updated tics paper

cicl-stanford · Oct 1, 2024 · f189fcf · f189fcf
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diff --git a/content/publication/beller2023language.md b/content/publication/beller2023language.md
diff --git a/content/publication/beller2024causation.md b/content/publication/beller2024causation.md
@@ -0,0 +1,33 @@
++++
+# 0 -> 'Forthcoming',
+# 1 -> 'Preprint',
+# 2 -> 'Journal',
+# 3 -> 'Conference Proceedings',
+# 4 -> 'Book chapter',
+# 5 -> 'Thesis'
+
+title = "Causation, Meaning, and Communication"
+date = "2024-09-20"
+authors = ["A. Beller","T. Gerstenberg"]
+publication_types = ["1"]
+publication_short = "_PsyArXiv_"
+publication = "Beller A., Gerstenberg T. (2024). Causation, Meaning, and Communication. _PsyArXiv_."
+abstract = "The words we use to describe what happened shape what comes to a listener's mind. How do speakers choose what causal expressions to use? How does that choice impact what listeners imagine? In this paper, we develop a computational model of how people use the causal expressions 'caused', 'enabled', 'affected', and 'made no difference'. The model first builds a causal representation of what happened. By running counterfactual simulations, the model computes several causal aspects that capture the different ways in which a candidate cause made a difference to the outcome. Logical combinations of these aspects define a semantics for the causal expressions. The model then uses pragmatic inference to decide what word to use in context. We test our model in a series of experiments and compare it to prior psychological accounts. In a set of psycholinguistic studies, we verify the model's semantics and pragmatics. We show that the causal expressions exist on a hierarchy of specificity, and that participants draw informative pragmatic inferences in line with this scale. In the next two studies, we demonstrate that our model quantitatively fits participant behavior in a speaker task and a listener task involving dynamic physical scenarios. We compare our model to two lesioned alternatives, one which removes pragmatic inference, and another which removes semantics and pragmatics. Our full model better accounts for participants' behavior than both alternatives. Taken together, these results suggest a new way forward for modeling the relationship between language and thought in the study of causality."
+image_preview = ""
+selected = false
+projects = []
+#url_pdf = "papers/beller2023language.pdf"
+url_preprint = "https://psyarxiv.com/xv8hf"
+url_code = ""
+url_dataset = ""
+url_slides = ""
+url_video = ""
+url_poster = ""
+url_source = ""
+url_custom = [{name = "Github", url = "https://github.com/cicl-stanford/causal_language"}]
+math = true
+highlight = true
+[header]
+# image = "publications/beller2023language.png"
+caption = ""
++++
diff --git a/docs/404.html b/docs/404.html
@@ -237,6 +237,10 @@ <h1>Page not found</h1>
 
   <h2>Publications</h2>
 
+  <ul>
+    <li><a href="https://cicl.stanford.edu/publication/beller2024causation/">Causation, Meaning, and Communication</a></li>
+  </ul>
+
   <ul>
     <li><a href="https://cicl.stanford.edu/publication/gandhi2024affective/">Human-like Affective Cognition in Foundation Models</a></li>
   </ul>
@@ -253,10 +257,6 @@ <h2>Publications</h2>
     <li><a href="https://cicl.stanford.edu/publication/amemiya2024disagreement/">Children use disagreement to infer what happened</a></li>
   </ul>
 
-  <ul>
-    <li><a href="https://cicl.stanford.edu/publication/wu2024whodunnit/">Whodunnit? Inferring what happened from multimodal evidence</a></li>
-  </ul>
-
 
 
 

diff --git a/docs/bibtex/cic_papers.bib b/docs/bibtex/cic_papers.bib
@@ -1,7 +1,7 @@
 %% This BibTeX bibliography file was created using BibDesk.
 %% https://bibdesk.sourceforge.io/
 
-%% Created for Tobias Gerstenberg at 2024-09-20 10:50:37 -0700 
+%% Created for Tobias Gerstenberg at 2024-09-20 15:09:53 -0700 
 
 
 %% Saved with string encoding Unicode (UTF-8) 
@@ -232,15 +232,15 @@ @article{amemiya2024disagreement
 	year = {2024},
 	bdsk-url-1 = {https://psyarxiv.com/y79sd/}}
 
-@article{beller2023language,
-	abstract = {The words we use to describe what happened shape the story a listener imagines. How do speakers choose what causal expression to use? How does that impact what listeners infer about what happened? In this paper, we develop a computational model of how people use the causal expressions "caused", "enabled", "affected", and "made no difference". The model first builds a causal representation of what happened. By running counterfactual simulations, the model computes causal aspects that capture the different ways in which a candidate cause made a difference to the outcome. Logical combinations of these aspects define a semantics for the different causal expressions. The model then uses pragmatic inference favoring informative utterances to decide what word to use in context. We test our model in a series of experiments. In a set of psycholinguistic studies, we verify semantic and pragmatic assumptions of our model. We show that the causal expressions exist on a hierarchy of informativeness, and that participants draw informative pragmatic inferences in line with this scale. In the next two studies, we demonstrate that our model quantitatively fits participant behavior in a speaker task and a listener task involving dynamic physical scenarios. We compare our model to two lesioned alternatives, one which removes the pragmatic inference component, and another which additionally removes the semantics of the causal expressions. Our full model better accounts for participants' behavior than both alternatives, suggesting that causal knowledge, semantics, and pragmatics are all important for understanding how people produce and comprehend causal language.},
+@article{beller2024causation,
+	abstract = {The words we use to describe what happened shape what comes to a listener's mind. How do speakers choose what causal expressions to use? How does that choice impact what listeners imagine? In this paper, we develop a computational model of how people use the causal expressions "caused", "enabled", "affected", and "made no difference". The model first builds a causal representation of what happened. By running counterfactual simulations, the model computes several causal aspects that capture the different ways in which a candidate cause made a difference to the outcome. Logical combinations of these aspects define a semantics for the causal expressions. The model then uses pragmatic inference to decide what word to use in context. We test our model in a series of experiments and compare it to prior psychological accounts. In a set of psycholinguistic studies, we verify the model's semantics and pragmatics. We show that the causal expressions exist on a hierarchy of specificity, and that participants draw informative pragmatic inferences in line with this scale. In the next two studies, we demonstrate that our model quantitatively fits participant behavior in a speaker task and a listener task involving dynamic physical scenarios. We compare our model to two lesioned alternatives, one which removes pragmatic inference, and another which removes semantics and pragmatics. Our full model better accounts for participants' behavior than both alternatives. Taken together, these results suggest a new way forward for modeling the relationship between language and thought in the study of causality.},
 	author = {Aaron Beller and Tobias Gerstenberg},
 	date-added = {2023-07-04 14:31:53 -0700},
-	date-modified = {2023-07-04 14:32:02 -0700},
+	date-modified = {2024-09-20 15:09:53 -0700},
 	journal = {PsyArXiv},
-	title = {A counterfactual simulation model of causal language},
+	title = {Causation, Meaning, and Communication},
 	url = {https://psyarxiv.com/xv8hf},
-	year = {2023},
+	year = {2024},
 	bdsk-url-1 = {https://psyarxiv.com/xv8hf}}
 
 @inproceedings{chase2023realism,

diff --git a/docs/index.xml b/docs/index.xml
@@ -9,6 +9,15 @@
     <lastBuildDate>Fri, 20 Sep 2024 00:00:00 +0000</lastBuildDate>
     <atom:link href="/" rel="self" type="application/rss+xml" />
 
+    <item>
+      <title>Causation, Meaning, and Communication</title>
+      <link>https://cicl.stanford.edu/publication/beller2024causation/</link>
+      <pubDate>Fri, 20 Sep 2024 00:00:00 +0000</pubDate>
+
+      <guid>https://cicl.stanford.edu/publication/beller2024causation/</guid>
+      <description></description>
+    </item>
+
     <item>
       <title>Human-like Affective Cognition in Foundation Models</title>
       <link>https://cicl.stanford.edu/publication/gandhi2024affective/</link>
@@ -135,14 +144,5 @@
       <description></description>
     </item>
 
-    <item>
-      <title>From Artifacts to Human Lives: Investigating the Domain-Generality of Judgments about Purposes</title>
-      <link>https://cicl.stanford.edu/publication/prinzing2024purpose/</link>
-      <pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate>
-
-      <guid>https://cicl.stanford.edu/publication/prinzing2024purpose/</guid>
-      <description></description>
-    </item>
-
   </channel>
 </rss>
diff --git a/docs/member/ari_beller/index.html b/docs/member/ari_beller/index.html
@@ -318,9 +318,9 @@ <h2 id="publications">Publications</h2>
   <span itemprop="author">
     A. Beller, T. Gerstenberg</span>
 
-    (2023).
+    (2024).
 
-  <a href="https://cicl.stanford.edu/publication/beller2023language/" itemprop="name">A counterfactual simulation model of causal language</a>.
+  <a href="https://cicl.stanford.edu/publication/beller2024causation/" itemprop="name">Causation, Meaning, and Communication</a>.
   <em>PsyArXiv</em>.
 
 

diff --git a/docs/member/tobias_gerstenberg/index.html b/docs/member/tobias_gerstenberg/index.html
@@ -356,6 +356,49 @@ <h2 id="publications">Publications</h2>
 
 
        <div class="pub-list-item" style="margin-bottom: 1rem" itemscope itemtype="http://schema.org/CreativeWork">
+  <span itemprop="author">
+    A. Beller, T. Gerstenberg</span>
+
+    (2024).
+
+  <a href="https://cicl.stanford.edu/publication/beller2024causation/" itemprop="name">Causation, Meaning, and Communication</a>.
+  <em>PsyArXiv</em>.
+
+
+
+
+  <p>
+
+
+
+
+<a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://psyarxiv.com/xv8hf" target="_blank" rel="noopener">
+  Preprint
+</a>
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+<a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://github.com/cicl-stanford/causal_language" target="_blank" rel="noopener">
+  Github
+</a>
+
+
+  </p>
+
+</div>
+<div class="pub-list-item" style="margin-bottom: 1rem" itemscope itemtype="http://schema.org/CreativeWork">
   <span itemprop="author">
     K. Gandhi, Z. Lynch, J. Fränken, K. Patterson, S. Wambu, T. Gerstenberg, D. C. Ong, N. D. Goodman</span>
 
@@ -1341,49 +1384,6 @@ <h2 id="publications">Publications</h2>
 
 
 
-  </p>
-
-</div>
-<div class="pub-list-item" style="margin-bottom: 1rem" itemscope itemtype="http://schema.org/CreativeWork">
-  <span itemprop="author">
-    A. Beller, T. Gerstenberg</span>
-
-    (2023).
-
-  <a href="https://cicl.stanford.edu/publication/beller2023language/" itemprop="name">A counterfactual simulation model of causal language</a>.
-  <em>PsyArXiv</em>.
-
-
-
-
-  <p>
-
-
-
-
-<a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://psyarxiv.com/xv8hf" target="_blank" rel="noopener">
-  Preprint
-</a>
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-<a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://github.com/cicl-stanford/causal_language" target="_blank" rel="noopener">
-  Github
-</a>
-
-
   </p>
 
 </div>

diff --git a/docs/papers/gerstenberg2024counterfactual.pdf b/docs/papers/gerstenberg2024counterfactual.pdf