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Society's problems cannot be alleviated via mere policy interventions, whether individual- or system-level, when the system is the problem. To bring about true and lasting change to the better, we must replace the present global political-economic system - oligarchic capitalism backed by the power of the state - with one that would let the people take charge of their lives.

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Statistical regularities and predictions can influence the earliest stages of visual processing. Studies examining their effects on detection, however, have yielded inconsistent results. In continuous flash suppression (CFS), where a static image projected to one eye is suppressed by a dynamic image presented to the other, the predictability of the suppressed signal may facilitate or delay detection. To identify the factors that differentiate these outcomes and dissociate the effects of expectation from those of behavioral relevance, we conducted three CFS experiments that addressed confounds related to the use of reaction time measures and complex images. In experiment 1, orientation recognition performance and visibility rates increased when a suppressed line segment completed a partial shape surrounding the CFS patch, demonstrating that valid configuration cues facilitate detection. In Experiment 2, however, predictive cues marginally affected visibility and did not modulate localization performance, challenging existing findings. In experiment 3, a relevance manipulation was introduced; participants pressed a key upon detecting lines of a particular orientation, ignoring the other possible orientation. Visibility and localization were enhanced for relevant orientations. Predictive cues modulated visibility, orientation recognition sensitivity, and response latencies, but not localization-an objective measure sensitive to partial breakthrough. Thus, while a consistent surround can strongly enhance detection during passive observation, predictive cueing primarily affects post-detection factors such as response readiness and recognition confidence. Relevance and predictability did not interact, suggesting that the contributions of these two processes to detection are mostly orthogonal.

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Evolutionary accounts of feelings, and in particular of negative affect and of pain, assume that creatures that feel and care about the outcomes of their behavior outperform those that do not in terms of their evolutionary fitness. Such accounts, however, can only work if feelings can be shown to contribute to fitness-influencing outcomes. Simply assuming that a learner that feels and cares about outcomes is more strongly motivated than one that does is not enough, if only because motivation can be tied directly to outcomes by incorporating an appropriate reward function, without leaving any apparent role to feelings (as it is done in state-of-the-art engineered systems based on reinforcement learning). Here, we propose a possible mechanism whereby pain contributes to fitness: an actor-critic functional architecture for reinforcement learning, in which pain reflects the costs imposed on actors in their bidding for control, so as to promote honest signaling and ultimately help the system optimize learning and future behavior.

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The human brain is able to learn difficult categorization tasks, even ones that have linearly inseparable boundaries; however, it is currently unknown how it achieves this computational feat. We investigated this by training participants on an animal categorization task with a linearly inseparable prototype structure in a morph shape space. Participants underwent fMRI scans before and after 4 days of behavioral training. Widespread representational changes were found throughout the brain, including an untangling of the categories' neural patterns that made them more linearly separable after behavioral training. These neural changes were task dependent, as they were only observed while participants were performing the categorization task, not during passive viewing. Moreover, they were found to occur in frontal and parietal areas, rather than ventral temporal cortices, suggesting that they reflected attentional and decisional reweighting, rather than changes in object recognition templates. These results illustrate how the brain can flexibly transform neural representational space to solve computationally challenging tasks.

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We propose that food-related uncertainty is but one of multiple cues that predicts harsh conditions and may activate "incentive hope." An evolutionarily adaptive response to these would have been to shift to a behavioral-metabolic phenotype geared toward facing hardship. In modernity, this phenotype may lead to pathologies such as obesity and hoarding. Our perspective suggests a novel therapeutic approach.

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Contemporary neurodynamical frameworks, such as coordination dynamics and winnerless competition, posit that the brain approximates symbolic computation by transitioning between metastable attractive states. This article integrates these accounts with electrophysiological data suggesting that coherent, nested oscillations facilitate information representation and transmission in thalamocortical networks. We review the relationship between criticality, metastability, and representational capacity, outline existing methods for detecting metastable oscillatory patterns in neural time series data, and evaluate plausible spatiotemporal coding schemes based on phase alignment. We then survey the circuitry and the mechanisms underlying the generation of coordinated alpha and gamma rhythms in the primate visual system, with particular emphasis on the pulvinar and its role in biasing visual attention and awareness. To conclude the review, we begin to integrate this perspective with longstanding theories of consciousness and cognition.

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Language plays a pivotal role in the evolution of human culture, yet the evolution of the capacity for language-uniquely within the hominin lineage-remains little understood. Bringing together insights from cognitive psychology, neuroscience, archaeology and behavioural ecology, we hypothesize that this singular occurrence was triggered by exaptation, or 'hijacking', of existing cognitive mechanisms related to sequential processing and motor execution. Observed coupling of the communication system with circuits related to complex action planning and control supports this proposition, but the prehistoric ecological contexts in which this coupling may have occurred and its adaptive value remain elusive. Evolutionary reasoning rules out most existing hypotheses regarding the ecological context of language evolution, which focus on ultimate explanations and ignore proximate mechanisms. Coupling of communication and motor systems, although possible in a short period on evolutionary timescales, required a multi-stepped adaptive process, involving multiple genes and gene networks. We suggest that the behavioural context that exerted the selective pressure to drive these sequential adaptations had to be one in which each of the systems undergoing coupling was independently necessary or highly beneficial, as well as frequent and recurring over evolutionary time. One such context could have been the teaching of tool production or tool use. In the present study, we propose the Cognitive Coupling hypothesis, which brings together these insights and outlines a unifying theory for the evolution of the capacity for language.This article is part of the theme issue 'Bridging cultural gaps: interdisciplinary studies in human cultural evolution'.

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A cognitive system faced with contingent events that cause rapid changes in sensory data may (i) incrementally incorporate new data into the ongoing perceptual and motor processing; or (ii) restart processing on each new event; or (iii) sample the data and hold onto the sample until its processing is complete, while disregarding any contingent changes. We offer a set of computational first-principles arguments for a hypothesis, according to which any system that contends with certain classes of perception and behavioral control tasks must include the sample-and-hold option (possibly alongside the other two, which may be useful in other tasks). This hypothesis has implications for understanding the dynamics of perception and action. In particular, a sample-and-hold channel necessarily processes sensory data on some kind of cycle (which does not imply precise periodicity). Further, being prepared to face the world at all times requires that the sampling that initiates each cycle be triggered by every significant action on part of the agent itself, such as saccades. We survey a range of evidence for the sample-and-hold functionality, touching upon diverse phenomena such as attentional blink and backward masking, the yoking of olfaction to respiration, thalamocortical interactions, and metastable brain dynamics in perception and consciousness.

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When humans and other animals make cultural innovations, they also change their environment, thereby imposing new selective pressures that can modify their biological traits. For example, there is evidence that dairy farming by humans favored alleles for adult lactose tolerance. Similarly, the invention of cooking possibly affected the evolution of jaw and tooth morphology. However, when it comes to cognitive traits and learning mechanisms, it is much more difficult to determine whether and how their evolution was affected by culture or by their use in cultural transmission. Here we argue that, excluding very recent cultural innovations, the assumption that culture shaped the evolution of cognition is both more parsimonious and more productive than assuming the opposite. In considering how culture shapes cognition, we suggest that a process-level model of cognitive evolution is necessary and offer such a model. The model employs relatively simple coevolving mechanisms of learning and data acquisition that jointly construct a complex network of a type previously shown to be capable of supporting a range of cognitive abilities. The evolution of cognition, and thus the effect of culture on cognitive evolution, is captured through small modifications of these coevolving learning and data-acquisition mechanisms, whose coordinated action is critical for building an effective network. We use the model to show how these mechanisms are likely to evolve in response to cultural phenomena, such as language and tool-making, which are associated with major changes in data patterns and with new computational and statistical challenges.

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As a highly consequential biological trait, a memory "bottleneck" cannot escape selection pressures. It must therefore co-evolve with other cognitive mechanisms rather than act as an independent constraint. Recent theory and an implemented model of language acquisition suggest that a limit on working memory may evolve to help learning. Furthermore, it need not hamper the use of language for communication.

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A computational theory of consciousness should include a quantitative measure of consciousness, or MoC, that (i) would reveal to what extent a given system is conscious, (ii) would make it possible to compare not only different systems, but also the same system at different times, and (iii) would be graded, because so is consciousness. However, unless its design is properly constrained, such an MoC gives rise to what we call the boundary problem: an MoC that labels a system as conscious will do so for some-perhaps most-of its subsystems, as well as for irrelevantly extended systems (e.g., the original system augmented with physical appendages that contribute nothing to the properties supposedly supporting consciousness), and for aggregates of individually conscious systems (e.g., groups of people). This problem suggests that the properties that are being measured are epiphenomenal to consciousness, or else it implies a bizarre proliferation of minds. We propose that a solution to the boundary problem can be found by identifying properties that are intrinsic or systemic: properties that clearly differentiate between systems whose existence is a matter of fact, as opposed to those whose existence is a matter of interpretation (in the eye of the beholder). We argue that if a putative MoC can be shown to be systemic, this ipso facto resolves any associated boundary issues. As test cases, we analyze two recent theories of consciousness in light of our definitions: the Integrated Information Theory and the Geometric Theory of consciousness.

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We offer and test a simple operationalization of hedonic and eudaimonic well-being ("happiness") as mediating variables that link outcomes to motivation. In six evolutionary agent-based simulation experiments, we compared the relative performance of agents endowed with different combinations of happiness-related traits (parameter values), under four types of environmental conditions. We found (i) that the effects of attaching more weight to longer-term than to momentary happiness and of extending the memory for past happiness are both stronger in an environment where food is scarce; (ii) that in such an environment "relative consumption," in which the agent's well-being is negatively affected by that of its neighbors, is more detrimental to survival when food is scarce; and (iii) that having a positive outlook, under which agents' longer-term happiness is increased by positive events more than it is decreased by negative ones, is generally advantageous.

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Animal acoustic communication often takes the form of complex sequences, made up of multiple distinct acoustic units. Apart from the well-known example of birdsong, other animals such as insects, amphibians, and mammals (including bats, rodents, primates, and cetaceans) also generate complex acoustic sequences. Occasionally, such as with birdsong, the adaptive role of these sequences seems clear (e.g. mate attraction and territorial defence). More often however, researchers have only begun to characterise - let alone understand - the significance and meaning of acoustic sequences. Hypotheses abound, but there is little agreement as to how sequences should be defined and analysed. Our review aims to outline suitable methods for testing these hypotheses, and to describe the major limitations to our current and near-future knowledge on questions of acoustic sequences. This review and prospectus is the result of a collaborative effort between 43 scientists from the fields of animal behaviour, ecology and evolution, signal processing, machine learning, quantitative linguistics, and information theory, who gathered for a 2013 workshop entitled, 'Analysing vocal sequences in animals'. Our goal is to present not just a review of the state of the art, but to propose a methodological framework that summarises what we suggest are the best practices for research in this field, across taxa and across disciplines. We also provide a tutorial-style introduction to some of the most promising algorithmic approaches for analysing sequences. We divide our review into three sections: identifying the distinct units of an acoustic sequence, describing the different ways that information can be contained within a sequence, and analysing the structure of that sequence. Each of these sections is further subdivided to address the key questions and approaches in that area. We propose a uniform, systematic, and comprehensive approach to studying sequences, with the goal of clarifying research terms used in different fields, and facilitating collaboration and comparative studies. Allowing greater interdisciplinary collaboration will facilitate the investigation of many important questions in the evolution of communication and sociality.

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The "problem of serial order in behavior," as formulated and discussed by Lashley (1951), is arguably more pervasive and more profound both than originally stated and than currently appreciated. We spell out two complementary aspects of what we term the generalized problem of behavior: (i) multimodality, stemming from the disparate nature of the sensorimotor variables and processes that underlie behavior, and (ii) concurrency, which reflects the parallel unfolding in time of these processes and of their asynchronous interactions. We illustrate these on a number of examples, with a special focus on language, briefly survey the computational approaches to multimodal concurrency, offer some hypotheses regarding the manner in which brains address it, and discuss some of the broader implications of these as yet unresolved issues for cognitive science.

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The skills required for the learning and use of language are the focus of extensive research, and their evolutionary origins are widely debated. Using agent-based simulations in a range of virtual environments, we demonstrate that challenges of foraging for food can select for cognitive mechanisms supporting complex, hierarchical, sequential learning, the need for which arises in language acquisition. Building on previous work, where we explored the conditions under which reinforcement learning is out-competed by seldom-reinforced continuous learning that constructs a network model of the environment, we now show that realistic features of the foraging environment can select for two critical advances: (i) chunking of meaningful sequences found in the data, leading to representations composed of units that better fit the prevalent statistical patterns in the environment; and (ii) generalization across units based on their contextual similarity. Importantly, these learning processes, which in our framework evolved for making better foraging decisions, had been earlier shown to reproduce a range of findings in language learning in humans. Thus, our results suggest a possible evolutionary trajectory that may have led from basic learning mechanisms to complex hierarchical sequential learning that can support advanced cognitive abilities of the kind needed for language acquisition.

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Natural behaviors, such as foraging, tool use, social interaction, birdsong, and language, exhibit branching sequential structure. Such structure should be learnable if it can be inferred from the statistics of early experience. We report that juvenile zebra finches learn such sequential structure in song. Song learning in finches has been extensively studied, and it is generally believed that young males acquire song by imitating tutors (Zann, 1996). Variability in the order of elements in an individual's mature song occurs, but the degree to which variation in a zebra finch's song follows statistical regularities has not been quantified, as it has typically been dismissed as production error (Sturdy et al., 1999). Allowing for the possibility that such variation in song is non-random and learnable, we applied a novel analytical approach, based on graph-structured finite-state grammars, to each individual's full corpus of renditions of songs. This method does not assume syllable-level correspondence between individuals. We find that song variation can be described by probabilistic finite-state graph grammars that are individually distinct, and that the graphs of juveniles are more similar to those of their fathers than to those of other adult males. This grammatical learning is a new parallel between birdsong and language. Our method can be applied across species and contexts to analyze complex variable learned behaviors, as distinct as foraging, tool use, and language.

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We introduce a set of biologically and computationally motivated design choices for modeling the learning of language, or of other types of sequential, hierarchically structured experience and behavior, and describe an implemented system that conforms to these choices and is capable of unsupervised learning from raw natural-language corpora. Given a stream of linguistic input, our model incrementally learns a grammar that captures its statistical patterns, which can then be used to parse or generate new data. The grammar constructed in this manner takes the form of a directed weighted graph, whose nodes are recursively (hierarchically) defined patterns over the elements of the input stream. We evaluated the model in seventeen experiments, grouped into five studies, which examined, respectively, (a) the generative ability of grammar learned from a corpus of natural language, (b) the characteristics of the learned representation, (c) sequence segmentation and chunking, (d) artificial grammar learning, and (e) certain types of structure dependence. The model's performance largely vindicates our design choices, suggesting that progress in modeling language acquisition can be made on a broad front-ranging from issues of generativity to the replication of human experimental findings-by bringing biological and computational considerations, as well as lessons from prior efforts, to bear on the modeling approach.

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Hoffman, Singh, and Prakash (2014) observe that perception evolves to serve as an interface between the perceiver and the world and proceed to reason that percepts need not, or even cannot, resemble their objects. I accept their premise, but argue that there are interesting ways in which perception can be truthful, with regard not to "objects" but to relations, and that evolutionary pressure is expected to favor rather than rule out such veridicality.

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Continuous, 'always on', learning of structure from a stream of data is studied mainly in the fields of machine learning or language acquisition, but its evolutionary roots may go back to the first organisms that were internally motivated to learn and represent their environment. Here, we study under what conditions such continuous learning (CL) may be more adaptive than simple reinforcement learning and examine how it could have evolved from the same basic associative elements. We use agent-based computer simulations to compare three learning strategies: simple reinforcement learning; reinforcement learning with chaining (RL-chain) and CL that applies the same associative mechanisms used by the other strategies, but also seeks statistical regularities in the relations among all items in the environment, regardless of the initial association with food. We show that a sufficiently structured environment favours the evolution of both RL-chain and CL and that CL outperforms the other strategies when food is relatively rare and the time for learning is limited. This advantage of internally motivated CL stems from its ability to capture statistical patterns in the environment even before they are associated with food, at which point they immediately become useful for planning.

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Complex network analysis (CNA), a subset of graph theory, is an emerging approach to the analysis of functional connectivity in the brain, allowing quantitative assessment of network properties such as functional segregation, integration, resilience, and centrality. Here, we show how a classification framework complements complex network analysis by providing an efficient and objective means of selecting the best network model characterizing given functional connectivity data. We describe a novel kernel-sum learning approach, block diagonal optimization (BDopt), which can be applied to CNA features to single out graph-theoretic characteristics and/or anatomical regions of interest underlying discrimination, while mitigating problems of multiple comparisons. As a proof of concept for the method's applicability to future neurodiagnostics, we apply BDopt classification to two resting state fMRI data sets: a trait (between-subjects) classification of patients with schizophrenia vs. controls, and a state (within-subjects) classification of wake vs. sleep, demonstrating powerful discriminant accuracy for the proposed framework.

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