A burgeoning interest in the intersection of neuroscience and architecture promises to offer biologically inspired insights into the design of spaces. The goal of such interdisciplinary approaches to architecture is to motivate construction of environments that would contribute to peoples' flourishing in behavior, health, and well-being. We suggest that this nascent field of neuroarchitecture is at a pivotal point in which neuroscience and architecture are poised to extend to a neuroscience of architecture. In such a research program, architectural experiences themselves are the target of neuroscientific inquiry. Here, we draw lessons from recent developments in neuroaesthetics to suggest how neuroarchitecture might mature into an experimental science. We review the extant literature and offer an initial framework from which to contextualize such research. Finally, we outline theoretical and technical challenges that lie ahead.

The posterior STS (pSTS) is an important brain region for perceptual analysis of social cognitive cues. This study seeks to characterize the pattern of network connectivity emerging from the pSTS in three core social perception localizers: biological motion perception, gaze recognition, and the interpretation of moving geometric shapes as animate. We identified brain regions associated with all three of these localizers and computed the functional connectivity pattern between them and the pSTS using a partial correlations metric that characterizes network connectivity. We find a core pattern of cortical connectivity that supports the hypothesis that the pSTS serves as a hub of the social brain network. The right pSTS was the most highly connected of the brain regions measured, with many long-range connections to pFC. Unlike other highly connected regions, connectivity to the pSTS was distinctly lateralized. We conclude that the functional importance of right pSTS is revealed when considering its role in the large-scale network of brain regions involved in various aspects of social cognition.

How is the processing of task information organized in the brain? Many views of brain function emphasize modularity, with different regions specialized for processing different types of information. However, recent accounts also highlight flexibility, pointing especially to the highly consistent pattern of frontoparietal activation across many tasks. Although early insights from functional imaging were based on overall activation levels during different cognitive operations, in the last decade many researchers have used multivoxel pattern analyses to interrogate the representational content of activations, mapping out the brain regions that make particular stimulus, rule, or response distinctions. Here, we drew on 100 searchlight decoding analyses from 57 published papers to characterize the information coded in different brain networks. The outcome was highly structured. Visual, auditory, and motor networks predominantly (but not exclusively) coded visual, auditory, and motor information, respectively. By contrast, the frontoparietal multiple-demand network was characterized by domain generality, coding visual, auditory, motor, and rule information. The contribution of the default mode network and voxels elsewhere was minor. The data suggest a balanced picture of brain organization in which sensory and motor networks are relatively specialized for information in their own domain, whereas a specific frontoparietal network acts as a domain-general “core” with the capacity to code many different aspects of a task.

Network science provides theoretical, computational, and empirical tools that can be used to understand the structure and function of the human brain in novel ways using simple concepts and mathematical representations. Network neuroscience is a rapidly growing field that is providing considerable insight into human structural connectivity, functional connectivity while at rest, changes in functional networks over time (dynamics), and how these properties differ in clinical populations. In addition, a number of studies have begun to quantify network characteristics in a variety of cognitive processes and provide a context for understanding cognition from a network perspective. In this review, we outline the contributions of network science to cognitive neuroscience. We describe the methodology of network science as applied to the particular case of neuroimaging data and review its uses in investigating a range of cognitive functions including sensory processing, language, emotion, attention, cognitive control, learning, and memory. In conclusion, we discuss current frontiers and the specific challenges that must be overcome to integrate these complementary disciplines of network science and cognitive neuroscience. Increased communication between cognitive neuroscientists and network scientists could lead to significant discoveries under an emerging scientific intersection known as cognitive network neuroscience.

According to prominent theories of aging, the brain may reorganize to compensate for neural deterioration and prevent or offset cognitive decline. A frequent and striking finding in functional imaging studies is that older adults recruit additional regions relative to young adults performing the same task. This is often interpreted as evidence for functional reorganization, suggesting that, as people age, different regions or networks may support the same cognitive functions. Associations between additional recruitment and better performance in older adults have led to the suggestion that the additional recruitment may contribute to preserved cognitive function in old age and may explain some of the variation among individuals in preservation of function. However, many alternative explanations are possible, and recent findings and methodological developments have highlighted the need for more systematic approaches to determine whether reorganization occurs with age and whether it benefits performance. We reevaluate current evidence for compensatory functional reorganization in the light of recent moves to address these challenges.

The use of prescription stimulants to enhance healthy cognition has significant social, ethical, and public health implications. The large number of enhancement users across various ages and occupations emphasizes the importance of examining these drugs' efficacy in a nonclinical sample. The present meta-analysis was conducted to estimate the magnitude of the effects of methylphenidate and amphetamine on cognitive functions central to academic and occupational functioning, including inhibitory control, working memory, short-term episodic memory, and delayed episodic memory. In addition, we examined the evidence for publication bias. Forty-eight studies (total of 1,409 participants) were included in the analyses. We found evidence for small but significant stimulant enhancement effects on inhibitory control and short-term episodic memory. Small effects on working memory reached significance, based on one of our two analytical approaches. Effects on delayed episodic memory were medium in size. However, because the effects on long-term and working memory were qualified by evidence for publication bias, we conclude that the effect of amphetamine and methylphenidate on the examined facets of healthy cognition is probably modest overall. In some situations, a small advantage may be valuable, although it is also possible that healthy users resort to stimulants to enhance their energy and motivation more than their cognition.

The pFC enables the essential human capacities for predicting future events and preadapting to them. These capacities rest on both the structure and dynamics of the human pFC. Structurally, pFC, together with posterior association cortex, is at the highest hierarchical level of cortical organization, harboring neural networks that represent complex goal-directed actions. Dynamically, pFC is at the highest level of the perception–action cycle, the circular processing loop through the cortex that interfaces the organism with the environment in the pursuit of goals. In its predictive and preadaptive roles, pFC supports cognitive functions that are critical for the temporal organization of future behavior, including planning, attentional set, working memory, decision-making, and error monitoring. These functions have a common future perspective and are dynamically intertwined in goal-directed action. They all utilize the same neural infrastructure: a vast array of widely distributed, overlapping, and interactive cortical networks of personal memory and semantic knowledge, named cognits, which are formed by synaptic reinforcement in learning and memory acquisition. From this cortex-wide reservoir of memory and knowledge, pFC generates purposeful, goal-directed actions that are preadapted to predicted future events.

Our intuitive concept of the relations between brain and mind is increasingly challenged by the scientific world view. Yet, although few neuroscientists openly endorse Cartesian dualism, careful reading reveals dualistic intuitions in prominent neuroscientific texts. Here, we present the “double-subject fallacy”: treating the brain and the entire person as two independent subjects who can simultaneously occupy divergent psychological states and even have complex interactions with each other—as in “my brain knew before I did.” Although at first, such writing may appear like harmless, or even cute, shorthand, a closer look suggests that it can be seriously misleading. Surprisingly, this confused writing appears in various cognitive-neuroscience texts, from prominent peer-reviewed articles to books intended for lay audience. Far from being merely metaphorical or figurative, this type of writing demonstrates that dualistic intuitions are still deeply rooted in contemporary thought, affecting even the most rigorous practitioners of the neuroscientific method. We discuss the origins of such writing and its effects on the scientific arena as well as demonstrate its relevance to the debate on legal and moral responsibility.

In primates, different vocalizations are produced, at least in part, by making different facial expressions. Not surprisingly, humans, apes, and monkeys all recognize the correspondence between vocalizations and the facial postures associated with them. However, one major dissimilarity between monkey vocalizations and human speech is that, in the latter, the acoustic output and associated movements of the mouth are both rhythmic (in the 3- to 8-Hz range) and tightly correlated, whereas monkey vocalizations have a similar acoustic rhythmicity but lack the concommitant rhythmic facial motion. This raises the question of how we evolved from a presumptive ancestral acoustic-only vocal rhythm to the one that is audiovisual with improved perceptual sensitivity. According to one hypothesis, this bisensory speech rhythm evolved through the rhythmic facial expressions of ancestral primates. If this hypothesis has any validity, we expect that the extant nonhuman primates produce at least some facial expressions with a speech-like rhythm in the 3- to 8-Hz frequency range. Lip smacking, an affiliative signal observed in many genera of primates, satisfies this criterion. We review a series of studies using developmental, x-ray cineradiographic, EMG, and perceptual approaches with macaque monkeys producing lip smacks to further investigate this hypothesis. We then explore its putative neural basis and remark on important differences between lip smacking and speech production. Overall, the data support the hypothesis that lip smacking may have been an ancestral expression that was linked to vocal output to produce the original rhythmic audiovisual speech-like utterances in the human lineage.

Jonathan Haidt's Moral Foundations Theory is an influential scientific account of morality incorporating psychological, developmental, and evolutionary perspectives. The theory proposes that morality is built upon five innate “foundations,” each of which is believed to have been selected for during human evolution and, subsequently, tuned-up by learning during development. We argue here that although some general elements of Haidt's theory are plausible, many other important aspects of his account are seriously flawed. First, innateness and modularity figure centrally in Haidt's account, but terminological and conceptual problems foster confusion and ambiguities. Second, both the theory's proposed number of moral foundations and its taxonomy of the moral domain appear contrived, ignoring equally good candidate foundations and the possibility of substantial intergroup differences in the foundations' contents. Third, the mechanisms (viz., modules) and categorical distinctions (viz., between foundations) proposed by the theory are not consilient with discoveries in contemporary neuroscience concerning the organization, functioning, and development of the brain. In light of these difficulties, we suggest that Haidt's theory is inadequate as a scientific account of morality. Nevertheless, the theory's weaknesses are instructive, and hence, criticism may be useful to psychologists, neuroscientists, and philosophers attempting to advance theories of morality, as well as to researchers wishing to invoke concepts such as innateness and modularity more generally.

The discovery of mirror neurons in macaque frontal cortex has sparked a resurgence of interest in motor/embodied theories of cognition. This critical review examines the evidence in support of one of these theories, namely, that mirror neurons provide the basis of action understanding. It is argued that there is no evidence from monkey data that directly tests this theory, and evidence from humans makes a strong case against the position.

Mental rotation is a hypothesized imagery process that has inspired controversy regarding the substrate of human spatial reasoning. Two central questions about mental rotation remain: Does mental rotation depend on analog spatial representations, and does mental rotation depend on motor simulation? A review and meta-analysis of neuroimaging studies help answer these questions. Mental rotation is accompanied by increased activity in the intraparietal sulcus and adjacent regions. These areas contain spatially mapped representations, and activity in these areas is modulated by parametric manipulations of mental rotation tasks, supporting the view that mental rotation depends on analog representations. Mental rotation also is accompanied by activity in the medial superior precentral cortex, particularly under conditions that favor motor simulation, supporting the view that mental rotation depends on motor simulation in some situations. The relationship between mental rotation and motor simulation can be understood in terms of how these two processes update spatial reference frames.

The goal of pattern-based classification of functional neuroimaging data is to link individual brain activation patterns to the experimental conditions experienced during the scans. These “brain-reading” analyses advance functional neuroimaging on three fronts. From a technical standpoint, pattern-based classifiers overcome fatal f laws in the status quo inferential and exploratory multivariate approaches by combining pattern-based analyses with a direct link to experimental variables. In theoretical terms, the results that emerge from pattern-based classifiers can offer insight into the nature of neural representations. This shifts the emphasis in functional neuroimaging studies away from localizing brain activity toward understanding how patterns of brain activity encode information. From a practical point of view, pattern-based classifiers are already well established and understood in many areas of cognitive science. These tools are familiar to many researchers and provide a quantitatively sound and qualitatively satisfying answer to most questions addressed in functional neuroimaging studies. Here, we examine the theoretical, statistical, and practical underpinnings of pattern-based classification approaches to functional neuroimaging analyses. Pattern-based classification analyses are well positioned to become the standard approach to analyzing functional neuroimaging data.