElementById(elId); + if (div) { + div.remove(); + } + }); + selectedAnnoteEl = undefined; + }; + // Attach click handler to the DT + const annoteDls = window.document.querySelectorAll('dt[data-target-cell]'); + for (const annoteDlNode of annoteDls) { + annoteDlNode.addEventListener('click', (event) => { + const clickedEl = event.target; + if (clickedEl !== selectedAnnoteEl) { + unselectCodeLines(); + const activeEl = window.document.querySelector('dt[data-target-cell].code-annotation-active'); + if (activeEl) { + activeEl.classList.remove('code-annotation-active'); + } + selectCodeLines(clickedEl); + clickedEl.classList.add('code-annotation-active'); + } else { + // Unselect the line + unselectCodeLines(); + clickedEl.classList.remove('code-annotation-active'); + } + }); + } + const findCites = (el) => { + const parentEl = el.parentElement; + if (parentEl) { + const cites = parentEl.dataset.cites; + if (cites) { + return { + el, + cites: cites.split(' ') + }; + } else { + return findCites(el.parentElement) + } + } else { + return undefined; + } + }; var bibliorefs = window.document.querySelectorAll('a[role="doc-biblioref"]'); for (var i=0; i diff --git a/data+analysis/01-pilots-1ab-choose-explain/02-data-exploration.qmd b/data+analysis/01-pilots-1ab-choose-explain/02-data-exploration.qmd index 506ac0c..62e96ee 100644 --- a/data+analysis/01-pilots-1ab-choose-explain/02-data-exploration.qmd +++ b/data+analysis/01-pilots-1ab-choose-explain/02-data-exploration.qmd @@ -56,7 +56,7 @@ Two pilot studies were run to test whether a statement like "X is associated wit N=200 participants recruited via Prolific (English Native, at least 10 previous studies, at least 90% approval rate). For technical reasons, N=`r d1$submission_id %>% unique() %>% length()` data sets were received for pilot 1a, and N=`r d2$submission_id %>% unique() %>% length()` for pilot 1b. Participants took about `r round((d2$experiment_duration %>% mean()) /60000, 2)` for pilot 1a, and about `r round((d2$experiment_duration %>% mean()) /60000, 2)` minutes for pilot 1b on average. They were paid 0.4 British Pounds. -The experiment code, data an material are [available online](https://github.com/magpie-ea/magpie3-causal-implicature). A live version of the experiment can be tested [here](https://magpie-ea.github.io/magpie3-causal-implicature/). +The experiment code, data and material are [available online](https://github.com/magpie-ea/magpie3-causal-implicature). A live version of the experiment can be tested [here](https://magpie-ea.github.io/magpie3-causal-implicature/). The experimental material consisted of four different conditions of which each participant saw exactly one (between-subject design). The critical trial involved a binary forced choice, see Figure @fig-screenshot-trial. @@ -124,6 +124,11 @@ plot2 <- make_barplot(d2, "Pilot 1b: w/ explicit costs") plot1 plot2 +ggplot2::ggsave(plot = plot1 + xlab("") + ylab("") + ggtitle(""), + filename = "results-Exp1a-barplot.pdf", width= 8, height = 5, scale=0.7) +ggplot2::ggsave(plot = plot2 + xlab("") + ylab("") + ggtitle(""), + filename = "results-Exp1b-barplot.pdf", width= 8, height = 5, scale=0.7) + ``` Descriptively, the order of choice rates across conditions matches expectations. We also see that mentioning a potential cost of ralocrop decreases the overall choice of the "BOTH" condition, which is intuitive. @@ -259,12 +264,17 @@ evaluate_fit <- function(fit) { } knitr::kable(evaluate_fit(fit1)) + +xtable::xtable(evaluate_fit(fit1)) + ``` For the data from Pilot 1b, the results look as follows: ```{r} knitr::kable(evaluate_fit(fit2)) + +xtable::xtable(evaluate_fit(fit2)) ``` In sum, we find the following critical results forall three cases (data combined, only data from Pilot 1a, only data from Pilot 1b): the choice of "BOTH" is credibly higher in the **association** condition than in the **deniedCausation** condition, and it is *not* credibly different from those in the **intervention** condition. diff --git a/data+analysis/02-pilot-2a-choose-explain-recall/02-data-exploration.html b/data+analysis/02-pilot-2a-choose-explain-recall/02-data-exploration.html deleted file mode 100644 index 17f51c7..0000000 --- a/data+analysis/02-pilot-2a-choose-explain-recall/02-data-exploration.html +++ /dev/null @@ -1,3009 +0,0 @@ - - - - - - - - - - -Causal Implicature - Pilot 2 data analysis - - - - - - - - - - - - - - - - - - - -
- -
- -
-
-

Causal Implicature - Pilot 2 data analysis

-
- - - -
- -
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Author
-
-

MF

-
-
- - -
- - -
- -
-
-Code -
library(tidyverse)
-library(tidyboot)
-library(aida)
-library(brms)
-library(faintr)
-library(patchwork)
-
-##################################################
-
-# these options help Stan run faster
-options(mc.cores = parallel::detectCores())
-
-# use the aida-theme for plotting
-theme_set(theme_aida())
-
-# global color scheme / non-optimized
-project_colors = c("#E69F00", "#56B4E9", "#009E73", 
-                   "#F0E442", "#0072B2", "#D55E00", 
-                   "#CC79A7", "#000000")
-
-# setting theme colors globally
-scale_colour_discrete <- function(...) {
-  scale_colour_manual(..., values = project_colors)
-}
-scale_fill_discrete <- function(...) {
-  scale_fill_manual(..., values = project_colors)
-} 
-
-##################################################
-
-# preprocessed data from pilot 2a
-d <- read_csv('01b-processed-data-pilot-2a.csv')
-
-
-

Pilot 2 uses similar methods to Pilots 1a and 1b. The main conceptual change is that an additional reproduction task was added at the end, in which participants were asked to reproduce the original statement by the Science Team regarding the relation between xeliherb and ralocrop. The purpose of this addition was to explore whether participants would use explicit causal language, possibly also depending on their choice (only xeliherb vs both crops).

-
-

Study details

-

N=31 participants were recruited via Prolific (English Native, at least 10 previous studies, at least 90% approval rate). Participants took about 5.23 minutes for Pilot 2 on average. They were paid 0.4 British Pounds.

-

The experiment code, data an material are available online. A live version of the experiment can be tested here.

-

The experimental material consisted of only the association condition from Pilot 1. As before, the critical trial involved a binary forced choice, but the information was presented in slightly layout in Pilot 2 in order to clearly demarcate what the information from the Science Team was (see Figure Figure 1).

-
-
-
-
-

-

Figure 1: Screenshot from the choice trial for Pilot 2.

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-

After the critical choice trials came a justification trial (as before). Following this, participants were presented with a reproduction trial as shown in Figure Figure 2.

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-

-

Figure 2: Screenshot from the reproduction trial for Pilot 2.

-
-
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-

Results

-
-
-Code -
proportion_both = d %>% filter(!is.na(response)) %>% 
-  group_by(response) %>% 
-  summarize(proportion = n() / (nrow(d)/3)) %>% 
-  .[1,2] %>% round(2)
-
-
-

The overal proportion of the BOTH option was 0.61 .

-

The reproductions given by participants were manually classified into three categories:

-
    -
  1. causal statements: directly implying a causal relationship (ralocrop causes high yield of xeliherb);
    2. -
  2. neutral statements: just reporting a correlation;
    3. -
  3. none: reproductions that failed to mention the relationship between the herbs.
    4. -
-
-
-
`summarise()` has grouped output by 'choice', 'reproduction'. You can override
-using the `.groups` argument.
-
-
-
-
-

-

Figure 3: Proportion of reproduction types per strategy choice.

-
-
-
-
-
-
-

List of all reproductions

-
-
-Code -
reproductions <- d %>% filter(!is.na(reproduction)) %>% 
-  select(submission_id, reproClass, reproduction)
-
-knitr::kable(reproductions)
-
-
-

association bothIf the batch of the essential plant doesn<U+2019>t grow properly then at least there is a plan B where it is likely to grow again.If the batch of the essential plant doesn’t grow properly then at least there is a plan B where it is likely to grow again.
pilot-1aassociation bothBecause hopefully we would end up with enough of the seeds to survive by not planting both it is a risk we won<U+2019>t.Because hopefully we would end up with enough of the seeds to survive by not planting both it is a risk we won’t.
pilot-1bassociation singleWe don<U+2019>t have enough resources to use bothWe don’t have enough resources to use both
pilot-1bcommonCause singleI would only cultivate xeliherb at first because even if it grows sparsely, there should be enough of it for the initial colonists to subsist on, provided the number of colonists is reasonably small. Then, once established, the colonists can learn more about how to grow it and hopefully find ways to grow it more easily and without the aid of the second plant (whose name I<U+2019>ve forgotten, I<U+2019>m sorry)I would only cultivate xeliherb at first because even if it grows sparsely, there should be enough of it for the initial colonists to subsist on, provided the number of colonists is reasonably small. Then, once established, the colonists can learn more about how to grow it and hopefully find ways to grow it more easily and without the aid of the second plant (whose name I’ve forgotten, I’m sorry)
pilot-1bcommonCause singleAs that<U+2019>s what we needAs that’s what we need
pilot-1bdeniedCausation singleThere<U+2019>s no larger benefit to doing both, it wastes resources with no guaranteed payoffThere’s no larger benefit to doing both, it wastes resources with no guaranteed payoff
pilot-1b
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submission_idreproClassreproduction
4368neutralTo achieve high yield of xeliherb need presence of ralocrap.
4375neutralTo survive, colonists must have constant supply of Xeliherb. Ralocrop is associated with a high yield of xeliherb. Ralocrop can be found elsewhere in the galaxy. Ralocrop does take up water and has costs to grow.
4363neutralYou need to harvest both for the best chance!
4371causalXeliherb can be harvested at a higher yield when grown with ralocrop.
4370neutralXeliherb yield is greatly increased in the presence of ralocrop.
4377noneRalocrop costs significantly more resources to cultivate than xeliherb. Ralocrop grows throughout the galaxy but xeliherb only grows on the planet you’re on.
4365noneTo survive on planet must have a large supply of xeliherb seeds
4386neutralRalocrop was discovered growing with xeliherb
4387neutralHigh yield of xeliherb is associated with the presence of ralocrop.
4392neutralRalocrop yields xeliherb.
4376noneThe science team reported that xeliherb is important to cultivate, but did not explicitly mention ralocrop.
4379noneRalocrop is another plant, not exclusive to the planet and is well-known throughout the galaxy. Cultivation of the crop is costly both in water and energy resources.
4385causalIt seems that xehliherb thrives with the addition of ralocrop.
4367neutralIf Ralocrop is present, there’s a high yield of Xeliherb.
4369neutralA high harvest of Xeliherb is associated with the presence of ralocrop.
4378neutralRalocrop is associated with a high yield of xeliherb.
4389neutralThe promoted growth of xeliherb is associated with the presence of ralocrop.
4364nonethere are conditions and technical problems
4366neutralA more abundant crop of xeliherb is associated with the proximity of ralocrop.
4374neutralHigh yields of xeliherb are associated with the presence of ralocrop
4383causalScience says there is a high yield of Xeliherb with ralocrop
4372noneScience team warned that to survive on the planet must have large secured stash of xeliherb seeds. So collect seeds build a storage for the seeds
4373causalRalocrop increases xeliherb yields
4384causalThe presence of ralocrop leads to higher yield of xeliherb according to our science team.
4390neutralA high yield of xeliherb is associated with the presence of ralocrop.
4380neutralThe presence of ralocrop is associated with a high yield of xeliherb.
4391nonegive priority to the cultivation of xeliherb since it is the best in terms of production costs
4381neutralThere is a higher yield of xeliherb in the presence of ralocrop
4382neutralThe cultivation of xeliherb on the planet is associated with a high presence of ralocrop.
4388causalThe existence of ralocrop enhances the growth and availability of xeliherb.
4393neutralHigh yield of xeliherb is associated with the presence of ralocrop.
-
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\n\n \n

How was your breakfast?

\n \n \n \n\n \n\n```\n\n\n```vue\n\n \n\n \n

How was your breakfast?

\n \n \n \n\n \n\n```\n\n```vue\n\n \n

Which sentence is ungrammatical?

\n banana went out to buy some groceries.',\n 'The bear raced past the barn froze.',\n 'The horse could not have been eaten why it was racing.',\n 'The bread was sour before it was made.'\n ]\">\n \n \n \n \n\n```\n\n\n\n