updated cv and publications

content/publication/goodman2023probabilistic.md

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+# 0 -> 'Forthcoming',
+# 1 -> 'Preprint',
+# 2 -> 'Journal',
+# 3 -> 'Conference Proceedings',
+# 4 -> 'Book chapter',
+# 5 -> 'Thesis'
+
+title = "Probabilistic programs as a unifying language of thought"
+date = "2023-01-01"
+year = "{in press}"
+authors = ["N. D. Goodman","T. Gerstenberg","J. B. Tenenbaum"]
+publication_types = ["4", "0"]
+publication_short = "_Reverse-engineering the mind: The Bayesian approach to cognitive science_"
+publication = "Goodman N. D., Gerstenberg T., Tenenbaum J. B. (in press). Probabilistic programs as a unifying language of thought. In _Reverse-engineering the mind: The Bayesian approach to cognitive science_."
+image_preview = ""
+selected = false
+projects = []
+#url_pdf = "papers/goodman2023probabilistic.pdf"
+url_preprint = ""
+url_code = ""
+url_dataset = ""
+url_slides = ""
+url_video = ""
+url_poster = ""
+url_source = ""
+#url_custom = [{name = "Github", url = ""}]
+math = true
+highlight = true
+[header]
+# image = "publications/goodman2023probabilistic.png"
+caption = ""
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content/publication/smith2022probabilistic.md

@@ -7,7 +7,7 @@
 # 5 -> 'Thesis'
 
 title = "Probabilistic models of physical reasoning"
-date = "2022-03-08"
+date = "2023-01-01"
 year = "{in press}"
 authors = ["K. A. Smith","J. B. Hamrick","A. N. Sanborn","P. W. Battaglia","T. Gerstenberg","T. D. Ullman","J. B. Tenenbaum"]
 publication_types = ["0", "4"]

content/publication/vasconcelos2023explanations.md

@@ -9,7 +9,7 @@
 title = "Explanations can reduce overreliance on AI systems during decision-making"
 date = "2023-01-01"
 authors = ['H. Vasconcelos','M. Jörke','M. Grunde-McLaughlin','T. Gerstenberg','M. Bernstein','R. Krishna']
-publication_types = ["2"]
+publication_types = ["3"]
 publication_short = "_Proceedings of the ACM on Human-Computer Interaction_"
 publication = "Vasconcelos, H., J\"orke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., Krishna, R. (2023). Explanations can reduce overreliance on ai systems during decision-making. In _Proceedings of the ACM on Human-Computer Interaction_, 7, 1--38."
 abstract = "Prior work has identified a resilient phenomenon that threatens the performance of human-AI decision-making teams: overreliance, when people agree with an AI, even when it is incorrect. Surprisingly, overreliance does not reduce when the AI produces explanations for its predictions, compared to only providing predictions. Some have argued that overreliance results from cognitive biases or uncalibrated trust, attributing overreliance to an inevitability of human cognition. By contrast, our paper argues that people strategically choose whether or not to engage with an AI explanation, demonstrating empirically that there are scenarios where AI explanations reduce overreliance. To achieve this, we formalize this strategic choice in a cost-benefit framework, where the costs and benefits of engaging with the task are weighed against the costs and benefits of relying on the AI. We manipulate the costs and benefits in a maze task, where participants collaborate with a simulated AI to find the exit of a maze. Through 5 studies (N = 731), we find that costs such as task difficulty (Study 1), explanation difficulty (Study 2, 3), and benefits such as monetary compensation (Study 4) affect overreliance. Finally, Study 5 adapts the Cognitive Effort Discounting paradigm to quantify the utility of different explanations, providing further support for our framework. Our results suggest that some of the null effects found in literature could be due in part to the explanation not sufficiently reducing the costs of verifying the AI's prediction."

content/publication/wu2023replacement.md

@@ -9,7 +9,7 @@
 title = "If not me, then who? Responsibility and replacement"
 date = "2023-10-02"
 authors = ["S. A. Wu","T. Gerstenberg"]
-publication_types = ["1"]
+publication_types = ["2"]
 publication_short = "_Cognition_"
 publication = "Wu S. A., Gerstenberg T. (accepted). If not me, then who? Responsibility and replacement. _Cognition_."
 abstract = "How do people hold others responsible? Responsibility judgments are affected not only by what actually happened, but also by what could have happened if things had turned out differently. Here, we look at how replaceability -- the ease with which a person could have been replaced by someone else -- affects responsibility. We develop the counterfactual replacement model which runs simulations of alternative scenarios to determine the probability that the outcome would have been different if the person of interest had been replaced. The model predicts that a person is held more responsible when it would have been more difficult to replace them. To test the model's predictions, we design a paradigm that quantitatively varies replaceability by manipulating the number of replacements as well as the probability with which each replacement would have been available. Across three experiments featuring increasingly complex scenarios, we show that the model explains participants' responsibility judgments well in both social and physical settings, and better than alternative models that rely only on features of what actually happened."

docs/bibtex/cic_papers.bib

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 %% This BibTeX bibliography file was created using BibDesk.
 %% https://bibdesk.sourceforge.io/
 
-%% Created for Tobias Gerstenberg at 2023-10-02 22:32:06 -0700 
+%% Created for Tobias Gerstenberg at 2023-10-10 11:28:38 -0700 
 
 
 %% Saved with string encoding Unicode (UTF-8) 
 
 
 
+@incollection{goodman2023probabilistic,
+	author = {Noah D. Goodman and Tobias Gerstenberg and Joshua B. Tenenbaum},
+	booktitle = {Reverse-engineering the mind: The Bayesian approach to cognitive science},
+	date-added = {2023-10-10 11:19:49 -0700},
+	date-modified = {2023-10-10 11:20:27 -0700},
+	editor = {Thomas L. Griffiths and Nick Chater and Joshua B. Tenenbaum},
+	title = {Probabilistic programs as a unifying language of thought},
+	year = {2023}}
+
 @article{amemiya2023disagreement,
 	abstract = {A challenge when figuring out what happened based on what others say is that they might disagree. Two preregistered experiments examined how children age 7 to 11 years use disagreement to make inferences about social events. Specifically, when there is no reason to question the reliability of either informant, can children use disagreement to infer that an ambiguous social event occurred? Experiment 1 (N = 52) found that children are indeed more likely to infer that an ambiguous social event occurred after learning that people disagreed (versus agreed) about what happened and that these inferences become stronger with age. Experiment 2 (N = 110) examined children's ability to predict that an ambiguous social event would cause disagreement and applied a computational model to examine the extent to which predictions explained their inferences. Children made the expected predictions and their inferences were consistent with the computational model, indicating that the ability to predict disagreement plays an important role for drawing inferences about what happened.},
 	author = {Jamie Amemiya and Gail D. Heyman and Tobias Gerstenberg},
 	date-added = {2023-09-09 11:57:43 -0700},
-	date-modified = {2023-09-09 11:58:57 -0700},
+	date-modified = {2023-10-10 11:27:30 -0700},
 	journal = {PsyArXiv},
 	title = {Children use disagreement to infer what happened},
 	url = {https://psyarxiv.com/y79sd/},
-	year = {2023},
-	bdsk-url-1 = {https://psyarxiv.com/y79sd/}}
+	year = {2023}}
 
 @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.},

docs/member/tobias_gerstenberg/index.html

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   Press: HAI blog
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+<div class="pub-list-item" style="margin-bottom: 1rem" itemscope itemtype="http://schema.org/CreativeWork">
+  <span itemprop="author">
+    K. A. Smith, J. B. Hamrick, A. N. Sanborn, P. W. Battaglia, T. Gerstenberg, T. D. Ullman, J. B. Tenenbaum</span>
+
+    (2023).
+
+  <a href="https://cicl.stanford.edu/publication/smith2022probabilistic/" itemprop="name">Probabilistic models of physical reasoning</a>.
+  <em>Reverse engineering the mind: Probabilistic models of cognition</em>.
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+<div class="pub-list-item" style="margin-bottom: 1rem" itemscope itemtype="http://schema.org/CreativeWork">
+  <span itemprop="author">
+    N. D. Goodman, T. Gerstenberg, J. B. Tenenbaum</span>
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+    (2023).
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+  <a href="https://cicl.stanford.edu/publication/goodman2023probabilistic/" itemprop="name">Probabilistic programs as a unifying language of thought</a>.
+  <em>Reverse-engineering the mind: The Bayesian approach to cognitive science</em>.
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-    K. A. Smith, J. B. Hamrick, A. N. Sanborn, P. W. Battaglia, T. Gerstenberg, T. D. Ullman, J. B. Tenenbaum</span>
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-    (2022).
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-  <a href="https://cicl.stanford.edu/publication/smith2022probabilistic/" itemprop="name">Probabilistic models of physical reasoning</a>.
-  <em>Reverse engineering the mind: Probabilistic models of cognition</em>.
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