This paper theoretically and empirically investigates the role of noisy cognition in perceptual judgment, focusing on the central tendency effect: the well-known empirical regularity that perceptual judgments are biased towards the center of the stimulus distribution. Based on a formal Bayesian framework, we generate predictions about the relationships between subjective confidence, central tendency, and response variability. Specifically, our model clarifies that lower subjective confidence as a measure of posterior uncertainty about a judgment should predict (i) a lower sensitivity of magnitude estimates to objective stimuli; (ii) a higher sensitivity to the mean of the stimulus distribution; (iii) a stronger central tendency effect at higher stimulus magnitudes; and (iv) higher response variability. To test these predictions, we collect a large-scale experimental data set and additionally re-analyze perceptual judgment data from several previous experiments. Across data sets, subjective confidence is strongly predictive of the central tendency effect and response variability, both correlationally and when we exogenously manipulate the magnitude of sensory noise. Our results are consistent with (but not necessarily uniquely explained by) Bayesian models of confidence and the central tendency.

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The attraction effect occurs when the presence of an inferior option (the decoy) increases the attractiveness of the option that dominates it (the target). Despite its prominence in behavioral science, recent evidence points to the puzzling existence of the opposite phenomenon—a repulsion effect. In this paper, we formally develop and experimentally test a normative account of the repulsion effect. Our theory is based on the idea that the true values of options are uncertain and must be inferred from available information, which includes the properties of other options. A low-value decoy can signal that the target also has low value when both are believed to be generated by a similar process. We formalize this logic using a hierarchical Bayesian cognitive model that makes predictions about how the strength of the repulsion effect should vary with statistical properties of the decision problem. This theory may help account for several documented phenomena linked to the repulsion effect across both economic and perceptual decision making, as well as new experimental data. Our results shed light on the key drivers of context-dependent judgment across multiple domains and sharpen our understanding of when decoys can be detrimental.

By collaborating with others, humans can pool their limited knowledge, skills, and resources to achieve goals that outstrip the abilities of any one person. What cognitive capacities make human collaboration possible? Here, we propose that collaboration is grounded in an intuitive understanding of how others think and of what they can do—in other words, of their mental states and competence. We present a belief-desire-competence framework that formalizes this proposal by extending existing models of commonsense psychological reasoning. Our framework predicts that agents recursively reason how much effort they and their partner will allocate to a task, based on the rewards at stake and on their own and their collaborator's competence. Across three experiments (N = 249), we show that the belief-desire-competence framework captures human judgments in a variety of contexts that are critical to collaboration, including predicting whether a joint activity will succeed (Experiment 1), selecting incentives for collaborators (Experiment 2), and choosing which individuals to recruit for a collaborative task (Experiment 3). Our work provides a theoretical framework for understanding how commonsense psychological reasoning contributes to collaborative achievements.

How do people judge responsibility in collaborative tasks? Past work has proposed a number of metrics that people may use to attribute blame and credit to others, such as effort, competence, and force. Some theories consider only the produced effort or force (individuals are more responsible if they produce more effort or force), whereas others consider counterfactuals (individuals are more responsible if some alternative behavior on their or their collaborator's part could have altered the outcome). Across four experiments (N = 717), we found that participants’ judgments are best described by a model that considers both produced and counterfactual effort. This finding generalized to an independent validation data set (N = 99). Our results thus support a dual-factor theory of responsibility attribution in collaborative tasks.