Most conceptual models of the organization of the cardiovascular system begin with the premise that the nervous system regulates the metabolic and nonmetabolic reflex adjustments of the circulation. These models assume that all the neurally mediated responses of the circulation are reactive, i.e., reflexes elicited by adequate stimuli. This target article suggests that the responses of the circulation are conditional in three senses. First, as Sherrington argued, reflexes are conditional in that they never operate in a vacuum but in a context together with other reflexes. Guided by functional utility, they interact rather than add. Second, as Pavlov argued, stimuli acquire meanings as a result of experience. This notion of stimulus effect plus the Sherringtonian notion of conditionality suggest that association is one of the ways stimuli eliciting cardiovascular reflexes acquire their meanings and thus their relative strengths. Finally, as Skinner and others have argued, operants are responses that act upon the environment to obtain consequences – that is, stimuli. As operants, cardiovascular responses fulfill a major biological need, functioning proactively. The cardiovascular response is an integral component of the animal's behavior regardless of whether it is an elicited reflex or the eliciting stimulus acquired its properties as a result of the genetic inheritance of the animal or through experience, or the cardiovascular response is emitted in anticipation of an environmental consequence. The main theses of this essay are: (1) behavior is an integrated set of responses and the circulation is one of the response systems comprising behavior; (2) behavior is, in part, determined by its functional significance within a context; (3) the contextual factors operative at the time of the behavior have a major role in determining which of the set of possible responses will determine the final act, that is, which behavior will be the effective response and which other behaviors will be concomitants.

Deep neural networks (DNNs) have had extraordinary successes in classifying photographic images of objects and are often described as the best models of biological vision. This conclusion is largely based on three sets of findings: (1) DNNs are more accurate than any other model in classifying images taken from various datasets, (2) DNNs do the best job in predicting the pattern of human errors in classifying objects taken from various behavioral datasets, and (3) DNNs do the best job in predicting brain signals in response to images taken from various brain datasets (e.g., single cell responses or fMRI data). However, these behavioral and brain datasets do not test hypotheses regarding what features are contributing to good predictions and we show that the predictions may be mediated by DNNs that share little overlap with biological vision. More problematically, we show that DNNs account for almost no results from psychological research. This contradicts the common claim that DNNs are good, let alone the best, models of human object recognition. We argue that theorists interested in developing biologically plausible models of human vision need to direct their attention to explaining psychological findings. More generally, theorists need to build models that explain the results of experiments that manipulate independent variables designed to test hypotheses rather than compete on making the best predictions. We conclude by briefly summarizing various promising modeling approaches that focus on psychological data.

We are delighted by the broad, intense, and fruitful discussion in reaction to our target article. A major point we take from the many comments is a prevailing feeling in the research community that we need significantly and urgently to advance resilience research, both by sharpening concepts and theories and by conducting empirical studies at a much larger scale and with a much more extended and sophisticated methodological arsenal than is the case currently. This advancement can be achieved only in a concerted international collaborative effort. In our response, we try to argue that an explicitly atheoretical, purely observational definition of resilience and a transdiagnostic, quantitative study framework can provide a suitable basis for empirically testing different competing resilience theories (sects. R1, R2, R6, R7). We are confident that it should be possible to unite resilience researchers from different schools, including from sociology and social psychology, behind such a pragmatic and theoretically neutral research strategy. In sections R3 to R5, we further specify and explain the positive appraisal style theory of resilience (PASTOR). We defend PASTOR as a comparatively parsimonious and translational theory that makes sufficiently concrete predictions to be evaluated empirically.

In our target article, we proposed that addiction could be envisioned as misperformance of a decision-making machinery described by two systems (deliberative and habit systems). Several commentators have argued that Pavlovian learning also produces actions. We agree and note that Pavlovian action-selection will provide several additional vulnerabilities. Several commentators have suggested that addiction arises from sociological parameters. We note in our response how sociological effects can change decision-making variables to provide additional vulnerabilities. Commentators generally have agreed that our theory provides a framework within which to site addiction and treatment, but additional work will be needed to determine whether our taxonomy will help identify and treat subpopulations within the addicted community.

This article argues that rapid eye movement (REM) dreaming is elaborative encoding for episodic memories. Elaborative encoding in REM can, at least partially, be understood through ancient art of memory (AAOM) principles: visualization, bizarre association, organization, narration, embodiment, and location. These principles render recent memories more distinctive through novel and meaningful association with emotionally salient, remote memories. The AAOM optimizes memory performance, suggesting that its principles may predict aspects of how episodic memory is configured in the brain. Integration and segregation are fundamental organizing principles in the cerebral cortex. Episodic memory networks interconnect profusely within the cortex, creating omnidirectional “landmark” junctions. Memories may be integrated at junctions but segregated along connecting network paths that meet at junctions. Episodic junctions may be instantiated during non–rapid eye movement (NREM) sleep after hippocampal associational function during REM dreams. Hippocampal association involves relating, binding, and integrating episodic memories into a mnemonic compositional whole. This often bizarre, composite image has not been present to the senses; it is not “real” because it hyperassociates several memories. During REM sleep, on the phenomenological level, this composite image is experienced as a dream scene. A dream scene may be instantiated as omnidirectional neocortical junction and retained by the hippocampus as an index. On episodic memory retrieval, an external stimulus (or an internal representation) is matched by the hippocampus against its indices. One or more indices then reference the relevant neocortical junctions from which episodic memories can be retrieved. Episodic junctions reach a processing (rather than conscious) level during normal wake to enable retrieval. If this hypothesis is correct, the stuff of dreams is the stuff of memory.

The notion of an enactive system requires thinking about the brain in a way that is different from the standard computational-representational models. In evolutionary terms, the brain does what it does and is the way that it is, across some scale of variations, because it is part of a living body with hands that can reach and grasp in certain limited ways, eyes structured to focus, an autonomic system, an upright posture, etc. coping with specific kinds of environments, and with other people. Changes to any of the bodily, environmental, or intersubjective conditions elicit responses from the system as a whole. On this view, rather than representing or computing information, the brain is better conceived as participating in the action.

Research in anthropology has shown that kin terminologies have a complex combinatorial structure and vary systematically across cultures. This article argues that universals and variation in kin terminology result from the interaction of (1) an innate conceptual structure of kinship, homologous with conceptual structure in other domains, and (2) principles of optimal, “grammatical” communication active in language in general. Kin terms from two languages, English and Seneca, show how terminologies that look very different on the surface may result from variation in the rankings of a universal set of constraints. Constraints on kin terms form a system: some are concerned with absolute features of kin (sex), others with the position (distance and direction) of kin in “kinship space,” others with groups and group boundaries (matrilines, patrilines, generations, etc.). Also, kin terms sometimes extend indefinitely via recursion, and recursion in kin terminology has parallels with recursion in other areas of language. Thus the study of kinship sheds light on two areas of cognition, and their phylogeny. The conceptual structure of kinship seems to borrow its organization from the conceptual structure of space, while being specialized for representing genealogy. And the grammar of kinship looks like the product of an evolved grammar faculty, opportunistically active across traditional domains of semantics, syntax, and phonology. Grammar is best understood as an offshoot of a uniquely human capacity for playing coordination games.

Contempt is typically studied as a uniquely human moral emotion. However, this approach has yielded inconclusive results. We argue this is because the folk affect concept “contempt” has been inaccurately mapped onto basic affect systems. “Contempt” has features that are inconsistent with a basic emotion, especially its protracted duration and frequently cold phenomenology. Yet other features are inconsistent with a basic attitude. Nonetheless, the features of “contempt” functionally cohere. To account for this, we revive and reconfigure the sentiment construct using the notion of evolved functional specialization. We develop the Attitude–Scenario–Emotion (ASE) model of sentiments, in which enduring attitudes represent others' social-relational value and moderate discrete emotions across scenarios. Sentiments are functional networks of attitudes and emotions. Distinct sentiments, including love, respect, like, hate, and fear, track distinct relational affordances, and each is emotionally pluripotent, thereby serving both bookkeeping and commitment functions within relationships. The sentiment contempt is an absence of respect; from cues to others' low efficacy, it represents them as worthless and small, muting compassion, guilt, and shame and potentiating anger, disgust, and mirth. This sentiment is ancient yet implicated in the ratcheting evolution of human ultrasocialty. The manifolds of the contempt network, differentially engaged across individuals and populations, explain the features of “contempt,” its translatability, and its variable experience as “hot” or “cold,” occurrent or enduring, and anger-like or disgust-like. This rapprochement between psychological anthropology and evolutionary psychology contributes both methodological and empirical insights, with broad implications for understanding the functional and cultural organization of social affect.

What is the primary function of consciousness in the nervous system? The answer to this question remains enigmatic, not so much because of a lack of relevant data, but because of the lack of a conceptual framework with which to interpret the data. To this end, we have developed Passive Frame Theory, an internally coherent framework that, from an action-based perspective, synthesizes empirically supported hypotheses from diverse fields of investigation. The theory proposes that the primary function of consciousness is well-circumscribed, serving the somatic nervous system. For this system, consciousness serves as a frame that constrains and directs skeletal muscle output, thereby yielding adaptive behavior. The mechanism by which consciousness achieves this is more counterintuitive, passive, and “low level” than the kinds of functions that theorists have previously attributed to consciousness. Passive frame theory begins to illuminate (a) what consciousness contributes to nervous function, (b) how consciousness achieves this function, and (c) the neuroanatomical substrates of conscious processes. Our untraditional, action-based perspective focuses on olfaction instead of on vision and is descriptive (describing the products of nature as they evolved to be) rather than normative (construing processes in terms of how they should function). Passive frame theory begins to isolate the neuroanatomical, cognitive-mechanistic, and representational (e.g., conscious contents) processes associated with consciousness.

The view that learning is governed by positive and negative consequences has dominated theory and application throughout this century. In some systems stimulus-response connections are stamped in or stamped out by the consequences of action, in others, cognitive expectancies are formed by experience with past consequences. The evidence from early experiments with rats and pigeons and the feedback principles of early servomechanisms seemed to offer both hard evidence and a plausible model for the law of effect in either its behaviorist or its cognitive form.

A large body of evidence demonstrates that the results of operant conditioning appear regardless of and often in spite of response-contingent consequences. Experiments designed to measure a residual effect of consequences exhibit an inevitable ex post facto error that vitiates all possible versions of this experimental design. Experiments designed to measure the effect of predictive contingency in Pavlovian conditioning exhibit a corresponding error. There appears to be a fundamental logical defect in all contingency models of the learning process.

Meanwhile, modern developments in ethology and computer science provide a unified feedforward model of the learning of adaptive and maladaptive behavior under both laboratory and field conditions. Because the feedforward model is more parsimonious, it is also more compatible with Darwinian principles of biological economy. Research on teaching new and challenging tasks to freeliving, well-fed subjects such as children and cross-fostered chimpanzees illustrates the wide applicability and practical effectiveness of feedforward.