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Developing a sense of internal safety and security depends mainly on others: numerous neuromodulators play a significant role in the homeostatic process, regulating the importance of proximity to a caregiver and experiencing feelings that enable us to regulate our interdependence with our conspecifics since birth. This array of neurofunctional structures have been called the SEPARATION DISTRESS system (now more commonly known as the PANIC/GRIEF system). This emotional system is mainly involved in the production of depressive symptoms. The disruption of this essential emotional balance leads to the onset of feelings of panic followed by depression. We will focus on the neuropeptides that play a crucial role in social approach behavior in mammals, which enhance prosocial behavior and facilitate the consolidation of social bonds. We propose that most prosocial behaviors are regulated through the specific neuromodulators acting on salient intersubjective stimuli, reflecting an increased sense of inner confidence (safety) in social relationships. This review considers the neurofunctional link between the feelings that may ultimately be at the base of a sense of inner safety and the central neuromodulatory systems. This link may shed light on the clinical implications for the development of early mother-infant bonding and the depressive clinical consequences when this bond is disrupted, such as in post-partum depression, depressive feelings connected to, addiction, neurofunctional disorders, and psychological trauma. [ABSTRACT FROM AUTHOR]

Developing a sense of internal safety and security depends mainly on others: numerous neuromodulators play a significant role in the homeostatic process, regulating the importance of proximity to a caregiver and experiencing feelings that enable us to regulate our interdependence with our conspecifics since birth. This array of neurofunctional structures have been called the SEPARATION DISTRESS system (now more commonly known as the PANIC/ GRIEF system). This emotional system is mainly involved in the production of depressive symptoms. The disruption of this essential emotional balance leads to the onset of feelings of panic followed by depression. We will focus on the neuropeptides that play a crucial role in social approach behavior in mammals, which enhance prosocial behavior and facilitate the consolidation of social bonds. We propose that most prosocial behaviors are regulated through the specific neuromodulators acting on salient intersubjective stimuli, reflecting an increased sense of inner confidence (safety) in social relationships. This review considers the neurofunctional link between the feelings that may ultimately be at the base of a sense of inner safety and the central neuromodulatory systems. This link may shed light on the clinical implications for the development of early mother-infant bonding and the depressive clinical consequences when this bond is disrupted, such as in post-partum depression, depressive feelings connected to, addiction, neurofunctional disorders, and psychological trauma.

Predictability plays an important role in the experience of musical pleasure. By leveraging expectations, music induces pleasure through tension and surprise. However, musical predictions draw on both prior knowledge and immediate context. Similarly, musical pleasure, which has been shown to depend on predictability, may also vary relative to the individual and context. Although research has demonstrated the influence of both long-term knowledge and stimulus features in influencing expectations, it is unclear how perceptions of a melody are influenced by comparisons to other music pieces heard in the same context. To examine the effects of context we compared how listeners' judgments of two distinct sets of stimuli differed when they were presented alone or in combination. Stimuli were excerpts from a repertoire of Western music and a set of experimenter created melodies. Separate groups of participants rated liking and predictability for each set of stimuli alone and in combination. We found that when heard together, the Repertoire stimuli were more liked and rated as less predictable than if they were heard alone, with the opposite pattern being observed for the Experimental stimuli. This effect was driven by a change in ratings between the Alone and Combined conditions for each stimulus set. These findings demonstrate a context-based shift of predictability ratings and derived pleasure, suggesting that judgments stem not only from the physical properties of the stimulus, but also vary relative to other options available in the immediate context. [ABSTRACT FROM AUTHOR]

Predictability plays an important role in the experience of musical pleasure. By leveraging expectations, music induces pleasure through tension and surprise. However, musical predictions draw on both prior knowledge and immediate context. Similarly, musical pleasure, which has been shown to depend on predictability, may also vary relative to the individual and context. Although research has demonstrated the influence of both long-term knowledge and stimulus features in influencing expectations, it is unclear how perceptions of a melody are influenced by comparisons to other music pieces heard in the same context. To examine the effects of context we compared how listeners’ judgments of two distinct sets of stimuli differed when they were presented alone or in combination. Stimuli were excerpts from a repertoire of Western music and a set of experimenter created melodies. Separate groups of participants rated liking and predictability for each set of stimuli alone and in combination. We found that when heard together, the Repertoire stimuli were more liked and rated as less predictable than if they were heard alone, with the opposite pattern being observed for the Experimental stimuli. This effect was driven by a change in ratings between the Alone and Combined conditions for each stimulus set. These findings demonstrate a context-based shift of predictability ratings and derived pleasure, suggesting that judgments stem not only from the physical properties of the stimulus, but also vary relative to other options available in the immediate context.

Purpose: Self-motion perception is a key factor in daily behaviours such as driving a car or piloting an aircraft. It is mainly based on visuo-vestibular integration, whose weighting mechanisms are modulated by the reliability properties of sensory inputs. Recently, it has been shown that the internal state of the operator can also modulate multisensory integration and may sharpen the representation of relevant inputs. In line with the concept of agency, it thus appears relevant to evaluate the impact of being in control of our own action on self-motion perception. Methodology: Here, we tested two conditions of motion control (active/manual trigger versus passive/observer condition), asking participants to discriminate between two consecutive longitudinal movements by identifying the larger displacement (displacement of higher intensity). We also tested motion discrimination under two levels of ambiguity by applying acceleration ratios that differed from our two "standard" displacements (i.e., 3 s; 0.012 m.s-2 and 0.030 m.s-2). Results: We found an effect of control condition, but not of the level of ambiguity on the way participants perceived the standard displacement, i.e., perceptual bias (Point of Subjective Equality; PSE). Also, we found a significant effect of interaction between the active condition and the level of ambiguity on the ability to discriminate between displacements, i.e., sensitivity (Just Noticeable Difference; JND). Originality: Being in control of our own motion through a manual intentional trigger of self-displacement maintains overall motion sensitivity when ambiguity increases. [ABSTRACT FROM AUTHOR]

The Bayesian approach of cognitive science largely takes the position that evolution drives perception to produce precepts that are veridical. However, some efforts utilizing evolutionary game theory simulations have shown that perception is more likely based on a fitness function, which promotes survival rather than promoting perceptual truth about the environment. Although these findings do not correspond well with the standard Bayesian approach to cognition, they may correspond with a behavioral functional contextual approach that is ontologically neutral (a-ontological). This approach, formalized through a post-Skinnerian account of behaviorism called relational frame theory (RFT), can, in fact, be shown to correspond well with an evolutionary fitness function, whereby contextual functions form that corresponds to a fitness function interface of the world. This fitness interface approach therefore may help provide a mathematical description for a functional contextual interface of phenomenological experience. Furthermore, this more broadly fits with a neurological active inference approach based on the free-energy principle (FEP) and more broadly with Lagrangian mechanics. These assumptions of how fitness beats truth (FBT) and FEP correspond to RFT are then discussed within a broader multidimensional and evolutionary framework called the extended evolutionary meta-model (EEMM) that has emerged out of the functional contextual behavioral science literature to incorporate principles of cognition, neurobiology, behaviorism, and evolution and are discussed in the context of a novel RFT framework called “Neurobiological and Natural Selection Relational Frame Theory” (N-frame). This framework mathematically connects RFT to FBT, FEP, and EEMM within a single framework that expands into dynamic graph networking. This is then discussed for its implications of empirical work at the non-ergodic process-based idiographic level as applied to individual and societal level dynamic modeling and clinical work. This discussion is framed within the context of individuals that are described as evolutionary adaptive and conscious (observer-self) agents that minimize entropy and can promote a prosocial society through group-level values and psychological flexibility. [ABSTRACT FROM AUTHOR]

PurposeSelf-motion perception is a key factor in daily behaviours such as driving a car or piloting an aircraft. It is mainly based on visuo-vestibular integration, whose weighting mechanisms are modulated by the reliability properties of sensory inputs. Recently, it has been shown that the internal state of the operator can also modulate multisensory integration and may sharpen the representation of relevant inputs. In line with the concept of agency, it thus appears relevant to evaluate the impact of being in control of our own action on self-motion perception.MethodologyHere, we tested two conditions of motion control (active/manual trigger versus passive/ observer condition), asking participants to discriminate between two consecutive longitudinal movements by identifying the larger displacement (displacement of higher intensity). We also tested motion discrimination under two levels of ambiguity by applying acceleration ratios that differed from our two “standard” displacements (i.e., 3 s; 0.012 m.s−2 and 0.030 m.s−2).ResultsWe found an effect of control condition, but not of the level of ambiguity on the way participants perceived the standard displacement, i.e., perceptual bias (Point of Subjective Equality; PSE). Also, we found a significant effect of interaction between the active condition and the level of ambiguity on the ability to discriminate between displacements, i.e., sensitivity (Just Noticeable Difference; JND).OriginalityBeing in control of our own motion through a manual intentional trigger of self-displacement maintains overall motion sensitivity when ambiguity increases.

The Bayesian approach of cognitive science largely takes the position that evolution drives perception to produce precepts that are veridical. However, some efforts utilizing evolutionary game theory simulations have shown that perception is more likely based on a fitness function, which promotes survival rather than promoting perceptual truth about the environment. Although these findings do not correspond well with the standard Bayesian approach to cognition, they may correspond with a behavioral functional contextual approach that is ontologically neutral (a-ontological). This approach, formalized through a post-Skinnerian account of behaviorism called relational frame theory (RFT), can, in fact, be shown to correspond well with an evolutionary fitness function, whereby contextual functions form that corresponds to a fitness function interface of the world. This fitness interface approach therefore may help provide a mathematical description for a functional contextual interface of phenomenological experience. Furthermore, this more broadly fits with a neurological active inference approach based on the free-energy principle (FEP) and more broadly with Lagrangian mechanics. These assumptions of how fitness beats truth (FBT) and FEP correspond to RFT are then discussed within a broader multidimensional and evolutionary framework called the extended evolutionary meta-model (EEMM) that has emerged out of the functional contextual behavioral science literature to incorporate principles of cognition, neurobiology, behaviorism, and evolution and are discussed in the context of a novel RFT framework called “Neurobiological and Natural Selection Relational Frame Theory” (N-frame). This framework mathematically connects RFT to FBT, FEP, and EEMM within a single framework that expands into dynamic graph networking. This is then discussed for its implications of empirical work at the non-ergodic process-based idiographic level as applied to individual and societal level dynamic modeling and clinical work. This discussion is framed within the context of individuals that are described as evolutionary adaptive and conscious (observer-self) agents that minimize entropy and can promote a prosocial society through group-level values and psychological flexibility.

Predictive coding theory is an influential view of perception and cognition. It proposes that subjective experience of the sensory information results from a comparison between the sensory input and the top-down prediction about this input, the latter being critical for shaping the final perceptual outcome. The theory is able to explain a wide range of phenomena extending from sensory experiences such as visual illusions to complex pathological states such as hallucinations and psychosis. In the current study we aimed at testing the proposed connection between different phenomena explained by the predictive coding theory by measuring the manifestation of top-down predictions at progressing levels of complexity, starting from bistable visual illusions (alternating subjective experience of the same sensory input) and pareidolias (alternative meaningful interpretation of the sensory input) to self-reports of hallucinations and delusional ideations in everyday life. Examining the correlation structure of these measures in 82 adult healthy subjects revealed a positive association between pareidolia proneness and a tendency for delusional ideations, yet without any relationship to bistable illusions. These results show that only a subset of the phenomena that are explained by the predictive coding theory can be attributed to one common underlying factor. Our findings thus support the hierarchical view of predictive processing with independent top-down effects at the sensory and cognitive levels. [ABSTRACT FROM AUTHOR]

Predictive coding theory is an influential view of perception and cognition. It proposes that subjective experience of the sensory information results from a comparison between the sensory input and the top-down prediction about this input, the latter being critical for shaping the final perceptual outcome. The theory is able to explain a wide range of phenomena extending from sensory experiences such as visual illusions to complex pathological states such as hallucinations and psychosis. In the current study we aimed at testing the proposed connection between different phenomena explained by the predictive coding theory by measuring the manifestation of top-down predictions at progressing levels of complexity, starting from bistable visual illusions (alternating subjective experience of the same sensory input) and pareidolias (alternative meaningful interpretation of the sensory input) to self-reports of hallucinations and delusional ideations in everyday life. Examining the correlation structure of these measures in 82 adult healthy subjects revealed a positive association between pareidolia proneness and a tendency for delusional ideations, yet without any relationship to bistable illusions. These results show that only a subset of the phenomena that are explained by the predictive coding theory can be attributed to one common underlying factor. Our findings thus support the hierarchical view of predictive processing with independent top-down effects at the sensory and cognitive levels.

This study investigated how children's drawings can provide insights into their cognitive development. It can be challenging to quantify the diversity of children's drawings across their developmental stages as well as between individuals. This study observed children's representational drawing ability by conducting a completion task where children could freely draw on partially drawn objects, and quantitatively analyzed di erences in children's drawing tendencies across age and between individuals. First, we conducted preregistered analyses, based on crowd-sourced adult ratings, to investigate the di erences of drawing style with the age and autistic traits of the children, where the latter was inspired by reports of atypical drawing among children with autism spectrum disorder (ASD). Additionally, the drawings were quantified using feature representations extracted with a deep convolutional neural network (CNN), which allowed an analysis of the drawings at different perceptual levels (i.e., local or global). Findings revealed a decrease in scribbling and an increase in completion behavior with increasing age. However, no correlation between drawing behavior and autistic traits was found. The network analysis demonstrated that older children adapted to the presented stimuli in a more adult-like manner than younger children. Furthermore, ways to quantify individual di erences in howchildren adapt to the presented stimuli are explored. Based on the predictive coding theory as a unified theory of how perception and behavior might emerge from integrating sensations and predictions, we suggest that our analyses may open up new possibilities for investigating children's cognitive development. [ABSTRACT FROM AUTHOR]

Generalized anxiety disorder is among the world’s most prevalent psychiatric disorders and often manifests as persistent and difficult to control apprehension. Despite its prevalence, there is no integrative, formal model of how anxiety and anxiety disorders arise. Here, we offer a perspective derived from the free energy principle; one that shares similarities with established constructs such as learned helplessness. Our account is simple: anxiety can be formalized as learned uncertainty. A biological system, having had persistent uncertainty in its past, will expect uncertainty in its future, irrespective of whether uncertainty truly persists. Despite our account’s intuitive simplicity— which can be illustrated with the mere flip of a coin—it is grounded within the free energy principle and hence situates the formation of anxiety within a broader explanatory framework of biological self-organization and self-evidencing. We conclude that, through conceptualizing anxiety within a framework of working generative models, our perspective might afford novel approaches in the clinical treatment of anxiety and its key symptoms. [ABSTRACT FROM AUTHOR]

This study investigated how children's drawings can provide insights into their cognitive development. It can be challenging to quantify the diversity of children's drawings across their developmental stages as well as between individuals. This study observed children's representational drawing ability by conducting a completion task where children could freely draw on partially drawn objects, and quantitatively analyzed differences in children's drawing tendencies across age and between individuals. First, we conducted preregistered analyses, based on crowd-sourced adult ratings, to investigate the differences of drawing style with the age and autistic traits of the children, where the latter was inspired by reports of atypical drawing among children with autism spectrum disorder (ASD). Additionally, the drawings were quantified using feature representations extracted with a deep convolutional neural network (CNN), which allowed an analysis of the drawings at different perceptual levels (i.e., local or global). Findings revealed a decrease in scribbling and an increase in completion behavior with increasing age. However, no correlation between drawing behavior and autistic traits was found. The network analysis demonstrated that older children adapted to the presented stimuli in a more adult-like manner than younger children. Furthermore, ways to quantify individual differences in how children adapt to the presented stimuli are explored. Based on the predictive coding theory as a unified theory of how perception and behavior might emerge from integrating sensations and predictions, we suggest that our analyses may open up new possibilities for investigating children's cognitive development.

Generalized anxiety disorder is among the world’s most prevalent psychiatric disorders and often manifests as persistent and difficult to control apprehension. Despite its prevalence, there is no integrative, formal model of how anxiety and anxiety disorders arise. Here, we offer a perspective derived from the free energy principle; one that shares similarities with established constructs such as learned helplessness. Our account is simple: anxiety can be formalized as learned uncertainty. A biological system, having had persistent uncertainty in its past, will expect uncertainty in its future, irrespective of whether uncertainty truly persists. Despite our account’s intuitive simplicity—which can be illustrated with the mere flip of a coin—it is grounded within the free energy principle and hence situates the formation of anxiety within a broader explanatory framework of biological self-organization and self-evidencing. We conclude that, through conceptualizing anxiety within a framework of working generative models, our perspective might afford novel approaches in the clinical treatment of anxiety and its key symptoms.

Predictive coding provides a compelling, unified theory of neural information processing, including for language. However, there is insufficient understanding of how predictive models adapt to changing contextual and environmental demands and the extent to which such adaptive processes differ between individuals. Here, we used electroencephalography (EEG) to track prediction error responses during a naturalistic language processing paradigm. In Experiment 1, 45 native speakers of English listened to a series of short passages. Via a speaker manipulation, we introduced changing intra-experimental adjective order probabilities for two-adjective noun phrases embedded within the passages and investigated whether prediction error responses adapt to reflect these intra-experimental predictive contingencies. To this end, we calculated a novel measure of speaker-based, intra-experimental surprisal (“speaker-based surprisal”) as defined on a trial-by-trial basis and by clustering together adjectives with a similar meaning. N400 amplitude at the position of the critical second adjective was used as an outcome measure of prediction error. Results showed that N400 responses attuned to speaker-based surprisal over the course of the experiment, thus indicating that listeners rapidly adapt their predictive models to reflect local environmental contingencies (here: the probability of one type of adjective following another when uttered by a particular speaker). Strikingly, this occurs in spite of the wealth of prior linguistic experience that participants bring to the laboratory. Model adaptation effects were strongest for participants with a steep aperiodic (1/f) slope in resting EEG and low individual alpha frequency (IAF), with idea density (ID) showing a more complex pattern. These results were replicated in a separate sample of 40 participants in Experiment 2, which employed a highly similar design to Experiment 1. Overall, our results suggest that individuals with a steep aperiodic slope adapt their predictive models most strongly to context-specific probabilistic information. Steep aperiodic slope is thought to reflect low neural noise, which in turn may be associated with higher neural gain control and better cognitive control. Individuals with a steep aperiodic slope may thus be able to more effectively and dynamically reconfigure their prediction-related neural networks to meet current task demands. We conclude that predictive mechanisms in language are highly malleable and dynamic, reflecting both the affordances of the present environment as well as intrinsic information processing capabilities of the individual. [ABSTRACT FROM AUTHOR]

The inconsistent relation between word frequency and free recall performance (sometimes a positive one, sometimes a negative one, and sometimes no relation) and the non-monotonic relation found between the two cannot all be explained by current theories. We propose a theoretical framework that can explain all extant results. Based on an ecological psychology analysis of the free recall situation in terms of environmental and informational resources available to the participants, we propose that because participants' cognitive system has been shaped by their native language, free recall performance is best understood as the end result of relational properties that preexist the experimental situation and of the way the words from the experimental list interact with those. In addition to this, we borrow from predictive coding theory the idea that the brain constantly predicts "what is coming next" so that it is mainly prediction errors that will propagate information forward. Our ecological psychology analysis indicates there will be "prediction errors" because the word frequency distribution in an experimental word list is inevitably different from the particular Zipf's law distribution of the words in the language that shaped participants' brains. We further propose the particular distributional discrepancies inherent to a given word list will trigger, as a function of the words that are included in the list, their order, and of the words that are absent from the list, a surprisal signal in the brain, something that is isomorphic to the concept of Bayesian surprise. The precise moment when Bayesian surprise is triggered will determine to what word of the list that Bayesian surprise will be associated with, and the word the Bayesian surprise will be associated with will benefit from it and become more memorable as a direct function of the magnitude of the surprisal. Two experiments are presented that show a proxy of Bayesian surprise explains the free recall performance and that no effect of word frequency is found above and beyond the effect of that proxy variable. We then discuss how our view can account for all data extant in the literature on the effect of word frequency on free recall. [ABSTRACT FROM AUTHOR]

Groove—defined as the pleasurable urge to move to a rhythm—depends on a fine-tuned interplay between predictability arising from repetitive rhythmic patterns, and surprise arising from rhythmic deviations, for example in the form of syncopation. The perfect balance between predictability and surprise is commonly found in rhythmic patterns with a moderate level of rhythmic complexity and represents the sweet spot of the groove experience. In contrast, rhythms with low or high complexity are usually associated with a weaker experience of groove because they are too boring to be engaging or too complex to be interpreted, respectively. Consequently, the relationship between rhythmic complexity and groove experience can be described by an inverted U-shaped function. We interpret this inverted U shape in light of the theory of predictive processing and provide perspectives on how rhythmic complexity and groove can help us to understand the underlying neural mechanisms linking temporal predictions, movement, and reward. A better understanding of these mechanisms can guide future approaches to improve treatments for patients with motor impairments, such as Parkinson’s disease, and to investigate prosocial aspects of interpersonal interactions that feature music, such as dancing. Finally, we present some open questions and ideas for future research. [ABSTRACT FROM AUTHOR]

Attention and memory for everyday experiences vary over time, wherein some moments are better attended and subsequently better remembered than others. These effects have been demonstrated in naturalistic viewing tasks with complex and relatively uncontrolled stimuli, as well as in more controlled laboratory tasks with simpler stimuli. For example, in the attentional boost effect (ABE), participants perform two tasks at once: memorizing a series of briefly presented stimuli (e.g., pictures of outdoor scenes) for a later memory test, and responding to other concurrently presented cues that meet pre-defined criteria (e.g., participants press a button for a blue target square and do nothing for a red distractor square). However, rather than increasing dual-task interference, attending to a target cue boosts, rather than impairs, subsequent memory for concurrently presented information. In this review we describe current data on the extent and limitations of the attentional boost effect and whether it may be related to activity in the locus coeruleus neuromodulatory system. We suggest that insight into the mechanisms that produce the attentional boost effect may be found in recent advances in the locus coeruleus literature and from understanding of how the neurocognitive system handles stability and change in everyday events. We consequently propose updates to an early account of the attentional boost effect, the dual-task interaction model, to better ground it in what is currently known about event cognition and the role that the LC plays in regulating brain states. [ABSTRACT FROM AUTHOR]

Pursuing a precise, focused train of thought requires cognitive effort. Even more effort is necessary when more alternatives need to be considered or when the imagined situation becomes more complex. Cognitive resources available to us limit the cognitive effort we can spend. In line with previous work, an information-theoretic, Bayesian brain approach to cognitive effort is pursued: to solve tasks in our environment, our brain needs to invest information, that is, negative entropy, to impose structure, or focus, away from a uniform structure or other task-incompatible, latent structures. To get a more complete formalization of cognitive effort, a resourceful event-predictive inference model (REPI) is introduced, which offers computational and algorithmic explanations about the latent structure of our generative models, the active inference dynamics that unfold within, and the cognitive effort required to steer the dynamics-to, for example, purposefully process sensory signals, decide on responses, and invoke their execution. REPI suggests that we invest cognitive resources to infer preparatory priors, activate responses, and anticipate action consequences. Due to our limited resources, though, the inference dynamics are prone to task-irrelevant distractions. For example, the task-irrelevant side of the imperative stimulus causes the Simon effect and, due to similar reasons, we fail to optimally switch between tasks. An actual model implementation simulates such task interactions and offers first estimates of the involved cognitive effort. The approach may be further studied and promises to offer deeper explanations about why we get quickly exhausted from multitasking, how we are influenced by irrelevant stimulus modalities, why we exhibit magnitude interference, and, during social interactions, why we often fail to take the perspective of others into account. [ABSTRACT FROM AUTHOR]

Language delay is often one of the first concerns of parents of toddlers with autism spectrum disorder (ASD), and early language abilities predict broader outcomes for children on the autism spectrum. Yet, mechanisms underlying language deficits in autistic children remain underspecified. One prominent component of linguistic behavior is the use of predictions or expectations during learning and processing. Several researcher teams have posited prediction deficit accounts of ASD. The basic assumption of the prediction accounts is that information is processed by making predictions and testing violations against expectations (prediction errors). Flexible (neurotypical) brains attribute differential weights to prediction errors to determine when new learning is appropriate, while autistic individuals are thought to assign disproportionate weight to prediction errors. According to some views, these prediction deficits are hypothesized to lead to higher levels of perceived novelty, resulting in "hyperplasticity" of learning based on the most recent input. In this article, we adopt the perspective that it would be useful to investigate whether language deficits in children with ASD can be attributed to atypical domain-general prediction processes. [ABSTRACT FROM AUTHOR]

The inconsistent relation between word frequency and free recall performance (sometimes a positive one, sometimes a negative one, and sometimes no relation) and the non-monotonic relation found between the two cannot all be explained by current theories. We propose a theoretical framework that can explain all extant results. Based on an ecological psychology analysis of the free recall situation in terms of environmental and informational resources available to the participants, we propose that because participants’ cognitive system has been shaped by their native language, free recall performance is best understood as the end result of relational properties that preexist the experimental situation and of the way the words from the experimental list interact with those. In addition to this, we borrow from predictive coding theory the idea that the brain constantly predicts “what is coming next” so that it is mainly prediction errors that will propagate information forward. Our ecological psychology analysis indicates there will be “prediction errors” because the word frequency distribution in an experimental word list is inevitably different from the particular Zipf’s law distribution of the words in the language that shaped participants’ brains. We further propose the particular distributional discrepancies inherent to a given word list will trigger, as a function of the words that are included in the list, their order, and of the words that are absent from the list, a surprisal signal in the brain, something that is isomorphic to the concept of Bayesian surprise. The precise moment when Bayesian surprise is triggered will determine to what word of the list that Bayesian surprise will be associated with, and the word the Bayesian surprise will be associated with will benefit from it and become more memorable as a direct function of the magnitude of the surprisal. Two experiments are presented that show a proxy of Bayesian surprise explains the free recall performance and that no effect of word frequency is found above and beyond the effect of that proxy variable. We then discuss how our view can account for all data extant in the literature on the effect of word frequency on free recall.

Predictive coding provides a compelling, unified theory of neural information processing, including for language. However, there is insufficient understanding of how predictive models adapt to changing contextual and environmental demands and the extent to which such adaptive processes differ between individuals. Here, we used electroencephalography (EEG) to track prediction error responses during a naturalistic language processing paradigm. In Experiment 1, 45 native speakers of English listened to a series of short passages. Via a speaker manipulation, we introduced changing intra-experimental adjective order probabilities for two-adjective noun phrases embedded within the passages and investigated whether prediction error responses adapt to reflect these intra-experimental predictive contingencies. To this end, we calculated a novel measure of speaker-based, intra-experimental surprisal (“speaker-based surprisal”) as defined on a trial-by-trial basis and by clustering together adjectives with a similar meaning. N400 amplitude at the position of the critical second adjective was used as an outcome measure of prediction error. Results showed that N400 responses attuned to speaker-based surprisal over the course of the experiment, thus indicating that listeners rapidly adapt their predictive models to reflect local environmental contingencies (here: the probability of one type of adjective following another when uttered by a particular speaker). Strikingly, this occurs in spite of the wealth of prior linguistic experience that participants bring to the laboratory. Model adaptation effects were strongest for participants with a steep aperiodic (1/f) slope in resting EEG and low individual alpha frequency (IAF), with idea density (ID) showing a more complex pattern. These results were replicated in a separate sample of 40 participants in Experiment 2, which employed a highly similar design to Experiment 1. Overall, our results suggest that individuals with a steep aperiodic slope adapt their predictive models most strongly to context-specific probabilistic information. Steep aperiodic slope is thought to reflect low neural noise, which in turn may be associated with higher neural gain control and better cognitive control. Individuals with a steep aperiodic slope may thus be able to more effectively and dynamically reconfigure their prediction-related neural networks to meet current task demands. We conclude that predictive mechanisms in language are highly malleable and dynamic, reflecting both the affordances of the present environment as well as intrinsic information processing capabilities of the individual.

Attention and memory for everyday experiences vary over time, wherein some moments are better attended and subsequently better remembered than others. These effects have been demonstrated in naturalistic viewing tasks with complex and relatively uncontrolled stimuli, as well as in more controlled laboratory tasks with simpler stimuli. For example, in the attentional boost effect (ABE), participants perform two tasks at once: memorizing a series of briefly presented stimuli (e.g., pictures of outdoor scenes) for a later memory test, and responding to other concurrently presented cues that meet pre-defined criteria (e.g., participants press a button for a blue target square and do nothing for a red distractor square). However, rather than increasing dual-task interference, attending to a target cue boosts, rather than impairs, subsequent memory for concurrently presented information. In this review we describe current data on the extent and limitations of the attentional boost effect and whether it may be related to activity in the locus coeruleus neuromodulatory system. We suggest that insight into the mechanisms that produce the attentional boost effect may be found in recent advances in the locus coeruleus literature and from understanding of how the neurocognitive system handles stability and change in everyday events. We consequently propose updates to an early account of the attentional boost effect, the dual-task interaction model, to better ground it in what is currently known about event cognition and the role that the LC plays in regulating brain states.

Language delay is often one of the first concerns of parents of toddlers with autism spectrum disorder (ASD), and early language abilities predict broader outcomes for children on the autism spectrum. Yet, mechanisms underlying language deficits in autistic children remain underspecified. One prominent component of linguistic behavior is the use of predictions or expectations during learning and processing. Several researcher teams have posited prediction deficit accounts of ASD. The basic assumption of the prediction accounts is that information is processed by making predictions and testing violations against expectations (prediction errors). Flexible (neurotypical) brains attribute differential weights to prediction errors to determine when new learning is appropriate, while autistic individuals are thought to assign disproportionate weight to prediction errors. According to some views, these prediction deficits are hypothesized to lead to higher levels of perceived novelty, resulting in “hyperplasticity” of learning based on the most recent input. In this article, we adopt the perspective that it would be useful to investigate whether language deficits in children with ASD can be attributed to atypical domain-general prediction processes.

Pursuing a precise, focused train of thought requires cognitive effort. Even more effort is necessary when more alternatives need to be considered or when the imagined situation becomes more complex. Cognitive resources available to us limit the cognitive effort we can spend. In line with previous work, an information-theoretic, Bayesian brain approach to cognitive effort is pursued: to solve tasks in our environment, our brain needs to invest information, that is, negative entropy, to impose structure, or focus, away from a uniform structure or other task-incompatible, latent structures. To get a more complete formalization of cognitive effort, a resourceful event-predictive inference model (REPI) is introduced, which offers computational and algorithmic explanations about the latent structure of our generative models, the active inference dynamics that unfold within, and the cognitive effort required to steer the dynamics—to, for example, purposefully process sensory signals, decide on responses, and invoke their execution. REPI suggests that we invest cognitive resources to infer preparatory priors, activate responses, and anticipate action consequences. Due to our limited resources, though, the inference dynamics are prone to task-irrelevant distractions. For example, the task-irrelevant side of the imperative stimulus causes the Simon effect and, due to similar reasons, we fail to optimally switch between tasks. An actual model implementation simulates such task interactions and offers first estimates of the involved cognitive effort. The approach may be further studied and promises to offer deeper explanations about why we get quickly exhausted from multitasking, how we are influenced by irrelevant stimulus modalities, why we exhibit magnitude interference, and, during social interactions, why we often fail to take the perspective of others into account.

Globally, mental and musculoskeletal disorders present with high prevalence, disease burden, and comorbidity. In order to improve the quality of care for patients with persistent physical and comorbid mental health conditions, person-centered care approaches addressing psychosocial factors are currently advocated. Central to successful person-centered care is a multidisciplinary collaboration between mental health and musculoskeletal specialists underpinned by a robust therapeutic alliance. Such a collaborative approach might be found in osteopathy, which is typically utilized to treat patients with musculoskeletal disorders but may arguably also benefit mental health outcomes. However, research and practice exploring the reputed effect of osteopathy on patients with mental health problems lack a robust framework. In this hypothesis and theory article, we build upon research from embodied cognition, predictive coding, interoception, and osteopathy to propose an embodied, predictive and interoceptive framework that underpins osteopathic person-centered care for individuals with persistent physical and comorbid mental health problems. Based on the premise that, for example, chronic pain and comorbid depression are underlined by overly precise predictions or imprecise sensory information, we hypothesize that osteopathic treatment may generate strong interoceptive prediction errors that update the generative model underpinning the experience of pain and depression. Thus, physical and mental symptoms may be reduced through active and perceptual inference. We discuss how these theoretical perspectives can inform future research into osteopathy and mental health to reduce the burden of comorbid psychological factors in patients with persistent physical symptoms and support person-centered multidisciplinary care in mental health. [ABSTRACT FROM AUTHOR]

According to the predictive coding theory of psychosis, hallucinations and delusions are explained by an overweighing of high-level prior expectations relative to sensory information that leads to false perceptions of meaningful signals. However, it is currently unclear whether the hypothesized overweighing of priors (1) represents a pervasive alteration that extends to the visual modality and (2) takes already effect at early automatic processing stages. Here, we addressed these questions by studying visual perception of socially meaningful stimuli in healthy individuals with varying degrees of psychosis proneness (n = 39). In a first task, we quantified participants’ prior for detecting faces in visual noise using a Bayesian decision model. In a second task, we measured participants’ prior for detecting direct gaze stimuli that were rendered invisible by continuous flash suppression. We found that the prior for detecting faces in noise correlated with hallucination proneness (r = 0.50, p = 0.001, Bayes factor 1/20.1) as well as delusion proneness (r = 0.46, p = 0.003, BF 1/9.4). The prior for detecting invisible direct gaze was significantly associated with hallucination proneness (r = 0.43, p = 0.009, BF 1/3.8) but not conclusively with delusion proneness (r = 0.30, p = 0.079, BF 1.7). Our results provide evidence for the idea that overly strong high-level priors for automatically detecting socially meaningful stimuli might constitute a processing alteration in psychosis.

According to the predictive coding theory of psychosis, hallucinations and delusions are explained by an overweighing of high-level prior expectations relative to sensory information that leads to false perceptions of meaningful signals. However, it is currently unclear whether the hypothesized overweighing of priors (1) represents a pervasive alteration that extends to the visual modality and (2) takes already effect at early automatic processing stages. Here, we addressed these questions by studying visual perception of socially meaningful stimuli in healthy individuals with varying degrees of psychosis proneness (n = 39). In a first task, we quantified participants' prior for detecting faces in visual noise using a Bayesian decision model. In a second task, we measured participants' prior for detecting direct gaze stimuli that were rendered invisible by continuous flash suppression. We found that the prior for detecting faces in noise correlated with hallucination proneness (r = 0.50, p = 0.001, Bayes factor 1/20.1) as well as delusion proneness (r = 0.46, p = 0.003, BF 1/9.4). The prior for detecting invisible direct gaze was significantly associated with hallucination proneness (r = 0.43, p = 0.009, BF 1/3.8) but not conclusively with delusion proneness (r = 0.30, p = 0.079, BF 1.7). Our results provide evidence for the idea that overly strong high-level priors for automatically detecting socially meaningful stimuli might constitute a processing alteration in psychosis. [ABSTRACT FROM AUTHOR]

Chronic post-surgical pain (CPSP) represents a highly prevalent and significant clinical problem. Both major and minor surgeries entail risks of developing CPSP, and cancer-related surgery is no exception. As an example, more than 40% of women undergoing breast cancer surgery struggle with CPSP years after surgery. While we do not fully understand the pathophysiology of CPSP, we know it is multifaceted with biological, social, and psychological factors contributing. The aim of this review is to advocate for the role of response outcome expectancies in the development of CPSP following breast cancer surgery. We propose the Cognitive Activation Theory of Stress (CATS) as an applicable theoretical framework detailing the potential role of cortisol regulation, inflammation, and inflammatory-induced sickness behavior in CPSP. Drawing on learning theory and activation theory, CATS offers psychobiological explanations for the relationship between stress and health, where acquired expectancies are crucial in determining the stress response and health outcomes. Based on existing knowledge about risk factors for CPSP, and in line with the CATS position, we propose the SURGEry outcome expectancy (SURGE) model of CPSP. According to SURGE, expectancies impact stress physiology, inflammation, and fear-based learning influencing the development and persistence of CPSP. SURGE further proposes that generalized response outcome expectancies drive adaptive or maladaptive stress responses in the time around surgery, where coping dampens the stress response, while helplessness and hopelessness sustains it. A sustained stress response may contribute to central sensitization, alterations in functional brain networks and excessive fear-based learning. This sets the stage for a prolonged state of inflammatory-induced sickness behavior – potentially driving and maintaining CPSP. Finally, as psychological factors are modifiable, robust and potent predictors of CPSP, we suggest hypnosis as an effective intervention strategy targeting response outcome expectancies. We here argue that presurgical clinical hypnosis has the potential of preventing CPSP in women with breast cancer. [ABSTRACT FROM AUTHOR]

The repetition of a stimulus often produces a shorter subjective duration than does the presentation of a novel item. To test whether familiarity mediates the repetition compression effect, the present study compared the influence of repeated words and pseudowords on apparent duration, using a duration discrimination task. We found a similar magnitude of temporal compression for the repeated-word and repeated-pseudoword conditions. When introducing a further experiment with two new conditions in which the standard–comparison pair shared a character at the first or second constituent position, we observed a shorter subjective duration for whole word (or whole pseudoword) repetition compared with the remaining conditions (i.e., first-character repetition, second-character repetition, and novel baseline). However, temporal compression for the first- and second-character repetitions was observed only for pseudowords but not for words. Our findings indicate that familiarity modulates the perception of duration in constituent character repetition. The results are discussed on the basis of the predictive coding theory.

The repetition of a stimulus often produces a shorter subjective duration than does the presentation of a novel item. To test whether familiarity mediates the repetition compression effect, the present study compared the influence of repeated words and pseudowords on apparent duration, using a duration discrimination task. We found a similar magnitude of temporal compression for the repeated-word and repeated-pseudoword conditions. When introducing a further experiment with two new conditions in which the standard–comparison pair shared a character at the first or second constituent position, we observed a shorter subjective duration for whole word (or whole pseudoword) repetition compared with the remaining conditions (i.e., first-character repetition, second-character repetition, and novel baseline). However, temporal compression for the first- and second-character repetitions was observed only for pseudowords but not for words. Our findings indicate that familiarity modulates the perception of duration in constituent character repetition. The results are discussed on the basis of the predictive coding theory. [ABSTRACT FROM AUTHOR]

Law enforcement personnel commonly make decisions in stressful circumstances, where the costs associated with errors are high and sometimes fatal. In this paper, we apply a powerful theoretical approach, the theory of constructed emotion (TCE), to understand decision making under evocative circumstances. This theory posits that the primary purpose of a brain is to predictively regulate physiological resources to coordinate the body’s motor activity and learning in the short term, and to meet the body’s needs for growth, survival, and reproduction in the long term. This process of managing the brain and body’s energy needs, called allostasis, is based on the premise that a brain anticipates bodily needs and attempts to meet those needs before they arise (e.g., vestibular activity that raises sympathetic nervous system activity before standing), because this is more efficient than responding to energetic needs after the fact. In this view, all mental events—cognition, emotion, perception, and action—are shaped by allostasis, and thus all decision making is embodied, predictive, and concerned with balancing energy needs. We also posit a key role for the autonomic nervous system (ANS) in regulating short-term energy expenditures, such that the ANS influences experience and behavior under stressful circumstances, including police decision making. In this paper, we first explain the core features of the TCE, and then offer insights for understanding police decision making in complex, real-world situations. In so doing, we describe how the TCE can be used to guide future studies of realistic decision making in occupations in which people commonly make decisions in evocative situations or under time pressure, such as in law enforcement.

Repetition suppression (RS), the relative lower neural response magnitude to repeated as compared to non-repeated stimuli, is often explained within the predictive coding framework. According to this theory, precise predictions (priors) together with less precise sensory evidences lead to decisions that are determined largely by the predictions and the other way around. In other words, the prediction error, namely the magnitude of RS, should depend on the precision of predictions and sensory inputs. In the current study, we aimed at testing this idea by manipulating the clarity and thereby the precision of the incoming sensory data by adding noise to the images. This resulted in an fMRI adaptation design with repeated or alternating trials showing clear or noisy face stimuli. Our results show a noise effect on the activity in the fusiform face area (FFA), namely less activation for noisy than for clear trials, which supports previous findings. No such effects could be found in OFA or LO. Data also showed reliable RS in the FFA (bilateral) and unilaterally in OFA (right) and LO (left). Interestingly, the noise added to the stimuli did not affect the magnitude of RS in any of the tested cortical areas. This suggests that the clarity of the sensory input is not crucial in determining the magnitude of RS.

In this study, we asked whether the event-related potentials associated to cue and target stimuli of a Central Cue Posner Paradigm (CCPP) may encode key parameters of Bayesian inference – prior expectation and surprise – on a trial-by-trial basis. Thirty-two EEG channel were recorded in a sample of 19 young adult subjects while performing a CCPP, in which a cue indicated (validly or invalidly) the position of an incoming auditory target. Three different types of blocks with validities of 50%, 64%, and 88%, respectively, were presented. Estimates of prior expectation and surprise were obtained on a trial-by-trial basis from participants’ responses, using a computational model implementing Bayesian learning. These two values were correlated on a trial-by-trial basis with the EEG values in all the electrodes and time bins. Therefore, a Spearman correlation metrics of the relationship between Bayesian parameters and the EEG was obtained. We report that the surprise parameter was able to classify the different validity blocks. Furthermore, the prior expectation parameter showed a significant correlation with the EEG in the cue-target period, in which the Contingent Negative Variation develops. Finally, in the post-target period the surprise parameter showed a significant correlation in the latencies and electrodes in which different event-related potentials are induced. Our results suggest that Bayesian parameters are coded in the EEG signals; and namely, the CNV would be related to prior expectation, while the post-target components P2a, P2, P3a, P3b, and SW would be related to surprise. This study thus provides novel support to the idea that human electrophysiological neural activity may implement a (Bayesian) predictive processing scheme.

The early right anterior negativity (ERAN) in event-related potentials (ERPs) is typically elicited by syntactically unexpected events in Western tonal music. We examined how visual predictive information influences syntactic processing, how musical or non-musical cues have different effects, and how they interact with sequential effects between trials, which could modulate with the strength of the sense of established tonality. The EEG was recorded from musicians who listened to chord sequences paired with one of four types of visual stimuli; two provided predictive information about the syntactic validity of the last chord through either musical notation of the whole sequence, or the word “regular” or “irregular,” while the other two, empty musical staves or a blank screen, provided no information. Half of the sequences ended with the syntactically invalid Neapolitan sixth chord, while the other half ended with the Tonic chord. Clear ERAN was observed in frontocentral electrodes in all conditions. A principal component analysis (PCA) was performed on the grand average response in the audio-only condition, to separate spatio-temporal dynamics of different scalp areas as principal components (PCs) and use them to extract auditory-related neural activities in the other visual-cue conditions. The first principal component (PC1) showed a symmetrical frontocentral topography, while the second (PC2) showed a right-lateralized frontal concentration. A source analysis confirmed the relative contribution of temporal sources to the former and a right frontal source to the latter. Cue predictability affected only the ERAN projected onto PC1, especially when the previous trial ended with the Tonic chord. The ERAN in PC2 was reduced in the trials following Neapolitan endings in general. However, the extent of this reduction differed between cue-styles, whereby it was nearly absent when musical notation was used, regardless of whether the staves were filled with notes or empty. The results suggest that the right frontal areas carry out the primary role in musical syntactic analysis and integration of the ongoing context, which produce schematic expectations that, together with the veridical expectation incorporated by the temporal areas, inform musical syntactic processing in musicians.

Law enforcement personnel commonly make decisions in stressful circumstances, where the costs associated with errors are high and sometimes fatal. In this paper, we apply a powerful theoretical approach, the theory of constructed emotion (TCE), to understand decision making under evocative circumstances. This theory posits that the primary purpose of a brain is to predictively regulate physiological resources to coordinate the body's motor activity and learning in the short term, and to meet the body's needs for growth, survival, and reproduction in the long term. This process of managing the brain and body's energy needs, called allostasis, is based on the premise that a brain anticipates bodily needs and attempts to meet those needs before they arise (e.g., vestibular activity that raises sympathetic nervous system activity before standing), because this is more efficient than responding to energetic needs after the fact. In this view, all mental events—cognition, emotion, perception, and action—are shaped by allostasis, and thus all decision making is embodied, predictive, and concerned with balancing energy needs. We also posit a key role for the autonomic nervous system (ANS) in regulating short-term energy expenditures, such that the ANS influences experience and behavior under stressful circumstances, including police decision making. In this paper, we first explain the core features of the TCE, and then offer insights for understanding police decision making in complex, real-world situations. In so doing, we describe how the TCE can be used to guide future studies of realistic decision making in occupations in which people commonly make decisions in evocative situations or under time pressure, such as in law enforcement. [ABSTRACT FROM AUTHOR]

Repetition suppression (RS), the relative lower neural response magnitude to repeated as compared to non-repeated stimuli, is often explained within the predictive coding framework. According to this theory, precise predictions (priors) together with less precise sensory evidences lead to decisions that are determined largely by the predictions and the other way around. In other words, the prediction error, namely the magnitude of RS, should depend on the precision of predictions and sensory inputs. In the current study, we aimed at testing this idea by manipulating the clarity and thereby the precision of the incoming sensory data by adding noise to the images. This resulted in an fMRI adaptation design with repeated or alternating trials showing clear or noisy face stimuli. Our results show a noise effect on the activity in the fusiform face area (FFA), namely less activation for noisy than for clear trials, which supports previous findings. No such effects could be found in OFA or LO. Data also showed reliable RS in the FFA (bilateral) and unilaterally in OFA (right) and LO (left). Interestingly, the noise added to the stimuli did not affect the magnitude of RS in any of the tested cortical areas. This suggests that the clarity of the sensory input is not crucial in determining the magnitude of RS. [ABSTRACT FROM AUTHOR]

Language-related event-related potential (ERP) components such as the N400 have traditionally been associated with linguistic or cognitive functional interpretations. By contrast, it has been considerably more difficult to relate these components to neurobiologically grounded accounts of language. Here, we propose a theoretical framework based on a predictive coding architecture, within which negative language-related ERP components such as the N400 can be accounted for in a neurobiologically plausible manner. Specifically, we posit that the amplitude of negative language-related ERP components reflects precision-weighted prediction error signals, i.e., prediction errors weighted by the relevance of the information source leading to the error. From this perspective, precision has a direct link to cue validity in a particular language and, thereby, to relevance of individual linguistic features for internal model updating. We view components such as the N400 and LAN as members of a family with similar functional characteristics and suggest that latency and topography differences between these components reflect the locus of prediction errors and model updating within a hierarchically organized cortical predictive coding architecture. This account has the potential to unify findings from the full range of the N400 literature, including word-level, sentence-, and discourse-level results as well as cross-linguistic differences. [ABSTRACT FROM AUTHOR]

This article is a comparative study between predictive processing (PP, or predictive coding) and cognitive dissonance (CD) theory. The theory of CD, one of the most influential and extensively studied theories in social psychology, is shown to be highly compatible with recent developments in PP. This is particularly evident in the notion that both theories deal with strategies to reduce perceived error signals. However, reasons exist to update the theory of CD to one of “predictive dissonance.” First, the hierarchical PP framework can be helpful in understanding varying nested levels of CD. If dissonance arises from a cascade of downstream and lateral predictions and consequent prediction errors, dissonance can exist at a multitude of scales, all the way up from sensory perception to higher order cognitions. This helps understand the previously problematic dichotomy between “dissonant cognitive relations” and “dissonant psychological states,” which are part of the same perception-action process while still hierarchically distinct. Second, since PP is action-oriented, it can be read to support recent action-based models of CD. Third, PP can potentially help us understand the recently speculated evolutionary origins of CD. Here, the argument is that responses to CD can instill meta-learning which serves to prevent the overfitting of generative models to ephemeral local conditions. This can increase action-oriented ecological rationality and enhanced capabilities to interact with a rich landscape of affordances. The downside is that in today’s world where social institutions such as science a priori separate noise from signal, some reactions to predictive dissonance might propagate ecologically unsound (underfitted, confirmation-biased) mental models such as climate denialism.

Do we have to presuppose a self to account for human self-consciousness? If so, how should we characterize the self? These questions are discussed in the context of two alternatives, i.e., the no-self position held by Metzinger (2003, 2009) and the claim that the only self we have to presuppose is a narrative self (Dennett, 1992; Schechtman, 2007; Hardcastle, 2008) which is primarily an abstract entity. In contrast to these theories, I argue that we have to presuppose an embodied self, although this is not a metaphysical substance, nor an entity for which stable necessary and jointly sufficient conditions can be given. Self-consciousness results from an integration of an embodied, basic affective flow with an intentional object (the self as agent or as center of imagination or thought), where this integration remains anchored in an embodied self. This embodied self is a flexible and variable entity, which we can account for only with a pattern theory of the self (in line with Gallagher, 2013). Furthermore, I outline how this pattern theory of the self fits into the predictive coding framework, which also answers the open question whether self-representation is prior to world-representation or the other way around. The principal organization of a mechanism of building up a self-model is such that both types of representations are always activated and developed in parallel. Modeling oneself is a process which is always activated when one interacts with the world – much as a shadow is present when a person walks in the sun.

The neural correlates of rapid eye movements (REMs) in sleep are extraordinarily robust; including REM-locked multisensory-motor integration and accompanying activation in the retrosplenial cortex, the supplementary eye field and areas encompassing cholinergic basal nucleus (Hong et al., 2009). The phenomenology of REMs speaks to the notion that perceptual experience in both sleep and wakefulness is a constructive process – in which we generate predictions of sensory inputs and then test those predictions through actively sampling the sensorium with eye movements. On this view, REMs during sleep may index an internalized active sampling or ‘scanning’ of self-generated visual constructs that are released from the constraints of visual input. If this view is correct, it renders REMs an ideal probe to study consciousness as “an exclusively internal affair” (Metzinger, 2009). In other words, REMs offer a probe of active inference – in the sense of predictive coding – when the brain is isolated from the sensorium in virtue of the natural blockade of sensory afferents during REM sleep. Crucially, REMs are temporally precise events that enable powerful inferences based on time series analyses. As a natural, task-free probe, (REMs) could be used in non-compliant subjects, including infants and animals. In short, REMs constitute a promising probe to study the ontogenetic and phylogenetic development of consciousness and perhaps the psychopathology of schizophrenia and autism, which have been considered in terms of aberrant predictive coding.

The object orientation effect describes shorter perceived distances to the front than to the back of oriented objects. The present work extends previous studies in showing that the object orientation effect occurs not only for egocentric distances between an observer and an object, but also for exocentric distances, that are between two oriented objects. Participants watched animated virtual humans (avatars) which were either facing each other or looking away, and afterward adjusted a bar to estimate the perceived length. In two experiments, participants judged avatars facing each other as closer than avatars facing away from each other. As the judged distance was between two objects and did not involve the observer, results rule out an explanation that observers perceive object fronts as closer to prepare for future interaction with them. The second experiment tested an explanation by predictive coding, this is the extrapolation of the current state of affairs to likely future states here that avatars move forward. We used avatars standing on bridges either connecting them or running orthogonal to the inter-avatar line thus preventing forward movement. This variation of walkability did not influence participants’ judgments. We conclude that if predictive coding was used by participants, they did not consider the whole scene layout for prediction, but concentrated on avatars. Another potential explanation of the effect assumes a general asymmetrical distribution of inter-person distances: people facing each other might typically be closer to each other than when facing away and that this asymmetry is reflected as a bias in perception.

The cerebral cortex predicts visual motion to adapt human behavior to surrounding objects moving in real time. Although the underlying mechanisms are still unknown, predictive coding is one of the leading theories. Predictive coding assumes that the brain's internal models (which are acquired through learning) predict the visual world at all times and that errors between the prediction and the actual sensory input further refine the internal models. In the past year, deep neural networks based on predictive coding were reported for a video prediction machine called PredNet. If the theory substantially reproduces the visual information processing of the cerebral cortex, then PredNet can be expected to represent the human visual perception of motion. In this study, PredNet was trained with natural scene videos of the self-motion of the viewer, and the motion prediction ability of the obtained computer model was verified using unlearned videos. We found that the computer model accurately predicted the magnitude and direction of motion of a rotating propeller in unlearned videos. Surprisingly, it also represented the rotational motion for illusion images that were not moving physically, much like human visual perception. While the trained network accurately reproduced the direction of illusory rotation, it did not detect motion components in negative control pictures wherein people do not perceive illusory motion. This research supports the exciting idea that the mechanism assumed by the predictive coding theory is one of basis of motion illusion generation. Using sensory illusions as indicators of human perception, deep neural networks are expected to contribute significantly to the development of brain research.

The notion of predictive coding assumes that perception is an iterative process between prior knowledge and sensory feedback. To date, this perspective has been primarily applied to exteroceptive perception as well as action and its associated phenomenological experiences such as agency. More recently, this predictive, inferential framework has been theoretically extended to interoception. This idea postulates that subjective feeling states are generated by top–down inferences made about internal and external causes of interoceptive afferents. While the processing of motor signals for action control and the emergence of selfhood have been studied extensively, the contributions of interoceptive input and especially the potential interaction of motor and interoceptive signals remain largely unaddressed. Here, we argue for a specific functional relation between motor and interoceptive awareness. Specifically, we implicate interoceptive predictions in the generation of subjective motor-related feeling states. Furthermore, we propose a distinction between reflexive and pre-reflexive modes of agentic action control and suggest that interoceptive input may affect each differently. Finally, we advocate the necessity of continuous interoceptive input for conscious forms of agentic action control. We conclude by discussing further research contributions that would allow for a fuller understanding of the interaction between agency and interoceptive awareness.