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.

I argue that Murayama and Jach's claim that higher-order motivational constructs face the “black-box” problem is misconceived because it doesn't clearly distinguish between personal and subpersonal explanations. To solve it they propose interpreting motivations as causal effects of mental computational processes. I suggest that their solution might be more compellingly presented as providing a fictionalist perspective on some personal-level constructs.

Murayama and Jach claim that current motivational constructs do not specify causal processes (black-box problem) and that mental computational processes solve this problem. We argue, process-focused research requires theoretical frameworks addressing situational variations, individual differences, and their interaction. Classic achievement motivation theory provides comprehensive models with empirically measurable process-related constructs and predictions. Recent developments build on this, addressing motivation, action, and their socio-cultural and lifespan context. Theory-free mental computational processes cannot do any of that.

Murayama and Jach rightfully aim to conceptualize motivation as an emergent property of a dynamic system of interacting elements. However, they do not embrace the ontological and paradigmatic constraints of the dynamic systems approach. They therefore miss the very process of emergence and how it can be formally modeled and tested by specific types of computer simulation.

Murayama and Jach offer a thoughtful and timely critique of motivation constructs. We largely concur with their basic premises, but offer additional input and clarification regarding the importance of carefully considering the energization and direction components of motivation, and fully attending to the hierarchical aspect of motivation rather than prioritizing particular levels of analysis.

Although in basic agreement with Murayama and Jach's call for greater attention to the black boxes underlying motivated behavior, we provide examples of our published suggestions regarding how subjective task value (and ability self-concepts) “gets into people's knowledge structures.” We suggest additional mental computational processes to investigate and call for a developmental and situated individual differences approach to this work.

Murayama and Jach raise a key problem in behavioral sciences, to which we suggest evolutionary science can provide a solution. We emphasize the role of adaptive mechanisms in shaping behavior and argue for the integration of hierarchical theories of goal-directed cognition and behavioral flexibility, in order to unravel the motivations behind actions that, in themselves, seem disconnected from adaptive goals.