In this commentary on Borsboom et al.’s target article, I address an inadequate, simplified use of the idea of “reductionism” in clinical psychology and psychiatry. This is important because reductionism is a fundamental methodology of science. Explaining mental states and processes in terms of biological and brain states and processes is fundamental for the science of psychology. I also briefly address a fundamental methodology of the goal of psychology as a hard science.

The thesis of multiple realisation that Borsboom et al. are relying on should not be taken for granted. In dissolving the apparent multiple realisation, the reductionist research strategies in psychopathology research (the Research Domain Criteria [RDoC] framework, in particular) are bound to lead to eliminativism rather than reductionism. Therefore, Borsboom et al. seem to be aiming at a wrong target.

Understanding how structure maps to function in the brain in terms of large-scale networks is critical to elucidating the brain basis of mental phenomena and mental disorders. Given that this mapping is many-to-many, I argue that researchers need to shift to a multivariate brain and behavior characterization to fully unravel the contributions of brain processes to typical and atypical function.

An engineer's viewpoint on psychiatry asks: What are the failure modes that underlie psychiatric dysfunction? And: How can we modify the system? Psychiatry has made great strides in understanding and treating disorders using biology; however, failure modes and modification access points can also exist extrinsically in environmental interactions. The network analysis suggested by Borsboom et al. in the target article provides a new viewpoint that should be incorporated into current theoretical constructs, not placed in opposition to them.

Use of network models to identify causal structure typically blocks reduction across the sciences. Entanglement of mental processes with environmental and intentional relationships, as Borsboom et al. argue, makes reduction of psychology to neuroscience particularly implausible. However, in psychiatry, a mental disorder can involve no brain disorder at all, even when the former crucially depends on aspects of brain structure. Gambling addiction constitutes an example.

We argue that the explanatory role of intentional content in connecting symptoms in a network approach to psychopathology hinges neither on causality nor on rationality. Instead, we argue that it hinges on a pluralistic body of practical and clinical know-how. Incorporating this practical approach to intentional state ascription in psychopathological cases expands and improves traditional interpretivism.

Borsboom et al. convincingly argue that, from their symptom network perspective, mental disorders cannot be reduced to brain disorders. While granting that network structures exist, I respond that there is no reason to think they are the only psychiatric phenomena worth explaining. From a pluralist perspective, what is required is not a full-scale rejection of explanatory reductionism but a critical attention to the circumstances of its application.

Autism spectrum disorder (ASD) subjects can present temporary behaviors of acute agitation and aggressiveness, named problem behaviors. They have been shown to be consistent with the self-organized criticality (SOC), a model wherein occasionally occurring “catastrophic events” are necessary in order to maintain a self-organized “critical equilibrium.” The SOC can represent the psychopathology network structures and additionally suggests that they can be considered as self-organized systems.

After the Darwinian revolution, biology is not only the study of the operation of structural elements (functional biology), but also the study of adaption and phylogenetic history (evolutionary biology). From an evolutionary perspective, the biology of mental disorders is not just “neurobiology and genetic constitution” but also adaptive reactions to adverse situations. Evolutionary explanations of mental disorders are biological and non-reductionist.

Network models block reductionism about psychiatric disorders only if models are interpreted in a realist manner – that is, taken to represent “what psychiatric disorders really are.” A flexible and more instrumentalist view of models is needed to improve our understanding of the heterogeneity and multifactorial character of psychiatric disorders.

Symptom network models (SNWMs) play an important role in identifying but not explaining patterns of symptoms. We discuss underlying assumptions of SNWMs and argue that they represent phenomenal models, best suited to detecting patterns among symptoms. SNWMs need to be supplemented with mechanistic models that provide constitutive and etiological explanations of each symptom (network nodes) once relevant patterns have been identified.

We address the commentaries on our target article in terms of four major themes. First, we note that virtually all commentators agree that mental disorders are not brain disorders in the common interpretation of these terms, and establish the consensus that explanatory reductionism is not a viable thesis. Second, we address criticisms to the effect that our article was misdirected or aimed at a straw man; we argue that this is unlikely, given the widespread communication of reductionist slogans in psychopathology research and society. Third, we tackle the question of whether intentionality, extended systems, and multiple realizability are as problematic as claimed in the target article, and we present a number of nuances and extensions with respect to our article. Fourth, we discuss the question of how the network approach should incorporate biological factors, given that wholesale reductionism is an unlikely option.

Food uncertainty has the effect of invigorating food-related responses. Psychologists have noted that mammals and birds respond more to a conditioned stimulus that unreliably predicts food delivery, and ecologists have shown that animals (especially small passerines) consume and/or hoard more food and can get fatter when access to that resource is unpredictable. Are these phenomena related? We think they are. Psychologists have proposed several mechanistic interpretations, while ecologists have suggested a functional interpretation: The effect of unpredictability on fat reserves and hoarding behavior is an evolutionary strategy acting against the risk of starvation when food is in short supply. Both perspectives are complementary, and we argue that the psychology of incentive motivational processes can shed some light on the causal mechanisms leading animals to seek and consume more food under uncertainty in the wild. Our theoretical approach is in agreement with neuroscientific data relating to the role of dopamine, a neurotransmitter strongly involved in incentive motivation, and its plausibility has received some explanatory and predictive value with respect to Pavlovian phenomena. Overall, we argue that the occasional and unavoidable absence of food rewards has motivational effects (called incentive hope) that facilitate foraging effort. We show that this hypothesis is computationally tenable, leading foragers in an unpredictable environment to consume more food items and to have higher long-term energy storage than foragers in a predictable environment.

We propose an elaboration of Anselme and Güntürkün's research that considers individuals’ foraging behavior as part of group efforts to cope with uncertainty. We discuss different possibilities for the interaction between individual and group mechanisms for risk reduction in uncertain environments, and we raise some open questions for future research.

Poverty-related food insecurity can be viewed as a form of economic and nutritional uncertainty that can lead, in some situations, to a desire for more filling and satisfying food. Given the current obesogenic food environment and the nature of the food supply, those food choices could engage a combination of sensory, neurophysiological, and genetic factors as potential determinants of obesity.

When an environment is uncertain, humans and other animals benefit from preparing for and attempting to predict potential outcomes. People respond to uncertainty both by conserving mental energy on tasks unrelated to the source of the uncertainty and by increasing their attentiveness to information related to the uncertainty. This mental hoarding and foraging allow people to prepare in uncertain situations.

For artificial agents trading off exploration (food seeking) versus (short-term) exploitation (or consumption), our experiments suggest that uncertainty (interpreted information, theoretically) magnifies food seeking. In more uncertain environments, with food distributed uniformly randomly, exploration appears to be beneficial. In contrast, in biassed (less uncertain) environments, with food concentrated in only one part, exploitation appears to be more advantageous. Agents also appear to do better in biassed environments.

Foraging for nutritional sustenance represents common significant learned/heritable survival strategies evolved for phylum-diverse cellular life on Earth. Unicellular aneural to multicellular neural foragers display conserved rational or irrational decision making depending on outcome predictions for noise-susceptible real/illusory homeodynamic and ambient dietary cues. Such context-dependent heuristic-guided foraging enables optimal, suboptimal, or fallacious decisions that drive organismal adaptation, health, longevity, and life history.