Pith. sign in

REVIEW 4 major objections 6 minor 33 references

Empathy in Explanation

T0 review · 4 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper claims that people craft explanations to manage the listener's emotion, specifically regret, and supports it with a fitted computational model.

desk verdict A clean behavioral demonstration that explanation choices are sensitive to listener regret, though the specific counterfactual-regret mechanism is not distinguished from simpler blame-avoidance. read the letter →

arxiv 2507.21081 v1 pith:GGLJC4ZJ submitted 2025-06-16 cs.HC cs.AI

Katherine M. Collins , Kartik Chandra , Adrian Weller , Jonathan Ragan-Kelley , Joshua B. Tenenbaum This is my paper · ORCID
classification cs.HCcs.AI
keywords explanationregretemotionrationalcommunicationdoctor-patientcounterfactualappraisalsocialcognitionempathy
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to show that people craft explanations not only to transfer information but to manage the listener's emotions, specifically regret. It extends a cooperative-communication model of explanation with a social cost equal to the counterfactual probability that a patient would have avoided disease had they not drunk, then tests the model on how a doctor should explain a fatal illness. The model fits human judgments well: for tactful participants, the full model reaches $r^2 = 0.87$ while the same model without the regret term drops to $0.58$. The paper concludes that some people genuinely reason about a listener's emotional state when choosing what to explain, a finding with stakes for medical communication and AI explanation systems.

What carries the argument

The central object is the explainer's utility function over possible utterances, $V(u) = \alpha_{\mathrm{explanandum}} V_{\mathrm{explanandum}}(u) + \alpha_{\mathrm{latents}} V_{\mathrm{latents}}(u) - \alpha_{\mathrm{social}} c_{\mathrm{social}}(u)$, with choices over the four possible explanations made by a softmax. Here $V_{\mathrm{explanandum}}$ measures how well the explanation resolves the patient's expectation violation, $V_{\mathrm{latents}}$ penalizes false inferences about unmentioned causes ('lying by omission'), and $c_{\mathrm{social}}(u) = \Pr(S=1 \mid \mathrm{do}(D=0), u)$ is the counterfactual probability of the disease had the patient abstained from alcohol — the term that operationalises regret. The paper fits this machinery to data separately for tactful and candid participants, and the fitted regret weight is what carries the empathic behaviour: large for insecure patients under tactful participants, indistinguishable from zero otherwise.

What would settle it

A cleaner version of the experiment's causal-structure contrast would settle it: in a disjunctive disease the virus alone can cause illness, so the model predicts tactful explainers should feel little need to hide the drinking cause (regret would be low even if it were mentioned). If tactful participants suppress the drinking cause just as strongly there as in conjunctive diseases, the regret mechanism is not the active ingredient.

Watch

Extended reading notes

Core claim

People do indeed reason about emotion when giving explanations. In a doctor–patient vignette, participants who described themselves as tactful suppressed a true but blameworthy cause (excessive drinking) when the patient was insecure about past choices, while candid participants reported all causes regardless of temperament. A computational model in which the explainer maximises a weighted utility — understanding, correct inference about unmentioned causes, and a penalty proportional to the patient's counterfactual regret $\Pr(S=1 \mid \mathrm{do}(D=0), u)$ — reproduces these choices: for tactful participants the fitted regret weight is large for insecure patients and near zero for confident ones, and the full model beats every ablation, most tellingly the no-regret version ($r^2 = 0.87$ vs $0.58$). The paper treats this as evidence that explanation is a cooperative social act sensitive to the listener's affective state, and replicates the pattern with milk instead of alcohol.

Load-bearing premise

The result rests on identifying the patient's felt regret with the counterfactual probability the disease would not have occurred had they not drunk; if that quantity does not track the emotion, the fitted regret term could instead be measuring blame avoidance, self-image protection, or some other motive.

Editorial extensions

If this is right

  • Medical-communication guidance should treat cause disclosure as an emotionally consequential choice, not merely an information-transfer problem.
  • Explainable-AI systems that ignore the user's emotional appraisal of an explanation will systematically fail to match what human explainers do.
  • The tactful/candid split implies that no single explanation policy fits all explainers; systems built to explain should infer or adapt to the explainer's own social-utility weights.
  • Swapping the regret appraisal variable for another emotion's appraisal (guilt, shame, pride) yields a concrete, testable family of models for when explainers will suppress or volunteer causes.
  • Because the model omits the listener's higher-order reasoning about why a cause was not mentioned, its predictions may break when the patient can infer omission; adding a level-2 patient is the paper's own stated next step.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A behavioural hidden-variable model could infer the tactful/candid split from choice data alone, removing the reliance on self-reports and testing whether the two groups are genuinely distinct or ends of a continuum.
  • The regret term conflates self-focused regret with other-focused blame: mentioning college drinking might trigger shame or perceived judgment, not only counterfactual regret. A variant that separates these would clarify which emotional cost actually shapes explanations.
  • The same machinery predicts the reverse phenomenon: an explainer may volunteer an external cause even when the true cause is internal, if doing so spares the listener regret — a form of face-saving that goes beyond the doctor vignette.
  • The weaker milk replication suggests the social cost should be scaled by the moral valence or stability of the act (alcohol is more blameworthy than milk); a parametric version of $c_{\mathrm{social}}$ that factors in blameworthiness would make the model more portable.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper asks whether explainers take listeners' emotions into account when crafting explanations. It extends Chandra et al.'s rational-communication framework by adding a social-cost term that penalizes the listener's counterfactual regret, defined as c_social(u) = Pr(S=1 | do(D=0), u). In a Prolific experiment, participants playing a doctor chose whether to mention a virus and a drinking-threshold factor when explaining a fatal disease to confident versus insecure patients. Participants were split by self-report into 'tactful' and 'candid' groups. The full model with separate regret weights for confident and insecure patients fits tactful participants well (R²=0.869) and significantly better than an ablation without the regret term (R²=0.58), while for candid participants the regret weights are near zero. A replication replacing alcohol with milk partially reproduces the findings. The authors conclude that people do indeed reason about emotion when giving explanations. However, all model parameters are fit in-sample, the regret term is not validated as tracking felt regret rather than a simpler blame-avoidance norm, and the causal-structure manipulation was collapsed after 20% of participants failed the comprehension quiz.

Significance. If the central mechanism were established, this would be a useful contribution to computational accounts of explanation: it extends rational-communication models to affective listener states, connects to appraisal-based emotion models, and includes a replication in a second domain. The paper's strengths are its clean experimental design, direct model comparisons via likelihood-ratio tests and bootstrap confidence intervals, and its honest acknowledgment that the milk replication shows a weaker regret effect. The main limitation is that the key social-cost term is not construct-validated, and the reported R² and LRT values are in-sample fits rather than predictive tests. As it stands, the paper demonstrates that a model with a regret-like cost term fits better than an ablation; it does not yet establish that people compute the specific counterfactual regret quantity. With additional validation and out-of-sample checks, the contribution would be solid and well within the scope of the journal.

major comments (4)
  1. [Abstract; §Model fit, Eq. (3), Tables 1-3] The paper repeatedly says the model 'predicts' human intuitions, but all six free parameters (plus the disease-probability weights) are fit to the same data on which R² and LRT statistics are computed. The likelihood-ratio tests and bootstrap confidence intervals therefore measure in-sample fit, not predictive accuracy. The claim that the full model wins because it includes regret is supported only as a nested-model comparison on the training data. To support the word 'prediction,' please add k-fold or leave-scenario-out cross-validation, or reframe the claims as model fitting and comparison. Also, the Abstract reports R²=0.87/0.97 and Figure 3 reports 0.869/0.969, while Table 3 reports 0.84/0.96 for the same full model; the discrepancy between full-data R² and bootstrap-subsample R² should be explained in the text.
  2. [§Computational model, Eq. (3), c_social definition] The paper equates the patient's regret with c_social(u) = Pr(S=1 | do(D=0), u), following Houlihan et al. It provides no validation that this counterfactual probability tracks felt regret. A doctor who simply wants to avoid blaming an insecure patient, preserve the patient's self-image, or maintain a comfortable clinical relationship would make the same utterance choices without computing a counterfactual utility. Because α_social is fit separately for confident (R=0) and insecure (R=1) patients, the model can absorb any temperament-dependent reluctance to mention drinking; the specific counterfactual form of c_social is not tested against that simpler alternative. Please add a control model with a non-counterfactual blame-avoidance cost (e.g., a term that penalizes mentioning the drinking threshold when R=1) and compare fits, or provide independent evidence, such as emotion ratings, that c_social tracks felt regret.
  3. [§Experiment; §Results] The causal-structure manipulation (conjunctive vs. disjunctive) was collapsed after 20% of participants failed the quiz, and in the model the disease probability is set to epsilon/0.25/0.5 based only on the number of present factors, independent of whether the structure is conjunctive or disjunctive. As a result, the 12 reported scenarios reduce to 6 effective cells (temperament × test result), and the paper does not actually test any claim about conjunctive versus disjunctive causal reasoning. This does not invalidate the main model comparison, but the discussion and abstract should not imply that the causal-structure factor was successfully manipulated, and the modeling assumption about disease probabilities should be flagged as a post hoc choice rather than a tested hypothesis.
  4. [§Replication with milk, Tables 4-6] In the milk replication, the LRT comparing the full model to the no-regret ablation is significant for tactful participants (Table 4, 22.84), but the fitted α_R=1_social has a 95% bootstrap CI (−0.02, 13.78) that includes zero (Table 6). Thus the only clean parameter-level evidence that the regret weight is positive for insecure patients comes from the alcohol study. The paper acknowledges this in the text, but the Abstract and Discussion still conclude broadly that 'people do indeed reason about emotion when giving explanations.' Please temper the general conclusion, or report a combined analysis across both studies that formally tests whether the regret weight is positive while accounting for domain differences.
minor comments (6)
  1. [Figures 2 and 3] The labels 'T actful' and 'T actful participants' contain a stray space; they should read 'Tactful' and 'Tactful participants.'
  2. [§Computational model; §Results] The disease-probability parameter epsilon is never given a numerical value. Please state the value used in the fits.
  3. [Table 1 and Table 6] The priors are reported as log Pr(V) and log Pr(T). Please state whether these are natural logs and how the reported values map to probabilities, so readers can reproduce the parameter values.
  4. [§Experiment, Procedure] The text says 125 participants were recruited and 25 failed the quiz, leaving 100; please state the final N explicitly after exclusions and clarify whether the 49%/51% split is computed on the remaining 100 participants.
  5. [§Experiment, Procedure] The manual reclassification of three participants based on their textual justifications could introduce experimenter bias; a blinded or pre-specified classification rule would be more robust. Please describe the procedure in more detail.
  6. [General] The manuscript does not include a data or code availability statement. Given that the model is implemented in the memo PPL and the comparisons depend on fitting details, releasing code and de-identified data would aid reproducibility.

Circularity Check

2 steps flagged · score 6.0 of 10

Headline predictive claims reduce to in-sample fits: the r² values are computed on the data used to fit the model, and the milk 'replication' is a re-fit rather than an out-of-sample prediction.

  1. fitted input called prediction [Abstract and 'Model fit' section, Eq. (1)-(3) with softmax Pr(u) ∝ exp(α·V(u))]
    "Our model predicts human intuitions well, better than emotion-agnostic ablations, suggesting that people do indeed reason about emotion when giving explanations. ... We fit the 6 free parameters of our model, separately for both tactful and candid participants (Figure 3). We find that our model captures both types of participants' judgments well (R2 =0.869 and R2 =0.969, respectively)."

    The r² values and LRT statistics are computed on the same behavioral data used to fit the six free parameters (Pr(T), Pr(V), α_explanandum, α_latents, α_social^{R=0}, α_social^{R=1}) by gradient descent. Pr(u) ∝ exp(α·V(u)) is evaluated on its training set, so the abstract's 'predicts' is a post-hoc goodness-of-fit, not a prediction. The full-versus-ablation comparison is a legitimate in-sample model comparison, but the headline predictive claim reduces to the fitting procedure; the paper would be more accurate to say 'fits' rather than 'predicts'.

  2. fitted input called prediction [Replication with milk section, Figure 4 and Table 5]
    "Our full model (re-fit to new data) continued to predict human responses well (Figure 4)."

    The replication re-fits all parameters to the new milk dataset and then reports r² on that same dataset. Because no parameters are frozen from the alcohol experiment, the model's agreement with the milk data is again in-sample. 'Continued to predict' equates training-set fit with predictive success; the replication therefore does not provide an out-of-sample test of the framework, only a second fit in a new domain.

full rationale

Most of the paper's machinery is not circular: the full model versus no-regret ablation is a nested model comparison on the same data, with parameters re-fit for each model, and the tactful/candid split comes from self-report rather than from the model. The central inference that the regret term matters rests on LRT and bootstrap CIs, which are legitimate in-sample model-selection tools. However, the paper repeatedly labels in-sample goodness-of-fit as 'prediction.' The r² values in Figure 3 and Tables 3/5 are correlations between the fitted softmax and the same behavioral data used to fit the six free parameters, so the abstract's 'predicts human intuitions well' is a fitted-input-called-prediction. The milk 'replication' re-fits all parameters to the new data and again reports r² on that same data, so it is a second fit rather than an out-of-sample test. These issues are partial circularity: the headline predictive claim reduces to the fitting procedure, while the model-comparison evidence retains independent content. The construct-validity question of whether c_social = Pr(S=1 | do(D=0), u) actually tracks felt regret is a separate empirical threat, not a circularity, and is not scored here.

Assumptions & free parameters 7 free parameters · 4 assumptions · 0 invented entities

The model relies on six fitted weights and priors plus a hand-set disease-probability function, and imports two external theories: Gricean rational communication and Bayesian emotion appraisal. No invented entities are introduced.

free parameters (7)
  • Patient prior over virus Pr(V) = Tactful logPr(V)=-5.08; candid logPr(V)=-2.70
    The patient's prior probability of having the virus, fit to choices and not derived from independent measurement.
  • Patient prior over drinking threshold Pr(T) = Tactful logPr(T)=-1.99; candid logPr(T)=-1.98
    The patient's prior probability of having drunk excessively, fit to choices.
  • alpha_explanandum = Tactful 5.22; candid 3.69
    Weight on reducing the patient's expectation violation, fit to choices.
  • alpha_latents = Tactful 2.75; candid 6.66
    Weight on avoiding false inferences about latent causes, fit to choices.
  • alpha_R=0_social = Tactful 0.09; candid 0.01
    Regret-cost weight for confident patients, fit to choices.
  • alpha_R=1_social = Tactful 15.12; candid 0.01
    Regret-cost weight for insecure patients, fit to choices; the key parameter behind the emotion claim.
  • Disease probability weights = epsilon, 0.25, 0.5
    Hand-set probabilities of disease given neither, one, or both causes, chosen after collapsing the conjunctive/disjunctive manipulation.
assumptions (4)
  • domain assumption Explanation behavior follows softmax rational choice over utility (Gricean/RSA framework).
    The model assumes doctors choose utterances by softmax over a utility with fitted weights; this framework is imported from Chandra et al. (2024) and Frank and Goodman (2012).
  • domain assumption Regret is captured by the counterfactual utility appraisal term Pr(S=1 | do(D=0), u) from Houlihan et al. (2023).
    The paper imports this appraisal model as the definition of the patient's regret; no independent validation of this equation against felt regret is provided.
  • ad hoc to paper Disease probability is a function of the number of present factors (epsilon, 0.25, 0.5), independent of conjunctive or disjunctive structure.
    Set after observing no behavioral difference between causal structures and after a 20% quiz-failure rate; collapses the causal-structure manipulation the experiment was designed to test.
  • domain assumption Participants' self-reported tactful or candid status accurately separates stable explanation strategies.
    The split is used to fit separate models; the paper corrects 3 of 125 classifications manually but does not test robustness to alternative codings.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Empathy in Explanation." pith.science (2026). https://pith.science/paper/GGLJC4ZJ

@misc{pith2026250721081,
  author       = {Pith},
  title        = {Pith review of: Empathy in Explanation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GGLJC4ZJ}},
  note         = {Machine review of arXiv:2507.21081}
}
read the original abstract

Why do we give the explanations we do? Recent work has suggested that we should think of explanation as a kind of cooperative social interaction, between a why-question-asker and an explainer. Here, we apply this perspective to consider the role that emotion plays in this social interaction. We develop a computational framework for modeling explainers who consider the emotional impact an explanation might have on a listener. We test our framework by using it to model human intuitions about how a doctor might explain to a patient why they have a disease, taking into account the patient's propensity for regret. Our model predicts human intuitions well, better than emotion-agnostic ablations, suggesting that people do indeed reason about emotion when giving explanations.

Figures

Figures reproduced from arXiv: 2507.21081 by the authors.

Figure 1
Figure 1. Schematic representation of our experimental [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Human responses and model predictions across 12 scenarios. Error bars show 95% confidence intervals across [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Correlation between humans and model. Each point [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: (Replication with milk.) Correlation between human responses and model predictions. Model Class r 2 tactful r 2 candid Full model 0.76 (0.65,0.87) 0.90 (0.86,0.94) No regret cost 0.70 (0.61,0.78) 0.90 (0.85,0.94) No inference 0.65 (0.57,0.73) 0.54 (0.51,0.58) Understan…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

33 extracted references · 20 canonical work pages

  1. [1]

    write newline

    " write newline " cite write " FUNCTION editor.postfix editor num.names #1 > "( )" "( )" if FUNCTION editor.trans.postfix editor num.names #1 > "( )" "( )" if FUNCTION trans.postfix translator num.names #1 > "( )" "( )" if FUNCTION authors.editors.reflist.apa5 'field := 'dot := field num.names 'numnames := numnames 'format.num.names := format.num.names na...

  2. [2]

    , Ho, T H

    camerer2004cognitive APACrefauthors Camerer, C F. , Ho, T H. \ Chong, J K. APACrefauthors \ 2004 . A cognitive hierarchy model of games A cognitive hierarchy model of games . The Quarterly Journal of Economics 119 3 861--898

    work page 2004

  3. [3]

    , Chen, T

    chandra2024cooperative APACrefauthors Chandra, K. , Chen, T. , Li, T M. , Ragan-Kelley, J. \ Tenenbaum, J. APACrefauthors \ 2024 . Cooperative Explanation as Rational Communication Cooperative explanation as rational communication . Proceedings of the Annual Meeting of the Cognitive Science Society Proceedings of the annual meeting of the cognitive scienc...

    2024

  4. [4]

    , Chen, T

    chandra2025memo APACrefauthors Chandra, K. , Chen, T. , Tenenbaum, J B. \ Ragan-Kelley, J. APACrefauthors \ 2025 . A Domain-Specific Probabilistic Programming Language for Reasoning About Reasoning (or: a memo on memo) A domain-specific probabilistic programming language for reasoning about reasoning (or: a memo on memo) . psyarxiv preprint . APACrefURL h...

    work page doi:10.31234/osf.io/pt863 2025

  5. [5]

    WatChat: Explaining perplexing programs by debugging mental models

    chandra2024watchat APACrefauthors Chandra, K. , Li, T M. , Nigam, R. , Tenenbaum, J. \ Ragan-Kelley, J. APACrefauthors \ 2024 . WatChat: Explaining perplexing programs by debugging mental models Watchat: Explaining perplexing programs by debugging mental models . arXiv preprint arXiv:2403.05334

    work page Pith review arXiv 2024

  6. [6]

    , Houlihan, S D

    chen2024intervening APACrefauthors Chen, T. , Houlihan, S D. , Chandra, K. , Tenenbaum, J. \ Saxe, R. APACrefauthors \ 2024 . Intervening on Emotions by Planning Over a Theory of Mind Intervening on emotions by planning over a theory of mind . Proceedings of the Annual Meeting of the Cognitive Science Society Proceedings of the annual meeting of the cogni...

    2024

  7. [7]

    , Sucholutsky, I

    collins2024building APACrefauthors Collins, K M. , Sucholutsky, I. , Bhatt, U. , Chandra, K. , Wong, L. , Lee, M. others APACrefauthors \ 2024 . Building Machines that Learn and Think with People Building machines that learn and think with people . Nature Human Behavior

    2024

  8. [8]

    , Becker, C

    faden1981disclosure APACrefauthors Faden, R R. , Becker, C. , Lewis, C. , Freeman, J. \ Faden, A I. APACrefauthors \ 1981 . Disclosure of information to patients in medical care Disclosure of information to patients in medical care . Medical care 19 7 718--733

    work page 1981

Show all 33 references
  1. [9]

    \ Goodman, N D

    frank2012predicting APACrefauthors Frank, M C. \ Goodman, N D. APACrefauthors \ 2012 . Predicting pragmatic reasoning in language games Predicting pragmatic reasoning in language games . Science 336 6084 998--998

    2012

  2. [10]

    APACrefauthors \ 1986

    frijda1986emotions APACrefauthors Frijda, N H. APACrefauthors \ 1986 . The emotions The emotions . Cambridge University Press

    1986

  3. [11]

    \ Icard, T

    gerstenberg2020expectations APACrefauthors Gerstenberg, T. \ Icard, T. APACrefauthors \ 2020 . Expectations affect physical causation judgments. Expectations affect physical causation judgments. Journal of Experimental Psychology: General 149 3 599

    2020

  4. [12]

    \ Frank, M C

    goodman2016pragmatic APACrefauthors Goodman, N D. \ Frank, M C. APACrefauthors \ 2016 . Pragmatic language interpretation as probabilistic inference Pragmatic language interpretation as probabilistic inference . Trends in cognitive sciences 20 11 818--829

    2016

  5. [13]

    APACrefauthors \ 1975

    grice1975logic APACrefauthors Grice, H P. APACrefauthors \ 1975 . Logic and conversation Logic and conversation . Speech acts Speech acts \ ( \ 41--58). Brill

    1975

  6. [14]

    , Gerstenberg, T

    harding2025communication APACrefauthors Harding, J. , Gerstenberg, T. \ Icard, T. APACrefauthors \ 2025 . A Communication-First Account of Explanation A communication-first account of explanation . arXiv preprint arXiv:2505.03732

    2025 arXiv

  7. [15]

    APACrefauthors \ 1996

    hilton1996mental APACrefauthors Hilton, D J. APACrefauthors \ 1996 . Mental models and causal explanation: Judgements of probable cause and explanatory relevance Mental models and causal explanation: Judgements of probable cause and explanatory relevance . Thinking and Reasoni...

    1996

  8. [16]

    , Kleiman-Weiner, M

    houlihan2023emotion APACrefauthors Houlihan, S D. , Kleiman-Weiner, M. , Hewitt, L B. , Tenenbaum, J B. \ Saxe, R. APACrefauthors \ 2023 . Emotion prediction as computation over a generative theory of mind Emotion prediction as computation over a generative theory of mind . Ph...

    2023

  9. [17]

    , Kominsky, J F

    icard2017normality APACrefauthors Icard, T F. , Kominsky, J F. \ Knobe, J. APACrefauthors \ 2017 . Normality and actual causal strength Normality and actual causal strength . Cognition 161 80--93

    2017

  10. [18]

    \ Josephson, S G

    josephson1996abductive APACrefauthors Josephson, J R. \ Josephson, S G. APACrefauthors \ 1996 . Abductive inference: Computation, philosophy, technology Abductive inference: Computation, philosophy, technology . Cambridge University Press

    1996

  11. [19]

    , Harding, J

    kirfel2024explain APACrefauthors Kirfel, L. , Harding, J. , Shin, J Y. , Xin, C. , Icard, T. \ Gerstenberg, T. APACrefauthors \ 2024 . Do as I explain: Explanations communicate optimal interventions Do as i explain: Explanations communicate optimal interventions . Proceedings ...

    2024

  12. [20]

    , Phillips, J

    kominsky2015causal APACrefauthors Kominsky, J F. , Phillips, J. , Gerstenberg, T. , Lagnado, D. \ Knobe, J. APACrefauthors \ 2015 . Causal superseding Causal superseding . Cognition 137 196--209

    2015

  13. [21]

    APACrefauthors \ 1991

    lazarus1991emotion APACrefauthors Lazarus, R S. APACrefauthors \ 1991 . Emotion and adaptation Emotion and adaptation \ ( 557). Oxford University Press

    1991

  14. [22]

    APACrefauthors \ 1990

    lipton1990contrastive APACrefauthors Lipton, P. APACrefauthors \ 1990 . Contrastive explanation Contrastive explanation . Royal Institute of Philosophy Supplements 27 247--266

    1990

  15. [23]

    APACrefauthors \ 2006

    lombrozo2006structure APACrefauthors Lombrozo, T. APACrefauthors \ 2006 . The structure and function of explanations The structure and function of explanations . Trends in cognitive sciences 10 10 464--470

    2006

  16. [24]

    APACrefauthors \ 2007

    lombrozo2007simplicity APACrefauthors Lombrozo, T. APACrefauthors \ 2007 . Simplicity and probability in causal explanation Simplicity and probability in causal explanation . Cognitive psychology 55 3 232--257

    2007

  17. [25]

    APACrefauthors \ 2012

    lombrozo2012explanation APACrefauthors Lombrozo, T. APACrefauthors \ 2012 . Explanation and abductive inference Explanation and abductive inference . Oxford handbook of thinking and reasoning 260--276

    2012

  18. [26]

    APACrefauthors \ 2002

    mcclure2002goal APACrefauthors McClure, J. APACrefauthors \ 2002 . Goal-based explanations of actions and outcomes Goal-based explanations of actions and outcomes . European review of social psychology 12 1 201--235

    2002

  19. [27]

    APACrefauthors \ 2019

    miller2019explanation APACrefauthors Miller, T. APACrefauthors \ 2019 . Explanation in artificial intelligence: Insights from the social sciences Explanation in artificial intelligence: Insights from the social sciences . Artificial intelligence 267 1--38

    2019

  20. [28]

    APACrefauthors \ 2014

    moors2014flavors APACrefauthors Moors, A. APACrefauthors \ 2014 . Flavors of appraisal theories of emotion Flavors of appraisal theories of emotion . Emotion Review 6 4 303--307

    2014

  21. [29]

    \ Schitter, C

    palan2018prolific APACrefauthors Palan, S. \ Schitter, C. APACrefauthors \ 2018 . Prolific.ac--- A subject pool for online experiments Prolific.ac--- A subject pool for online experiments . Journal of Behavioral and Experimental Finance 17 22--27

    2018

  22. [30]

    \ Goodman, N

    poesia2022left APACrefauthors Poesia Reis e Silva, G. \ Goodman, N. APACrefauthors \ 2022 . Left to the Reader: Abstracting Solutions in Mathematical Reasoning Left to the reader: Abstracting solutions in mathematical reasoning . 44 44 . APACrefURL https://escholarship.org/uc/...

    2022

  23. [31]

    \ Thill, S

    riveiro2021thats APACrefauthors Riveiro, M. \ Thill, S. APACrefauthors \ 2021 . ``That's (not) the output I expected!'' On the role of end user expectations in creating explanations of AI systems ``that's (not) the output i expected!'' on the role of end user expectations in c...

    2021

  24. [32]

    , Ho, M K

    sumers2023reconciling APACrefauthors Sumers, T R. , Ho, M K. , Griffiths, T L. \ Hawkins, R D. APACrefauthors \ 2023 . Reconciling truthfulness and relevance as epistemic and decision-theoretic utility. Reconciling truthfulness and relevance as epistemic and decision-theoretic...

    2023

  25. [33]

    , Tessler, M H

    yoon2020polite APACrefauthors Yoon, E J. , Tessler, M H. , Goodman, N D. \ Frank, M C. APACrefauthors \ 2020 . Polite speech emerges from competing social goals Polite speech emerges from competing social goals . Open Mind 4 71--87

    2020

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.