Above and beyond the concrete: The diverse representational substrates of the predictive brain

The Wise Mind Balances the Abstract and the Concrete

We explored how individuals' mental representations of complex and uncertain situations impact their ability to reason wisely. To this end, we introduce situated methods to capture abstract and concrete mental representations and the switching between them when reflecting on social challenges. Using these methods, we evaluated the alignment of abstractness and concreteness with four integral facets of wisdom: intellectual humility, open-mindedness, perspective-taking, and compromise-seeking. Data from North American and UK participants (N = 1,151) revealed that both abstract and concrete construals significantly contribute to wise reasoning, even when controlling for a host of relevant covariates and potential response bias. Natural language processing of unstructured texts among high (top 25%) and low (bottom 25%) wisdom participants corroborated these results: semantic networks of the high wisdom group reveal greater use of both abstract and concrete themes compared to the low wisdom group. Finally, employing a repeated strategy-choice method as an additional measure, our findings demonstrated that individuals who showed a greater balance and switching between these construal types exhibited higher wisdom. Our findings advance understanding of individual differences in mental representations and how construals shape reasoning across contexts in everyday life.

Invariant representations in abstract concept grounding - the physical world in grounded cognition

Grounded cognition states that mental representations of concepts consist of experiential aspects. For example, the concept "cup" consists of the sensorimotor experiences from interactions with cups. Typical modalities in which concepts are grounded are: The sensorimotor system (including interoception), emotion, action, language, and social aspects. Here, we argue that this list should be expanded to include physical invariants (unchanging features of physical motion; e.g., gravity, momentum, friction). Research on physical reasoning consistently demonstrates that physical invariants are represented as fundamentally as other grounding substrates, and therefore should qualify. We assess several theories of concept representation (simulation, conceptual metaphor, conceptual spaces, predictive processing) and their positions on physical invariants. We find that the classic grounded cognition theories, simulation and conceptual metaphor theory, have not considered physical invariants, while conceptual spaces and predictive processing have. We conclude that physical invariants should be included into grounded cognition theories, and that the core mechanisms of simulation and conceptual metaphor theory are well suited to do this. Furthermore, conceptual spaces and predictive processing are very promising and should also be integrated with grounded cognition in the future.

Simplifying social learning

Social learning is complex, but people often seem to navigate social environments with ease. This ability creates a puzzle for traditional accounts of reinforcement learning (RL) that assume people negotiate a tradeoff between easy-but-simple behavior (model-free learning) and complex-but-difficult behavior (e.g., model-based learning). We offer a theoretical framework for resolving this puzzle: although social environments are complex, people have social expertise that helps them behave flexibly with low cognitive cost. Specifically, by using familiar concepts instead of focusing on novel details, people can turn hard learning problems into simpler ones. This ability highlights social learning as a prototype for studying cognitive simplicity in the face of environmental complexity and identifies a role for conceptual knowledge in everyday reward learning.

Tomorrow versus a year from now: Do children represent the near and distant future differently?

Adults represent the near future more concretely and vividly than the distant future, with important implications for future-oriented behavior (e.g., planning, self-control). Although children are adept at describing future events at around 5 years of age, we know little about how temporal distance (i.e., "near" vs "distant") affects their future event representations. In a series of three experiments, we sought to determine the effects of temporal distance, age, and event frequency on children's future event representations. Participants, 5- to 9-year-olds, were asked to describe frequent (e.g., snack) and infrequent (e.g., party) events, with half of children imagining that these events would happen in the near future and the other half imagining that they would happen in the distant future. We investigated the effect of temporal distance on numerous event representation indicators (e.g., clarity, details, pronouns), all theoretically grounded in previous literature. Although children perceived near events as closer in time than distant events (Experiments 2 and 2b) and temporal distance affected the clarity of event representations (Experiment 2), most indicators were not affected by temporal distance. In contrast, event frequency (examined in Experiment 1) played an important role in children's event representations, with infrequent events being described more concretely than frequent events. Results suggest that young children may begin perceiving differences in temporal distance but that this does not translate to their event representations (e.g., clarity, pronouns) until later in development. Implications for children's future thinking and future research are discussed.

Modal and amodal cognition: an overarching principle in various domains of psychology

Accounting for how the human mind represents the internal and external world is a crucial feature of many theories of human cognition. Central to this question is the distinction between modal as opposed to amodal representational formats. It has often been assumed that one but not both of these two types of representations underlie processing in specific domains of cognition (e.g., perception, mental imagery, and language). However, in this paper, we suggest that both formats play a major role in most cognitive domains. We believe that a comprehensive theory of cognition requires a solid understanding of these representational formats and their functional roles within and across different domains of cognition, the developmental trajectory of these representational formats, and their role in dysfunctional behavior. Here we sketch such an overarching perspective that brings together research from diverse subdisciplines of psychology on modal and amodal representational formats so as to unravel their functional principles and their interactions.

The Human Affectome

Over the last decades, theoretical perspectives in the interdisciplinary field of the affective sciences have proliferated rather than converged due to differing assumptions about what human affective phenomena are and how they work. These metaphysical and mechanistic assumptions, shaped by academic context and values, have dictated affective constructs and operationalizations. However, an assumption about the purpose of affective phenomena can guide us to a common set of metaphysical and mechanistic assumptions. In this capstone paper, we home in on a nested teleological principle for human affective phenomena in order to synthesize metaphysical and mechanistic assumptions. Under this framework, human affective phenomena can collectively be considered algorithms that either adjust based on the human comfort zone (affective concerns) or monitor those adaptive processes (affective features). This teleologically-grounded framework offers a principled agenda and launchpad for both organizing existing perspectives and generating new ones. Ultimately, we hope the Human Affectome brings us a step closer to not only an integrated understanding of human affective phenomena, but an integrated field for affective research.

Association between abstraction level and time: Are future and past more abstract than the present?

Construal level theory suggests that objects or events are represented differently depending on their psychological distance from ourselves. Specifically, objects and events should be represented more abstractly the farther they are removed from direct experience through distance in the spatial, temporal, social, or hypotheticality domains. Bar-Anan et al. reported a key finding supporting this assumed association of the various distance dimensions and abstraction level. In their study, participants responded faster in an Implicit Association Task when temporally near and concrete concepts, as well as temporally far and abstract concepts, were mapped to the same rather than different response keys. In this study, we conceptually replicated this basic finding when employing temporal adverbs relating to present versus future time, and nouns referring to concrete versus abstract concepts (Experiment 1). Evidence for such an association, however, was largely absent (and significantly weaker than in Experiment 1) when temporal adverbs relating to the past were employed as instances of the large temporal distance category (Experiment 2). We propose that the uncertainty associated with the future, as opposed to the past, might play an important role in this temporal asymmetry by increasing psychological distance.

Investigation of advanced mindfulness meditation "cessation" experiences using EEG spectral analysis in an intensively sampled case study

Mindfulness meditation is a contemplative practice informed by Buddhism that targets the development of present-focused awareness and non-judgment of experience. Interest in mindfulness is burgeoning, and it has been shown to be effective in improving mental and physical health in clinical and non-clinical contexts. In this report, for the first time, we used electroencephalography (EEG) combined with a neurophenomenological approach to examine the neural signature of "cessation" events, which are dramatic experiences of complete discontinuation in awareness similar to the loss of consciousness, which are reported to be experienced by very experienced meditators, and are proposed to be evidence of mastery of mindfulness meditation. We intensively sampled these cessations as experienced by a single advanced meditator (with over 23,000 h of meditation training) and analyzed 37 cessation events collected in 29 EEG sessions between November 12, 2019, and March 11, 2020. Spectral analyses of the EEG data surrounding cessations showed that these events were marked by a large-scale alpha-power decrease starting around 40 s before their onset, and that this alpha-power was lowest immediately following a cessation. Region-of-interest (ROI) based examination of this finding revealed that this alpha-suppression showed a linear decrease in the occipital and parietal regions of the brain during the pre-cessation time period. Additionally, there were modest increases in theta power for the central, parietal, and right temporal ROIs during the pre-cessation timeframe, whereas power in the Delta and Beta frequency bands were not significantly different surrounding cessations. By relating cessations to objective and intrinsic measures of brain activity (i.e., EEG power) that are related to consciousness and high-level psychological functioning, these results provide evidence for the ability of experienced meditators to voluntarily modulate their state of consciousness and lay the foundation for studying these unique states using a neuroscientific approach.

Depth and hierarchies in the predictive brain: From reaction to action

The human brain is a prediction device, a view widely accepted in neuroscience. Prediction is a rational and efficient response that relies on the brain's ability to create and employ generative models to optimize actions over unpredictable time horizons. We argue that extant predictive frameworks while compelling, have not explicitly accounted for the following: (a) The brain's generative models must incorporate predictive depth (i.e., rely on degrees of abstraction to enable predictions over different time horizons); (b) The brain's implementation scheme to account for varying predictive depth relies on dynamic predictive hierarchies formed using the brain's functional networks. We show that these hierarchies incorporate the ascending processes (driven by reaction), and the descending processes (related to prediction), eventually driving action. Because they are dynamically formed, predictive hierarchies allow the brain to address predictive challenges in virtually any domain. By way of application, we explain how this framework can be applied to heretofore poorly understood processes of human behavioral thermoregulation. Although mammalian thermoregulation has been closely tied to deep brain structures engaged in autonomic control such as the hypothalamus, this narrow conception does not translate well to humans. In addition to profound differences in evolutionary history, the human brain is bestowed with substantially increased functional complexity (that itself emerged from evolutionary differences). We argue that behavioral thermoregulation in humans is possible because, (a) ascending signals shaped by homeostatic sub-networks, interject with (b) descending signals related to prediction (implemented in interoceptive and executive sub-networks) and action (implemented in executive sub-networks). These sub-networks cumulatively form a predictive hierarchy for human thermoregulation, potentiating a range of viable responses to known and unknown thermoregulatory challenges. We suggest that our proposed extensions to the predictive framework provide a set of generalizable principles that can further illuminate the many facets of the predictive brain. This article is categorized under: Neuroscience > Behavior Philosophy > Action Psychology > Prediction.

Social Concepts Simplify Complex Reinforcement Learning

Humans often generalize rewarding experiences across abstract social roles. Theories of reward learning suggest that people generalize through model-based learning, but such learning is cognitively costly. Why do people seem to generalize across social roles with ease? Humans are social experts who easily recognize social roles that reflect familiar semantic concepts (e.g., "helper" or "teacher"). People may associate these roles with model-free reward (e.g., learning that helpers are rewarding), allowing them to generalize easily (e.g., interacting with novel individuals identified as helpers). In four online experiments with U.S. adults (N = 577), we found evidence that social concepts ease complex learning (people generalize more and at faster speed) and that people attach reward directly to abstract roles (they generalize even when roles are unrelated to task structure). These results demonstrate how familiar concepts allow complex behavior to emerge from simple strategies, highlighting social interaction as a prototype for studying cognitive ease in the face of environmental complexity.

Are perceptions of climate change in Amazonian coastal communities influenced by socioeconomic and cultural factors?

Climate changes have become undisputed, as have their consequences for global ecosystems and mankind. The coastal areas are among the most affected areas on the planet due to their geographical location. The effects suffered by coastal areas can render the residing populations homeless, as well as compromise the continuity of the history and culture of these environments. The Marine Extractive Reserve of the city of Soure (coastal area of eastern Amazonia) stands out for housing populations that have developed an intimate relationship with nature and have knowledge that can explain people's perception of climate changes. In this context, this study investigated how local residents perceive climate change and its consequences considering different temporal and spatial scales. To this end, questionnaires were developed and applied using a 5-point Likert scale. Our results indicate that perception is shaped by socioeconomic and demographic factors, and that they are perceived on different time scales and geographic space. These findings reflect the awareness-raising efforts of the management body of this Conservation Unit and the local knowledge, derived from the relationship of the residents with the natural environment, which, together, provided the population with assertive information that favor a better understanding of this phenomenon.

Development and validation of the Mental-Physical Verb Norms (MPVN): A text analysis measure of mental state attribution

Attribution of mental states to self and others, i.e., mentalizing, is central to human life. Current measures are lacking in the ability to directly gauge the extent to which individuals engage in spontaneous mentalizing. Focusing on natural language use as an expression of inner psychological processes, we developed the Mental-Physical Verb Norms (MPVN). These norms are participant-derived ratings of the extent to which common verbs reflect mental (vs physical) activities and occurrences, covering a majority of verbs appearing in a given English text. Content validity was assessed against existing expert-compiled dictionaries of mental states and cognitive processes, as well as against normative ratings of verb concreteness. Criterion Validity was assessed through natural text analysis of both experimental data, and natural language use in a real-world online setting. Finally, incremental validity was assessed through a classification analysis. Results indicate the unique contribution of the MPVN ratings as a measure of the degree to which individuals adopt the intentional stance in describing targets, by describing both self and others in mental, opposite physical, terms. We discuss potential uses for future research across various psychological and neurocognitive disciplines, as well as theoretical implications regarding the use of mentalizing language within spontaneous contexts.

The lack of temporal brain dynamics asymmetry as a signature of impaired consciousness states

Life is a constant battle against equilibrium. From the cellular level to the macroscopic scale, living organisms as dissipative systems require the violation of their detailed balance, i.e. metabolic enzymatic reactions, in order to survive. We present a framework based on temporal asymmetry as a measure of non-equilibrium. By means of statistical physics, it was discovered that temporal asymmetries establish an arrow of time useful for assessing the reversibility in human brain time series. Previous studies in human and non-human primates have shown that decreased consciousness states such as sleep and anaesthesia result in brain dynamics closer to the equilibrium. Furthermore, there is growing interest in the analysis of brain symmetry based on neuroimaging recordings and since it is a non-invasive technique, it can be extended to different brain imaging modalities and applied at different temporo-spatial scales. In the present study, we provide a detailed description of our methodological approach, paying special attention to the theories that motivated this work. We test, for the first time, the reversibility analysis in human functional magnetic resonance imaging data in patients suffering from disorder of consciousness. We verify that the tendency of a decrease in the asymmetry of the brain signal together with the decrease in non-stationarity are key characteristics of impaired consciousness states. We expect that this work will open the way for assessing biomarkers for patients' improvement and classification, as well as motivating further research on the mechanistic understanding underlying states of impaired consciousness.

Psychological proximity improves reasoning in academic aptitude tests

Performance on standardized academic aptitude tests (AAT) can determine important life outcomes. However, it is not clear whether and which aspects of the content of test questions affect performance. We examined the effect of psychological distance embedded in test questions. In Study 1 (N = 41,209), we classified the content of existing AAT questions as invoking proximal versus distal details. We found better performance with proximal compared to distal questions, especially for low-achieving examinees. Studies 2 and 3 manipulated the distance of questions adapted from AATs and examined three moderators: overall AAT score, working-memory capacity, and presence of irrelevant information. In Study 2 (N = 129), proximity (versus distance) improved the performance of low-achieving participants. In Study 3 (N = 1744), a field study, among low-achieving examinees, proximity improved performance on questions that included irrelevant information. Together, these results suggest that the psychological distance that is invoked by the content of test questions has important consequences for performance in real-life high-stakes tests.

Temporal irreversibility of neural dynamics as a signature of consciousness

Dissipative systems evolve in the preferred temporal direction indicated by the thermodynamic arrow of time. The fundamental nature of this temporal asymmetry led us to hypothesize its presence in the neural activity evoked by conscious perception of the physical world, and thus its covariance with the level of conscious awareness. We implemented a data-driven deep learning framework to decode the temporal inversion of electrocorticography signals acquired from non-human primates. Brain activity time series recorded during conscious wakefulness could be distinguished from their inverted counterparts with high accuracy, both using frequency and phase information. However, classification accuracy was reduced for data acquired during deep sleep and under ketamine-induced anesthesia; moreover, the predictions obtained from multiple independent neural networks were less consistent for sleep and anesthesia than for conscious wakefulness. Finally, the analysis of feature importance scores highlighted transitions between slow ($\approx$20 Hz) and fast frequencies (>40 Hz) as the main contributors to the temporal asymmetry observed during conscious wakefulness. Our results show that a preferred temporal direction is manifest in the neural activity evoked by conscious mentation and in the phenomenology of the passage of time, establishing common ground to tackle the relationship between brain and subjective experience.

On the Role of Theory and Modeling in Neuroscience

In recent years, the field of neuroscience has gone through rapid experimental advances and a significant increase in the use of quantitative and computational methods. This growth has created a need for clearer analyses of the theory and modeling approaches used in the field. This issue is particularly complex in neuroscience because the field studies phenomena that cross a wide range of scales and often require consideration at varying degrees of abstraction, from precise biophysical interactions to the computations they implement. We argue that a pragmatic perspective of science, in which descriptive, mechanistic, and normative models and theories each play a distinct role in defining and bridging levels of abstraction, will facilitate neuroscientific practice. This analysis leads to methodological suggestions, including selecting a level of abstraction that is appropriate for a given problem, identifying transfer functions to connect models and data, and the use of models themselves as a form of experiment.

Disruption of Social Orders in Societal Transitions as Affective Control of Uncertainty

Bayesian affect control theory is a model of affect-driven social interaction under conditions of uncertainty. In this paper, we investigate how the operationalization of uncertainty in the model can be related to the disruption of social orders-societal pressures to adapt to ongoing environmental and technological change. First, we study the theoretical tradeoffs between three kinds of uncertainty as groups navigate external problems: validity (the predictability of the environment, including of other agents), coherence (the predictability of interpersonal affective dynamics), and dependence (the predictability of affective meanings). Second, we discuss how these uncertainty tradeoffs are related to contemporary political conflict and polarization in the context of societal transitions. To illustrate the potential of our model to analyze the socio-emotional consequences of uncertainty, we present a simulation of diverging individual affective meanings of occupational identities under uncertainty in a climate change mitigation scenario based on events in Germany. Finally, we sketch a possible research agenda to substantiate the novel, but yet mostly conjectural, ideas put forward in this paper.

Temporal Construal Effects Are Independent of Episodic Future Thought

Human thought is prone to biases. Some biases serve as beneficial heuristics to free up limited cognitive resources or improve well-being, but their neurocognitive basis is unclear. One such bias is a tendency to construe events in the distant future in abstract, general terms and events in the near future in concrete, detailed terms. Temporal construal may rely on our capacity to orient toward and/or imagine context-rich future events. We tested 21 individuals with impaired episodic future thinking resulting from lesions to the hippocampus or ventromedial prefrontal cortex (vmPFC) and 57 control participants (aged 45-76 years) from Canada and Italy on measures sensitive to temporal construal. We found that temporal construal persisted in most patients, even those with impaired episodic future thinking, but was abolished in some vmPFC cases, possibly in relation to difficulties forming and maintaining future intentions. The results confirm the fractionation of future thinking and that parts of vmPFC might critically support our ability to flexibly conceive and orient ourselves toward future events.

Counterfactual curiosity: motivated thinking about what might have been

Counterfactual information, information about what might have been, forms the content of counterfactual thoughts and emotions like regret and relief. Recent research suggests that human adults and children, as well as rhesus monkeys, demonstrate ‘counterfactual curiosity’: they are motivated to seek out counterfactual information after making decisions. Based on contemporary theories of curiosity and information seeking and a broad range of empirical literature, we suggest multiple heterogeneous psychological processes that contribute to people's motivation for counterfactual information. This includes processes that are identified in the curiosity literature more generally—the potential use of counterfactual information for adaptive decision making (its long-term instrumental value) and the drive to reduce uncertainty. Additionally, we suggest that counterfactual information may be particularly alluring because of its role in causal reasoning; its relationship with prediction and decision making; and its potential to fulfil emotion regulation and self-serving goals. Some future directions have been suggested, including investigating the role of individual differences in counterfactual curiosity on learning and wellbeing.

This article is part of the theme issue ‘Thinking about possibilities: mechanisms, ontogeny, functions and phylogeny’.

Abstract words processing induces parasympathetic activation: A thermal imaging study

Abstract words (e.g., freedom) compose a significant part of speech. Despite this, learning them is complicated. Abstract concepts collect more heterogeneous exemplars and are more detached from sensory modalities than concrete concepts. Recent views propose that, because of their complexity, other people are pivotal for abstract concepts’ acquisition and use, e.g., to explain their meaning. We tested this hypothesis using a combined behavioral and thermal imaging paradigm. Twenty-one Italian children (10\F, mean age: 6 years) determined whether acoustic stimuli (concrete and abstract words; non-words) were or not correct Italian words (lexical decision). Concrete terms yielded faster responses than abstract ones: for the first time, this effect appears with response times in very young children. More crucially, the higher increase in temperature of the nasal tip (i.e., typically associated with parasympathetic dominance of the neurovegetative response) suggests that, with abstract concepts, children might be more socially and cognitively engaged.

How childhood maltreatment alters perception and cognition - the predictive processing account of borderline personality disorder

Borderline personality disorder (BPD) is a severe mental disorder, comprised of heterogeneous psychological and neurobiological pathologies. Here, we propose a predictive processing (PP) account of BPD to integrate these seemingly unrelated pathologies. In particular, we argue that the experience of childhood maltreatment, which is highly prevalent in BPD, leaves a developmental legacy with two facets: first, a coarse-grained, alexithymic model of self and others - leading to a rigidity and inflexibility concerning beliefs about self and others. Second, this developmental legacy leads to a loss of confidence or precision afforded beliefs about the consequences of social behavior. This results in an over reliance on sensory evidence and social feedback, with concomitant lability, impulsivity and hypersensitivity. In terms of PP, people with BPD show a distorted belief updating in response to new information with two opposing manifestations: rapid changes in beliefs and a lack of belief updating despite disconfirmatory evidence. This account of distorted information processing has the potential to explain both the instability (of affect, self-image, and interpersonal relationships) and the rigidity (of beliefs about self and others) which is typical of BPD. At the neurobiological level, we propose that enhanced levels of dopamine are associated with the increased integration of negative social feedback, and we also discuss the hypothesis of an impaired inhibitory control of the prefrontal cortex in the processing of negative social information. Our account may provide a new understanding not only of the clinical aspects of BPD, but also a unifying theory of the corresponding neurobiological pathologies. We conclude by outlining some directions for future research on the behavioral, neurobiological, and computational underpinnings of this model, and point to some clinical implications of it.

Machines That Feel and Think: The Role of Affective Feelings and Mental Action in (Artificial) General Intelligence

What role do affective feelings (feelings/emotions/moods) play in adaptive behaviour? What are the implications of this for understanding and developing artificial general intelligence? Leading theoretical models of brain function are beginning to shed light on these questions. While artificial agents have excelled within narrowly circumscribed and specialised domains, domain-general intelligence has remained an elusive goal in artificial intelligence research. By contrast, humans and nonhuman animals are characterised by a capacity for flexible behaviour and general intelligence. In this article I argue that computational models of mental phenomena in predictive processing theories of the brain are starting to reveal the mechanisms underpinning domain-general intelligence in biological agents, and can inform the understanding and development of artificial general intelligence. I focus particularly on approaches to computational phenomenology in the active inference framework. Specifically, I argue that computational mechanisms of affective feelings in active inference-affective self-modelling-are revealing of how biological agents are able to achieve flexible behavioural repertoires and general intelligence. I argue that (i) affective self-modelling functions to "tune" organisms to the most tractable goals in the environmental context; and (ii) affective and agentic self-modelling is central to the capacity to perform mental actions in goal-directed imagination and creative cognition. I use this account as a basis to argue that general intelligence of the level and kind found in biological agents will likely require machines to be implemented with analogues of affective self-modelling.

A prediction-focused approach to personality modeling

In the current study, we set out to examine the viability of a novel approach to modeling human personality. Research in psychology suggests that people's personalities can be effectively described using five broad dimensions (the Five-Factor Model; FFM); however, the FFM potentially leaves room for improved predictive accuracy. We propose a novel approach to modeling human personality that is based on the maximization of the model's predictive accuracy. Unlike the FFM, which performs unsupervised dimensionality reduction, we utilized a supervised machine learning technique for dimensionality reduction of questionnaire data, using numerous psychologically meaningful outcomes as data labels (e.g., intelligence, well-being, sociability). The results showed that our five-dimensional personality summary, which we term the "Predictive Five" (PF), provides predictive performance that is better than the FFM on two independent validation datasets, and on a new set of outcome variables selected by an independent group of psychologists. The approach described herein has the promise of eventually providing an interpretable, low-dimensional personality representation, which is also highly predictive of behavior.

Abstract concepts: external influences, internal constraints, and methodological issues

There is a longstanding and widely held misconception about the relative remoteness of abstract concepts from concrete experiences. This review examines the current evidence for external influences and internal constraints on the processing, representation, and use of abstract concepts, like truth, friendship, and number. We highlight the theoretical benefit of distinguishing between grounded and embodied cognition and then ask which roles do perception, action, language, and social interaction play in acquiring, representing and using abstract concepts. By reviewing several studies, we show that they are, against the accepted definition, not detached from perception and action. Focussing on magnitude-related concepts, we also discuss evidence for cultural influences on abstract knowledge and explore how internal processes such as inner speech, metacognition, and inner bodily signals (interoception) influence the acquisition and retrieval of abstract knowledge. Finally, we discuss some methodological developments. Specifically, we focus on the importance of studies that investigate the time course of conceptual processing and we argue that, because of the paramount role of sociality for abstract concepts, new methods are necessary to study concepts in interactive situations. We conclude that bodily, linguistic, and social constraints provide important theoretical limitations for our theories of conceptual knowledge.

The Role of Predictions, Their Confirmation, and Reward in Maintaining the Self-Concept

The predictive processing framework posits that people continuously use predictive principles when interacting with, learning from, and interpreting their surroundings. Here, we suggest that the same framework may help explain how people process self-relevant knowledge and maintain a stable and positive self-concept. Specifically, we recast two prominent self-relevant motivations, self-verification and self-enhancement, in predictive processing (PP) terms. We suggest that these self-relevant motivations interact with the self-concept (i.e., priors) to create strong predictions. These predictions, in turn, influence how people interpret information about themselves. In particular, we argue that these strong self-relevant predictions dictate how prediction error, the deviation from the original prediction, is processed. In contrast to many implementations of the PP framework, we suggest that predictions and priors emanating from stable constructs (such as the self-concept) cultivate belief-maintaining, rather than belief-updating, dynamics. Based on recent findings, we also postulate that evidence supporting a predicted model of the self (or interpreted as such) triggers subjective reward responses, potentially reinforcing existing beliefs. Characterizing the role of rewards in self-belief maintenance and reframing self-relevant motivations and rewards in predictive processing terms offers novel insights into how the self is maintained in neurotypical adults, as well as in pathological populations, potentially pointing to therapeutic implications.

The Inanimate Third: Going Beyond Psychodynamic Approaches for Remote Psychotherapy during the COVID ‐19 Pandemic

The COVID‐19 pandemic exposed the field of psychotherapy to the need to provide treatment remotely. We discuss the question of whether remote therapy can be curative and if the electronic device used to manage these sessions unites or separates the therapist and the patient. We term the electronic device as ‘the inanimate third’ in the therapeutic process and discuss the objectivity of the device as opposed to the subjective emotional processes involved. We deal with emotional themes relevant to the COVID‐19 pandemic and associated social distancing practices, such as longing, loneliness, the perception of the future and the lost past, and the efficacy of the therapeutic stimulation of fantasy and hope. We also evaluate the possibility of existing transference and countertransference processes while working remotely. We suggest the term ‘social paradox’ to describe the situation in which an objective entity such as the digital media symbolizes both distance and intimacy as well as separation and unity. We conclude by stating that containment of the social paradox by the therapeutic dialogue is possible as the existence of the dialogue eliminates elements of the paradox.

A First Principles Approach to Subjective Experience

Understanding the neural bases of subjective experience remains one of the great challenges of the natural sciences. Higher-order theories of consciousness are typically defended by assessments of neural activity in higher cortical regions during perception, often with disregard to the nature of the neural computations that these regions execute. We have sought to refocus the problem toward identification of those neural computations that are necessary for subjective experience with the goal of defining the sorts of neural architectures that can perform these operations. This approach removes reliance on behaviour and brain homologies for appraising whether non-human animals have the potential to subjectively experience sensory stimuli. Using two basic principles—first, subjective experience is dependent on complex processing executing specific neural functions and second, the structure-determines-function principle—we have reasoned that subjective experience requires a neural architecture consisting of stacked forward models that predict the output of neural processing from inputs. Given that forward models are dependent on appropriately connected processing modules that generate prediction, error detection and feedback control, we define a minimal neural architecture that is necessary (but not sufficient) for subjective experience. We refer to this framework as the hierarchical forward models algorithm. Accordingly, we postulate that any animal lacking this neural architecture will be incapable of subjective experience.

Emotion Regulation by Psychological Distance and Level of Abstraction: Two Meta-Analyses

Self-reflection is suggested to attenuate feelings, yet researchers disagree on whether adopting a distant or near perspective, or processing the experience abstractly or concretely, is more effective. Given the relationship between psychological distance and level of abstraction, we suggest the "construal-matching hypothesis": Psychological distance and abstraction differently influence emotion intensity, depending on whether the emotion's appraisal involves low-level or high-level construal. Two meta-analyses tested the effects of psychological distance (k = 230) and level-of-abstraction (k = 98) manipulations on emotional experience. A distant perspective attenuated emotional experience (g = 0.52) but with weaker effects for high-level (g = 0.29; for example, self-conscious emotions) than low-level emotions (g= 0.64; for example, basic emotions). Level of abstraction only attenuated the experience of low-level emotions (g = 0.2) and showed a reverse (nonsignificant) effect for high-level emotions (g = -0.13). These results highlight differences between distancing and level-of-abstraction manipulations and the importance of considering the type of emotion experienced in emotion regulation.

Psychological closeness and concrete construal may underlie high-fidelity social emulation

We compare bifocal stance theory's (BST) approach to social learning to construal level theory's (CLT) – a social-cognitive theory positing that psychological closeness to a model influences action-representation and thus modulates how concretely or abstractly observers emulate models. Whereas BST argues that social motives produce higher fidelity emulation, CLT argues that psychological closeness impacts cognitive construal and produces more concrete emulation across diverse motivations for emulation.

Overview of the Construal Level Theory: history of formation, main provisions and research potential

This paper provides an overview of Construal Level Theory (CLT), which was founded by Nira Liberman and Yakov Trope. CLT describes the relationship between psychological distance (subjective experience of remoteness of a phenomenon) and the level of its construal (representation) in consciousness. According to this theory, phenomena perceived more distant, will be presented in a more abstract way in the mind of the subject. At the moment CLT is one of the most actively developing interdisciplinary psychological theories. At the same time, it remains virtually unknown within the Russian-speaking psychological community. The main purpose of the article is to help the reader get an idea of the history of CLT development, its main assumptions, and to demonstrate its high research potential. The first part presents the most significant works, which determined the direction of the theory’s development. The second part summarizes its key points. The last part considers the possibilities of CLT in building a deeper understanding of the nature of prediction, evaluation, choice, self-regulation, and a number of other processes.

Framing is a motivated process

Frames group choices into categories, thus modifying the incentives for them. This effect makes framing itself a motivated choice rather than a neutral cognition. In particular, framing an inferior choice with a high short-term payoff as part of a broad category of choices recruits incentive to reject it; but this must be motivated by its being a test case.

Effects of Social Information on Risk Taking and Performance: Understanding Others’ Decisions vs. Comparing Oneself with Others in Short-Term Performance

When a problem leaves decision makers uncertain as to how to approach it, observing others’ decisions can improve one’s own decisions by promoting more accurate judgments and a better insight into the problem. However, observing others’ decisions may also activate motives that prevent this potential from being realized, for instance, ego concerns that prompt excessive risk taking. Our experimental study investigates how two features of the social environment influence the effect of observing others’ decisions on individual risk taking and performance. We manipulated (1) the psychological distance to others whose decisions could be observed (and thereby the tendency to seek self-enhancing social comparison) and (2) the opportunity for interaction (and thereby for a cumulative effect of any such tendency on decisions over time and for an effect on social information itself). Because the two features covary in real-world settings, we designed two treatments corresponding to the two natural combinations. Both treatments provided participants with two other participants’ period decisions in a multiperiod problem under uncertainty. No new objective information about the problem could be inferred from these decisions. We predicted that participants who observed the decisions of distant others (who had solved the same problem earlier) would perform better than participants in a control sample without any information about others’ decisions and that participants who observed the decisions of proximal others (with whom interaction could arise) would take more risk and perform worse than those who observed distant others’ decisions. The data corroborate our predictions. We discuss implications for organizational learning.

Equality and Freedom as Uncertainty in Groups

In this paper, I investigate a connection between a common characterisation of freedom and how uncertainty is managed in a Bayesian hierarchical model. To do this, I consider a distributed factorization of a group's optimization of free energy, in which each agent is attempting to align with the group and with its own model. I show how this can lead to equilibria for groups, defined by the capacity of the model being used, essentially how many different datasets it can handle. In particular, I show that there is a "sweet spot" in the capacity of a normal model in each agent's decentralized optimization, and that this "sweet spot" corresponds to minimal free energy for the group. At the sweet spot, an agent can predict what the group will do and the group is not surprised by the agent. However, there is an asymmetry. A higher capacity model for an agent makes it harder for the individual to learn, as there are more parameters. Simultaneously, a higher capacity model for the group, implemented as a higher capacity model for each member agent, makes it easier for a group to integrate a new member. To optimize for a group of agents then requires one to make a trade-off in capacity, as each individual agent seeks to decrease capacity, but there is pressure from the group to increase capacity of all members. This pressure exists because as individual agent's capacities are reduced, so too are their abilities to model other agents, and thereby to establish pro-social behavioural patterns. I then consider a basic two-level (dual process) Bayesian model of social reasoning and a set of three parameters of capacity that are required to implement such a model. Considering these three capacities as dependent elements in a free energy minimization for a group leads to a "sweet surface" in a three-dimensional space defining the triplet of parameters that each agent must use should they hope to minimize free energy as a group. Finally, I relate these three parameters to three notions of freedom and equality in human social organization, and postulate a correspondence between freedom and model capacity. That is, models with higher capacity, have more freedom as they can interact with more datasets.

Extracting Summary Statistics of Rapid Numerical Sequences

We examine the ability of observers to extract summary statistics (such as the mean and the relative-variance) from rapid numerical sequences of two digit numbers presented at a rate of 4/s. In four experiments (total N = 100), we find that the participants show a remarkable ability to extract such summary statistics and that their precision in the estimation of the sequence-mean improves with the sequence-length (subject to individual differences). Using model selection for individual participants we find that, when only the sequence-average is estimated, most participants rely on a holistic process of frequency based estimation with a minority who rely on a (rule-based and capacity limited) mid-range strategy. When both the sequence-average and the relative variance are estimated, about half of the participants rely on these two strategies. Importantly, the holistic strategy appears more efficient in terms of its precision. We discuss implications for the domains of two pathways numerical processing and decision-making.

From Affordances to Abstract Words: The Flexibility of Sensorimotor Grounding

The sensorimotor system plays a critical role in several cognitive processes. Here, we review recent studies documenting this interplay at different levels. First, we concentrate on studies that have shown how the sensorimotor system is flexibly involved in interactions with objects. We report evidence demonstrating how social context and situations influence affordance activation, and then focus on tactile and kinesthetic components in body-object interactions. Then, we turn to word use, and review studies that have shown that not only concrete words, but also abstract words are grounded in the sensorimotor system. We report evidence that abstract concepts activate the mouth effector more than concrete concepts, and discuss this effect in light of studies on adults, children, and infants. Finally, we pinpoint possible sensorimotor mechanisms at play in the acquisition and use of abstract concepts. Overall, we show that the involvement of the sensorimotor system is flexibly modulated by context, and that its role can be integrated and flanked by that of other systems such as the linguistic system. We suggest that to unravel the role of the sensorimotor system in cognition, future research should fully explore the complexity of this intricate, and sometimes slippery, relation.

Consciousness in active inference: Deep self-models, other minds, and the challenge of psychedelic-induced ego-dissolution

Predictive processing approaches to brain function are increasingly delivering promise for illuminating the computational underpinnings of a wide range of phenomenological states. It remains unclear, however, whether predictive processing is equipped to accommodate a theory of consciousness itself. Furthermore, objectors have argued that without specification of the core computational mechanisms of consciousness, predictive processing is unable to inform the attribution of consciousness to other non-human (biological and artificial) systems. In this paper, I argue that an account of consciousness in the predictive brain is within reach via recent accounts of phenomenal self-modelling in the active inference framework. The central claim here is that phenomenal consciousness is underpinned by 'subjective valuation'-a deep inference about the precision or 'predictability' of the self-evidencing ('fitness-promoting') outcomes of action. Based on this account, I argue that this approach can critically inform the distribution of experience in other systems, paying particular attention to the complex sensory attenuation mechanisms associated with deep self-models. I then consider an objection to the account: several recent papers argue that theories of consciousness that invoke self-consciousness as constitutive or necessary for consciousness are undermined by states (or traits) of 'selflessness'; in particular the 'totally selfless' states of ego-dissolution occasioned by psychedelic drugs. Drawing on existing work that accounts for psychedelic-induced ego-dissolution in the active inference framework, I argue that these states do not threaten to undermine an active inference theory of consciousness. Instead, these accounts corroborate the view that subjective valuation is the constitutive facet of experience, and they highlight the potential of psychedelic research to inform consciousness science, computational psychiatry and computational phenomenology.

Unification by Fiat: Arrested Development of Predictive Processing

Predictive processing (PP) has been repeatedly presented as a unificatory account of perception, action, and cognition. In this paper, we argue that this is premature: As a unifying theory, PP fails to deliver general, simple, homogeneous, and systematic explanations. By examining its current trajectory of development, we conclude that PP remains only loosely connected both to its computational framework and to its hypothetical biological underpinnings, which makes its fundamentals unclear. Instead of offering explanations that refer to the same set of principles, we observe systematic equivocations in PP‐based models, or outright contradictions with its avowed principles. To make matters worse, PP‐based models are seldom empirically validated, and they are frequently offered as mere just‐so stories. The large number of PP‐based models is thus not evidence of theoretical progress in unifying perception, action, and cognition. On the contrary, we maintain that the gap between theory and its biological and computational bases contributes to the arrested development of PP as a unificatory theory. Thus, we urge the defenders of PP to focus on its critical problems instead of offering mere re‐descriptions of known phenomena, and to validate their models against possible alternative explanations that stem from different theoretical assumptions. Otherwise, PP will ultimately fail as a unified theory of cognition.

Human Cooperation and the Crises of Climate Change, COVID-19, and Misinformation

Contemporary society is facing many social dilemmas-including climate change, COVID-19, and misinformation-characterized by a conflict between short-term self-interest and longer-term collective interest. The climate crisis requires paying costs today to benefit distant others (and oneself) in the future. The COVID-19 crisis requires the less vulnerable to pay costs to benefit the more vulnerable in the face of great uncertainty. The misinformation crisis requires investing effort to assess truth and abstain from spreading attractive falsehoods. Addressing these crises requires an understanding of human cooperation. To that end, we present (a) an overview of mechanisms for the evolution of cooperation, including mechanisms based on similarity and interaction; (b) a discussion of how reputation can incentivize cooperation via conditional cooperation and signaling; and (c) a review of social preferences that undergird the proximate psychology of cooperation, including positive regard for others, parochialism, and egalitarianism. We discuss the three focal crises facing our society through the lens of cooperation, emphasizing how cooperation research can inform our efforts to address them. Expected final online publication date for the Annual Review of Psychology, Volume 73 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Abstract concepts in interaction: the need of others when guessing abstract concepts smooths dyadic motor interactions

concepts (ACs, e.g. 'justice') are more complex compared with concrete concepts (CCs) (e.g. 'table'). Indeed, they do not possess a single object as a referent, they assemble quite heterogeneous members and they are more detached from exteroceptive and more grounded in interoceptive experience. Recent views have hypothesized that interpersonal communication is particularly crucial to acquire and use ACs. The current study investigates the reliance of ACs/CCs representation on interpersonal behaviour. We asked participants to perform a motor interaction task with two avatars who embodied two real confederates. Before and after the motor interaction task, the two confederates provided participants with hints in a concept guessing task associated with visual stimuli: one helped in guessing ACs and the other, CCs. A control study we performed both with the materials employed in the main experiment and with other materials, confirmed that associating verbal concepts with visual images was more difficult with ACs than with CCs. Consistently, the results of the main experiment showed that participants asked for more hints with ACs than CCs and were more synchronous when interacting with the avatar corresponding to the AC's confederate. The results highlight an important role of sociality in grounding ACs.

Does Temporal Distance Influence Abstraction? A Large Pre-Registered Experiment

Construal level theory has been extraordinarily generative both within and beyond social psychology, yet the individual effects that form the empirical foundation of the theory have yet to be carefully probed and precisely estimated using large samples and preregistered analysis plans. In a highly powered and preregistered study, we tested the effect of temporal distance on abstraction, using one of the most common operationalizations of temporal distance (thinking about a future point in time that is one day vs. one year from today) and one of the most common operationalizations of abstraction (preference for more abstract vs. concrete action representations, as assessed by the Behavioral Identification Form). Participants preferred significantly more abstract action representations in the distant (vs. near) future condition, with an effect size of d = .276, 95% CI [.097, .455]. We discuss implications, future directions, and constraints on the generality of these results.

Psychological distance: How to make climate change less abstract and closer to the self

Scientists have been warning the world of the threatening consequences of climate change for decades. Yet, only a few countries have made climate change mitigation a priority. One of the chief issues regarding climate change is its abstractness: consequences for the collective in the long-term are much more abstract than consequences for the self in the here-and-now. To combat climate change, individuals, communities, and governments must work together to reduce the psychological distance of climate change and designate the future of the planet as the prime concern.

Grounded separation: can the sensorimotor be grounded in the symbolic?

According to Lee and Schwarz, the sensorimotor experience of cleansing involves separating one physical entity from another and grounds mental separation of one psychological entity from another. We propose that cleansing effects may result from symbolic cognition. Instead of viewing abstract meanings as emerging from concrete physical acts of cleansing, this physical act may be appended with pre-existing, symbolic meaning.

Building semantic memory from embodied and distributional language experience

Humans seamlessly make sense of a rapidly changing environment, using a seemingly limitless knowledgebase to recognize and adapt to most situations we encounter. This knowledgebase is called semantic memory. Embodied cognition theories suggest that we represent this knowledge through simulation: understanding the meaning of coffee entails reinstantiating the neural states involved in touching, smelling, seeing, and drinking coffee. Distributional semantic theories suggest that we are sensitive to statistical regularities in natural language, and that a cognitive mechanism picks up on these regularities and transforms them into usable semantic representations reflecting the contextual usage of language. These appear to present contrasting views on semantic memory, but do they? Recent years have seen a push toward combining these approaches under a common framework. These hybrid approaches augment our understanding of semantic memory in important ways, but current versions remain unsatisfactory in part because they treat sensory-perceptual and distributional-linguistic data as interacting but distinct types of data that must be combined. We synthesize several approaches which, taken together, suggest that linguistic and embodied experience should instead be considered as inseparably entangled: just as sensory and perceptual systems are reactivated to understand meaning, so are experience-based representations endemic to linguistic processing; further, sensory-perceptual experience is susceptible to the same distributional principles as language experience. This conclusion produces a characterization of semantic memory that accounts for the interdependencies between linguistic and embodied data that arise across multiple timescales, giving rise to concept representations that reflect our shared and unique experiences. This article is categorized under: Psychology > Language Neuroscience > Cognition Linguistics > Language in Mind and Brain.

Regulatory Scope and Its Mental and Social Supports

Adaptive functioning requires the ability to both immerse oneself in the here and now as well as to move beyond current experience. We leverage and expand construal-level theory to understand how individuals and groups regulate thoughts, feelings, and behavior to address both proximal and distal ends. To connect to distant versus proximal events in a way that meaningfully informs and guides responses in the immediate here and now, people must expand versus contract their regulatory scope. We propose that humans have evolved a number of mental and social tools that enable the modulation of regulatory scope and address the epistemic, emotive, and executive demands of regulation. Critically, across these tools, it is possible to distinguish a hierarchy that varies in abstractness. Whereas low-level tools enable contractive scope, high-level tools enable expansion. We review empirical results that support these assertions and highlight the novel insights that a regulatory-scope framework provides for understanding diverse phenomena.

The Computational Boundary of a "Self": Developmental Bioelectricity Drives Multicellularity and Scale-Free Cognition

All epistemic agents physically consist of parts that must somehow comprise an integrated cognitive self. Biological individuals consist of subunits (organs, cells, and molecular networks) that are themselves complex and competent in their own native contexts. How do coherent biological Individuals result from the activity of smaller sub-agents? To understand the evolution and function of metazoan creatures' bodies and minds, it is essential to conceptually explore the origin of multicellularity and the scaling of the basal cognition of individual cells into a coherent larger organism. In this article, I synthesize ideas in cognitive science, evolutionary biology, and developmental physiology toward a hypothesis about the origin of Individuality: "Scale-Free Cognition." I propose a fundamental definition of an Individual based on the ability to pursue goals at an appropriate level of scale and organization and suggest a formalism for defining and comparing the cognitive capacities of highly diverse types of agents. Any Self is demarcated by a computational surface - the spatio-temporal boundary of events that it can measure, model, and try to affect. This surface sets a functional boundary - a cognitive "light cone" which defines the scale and limits of its cognition. I hypothesize that higher level goal-directed activity and agency, resulting in larger cognitive boundaries, evolve from the primal homeostatic drive of living things to reduce stress - the difference between current conditions and life-optimal conditions. The mechanisms of developmental bioelectricity - the ability of all cells to form electrical networks that process information - suggest a plausible set of gradual evolutionary steps that naturally lead from physiological homeostasis in single cells to memory, prediction, and ultimately complex cognitive agents, via scale-up of the basic drive of infotaxis. Recent data on the molecular mechanisms of pre-neural bioelectricity suggest a model of how increasingly sophisticated cognitive functions emerge smoothly from cell-cell communication used to guide embryogenesis and regeneration. This set of hypotheses provides a novel perspective on numerous phenomena, such as cancer, and makes several unique, testable predictions for interdisciplinary research that have implications not only for evolutionary developmental biology but also for biomedicine and perhaps artificial intelligence and exobiology.