We use psychological concepts (e.g., intention and desire) when we ascribe psychological states to others for purposes of describing, explaining, and predicting their actions. Does the evidence reported by Knobe show, as he thinks, that moral evaluation shapes our mastery of psychological concepts? We argue that the evidence so far shows instead that moral evaluation shapes the way we report, not the way we think about, others' psychological states.

The network approach to psychiatric phenomena has the potential to clarify and enhance psychiatric diagnosis and classification. However, its generally well-justified anti-essentialism views psychiatric disorders as invariably fuzzy and arbitrary, and overlooks the likelihood that the domain includes some latent categories. Network models misrepresent these categories, and fail to recognize that some comorbidity may represent valid co-occurrence of discrete conditions.

Cramer et al.'s account of comorbidity comes with a substantive philosophical view concerning the nature of psychological disorders. Although the network account is responsive to problems with extant approaches, it faces several practical and conceptual challenges of its own, especially in cases where the individual differences in network structures require the analysis of intra-individual time-series data.

Moral considerations and our normative expectations influence not only our judgments about intentional action or causation but also our judgments about exact probabilities and quantities. Whereas those cases support the competence theory proposed by Knobe in his paper, they remain compatible with a modular conception of the interaction between moral and nonmoral cognitive faculties in each of those domains.

Moral judgments often affect scientific judgments in real-world contexts, but Knobe's examples in the target article do not capture this phenomenon.

First, we question whether Cramer et al.'s proposed network model can provide a viable scientific foundation for investigating comorbidity without invoking latent variables in some form. Second, the authors' claim that the network perspective is radically different from a latent variable perspective rests upon an undemonstrated premise. Without being demonstrated, we think the premise is potentially misleading.

Cramer et al. contrast two possible explanations for psychological symptoms: latent variables (i.e., specific cause) versus a network of causality between symptoms. There is a third explanation: The reason for comorbidity and the reported network structure of psychological symptoms is that the underlying biological cause is a psychoneuroimmunoendocrine information network which, when dysregulated, leads to several maladaptive psychological and somatic symptoms.

We have found that moral considerations interact with belief ascription in determining intentionality judgment. We attribute this finding to a differential availability of plausible counterfactual alternatives that undo the negative side-effect of an action. We conclude that Knobe's thesis does not account for processes by which counterfactuals are generated and how these processes affect moral evaluations.

Latent variable modeling has revealed important conundrums in the DSM classification system. We agree that the network perspective has potential to inspire new insights and resolve some of these conundrums. We note, however, that alone it cannot really help us understand etiology. Etiology, not comorbidity, is the fundamental question.

Moral judgment – even the type discussed by Knobe – necessarily relies on substantial information about an agent's mental states, especially regarding beliefs and attitudes. Moreover, the effects described by Knobe can be attributed to norm violations in general, rather than moral concerns in particular. Consequently, Knobe's account overstates the influence of moral judgment on assessments of mental states and causality.

Cramer et al. articulate a novel perspective on comorbidity. However, their network models must be compared with more parsimonious latent variable models before conclusions can be drawn about network models as plausible accounts of comorbidity. Latent variable models have proven generative in studying psychopathology and its external correlates, and we doubt network models will prove as useful for psychopathology research.

In criminal law, foresight betrays a guilty mind as much as intent does: both reveal that the agent is not properly motivated to avoid an illegal state of affairs. This commonality warrants our judgment that the state is brought about intentionally, even when unintended. In contrast to Knobe, I thus retain the idea that acting intentionally is acting with a certain frame of mind.

Cramer et al. present a thoughtful application of network analysis to symptoms, but certain questions remain open. These questions involve the intended causal interpretation, the critique of latent variables, individual variation in causal networks, Borsboom's idea of networks as measurement models, and how well the data support the stability of the network results.

Scientists are generally more moral, and moralists more scientific, than Knobe suggests. His own experiments show that people, rather than making unscientific judgements about the moral intentions of others, are behaving as good Bayesians who take account of prior knowledge.

In this commentary we explore Knobe's ideas of moral judgments leading to moral intuitions in the context of the moral thought and moral action debate. We suggest that Knobe's primary moral judgment and the setting of a continuum with a default point is in essence a form of cultural capital, different from moral action, which is more akin to social capital.

In the target article, Cramer et al. suggest that diagnostic classification is improved by modeling the relationship between manifest variables (i.e., symptoms) rather than modeling unobservable latent variables (i.e., diagnostic categories such as Generalized Anxiety Disorder). This commentary discusses whether symptoms represent manifest or latent variables and the implications of this distinction for diagnosis and treatment.

For Knobe, observers evaluate mental states by comparing agents' statements with “defaults,” the attitudes they are expected to hold. In our analysis, Knobe's model relies primarily on what agents should think, and little on expectancies of what they would think. We show the importance and complexity of including descriptive and prescriptive norms if one is to take expectancies seriously.

Cramer et al. present an original and interesting network perspective on comorbidity and contrast this perspective with a more traditional interpretation of comorbidity in terms of latent variable theory. My commentary focuses on the relationship between the two perspectives; that is, it aims to qualify the presumed contrast between interpretations in terms of networks and latent variables.

Children learn from people and about people simultaneously; that is, children consider evidentiary qualities of human actions which cross traditional domain boundaries. We propose that Knobe's moral asymmetries are a natural consequence of this learning process: the way “child scientists” gather evidence for causation, intention, and morality through early social experiences.

Cramer et al. persuasively conceptualize major depressive disorder (MDD) and generalized anxiety disorder (GAD) as network disorders, rejecting latent variable accounts. But how does their radical picture generalize across the suite of mental and personality disorders? Addictions are Axis I disorders that may be better characterized by latent variables. Their comorbidity relationships could be captured by inserting them as nodes in a super-network of Axis I conditions.