How We Know What Not To Think

Phillips, Jonathan; Morris, Adam; Cushman, Fiery

doi:10.1016/j.tics.2019.09.007

Cited by 114 publications

(106 citation statements)

References 98 publications

(155 reference statements)

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“…If the Queen of England had watered the plants, they would not have died either. While this counterfactual statement is true, it strikes us as irrelevant (Kominsky et al, 2015;Phillips & Cushman, 2017;Phillips, Morris, & Cushman, 2019). One popular strategy to justify why the gardener and not the Queen of England caused the plants to die, is to rely on the role of norms and expectations.…”

Section: Future Directions and Open Challengesmentioning

confidence: 99%

A counterfactual simulation model of causal judgments for physical events

Goodman¹,

Lagnado²,

Tenenbaum³

2020

Preprint

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How do people make causal judgments? We propose the counterfactual simulation model (CSM) of causal judgment which predicts causal judgments by comparing what actually happened with counterfactual simulations of what would have happened in relevant contingencies. It postulates different aspects of causation that capture whether the cause made a difference to whether and how the outcome occurred, and if the cause was sufficient and robust. We test the CSM in three experiments that ask participants to make judgments about dynamic collision events. Experiment 1 reveals a very close quantitative mapping between causal judgments, and participants’ belief that the outcome would have been different without the cause. Experiment 2 demonstrates that counterfactual contrasts are necessary for explaining causal judgments. Participants’ judgments differ dramatically between pairs of situations in which what actually happened was identical, but what would have happened differed. Experiment 3 features two candidate causes and shows that participants’ judgments are sensitive to different aspects of causation. The CSM provides a better fit to participants’ judgments than a heuristic model which uses features based on what actually happened. We discuss how the CSM can be used to model the semantics of different causal verbs, deal with causation by omission, and be extended beyond the physical domain.

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Section: Future Directions and Open Challengesmentioning

confidence: 99%

A counterfactual simulation model of causal judgments for physical events

Goodman¹,

Lagnado²,

Tenenbaum³

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…In contrast, in trinary choice it is also optimal to ignore the item with the lowest posterior mean. Our model thus provides a normative foundation for the allocation of attention to high-value options [4,25,26,28,51,58,59]. However, although the data we present suggest that human attention is less affected by estimated value in the two-item case, it is not clear that there is no effect (Figs.…”

Section: Discussionmentioning

confidence: 70%

“…Studies in psychology, economics, and neuroscience have provided useful insights into this question [1]. In choices involving many options, only a subset of items are fixated and only fixated items are chosen, a phenomenon captured by two-stage consideration set models [2][3][4][5][6]. Among the attended items, those that are fixated longer are more likely to be chosen [7][8][9][10][11][12][13][14], unless the items are aversive, in which case more-attended items are chosen less frequently [8,15].…”

Section: Introductionmentioning

confidence: 99%

Fixation patterns in simple choice reflect optimal information sampling

Callaway¹,

Rangel²,

Griffiths³

2019

Preprint

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When faced with a decision between several options, people rarely fully consider every alternative. Instead, we direct our attention to the most promising candidates, focusing our limited cognitive resources on evaluating the options that we are most likely to choose. A growing body of empirical work has shown that attention plays an important role in human decision making, but it is still unclear how people choose with option to attend to at each moment in the decision making process. In this paper, we present an analysis of how a rational decision maker should allocate her attention. We cast attention allocation in decision making as a sequential sampling problem, in which the decision maker iteratively selects from which distribution to sample in order to update her beliefs about the values of the available alternatives. By approximating the optimal solution to this problem, we derive a model in which both the selection and integration of evidence are rational. This model predicts choices and reaction times, as well as sequences of visual fixations. Applying the model to a ternary-choice dataset, we find that its predictions align well with human data.

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“…As such, "motivated intuition" is perhaps a better description of the current findings than "motivated reasoning". Ultimately, the corrective effect of slow reflection might rely on a key function of consciousness: The ability to simulate the future as a collection of multiple possibilities (Baumeister, Maranges, & Sjåstad, 2018;Phillips, Morris, & Cushman, 2019).…”

Section: Discussionmentioning

confidence: 99%

Fast optimism, slow realism? Causal evidence for a two-step model of future thinking

Sjåstad¹,

Baumeister²

2020

Preprint

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Future optimism is a widespread phenomenon, often attributed to the psychology of intuition. However, causal evidence for this explanation is lacking, and sometimes cautious realism is found. One resolution is that thoughts about the future have two steps: A first step imagining the desired outcome, and then a sobering reflection on how to get there. Four pre-registered experiments supported this two-step model, showing that fast predictions are more optimistic than slow predictions. The total sample consisted of 2,116 participants from USA and Norway, providing 9,036 predictions. In Study 1, participants in the fast-response condition thought positive events were more likely to happen and that negative events were less likely, as compared to participants in the slow-response condition. Although the predictions were optimistically biased in both conditions, future optimism was significantly stronger among fast responders. Participants in the fast-response condition also relied more on intuitive heuristics (CRT). Studies 2 and 3 focused on future health problems (e.g., getting a heart attack or diabetes), in which participants in the fast-response condition thought they were at lower risk. Study 4 provided a direct replication, with the additional finding that fast predictions were more optimistic only for the self (vs. the average person). The results suggest that when people think about their personal future, the first response is optimistic, which only later may be followed by a second step of reflective realism. Current health, income, trait optimism, perceived control and happiness were negatively correlated with health-risk predictions, but did not moderate the fast-optimism effect.

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How We Know What Not To Think

Cited by 114 publications

References 98 publications

A counterfactual simulation model of causal judgments for physical events

A counterfactual simulation model of causal judgments for physical events

Fixation patterns in simple choice reflect optimal information sampling

Fast optimism, slow realism? Causal evidence for a two-step model of future thinking

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