We discuss some of the most central problems and concepts elaborated within the social sciences, especially sociology, which are not or only tangentially exposed by Moffett. Then, we will exemplify of how identity, which is a central constituent of Moffett's definition of society, cannot be opposed to interaction despite his claims. Rather it is to be studied as interactional achievement.

While ambitious, interesting, and generally corresponding to usage in archaeology history, and anthropology, Moffett's paper seems more philosophy of science (conceptual analysis) than science (their use in explanations). It avoids explanations of how “markers of identity” and “their recognition” are acquired (e.g., by biological evolution, individual learning, social learning, or sociocultural evolution) and what the concept of “a society” explains.

Moffett's definition of societies could be augmented by recognizing society's organizing systems that coordinate diverse individuals' behavior for collective good. Viewing humans as cultural animals indicates three reasons for ever larger societies: More shared information, bigger and better marketplace for exchange, and military superiority in numbers. Sports teams are societies offering a promising venue for empirical work.

If human societies are understood as identity groups, then our psychology should include design for the production and detection of credible identity signals. We argue that vocalizations are ideal identity signals because the human auditory system is sensitive to subtle acoustic features; vocal signals are efficient; and speech and song are highly complex, enabling the embedding therein of identity signals.

Research in community ecology, anthropology, and ethnoprimatology has identified mixed-species animal groups, and we argue that Moffett's definition of society allows these groups to qualify as societies. The existence of mixed-species society has two implications – that societies are structured by social norms, and that it may be more common to belong to multiple societies than Moffett suggests.

I propose the need to establish a comparative study of societies, conceived of specificially here as bounded groups beyond a simple, immediate family that have the potential to endure for generations, whose constituent individuals recognize one another as members, and that maintain control over a physical space. This definition, with refinements and ramifications I explore, serves for cross-disciplinary research because it applies not just to nations but to diverse hunter–gatherer and tribal groups with a pedigree that likely traces back to the societies of our common ancestor with the chimpanzees. It also applies to groups among other species for which comparison to humans can be instructive. Notably, it describes societies in terms of shared group identification rather than social interactions. An expansive treatment of the topic is overdue given that the concept of a society (even the use of such synonyms as primate “troop”) has fallen out of favor among biologists, resulting in a semantic mess; whereas sociologists rarely consider societies beyond nations, and social psychologists predominantly focus on ethnicities and other component groups of societies. I examine the relevance of societies across realms of inquiry, discussing the ways member recognition is achieved; how societies compare to other organizational tiers; and their permeability, territoriality (allowing for mobile territories), relation to social networks and kinship, and impermanence. We have diverged from our ancestors in generating numerous affiliations within and between societies while straining the expectation of society memberships by assimilating diverse populations. Nevertheless, if, as I propose, societies were the first, and thereafter the primary, ingroups of prehistory, how we came to register society boundaries may be foundational to all human “groupiness.” A discipline-spanning approach to societies should further our understanding of what keeps societies together and what tears them apart.

The target article argued that motivation constructs are treated as black boxes and called for work that specifies the mental computational processes underlying motivated behavior. In response to critical commentaries, we clarify our philosophical standpoint, elaborate on the meaning of mental computational processes and why past work was not sufficient, and discuss the opportunities to expand the scope of the framework.

Although higher-level constructs often fail to explain the mechanisms underlying motivation, we argue that purely mechanistic approaches have limitations. Lower-level neural data help us identify “biologically plausible” mechanisms, while higher-level constructs are critical to formulate measurable behavioral outcomes when constructing computational models. Therefore, we propose that a multi-level, multi-measure approach is required to fully unpack the black box of motivated behavior.

Murayama and Jach critically evaluate the idea that motivation is a dynamic that determines behavior and propose alternatively that it might be an emergent property that people construe through perceived regularities in experience and action. The critique has value but fails to appreciate the progress that has been made in moving beyond the idea of which the authors are critical.

Understanding the psychological computations underlying motivation can shed light onto motivational constructs as emergent phenomena. According to Murayama and Jach, reward-learning is a key candidate mechanism. However, there's no such thing as a free lunch: Not only benefits (like reward), but also costs inherent to motivated behaviors (like effort, or uncertainty) are an essential part of the picture.

Integrating the predictive processing framework into our understanding of motivation offers promising avenues for theoretical development, while shedding light on the computational processes underlying motivated behavior. Here we decompose expected free energy into intrinsic value (i.e., epistemic affordance) and extrinsic value (i.e., instrumental affordance) to provide insights into how individuals adapt to and interact with their environment.

There are indeed questionable motivation constructs in psychology. The diagnosis and proposed remedies in the target article both neglect the crucial consideration that all tendencies to behaviour compete for the same finite set of degrees of freedom. Action selection also has irreducibly economic aspects which should constrain motivation constructs and already inform healthy research programmes.

We argue that the target article's computational/reductionistic approach to motivation is insufficient to explain the energization of human behavior, because such explanation requires broad consideration of “what people are trying to do.” We illustrate what is gained by retaining (rather than jettisoning) higher-order motivation constructs and show that the authors’ approach assumes, but fails to name, such constructs.

Though we see the potential for benefits from the development of process-oriented approaches, we argue that it falls prey to many of the same critiques raised about the existing construct level of analysis. The construct-level approach will likely dominate motivation research until we develop computational models that are not only accurate, but also broadly usable.

Murayama and Jach criticize the use of high-level motivational constructs in psychology, urging psychologists to “unpack” the black box. These constructs are alleged to be “psychological constructions” with no causal powers of their own. I argue that this view is mistaken, and that high-level motivational constructs are causal even when unpacked in terms of underlying computational, algorithmic, and implementational processes.

Some constructs in motivation science are certainly underdeveloped and some motivation researchers may work with underspecified constructs, as suggested by Murayama and Jach (M&J). However, this is not indicative of a general problem in motivation science. Many motivation theories focus on specific mechanisms underlying motivated behavior and thus have already adopted the computational process perspective that M&J call for.

Metacognitive feelings are an integral part of mental computational processes and influence the outcome of computations. We review supporting evidence on affect inherent in perceptual processes, fluency in study decisions, metacognitive feelings in aha-experiences and intuition, and affect in early phases of interest development. These findings connect to recent theories that combine metacognitive feelings with computational models.

Whole Trait Theory (and other dynamic theories of personality) can illuminate the process by which motivational states become traits. Mental computational processes constitute part of the explanatory mechanisms that drive trait manifestations. Empirical work on Whole Trait Theory may inform future research directions on mental computational processes.

The proposed “black-box” problem and its solution are drawn from the same substance-oriented framework. This framework's assumptions have consequences that re-create the black-box problem at a foundational level. Specifically, Murayama and Jach's solution fails to explain novel behavior that emerges through an organism's development. A process-oriented theoretical shift provides an ontological explanation for emergent behavior and eliminates the black-box problem altogether.

In their proposal for unpacking the black box of motivation, Murayama and Jach (M&J) propose three types of reductions: From high-level to low-level motivational constructs, from motivation to cognition, and from contentful to contentless explanations. Although these reductions come with the promise of parsimony, they carry the risk of losing vital explanatory power.