within formalized cognitive architectures. Pioneering artificial in- telligence and cognitive science researcher Alan Newell, argued (e.g., 1973; 1990) that psychological science would benefit by moving beyond mere verbal (qualitative) hypotheses, such as simple dichotomies (e.g., nature vs. nurture), toward formalized (quantitative) hypotheses. Additionally, he suggested that one path toward a unified theory of mind is by developing cognitive architectures. A number of cognitive architectures have been de- veloped, such as Soar (Newell 1990), EPIC (Meyer & Kieras 1997) and ACT-R (Anderson & Lebiere 1998; see Langley et al. 2008 for a review of different architectures). Take ACT-R, for example (see Anderson 2007 for details). This model incorporates decades of research to describe a full range of cognitive processes, from perception to action, and can provide fine-grained predica- tions about reaction times, neuroimaging measurements, eye- tracking data, as well as behavioral responses. In our view, it is quite stunning that, thus far, there have been relatively few at- tempts to incorporate affective components into architectural models of cognition and behavior. For the purpose of this com- mentary, the most noteworthy aspect of cognitive architectures relates to understanding and hypothesizing about interactions between different perceptual, motor, and cognitive components that naturally arise while modeling behavioral tasks. Within Pessoa’s book and elsewhere (e.g., McGaugh 2000), affective aspects of behavior such as stress, motivation, and arousal have been shown to modulate cognitive processes such as attention and memory, and we believe that developing these affective com- ponents within cognitive architectures can afford researchers the ability to precisely define how and where these types of interac- tions may take place within a human system. Additionally, when one or more aspects of cognition are qualified based on an affec- tive state and a possible system-wide chain of interactions occurs, cognitive architectures may be the best tool for dealing with the high level of complexity. How can affective components be implemented within cogni- tive architectures? The approach that several authors have called for or begun working with is to define how affective states might modulate the underlying cognitive processes (e.g., at- tention, working memory) within the architecture (e.g., Belavkin 2001; Cochran et al. 2006; Dancy et al. 2013; Hudlicka 2004; Ritter et al. 2007; see also Gunzelmann et al. 2009 for similar work related to fatigue). This can translate to adjusting certain pa- rameters within existing architectures. For example, Cochran et al. (2006) provide a relatively simple demonstration of this ap- proach, in which they model the effect of one aspect of emotion (arousal) within one cognitive module of ACT-R (declarative memory). Cochran et al. (2006) point out that the standard ACT-R model is not able to predict the results of the classic study by Kleinsmith and Kaplan (1964), which found that study of high emotional arousal stimuli led to short-term forgetting and long-term remembering compared with low emotional arousal stimuli. To implement this impact of arousal on memory within ACT-R, Cochran et al. (2006) redefined and expanded certain parameters (specifically, within the declarative memory module) to produce a pattern similar to the behavioral data. Sim- ilarly, in another paper, Ritter et al. (2007) developed a model within ACT-R to predict performance on a serial subtraction task, in which certain cognitive mechanisms within the architec- ture (e.g., attention, working memory) were modified to represent the impact of stress. Much more, we suspect that it would be worthwhile to explore how the findings and theories presented within Pessoa’s book can be modeled within cognitive architec- tures in similar ways. Many cognitive architectures (ACT-R in particular) not only attempt to model the processes underlying human behavior, but they also incorporate neuroimaging findings to develop a brain- like system of structures and processes (e.g., Anderson 2007; Just & Varma 2007). Indeed, within ACT-R different cognitive modules are associated with certain brain structures. Because of this design approach, (1) neuropsychological findings can be used to guide and constrain model development, and (2) neuroim- aging data (such as fMRI) can be used in conjunction with behav- ioral measurements to help validate models (e.g., Borst & Anderson 2014). Because ACT-R provides latency information for different cognitive processes (e.g., visually encoding a stimulus, retrieving information from memory, producing a motor re- sponse), this pattern of activity can be translated into predictions for neuroimaging data in correspondence with the brain areas as- sociated with the different cognitive modules. We suspect that this facet of cognitive architectures may be especially compelling for the development of affective components because, as Pessoa de- scribes, certain brain structures (such as the amygdala) are associ- ated with a variety of processes. These types of neuropsychological research findings can be taken into account when exploring how affective aspects might modulate particular processes within an architecture. There is, perhaps, no better way to conclude this short com- mentary than by turning to one of the conceptual founders of in- tegrative approaches to behavior and cognition. In many ways, Pessoa’s book echoes Newell’s(1990) argument that, “A single system (mind) produces all aspects of behavior. It is one mind that minds them all. Even if the mind has parts, modules, compo- nents, or whatever, they all mesh together to produce behavior.… If a theory covers only one part or component, it flirts with trouble from the start” (p. 17). In short, Pessoa contends that, given the high level of overlap between aspects of cognition and emotion, the two should not be considered separately. We agree with this and believe that the ideal research approach for pursuing this in- tegration of theories includes cognitive architectures. Neuropsychology still needs to model organismic processes “from within” doi:10.1017/S0140525X14000983, e83 Juan Pascual-Leone, a Antonio Pascual-Leone, b and Marie Arsalidou a,c a Department of Psychology, York University, Toronto, Ontario, Canada; b Department of Psychology, University of Windsor, Windsor, Ontario, Canada; c Department of Psychology, National Research University Higher School of Economics, Moscow, 101000 Russia. juanpl@yorku.ca http://tcolab.blog.yorku.ca/ apl@uwindsor.ca http://www1.uwindsor.ca/people/apl/ marie.arsalidou@gmail.com http://www.hse.ru/en/org/persons/133641316 Abstract: Four issues are discussed: (1) differences between cognition and emotion; (2) affect, emotion, and motivation differentials, including a neuropsychological model of motivation; (3) mental attention (working memory) as a resource neither affective nor cognitive, but applicable to both; and (4) explication of neuropsychological scheme units, which have neuronal circuits as functional infrastructure, thus helping to clarify the semantics of functional connectivity. Pessoa’s The Cognitive-Emotional Brain (2013) is important because it attempts to clarify in broad detail neuroscience rela- tions among cognition, emotion, and motivation. Pessoa sees these constructs as intertwined in the brain networks but does not make apparent how cognition, emotion, and motivation func- tionally complement each other as different modes of processing. A number of important issues remain unanswered. Do Pessoa’s multiple waves and dual competition models (pp. 70–72, and Chapter 7 of his book) imply that performance is overdeter- mined – as Freud would have said – by many actively self-propel- ling, often connected brain processes? How are external “contexts” and related internal processes expressed in the brain? Commentary/Pessoa: Précis on The Cognitive-Emotional Brain BEHAVIORAL AND BRAIN SCIENCES, 38 (2015) 33