The social sciences needs more than integrative experimental designs: We need better theories.

Almaatouq et al.'s prescription for more integrative experimental designs is welcome but does not address an equally important problem: Lack of adequate theories. We highlight two features theories ought to satisfy: "Well-specified" and "grounded." We discuss the importance of these features, some positive exemplars, and the complementarity between the target article's prescriptions and improved theorizing.

Evolution of conditional and unconditional commitment.

We show that altruism can evolve as a signaling device designed to solve commitment problems in interactions with outside options. In a simple evolutionary game-theoretic model, uncertainty about agents' incentives to stay in a relationship can cause the relationship to collapse, because of a vicious circle where being skeptical about one's partner's commitment makes one even more likely to leave the relationship. When agents have the possibility to send costly gifts to each other, analytical modeling and agent-based simulations show that gift-giving can evolve as a credible signal of commitment, which decreases the likelihood of relationship dissolution. Interestingly, different conventions can determine the meaning of the signal conveyed by the gift. Exactly two kinds of conventions are evolutionarily stable: according to the first convention, an agent who sends a gift signals that he intends to stay in the relationship if and only if he also receives a gift; according to the second convention, a gift signals unconditional commitment.

Cross-cultural regularities in the cognitive architecture of pride.

Pride occurs in every known culture, appears early in development, is reliably triggered by achievements and formidability, and causes a characteristic display that is recognized everywhere. Here, we evaluate the theory that pride evolved to guide decisions relevant to pursuing actions that enhance valuation and respect for a person in the minds of others. By hypothesis, pride is a neurocomputational program tailored by selection to orchestrate cognition and behavior in the service of: (i) motivating the cost-effective pursuit of courses of action that would increase others' valuations and respect of the individual, (ii) motivating the advertisement of acts or characteristics whose recognition by others would lead them to enhance their evaluations of the individual, and (iii) mobilizing the individual to take advantage of the resulting enhanced social landscape. To modulate how much to invest in actions that might lead to enhanced evaluations by others, the pride system must forecast the magnitude of the evaluations the action would evoke in the audience and calibrate its activation proportionally. We tested this prediction in 16 countries across 4 continents (n = 2,085), for 25 acts and traits. As predicted, the pride intensity for a given act or trait closely tracks the valuations of audiences, local (mean r = +0.82) and foreign (mean r = +0.75). This relationship is specific to pride and does not generalize to other positive emotions that coactivate with pride but lack its audience-recalibrating function.

Universal modesty in signal-burying games.

Why would individuals hide positive information about themselves? Evolutionary game theorists have recently developed the signal-burying game as a simple model to shed light on this puzzle. They have shown that the game has an equilibrium where some agents are better off deliberately reducing the visibility of the signal by which they broadcast their positive traits. However, this equilibrium also features individuals who fully broadcast their positive traits. Here, we show that the signal-burying framework can also explain modesty norms that everyone adheres to: the game contains an equilibrium where all agents who send a signal voluntarily reduce its conspicuousness. Surprisingly, the stability of the two kinds of equilibria rely on very different principles. The equilibrium where some agents brag is stable because of costly signalling dynamics. By contrast, the universal modesty equilibrium exists because buried signals contain probabilistic information about a sender's type, and receivers make optimal use of this information. In the latter equilibrium, burying a signal can be understood as a handicap which makes the signal more honest, but honesty is not achieved through standard costly signalling dynamics.

Rational information search in welfare-tradeoff cognition.

One of the most important dimensions along which we evaluate others is their propensity to value our welfare: we like people who are disposed to incur costs for our benefit and who refrain from imposing costs on us to benefit themselves. The evolutionary importance of social valuation in our species suggests that humans have cognitive mechanisms that are able to efficiently extract information about how much another person values them. Here I test the hypothesis that people are spontaneously interested in the kinds of events that have the most potential to reveal such information. In two studies, I presented participants (Ns = 216; 300) with pairs of dilemmas that another individual faced in an economic game; for each pair, I asked them to choose the dilemma for which they would most like to see the decision that the individual had made. On average, people spontaneously selected the choices that had the potential to reveal the most information about the individual's valuation of the participant, as quantified by a Bayesian ideal search model. This finding suggests that human cooperation is supported by sophisticated cognitive mechanisms for information-gathering.

Rational inferences about social valuation.

The decisions made by other people can contain information about the value they assign to our welfare-for example how much they are willing to sacrifice to make us better off. An emerging body of research suggests that we extract and use this information, responding more favorably to those who sacrifice more even if they provide us with less. The magnitude of their trade-offs governs our social responses to them-including partner choice, giving, and anger. This implies that people have well-designed cognitive mechanisms for estimating the weight someone else assigns to their welfare, even when the amounts at stake vary and the information is noisy or sparse. We tested this hypothesis in two studies (N=200; US samples) by asking participants to observe a partner make two trade-offs, and then predict the partner's decisions in other trials. Their predictions were compared to those of a model that uses statistically optimal procedures, operationalized as a Bayesian ideal observer. As predicted, (i) the estimates people made from sparse evidence matched those of the ideal observer, and (ii) lower welfare trade-offs elicited more anger from participants, even when their total payoffs were held constant. These results support the view that people efficiently update their representations of how much others value them. They also provide the most direct test to date of a key assumption of the recalibrational theory of anger: that anger is triggered by cues of low valuation, not by the infliction of costs.

Counterfactuals and the logic of causal selection.

Everything that happens has a multitude of causes, but people make causal judgments effortlessly. How do people select one particular cause (e.g., the lightning bolt that set the forest ablaze) out of the set of factors that contributed to the event (the oxygen in the air, the dry weather … )? Cognitive scientists have suggested that people make causal judgments about an event by simulating alternative ways things could have happened. We argue that this counterfactual theory explains many features of human causal intuitions, given two simple assumptions. First, people tend to imagine counterfactual possibilities that are both a priori likely and similar to what actually happened. Second, people judge that a factor C caused effect E if C and E are highly correlated across these counterfactual possibilities. In a reanalysis of existing empirical data, and a set of new experiments, we find that this theory uniquely accounts for people's causal intuitions. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A Context-Dependent Bayesian Account for Causal-Based Categorization.

The causal view of categories assumes that categories are represented by features and their causal relations. To study the effect of causal knowledge on categorization, researchers have used Bayesian causal models. Within that framework, categorization may be viewed as dependent on a likelihood computation (i.e., the likelihood of an exemplar with a certain combination of features, given the category's causal model) or as a posterior computation (i.e., the probability that the exemplar belongs to the category, given its features). Across three experiments, in combination with computational modeling, we offer evidence that categorization is better accounted for by assuming that people compute posteriors and not likelihoods, though both probabilities are closely related. This result contrasts with existing analyses of causal-based categorization, which assume that likelihood computations give a good approximation of human judgments. We also find that people are able to compute likelihoods in a closely related task that elicits judgments of consistency rather than category membership judgments. Our analyses show that people do use causal probabilistic information as prescribed by a Bayesian model but that they flexibly compute likelihoods or posteriors depending on the task. We discuss our results in relation to the relevant literature on the topic.

Local Search and the Evolution of World Models.

An open question regarding how people develop their models of the world is how new candidates are generated for consideration out of infinitely many possibilities. We discuss the role that evolutionary mechanisms play in this process. Specifically, we argue that when it comes to developing a global world model, innovation is necessarily incremental, involving the generation and selection among random local mutations and recombinations of (parts of) one's current model. We argue that, by narrowing and guiding exploration, this feature of cognitive search is what allows human learners to discover better theories, without ever grappling directly with the problem of finding a "global optimum," or best possible world model. We suggest this aspect of cognitive processing works analogously to how blind variation and selection mechanisms drive biological evolution. We propose algorithms developed for program synthesis provide candidate mechanisms for how human minds might achieve this. We discuss objections and implications of this perspective, finally suggesting that a better process-level understanding of how humans incrementally explore compositional theory spaces can shed light on how we think, and provide explanatory traction on fundamental cognitive biases, including anchoring, probability matching, and confirmation bias.

Causal Judgment in the Wild: Evidence from the 2020 U.S. Presidential Election.

When explaining why an event occurred, people intuitively highlight some causes while ignoring others. How do people decide which causes to select? Models of causal judgment have been evaluated in simple and controlled laboratory experiments, but they have yet to be tested in a complex real-world setting. Here, we provide such a test, in the context of the 2020 U.S. presidential election. Across tens of thousands of simulations of possible election outcomes, we computed, for each state, an adjusted measure of the correlation between a Biden victory in that state and a Biden election victory. These effect size measures accurately predicted the extent to which U.S. participants (N = 207, preregistered) viewed victory in a given state as having caused Biden to win the presidency. Our findings support the theory that people intuitively select as causes of an outcome the factors with the largest standardized causal effect on that outcome across possible counterfactual worlds.

A simple definition of 'intentionally'.

Cognitive scientists have been debating how the folk concept of intentional action works. We suggest a simple account: people consider that an agent did X intentionally to the extent that X was causally dependent on how much the agent wanted X to happen (or not to happen). Combined with recent models of human causal cognition, this definition provides a good account of the way people use the concept of intentional action, and offers natural explanations for puzzling phenomena such as the side-effect effect. We provide empirical support for our theory, in studies where we show that people's causation and intentionality judgments track each other closely, in everyday situations as well as in scenarios with unusual causal structures. Study 5 additionally shows that the effect of norm violations on intentionality judgments depends on the causal structure of the situation, in a way uniquely predicted by our theory. Taken together, these results suggest that the folk concept of intentional action has been difficult to define because it is made of cognitive building blocks, such as our intuitive concept of causation, whose logic cognitive scientists are just starting to understand.

When do we think that X caused Y?

When judging what caused an event, people do not treat all factors equally - for instance, they will say that a forest fire was caused by a lit match, and not mention the oxygen in the air which helped fuel the fire. We develop a computational model formalizing the idea that causal judgment is designed to identify "portable" causes - causes that are likely to generalize across a variety of background circumstances. Under minimal assumptions, the model is surprisingly simple: a factor is regarded as a cause of an outcome to the extent that it is, across counterfactual worlds, correlated with that outcome. The model explains why causal judgment is influenced by the normality of candidate causes, and outperforms other known computational models when tested against an existing fine-grained dataset of human graded causal judgments (Morris, A., Phillips, J., Gerstenberg, T., & Cushman, F. (2019). Quantitative causal selection patterns in token causation. PloS one, 14(8).).