Computational Grounded Cognition: a new alliance between grounded cognition and computational modeling

The impact of early and late blindness on language and verbal working memory: A brain-constrained neural model

Neural circuits related to language exhibit a remarkable ability to reorganize and adapt in response to visual deprivation. Particularly, early and late blindness induce distinct neuroplastic changes in the visual cortex, repurposing it for language and semantic processing. Interestingly, these functional changes provoke a unique cognitive advantage - enhanced verbal working memory, particularly in early blindness. Yet, the underlying neuromechanisms and the impact on language and memory-related circuits remain not fully understood. Here, we applied a brain-constrained neural network mimicking the structural and functional features of the frontotemporal-occipital cortices, to model conceptual acquisition in early and late blindness. The results revealed differential expansion of conceptual-related neural circuits into deprived visual areas depending on the timing of visual loss, which is most prominent in early blindness. This neural recruitment is fundamentally governed by the biological principles of neural circuit expansion and the absence of uncorrelated sensory input. Critically, the degree of these changes is constrained by the availability of neural matter previously allocated to visual experiences, as in the case of late blindness. Moreover, we shed light on the implication of visual deprivation on the neural underpinnings of verbal working memory, revealing longer reverberatory neural activity in 'blind models' as compared to the sighted ones. These findings provide a better understanding of the interplay between visual deprivations, neuroplasticity, language processing and verbal working memory.

Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts

Active reinforcement learning enables dynamic prediction and control, where one should not only maximize rewards but also minimize costs such as of inference, decisions, actions, and time. For an embodied agent such as a human, decisions are also shaped by physical aspects of actions. Beyond the effects of reward outcomes on learning processes, to what extent can modeling of behavior in a reinforcement-learning task be complicated by other sources of variance in sequential action choices? What of the effects of action bias (for actions per se) and action hysteresis determined by the history of actions chosen previously? The present study addressed these questions with incremental assembly of models for the sequential choice data from a task with hierarchical structure for additional complexity in learning. With systematic comparison and falsification of computational models, human choices were tested for signatures of parallel modules representing not only an enhanced form of generalized reinforcement learning but also action bias and hysteresis. We found evidence for substantial differences in bias and hysteresis across participants-even comparable in magnitude to the individual differences in learning. Individuals who did not learn well revealed the greatest biases, but those who did learn accurately were also significantly biased. The direction of hysteresis varied among individuals as repetition or, more commonly, alternation biases persisting from multiple previous actions. Considering that these actions were button presses with trivial motor demands, the idiosyncratic forces biasing sequences of action choices were robust enough to suggest ubiquity across individuals and across tasks requiring various actions. In light of how bias and hysteresis function as a heuristic for efficient control that adapts to uncertainty or low motivation by minimizing the cost of effort, these phenomena broaden the consilient theory of a mixture of experts to encompass a mixture of expert and nonexpert controllers of behavior.

Generating meaning: active inference and the scope and limits of passive AI

Prominent accounts of sentient behavior depict brains as generative models of organismic interaction with the world, evincing intriguing similarities with current advances in generative artificial intelligence (AI). However, because they contend with the control of purposive, life-sustaining sensorimotor interactions, the generative models of living organisms are inextricably anchored to the body and world. Unlike the passive models learned by generative AI systems, they must capture and control the sensory consequences of action. This allows embodied agents to intervene upon their worlds in ways that constantly put their best models to the test, thus providing a solid bedrock that is - we argue - essential to the development of genuine understanding. We review the resulting implications and consider future directions for generative AI.

An active inference model of hierarchical action understanding, learning and imitation

We advance a novel active inference model of the cognitive processing that underlies the acquisition of a hierarchical action repertoire and its use for observation, understanding and imitation. We illustrate the model in four simulations of a tennis learner who observes a teacher performing tennis shots, forms hierarchical representations of the observed actions, and imitates them. Our simulations show that the agent's oculomotor activity implements an active information sampling strategy that permits inferring the kinematic aspects of the observed movement, which lie at the lowest level of the action hierarchy. In turn, this low-level kinematic inference supports higher-level inferences about deeper aspects of the observed actions: proximal goals and intentions. Finally, the inferred action representations can steer imitative responses, but interfere with the execution of different actions. Our simulations show that hierarchical active inference provides a unified account of action observation, understanding, learning and imitation and helps explain the neurobiological underpinnings of visuomotor cognition, including the multiple routes for action understanding in the dorsal and ventral streams and mirror mechanisms.

Holistic Functioning from a Developmental Perspective: A New Synthesis with a Focus on a Multi-tiered System Support Structure

While research in specific academic disciplines has individually advanced knowledge and practice for promoting multiple aspects of health and well-being in children and adolescents, still missing is an understanding of the interconnectedness of many critical aspects of development and how to intentionally weave these factors to advance a more holistic approach. The need for a more holistic and inclusive approach to child and adolescent development is increasingly evident to promote long-term health and well-being as the overall percentage of children, adolescents, and adults who suffer from mental health disorders is increasing. To address this issue, our authorship team consists of researchers in the areas of developmental psychology, neuroscience, motor development, exercise science, and mental health. The collective ideas outlined in this paper are aligned to address the need to remove disciplinary-specific boundaries and elucidate synergistic linkages across multiple research domains that support holistic development and lifespan health and wellness. We propose a conceptual framework that comprehensively addresses the integration of physical, cognitive, psychological, social, and emotional domains of child and adolescent development. In addition, we also provide a holistic preventative approach that is aligned with a contemporary intervention structure (i.e., Multi-tiered Systems of Support) to promote, from a developmental perspective, positive trajectories of health and well-being across childhood and adolescence.

Alternative Object Use in Adults and Children: Embodied Cognitive Bases of Creativity

Why does one need creativity? On a personal level, improvisation with available resources is needed for online coping with unforeseen environmental stimuli when existing knowledge and apparent action strategies do not work. On a cultural level, the exploitation of existing cultural means and norms for the deliberate production of novel and valuable artifacts is a basis for cultural and technological development and extension of human action possibilities across various domains. It is less clear, however, how creativity develops and how exactly one arrives at generating new action possibilities and producing multiple alternative action strategies using familiar objects. In this theoretical paper, we first consider existing accounts of the creative process in the Alternative Uses Task and then present an alternative interpretation, drawing on sociocultural views and an embodied cognition approach. We explore similarities between the psychological processes underlying the generation of new uses in the Alternative Uses Task and children’s pretend play. We discuss possible cognitive mechanisms and speculate how the generation of new action possibilities for common objects in pretend play can be related to adults’ ability to generate new action strategies associated with object use. Implications for creativity development in humans and embodied artificial agents are discussed.

Grounding Context in Embodied Cognitive Robotics

Biological agents are context-dependent systems that exhibit behavioral flexibility. The internal and external information agents process, their actions, and emotions are all grounded in the context within which they are situated. However, in the field of cognitive robotics, the concept of context is far from being clear with most studies making little to no reference to it. The aim of this paper is to provide an interpretation of the notion of context and its core elements based on different studies in natural agents, and how these core contextual elements have been modeled in cognitive robotics, to introduce a new hypothesis about the interactions between these contextual elements. Here, global context is categorized as agent-related, environmental, and task-related context. The interaction of their core elements, allows agents to first select self-relevant tasks depending on their current needs, or for learning and mastering their environment through exploration. Second, to perform a task and continuously monitor its performance. Third, to abandon a task in case its execution is not going as expected. Here, the monitoring of prediction error, the difference between sensorimotor predictions and incoming sensory information, is at the core of behavioral flexibility during situated action cycles. Additionally, monitoring prediction error dynamics and its comparison with the expected reduction rate should indicate the agent its overall performance on executing the task. Sensitivity to performance evokes emotions that function as the driving element for autonomous behavior which, at the same time, depends on the processing of the interacting core elements. Taking all these into account, an interactionist model of contexts and their core elements is proposed. The model is embodied, affective, and situated, by means of the processing of the agent-related and environmental core contextual elements. Additionally, it is grounded in the processing of the task-related context and the associated situated action cycles during task execution. Finally, the model proposed here aims to guide how artificial agents should process the core contextual elements of the agent-related and environmental context to give rise to the task-related context, allowing agents to autonomously select a task, its planning, execution, and monitoring for behavioral flexibility.

Education shapes the structure of semantic memory and impacts creative thinking

Education is central to the acquisition of knowledge, such as when children learn new concepts. It is unknown, however, whether educational differences impact not only what concepts children learn, but how those concepts come to be represented in semantic memory-a system that supports higher cognitive functions, such as creative thinking. Here we leverage computational network science tools to study hidden knowledge structures of 67 Swiss schoolchildren from two distinct educational backgrounds-Montessori and traditional, matched on socioeconomic factors and nonverbal intelligence-to examine how educational experience shape semantic memory and creative thinking. We find that children experiencing Montessori education show a more flexible semantic network structure (high connectivity/short paths between concepts, less modularity) alongside higher scores on creative thinking tests. The findings indicate that education impacts how children represent concepts in semantic memory and suggest that different educational experiences can affect higher cognitive functions, including creative thinking.

Grounding (fairly) complex numerical knowledge: an educational example

In this article, we contextualize and discuss an on-line contribution to this special issue in which a video-recorded lecture demonstrates the teaching of an abstract mathematical concept, namely regression to the mean. We first motivate the pertinence of this example from the perspective of embodied cognition. Then, we identify mechanisms of teaching that reflect embodied cognitive practices, such as the concreteness fading approach. Rather than a comprehensive review of multiple extensive literatures, this article provides the interested reader with several sources or entries into those literatures.

Separation/connection procedures: From cleansing behavior to numerical cognition

Lee and Schwarz (L&S) suggest that separation is the grounded procedure underlying cleansing effects in different psychological domains. Here, we interpret L&S's account from a hierarchical view of cognition that considers the influence of physical properties and sensorimotor constraints on mental representations. This approach allows theoretical integration and generalization of L&S's account to the domain of formal quantitative reasoning.

Decision-Making in the Human-Machine Interface

If our choices make us who we are, then what does that mean when these choices are made in the human-machine interface? Developing a clear understanding of how human decision making is influenced by automated systems in the environment is critical because, as human-machine interfaces and assistive robotics become even more ubiquitous in everyday life, many daily decisions will be an emergent result of the interactions between the human and the machine – not stemming solely from the human. For example, choices can be influenced by the relative locations and motor costs of the response options, as well as by the timing of the response prompts. In drift diffusion model simulations of response-prompt timing manipulations, we find that it is only relatively equibiased choices that will be successfully influenced by this kind of perturbation. However, with drift diffusion model simulations of motor cost manipulations, we find that even relatively biased choices can still show some influence of the perturbation. We report the results of a two-alternative forced-choice experiment with a computer mouse modified to have a subtle velocity bias in a pre-determined direction for each trial, inducing an increased motor cost to move the cursor away from the pre-designated target direction. With queries that have each been normed in advance to be equibiased in people’s preferences, the participant will often begin their mouse movement before their cognitive choice has been finalized, and the directional bias in the mouse velocity exerts a small but significant influence on their final choice. With queries that are not equibiased, a similar influence is observed. By exploring the synergies that are developed between humans and machines and tracking their temporal dynamics, this work aims to provide insight into our evolving decisions.

Changes in Loss Sensitivity During Treatment in Concurrent Disorders Inpatients: A Computational Model Approach to Assessing Risky Decision-Making

BACKGROUND

Recent studies have employed computational modeling to characterize deficits in aspects of decision-making not otherwise detected using traditional behavioral task outcomes. While prospect utility-based modeling has shown to differentiate decision-making patterns between users of different drugs, its relevance in the context of treatment has yet to be examined. This study investigated model-based decision-making as it relates to treatment outcome in inpatients with co-occurring mental health and substance use disorders.

METHODS

50 patients (Mage = 38.5, SD = 11.4; 16F) completed the Cambridge Gambling Task (CGT) within 2 weeks of admission (baseline) and 6 months into treatment (follow-up), and 50 controls (Mage = 31.9, SD = 10.0; 25F) completed CGT under a single outpatient session. We evaluated 4 traditional CGT outputs and 5 decisional processes derived from the Cumulative Model. Psychiatric diagnoses and discharge data were retrieved from patient health records.

RESULTS

Groups were similar in age, sex, and premorbid IQ. Differences in years of education were included as covariates across all group comparisons. All patients had ≥1 mental health diagnosis, with 80% having >1 substance use disorder. On the CGT, patients showed greater Deliberation Time and Delay Aversion than controls. Estimated model parameters revealed higher Delayed Reward Discounting, and lower Probability Distortion and Loss Sensitivity in patients relative to controls. From baseline to follow-up, patients (n = 24) showed a decrease in model-derived Loss Sensitivity and Color Choice Bias. Lastly, poorer Quality of Decision-Making and Choice Consistency, and greater Color Choice Bias independently predicted higher likelihood of treatment dropout, while none were significant in relation to treatment length of stay.

CONCLUSION

This is the first study to assess a computational model of decision-making in the context of treatment for concurrent disorders. Patients were more impulsive and slower to deliberate choice than controls. While both traditional and computational outcomes predicted treatment adherence in patients, findings suggest computational methods are able to capture treatment-sensitive aspects of decision-making not accessible via traditional methods. Further research is needed to confirm findings as well as investigate the relationship between model-based decision-making and post-treatment outcomes.

An Algorithmic Metaphysics of Self-Patterns

The paper draws on an algorithmic criterion to demonstrate that the self (as initially described in Shaun Gallagher’s a pattern theory of self) is a composite, scattered, and patterned object. It also addresses the question of extendedness of the self-pattern. Based on the criteria drawn from algorithmic complexity, I argue that although the self-pattern possesses a genuinely extended aspect (and in this sense, the self-pattern is minimally extended) the self-pattern and its environment do not constitute a genuine composite object.

The Brain’s Asymmetric Frequency Tuning: Asymmetric Behavior Originates from Asymmetric Perception

To construct a coherent multi-modal percept, vertebrate brains extract low-level features (such as spatial and temporal frequencies) from incoming sensory signals. However, because frequency processing is lateralized with the right hemisphere favouring low frequencies while the left favours higher frequencies, this introduces asymmetries between the hemispheres. Here, we describe how this lateralization shapes the development of several cognitive domains, ranging from visuo-spatial and numerical cognition to language, social cognition, and even aesthetic appreciation, and leads to the emergence of asymmetries in behaviour. We discuss the neuropsychological and educational implications of these emergent asymmetries and suggest future research approaches.

Computational animal welfare: towards cognitive architecture models of animal sentience, emotion and wellbeing

To understand animal wellbeing, we need to consider subjective phenomena and sentience. This is challenging, since these properties are private and cannot be observed directly. Certain motivations, emotions and related internal states can be inferred in animals through experiments that involve choice, learning, generalization and decision-making. Yet, even though there is significant progress in elucidating the neurobiology of human consciousness, animal consciousness is still a mystery. We propose that computational animal welfare science emerges at the intersection of animal behaviour, welfare and computational cognition. By using ideas from cognitive science, we develop a functional and generic definition of subjective phenomena as any process or state of the organism that exists from the first-person perspective and cannot be isolated from the animal subject. We then outline a general cognitive architecture to model simple forms of subjective processes and sentience. This includes evolutionary adaptation which contains top-down attention modulation, predictive processing and subjective simulation by re-entrant (recursive) computations. Thereafter, we show how this approach uses major characteristics of the subjective experience: elementary self-awareness, global workspace and qualia with unity and continuity. This provides a formal framework for process-based modelling of animal needs, subjective states, sentience and wellbeing.

Searching for Models for Psychological Science: A Possible Contribution of Simulation

The problem of the theoretical precariousness of psychology requires defining, at an epistemological level, its concepts and languages and the use of models for finding core concepts and building more or less 'hard' theories. After reviewing some main aims and models of psychology, and comparing them with those of other sciences, the commentary deals with the possibility of simulating life as a tool for building evolutionary psychological models. In particular, evolutionary and developmental robotics and the evo-devo models can simulate the simultaneous adaptation effects of the phylogenetic and ontogenetic development of the embodied mind in response to the characteristics of the environment. The simulation using Artificial Intelligence and robotics could contribute significantly to the definition, experimentation, and validation of the models that make up the theories of psychology in its different domains, enhancing the interdisciplinarity with other sciences that study the same objects. Further studies, both epistemological and empirical, are suggested to confirm whether this evo-devo approach can be integrated with "evolutionary psychology" and contribute to a unifying meta-theory of psychological science.

A biological foundation for spatial-numerical associations: the brain's asymmetric frequency tuning

"Left" and "right" coordinates control our spatial behavior and even influence abstract thoughts. For number concepts, horizontal spatial-numerical associations (SNAs) have been widely documented: we associate few with left and many with right. Importantly, increments are universally coded on the right side even in preverbal humans and nonhuman animals, thus questioning the fundamental role of directional cultural habits, such as reading or finger counting. Here, we propose a biological, nonnumerical mechanism for the origin of SNAs on the basis of asymmetric tuning of animal brains for different spatial frequencies (SFs). The resulting selective visual processing predicts both universal SNAs and their context-dependence. We support our proposal by analyzing the stimuli used to document SNAs in newborns for their SF content. As predicted, the SFs contained in visual patterns with few versus many elements preferentially engage right versus left brain hemispheres, respectively, thus predicting left-versus rightward behavioral biases. Our "brain's asymmetric frequency tuning" hypothesis explains the perceptual origin of horizontal SNAs for nonsymbolic visual numerosities and might be extensible to the auditory domain.

Number concepts: abstract and embodied

Numerical knowledge, including number concepts and arithmetic procedures, seems to be a clear-cut case for abstract symbol manipulation. Yet, evidence from perceptual and motor behaviour reveals that natural number knowledge and simple arithmetic also remain closely associated with modal experiences. Following a review of behavioural, animal and neuroscience studies of number processing, we propose a revised understanding of psychological number concepts as grounded in physical constraints, embodied in experience and situated through task-specific intentions. The idea that number concepts occupy a range of positions on the continuum between abstract and modal conceptual knowledge also accounts for systematic heuristics and biases in mental arithmetic, thus inviting psycho-logical approaches to the study of the mathematical mind.This article is part of the theme issue 'Varieties of abstract concepts: development, use and representation in the brain'.

Re-enacting the Bodily Self on Stage: Embodied Cognition Meets Psychoanalysis

The embodied approach to cognition consists in a range of theoretical proposals sharing the idea that our concepts are constitutively shaped by the physical and social constraints of our body and environment. Still far from a mutually enriching interplay, in recent years embodied and psychoanalytic approaches are converging on similar constructs as the ones of intersubjectivity, bodily self, and affective quality of verbal communication. Some efforts to cope with the sentient subject were already present in classical cognitivism: having expunged desires and conflicts from the cognitive harmony, bodily emotions re-emerged but only as a noisy dynamic friction. In contrast, the new, neural, embodied cognitive science with its focus on bodily effects/affects has enabled a dialogue between neuro-cognitive perspectives and clinic-psychological ones, through shared conceptual frameworks. I will address crucial issues that should be faced on this reconciling path. With reference to two kinds of contemporary addictions - internet addiction disorder and eating disorders - I will introduce a possible therapeutic approach that is built upon the core role of the acting-sentient bodily self in a dynamic-social and affective environment. In Psychoanalytic Psychodrama, the spontaneous re-enactment of a past (socially and physically constrained) experience is actualized by means of the other, the Auxiliary Ego. This allows homeostatic and social-emotional affects, i.e., drives and instincts, to be re-experienced by the agent, the Protagonist, in a safe scenario. The director-psychoanalyst smoothly traces back this simulation to the motivated, and constrained, early proximal embodied interactions with significant others, and to the related instinctual conflicting aims. The psychoanalytic reframing of classical psychodrama does not merely exploit its original cathartic function, rather stands out for exploring the interpersonal constitution of the self, through an actual "re-somatization" of psychoanalytic therapy. Unspoken/unspeakable feelings pop up on stage: the strength of this treatment mainly rests on re-establishing the priority of the embodied Self over the narrative Self. By pointing out the possible conflicts between these two selves, this method can broaden the embodied cognition perspective. The psychodramatic approach will be briefly discussed in light of connectionist models, to finally address linguistic and methodological pivotal issues.

Concepts dissolve artificial boundaries in the study of emotion and cognition, uniting body, brain, and mind

Theories of emotion have often maintained artificial boundaries: for instance, that cognition and emotion are separable, and that an emotion concept is separable from the emotional events that comprise its category (e.g. "fear" is distinct from instances of fear). Over the past several years, research has dissolved these artificial boundaries, suggesting instead that conceptual construction is a domain-general process-a process by which the brain makes meaning of the world. The brain constructs emotion concepts, but also cognitions and perceptions, all in the service of guiding action. In this view, concepts are multimodal constructions, dynamically prepared from a set of highly variable instances. This approach obviates old questions (e.g. how does cognition regulate emotion?) but generates new ones (e.g. how does a brain learn emotion concepts?). In this paper, we review this constructionist, predictive coding account of emotion, considering its implications for health and well-being, culture and development.

Cerebellar Gray Matter Volume, Executive Function, and Insomnia: Gender Differences in Adolescents

The cerebellum is an important region responsible for adolescent cognitive function and sleep, and their correlation is expected to show different patterns depending on age and gender. We examined the regional cerebellar gray matter volume (GMV), executive function (EF) and insomnia symptoms to identify their correlation and gender differences in adolescents. Data for a total of 55 subjects' (M = 31, F = 24, 14.80 ± 1.39 years old) were analyzed. The correlations between cerebellar regional GMV and Wisconsin card sorting test (WCST) subcategories showed that EF was better with larger GMV both in males and females. Far more overall correlations with cerebellar regions were observed in boys, with corresponding correlation strength being higher, and differences in localization were also observed in contrast to girls. Larger cerebellar GMV corresponded to better EF in adolescents. Insomnia did not influence the correlations between cerebellar regional GMV and EF, but more severe insomnia in boys correlated to smaller GMV in the right flocculonodular lobe. These results might implicate that the adolescent cerebellum is involved differently in EF dependent on gender.

Toward mechanistic models of action-oriented and detached cognition

To be successful, the research agenda for a novel control view of cognition should foresee more detailed, computationally specified process models of cognitive operations including higher cognition. These models should cover all domains of cognition, including those cognitive abilities that can be characterized as online interactive loops and detached forms of cognition that depend on internally generated neuronal processing.

Increased heart rate after exercise facilitates the processing of fearful but not disgusted faces

Embodied theories of emotion assume that emotional processing is grounded in bodily and affective processes. Accordingly, the perception of an emotion re-enacts congruent sensory and affective states; and conversely, bodily states congruent with a specific emotion facilitate emotional processing. This study tests whether the ability to process facial expressions (faces having a neutral expression, expressing fear, or disgust) can be influenced by making the participants’ body state congruent with the expressed emotion (e.g., high heart rate in the case of faces expressing fear). We designed a task requiring participants to categorize pictures of male and female faces that either had a neutral expression (neutral), or expressed emotions whose linkage with high heart rate is strong (fear) or significantly weaker or absent (disgust). Critically, participants were tested in two conditions: with experimentally induced high heart rate (Exercise) and with normal heart rate (Normal). Participants processed fearful faces (but not disgusted or neutral faces) faster when they were in the Exercise condition than in the Normal condition. These results support the idea that an emotionally congruent body state facilitates the automatic processing of emotionally-charged stimuli and this effect is emotion-specific rather than due to generic factors such as arousal.

The Theory of Localist Representation and of a Purely Abstract Cognitive System: The Evidence from Cortical Columns, Category Cells, and Multisensory Neurons

The debate about representation in the brain and the nature of the cognitive system has been going on for decades now. This paper examines the neurophysiological evidence, primarily from single cell recordings, to get a better perspective on both the issues. After an initial review of some basic concepts, the paper reviews the data from single cell recordings - in cortical columns and of category-selective and multisensory neurons. In neuroscience, columns in the neocortex (cortical columns) are understood to be a basic functional/computational unit. The paper reviews the fundamental discoveries about the columnar organization and finds that it reveals a massively parallel search mechanism. This columnar organization could be the most extensive neurophysiological evidence for the widespread use of localist representation in the brain. The paper also reviews studies of category-selective cells. The evidence for category-selective cells reveals that localist representation is also used to encode complex abstract concepts at the highest levels of processing in the brain. A third major issue is the nature of the cognitive system in the brain and whether there is a form that is purely abstract and encoded by single cells. To provide evidence for a single-cell based purely abstract cognitive system, the paper reviews some of the findings related to multisensory cells. It appears that there is widespread usage of multisensory cells in the brain in the same areas where sensory processing takes place. Plus there is evidence for abstract modality invariant cells at higher levels of cortical processing. Overall, that reveals the existence of a purely abstract cognitive system in the brain. The paper also argues that since there is no evidence for dense distributed representation and since sparse representation is actually used to encode memories, there is actually no evidence for distributed representation in the brain. Overall, it appears that, at an abstract level, the brain is a massively parallel, distributed computing system that is symbolic. The paper also explains how grounded cognition and other theories of the brain are fully compatible with localist representation and a purely abstract cognitive system.

ReaCog, a Minimal Cognitive Controller Based on Recruitment of Reactive Systems

It has often been stated that for a neuronal system to become a cognitive one, it has to be large enough. In contrast, we argue that a basic property of a cognitive system, namely the ability to plan ahead, can already be fulfilled by small neuronal systems. As a proof of concept, we propose an artificial neural network, termed reaCog, that, first, is able to deal with a specific domain of behavior (six-legged-walking). Second, we show how a minor expansion of this system enables the system to plan ahead and deploy existing behavioral elements in novel contexts in order to solve current problems. To this end, the system invents new solutions that are not possible for the reactive network. Rather these solutions result from new combinations of given memory elements. This faculty does not rely on a dedicated system being more or less independent of the reactive basis, but results from exploitation of the reactive basis by recruiting the lower-level control structures in a way that motor planning becomes possible as an internal simulation relying on internal representation being grounded in embodied experiences.

Situating Machine Intelligence Within the Cognitive Ecology of the Internet

The Internet is an important focus of attention for the philosophy of mind and cognitive science communities. This is partly because the Internet serves as an important part of the material environment in which a broad array of human cognitive and epistemic activities are situated. The Internet can thus be seen as an important part of the ‘cognitive ecology’ that helps to shape, support and (on occasion) realize aspects of human cognizing. Much of the previous philosophical work in this area has sought to analyze the cognitive significance of the Internet from the perspective of human cognition. There has, as such, been little effort to assess the cognitive significance of the Internet from the perspective of ‘machine cognition’. This is unfortunate, because the Internet is likely to exert a significant influence on the shape of machine intelligence. The present paper attempts to evaluate the extent to which the Internet serves as a form of cognitive ecology for synthetic (machine-based) forms of intelligence. In particular, the phenomenon of Internet-situated machine intelligence is analyzed from the perspective of a number of approaches that are typically subsumed under the heading of situated cognition. These include extended, embedded, scaffolded and embodied approaches to cognition. For each of these approaches, the Internet is shown to be of potential relevance to the development and operation of machine-based cognitive capabilities. Such insights help us to appreciate the role of the Internet in advancing the current state-of-the-art in machine intelligence.

Active inference and cognitive-emotional interactions in the brain

All organisms must integrate cognition, emotion, and motivation to guide action toward valuable (goal) states, as described by active inference. Within this framework, cognition, emotion, and motivation interact through the (Bayesian) fusion of exteroceptive, proprioceptive, and interoceptive signals, the precision-weighting of prediction errors, and the "affective tuning" of neuronal representations. Crucially, misregulation of these processes may have profound psychopathological consequences.

The body of knowledge: On the role of the living body in grounding embodied cognition

Embodied cognition is a hot topic in both cognitive science and AI, despite the fact that there still is relatively little consensus regarding what exactly constitutes 'embodiment'. While most embodied AI and cognitive robotics research views the body as the physical/sensorimotor interface that allows to ground computational cognitive processes in sensorimotor interactions with the environment, more biologically-based notions of embodied cognition emphasize the fundamental role that the living body - and more specifically its homeostatic/allostatic self-regulation - plays in grounding both sensorimotor interactions and embodied cognitive processes. Adopting the latter position - a multi-tiered affectively embodied view of cognition in living systems - it is further argued that modeling organisms as layered networks of bodily self-regulation mechanisms can make significant contributions to our scientific understanding of embodied cognition.

Resonant Dynamics of Grounded Cognition: Explanation of Behavioral and Neuroimaging Data Using the ART Neural Network

Research on grounded cognition suggests that the processing of a word or concept reactivates the perceptual representations that are associated with the referent object. The objective of this work is to demonstrate how behavioral and functional neuroimaging data on grounded cognition can be understood as different manifestations of the same cortical circuit designed to achieve stable category learning, as proposed by the adaptive resonance theory (ART). We showed that the ART neural network provides a mechanistic explanation of why reaction times in behavioral studies depend on the expectation or attentional priming created by the word meaning (Richter and Zwaan, 2009). A mismatch between top-down expectation and bottom-up sensory data activates an orienting subsystem that slows execution of the current task. Furthermore, we simulated the data from functional neuroimaging studies of color knowledge retrieval that showed anterior shift (Chao and Martin, 1999; Thompson-Schill, 2003) and an overlap effect (Simmons et al., 2007; Hsu et al., 2011) in the left fusiform gyrus. We explain the anterior effect as a result of the partial activation of different components of the same ART circuit in the condition of passive viewing. Conversely, a demanding perceptual task requires activation of the whole ART circuit. This condition is reflected in the fMRI image as an overlap between cortical activation during perceptual and conceptual processing. We conclude that the ART neural network is able to explain how the brain grounds symbols in perception via perceptual simulation.

How do you hold your mouse? Tracking the compatibility effect between hand posture and stimulus size

In keeping with the idea that observing objects activates possible motor responses, several experiments revealed compatibility effects between the hand postures used to report a choice and some characteristics of the stimuli. The real-time dynamics of such compatibility effects are currently unknown. We tracked the time course of a categorization experiment requiring subjects to categorize as natural or artifact figures of big and small objects. Participants reported their choice using either a big mouse (requiring a power grip: a hand posture compatible with the grasping of big objects) or a small mouse (requiring a precision grip: a hand posture compatible with the grasping of small objects). We found a compatibility effect between the grip required by the mouse and the grip elicited by objects, even if it was irrelevant to the task. In a following experiment with the same paradigm, lexical stimuli failed to reproduce the same effect. Nevertheless, a compatibility effect mediated by the target-word category (artificial vs. natural) was observed. We discuss the results in the context of affordance effects literature and grounded theories of cognition.

How active perception and attractor dynamics shape perceptual categorization: a computational model

We propose a computational model of perceptual categorization that fuses elements of grounded and sensorimotor theories of cognition with dynamic models of decision-making. We assume that category information consists in anticipated patterns of agent-environment interactions that can be elicited through overt or covert (simulated) eye movements, object manipulation, etc. This information is firstly encoded when category information is acquired, and then re-enacted during perceptual categorization. The perceptual categorization consists in a dynamic competition between attractors that encode the sensorimotor patterns typical of each category; action prediction success counts as "evidence" for a given category and contributes to falling into the corresponding attractor. The evidence accumulation process is guided by an active perception loop, and the active exploration of objects (e.g., visual exploration) aims at eliciting expected sensorimotor patterns that count as evidence for the object category. We present a computational model incorporating these elements and describing action prediction, active perception, and attractor dynamics as key elements of perceptual categorizations. We test the model in three simulated perceptual categorization tasks, and we discuss its relevance for grounded and sensorimotor theories of cognition.

Communication, concepts and grounding

This article discusses the relation between communication and conceptual grounding. In the brain, neurons, circuits and brain areas are involved in the representation of a concept, grounding it in perception and action. In terms of grounding we can distinguish between communication within the brain and communication between humans or between humans and machines. In the first form of communication, a concept is activated by sensory input. Due to grounding, the information provided by this communication is not just determined by the sensory input but also by the outgoing connection structure of the conceptual representation, which is based on previous experiences and actions. The second form of communication, that between humans or between humans and machines, is influenced by the first form. In particular, a more successful interpersonal communication might require forms of situated cognition and interaction in which the entire representations of grounded concepts are involved.

Shifting the paradigm of music instruction: implications of embodiment stemming from an augmented reality guitar learning system

Musical instruction often includes materials that can act as a barrier to learning. New technologies using augmented reality may aid in reducing the initial difficulties involved in learning music by lowering these barriers characteristic of traditional instructional materials. Therefore, this set of studies examined a novel augmented reality guitar learning system (i.e., the Fretlight® guitar) in regards to current theories of embodied music cognition. Specifically, we examined the effects of using this system in comparison to a standard instructional material (i.e., diagrams). First, we review major theories related to musical embodiment and specify a niche within this research space we call embodied music technology for learning. Following, we explicate two parallel experiments that were conducted to address the learning effects of this system. Experiment 1 examined short-term learning effects within one experimental session, while Experiment 2 examined both short-term and long-term effects across two sessions spaced at a 2-week interval. Analyses demonstrated that, for many of our dependent variables, all participants increased in performance across time. Further, the Fretlight® condition consistently led to significantly better outcomes via interactive effects, including significantly better long term retention for the learned information across a 2 week time interval. These results are discussed in the context of embodied cognition theory as it relates to music. Potential limitations and avenues for future research are described.

The cerebellum for jocks and nerds alike

Historically the cerebellum has been implicated in the control of movement. However, the cerebellum's role in non-motor functions, including cognitive and emotional processes, has also received increasing attention. Starting from the premise that the uniform architecture of the cerebellum underlies a common mode of information processing, this review examines recent electrophysiological findings on the motor signals encoded in the cerebellar cortex and then relates these signals to observations in the non-motor domain. Simple spike firing of individual Purkinje cells encodes performance errors, both predicting upcoming errors as well as providing feedback about those errors. Further, this dual temporal encoding of prediction and feedback involves a change in the sign of the simple spike modulation. Therefore, Purkinje cell simple spike firing both predicts and responds to feedback about a specific parameter, consistent with computing sensory prediction errors in which the predictions about the consequences of a motor command are compared with the feedback resulting from the motor command execution. These new findings are in contrast with the historical view that complex spikes encode errors. Evaluation of the kinematic coding in the simple spike discharge shows the same dual temporal encoding, suggesting this is a common mode of signal processing in the cerebellar cortex. Decoding analyses show the considerable accuracy of the predictions provided by Purkinje cells across a range of times. Further, individual Purkinje cells encode linearly and independently a multitude of signals, both kinematic and performance errors. Therefore, the cerebellar cortex's capacity to make associations across different sensory, motor and non-motor signals is large. The results from studying how Purkinje cells encode movement signals suggest that the cerebellar cortex circuitry can support associative learning, sequencing, working memory, and forward internal models in non-motor domains.

A goal-based mechanism for delayed motor intention: considerations from motor skills, tool use and action memory

Thinking about our behaviors for a future recall like playing a piano sonata during the next weekend (i.e., delayed motor intention) should engage at some level sensorimotor-based representations. Theoretically, such representations can be stored through both an action- and a goal-based mechanism. An action-based mechanism is related to the specific motor sequence of fingers like the key presses on the piano, and a goal-based mechanism is related to the musical tones generated by the key presses. From these considerations, the present article tries to explore whether the cognitive nature of delayed motor intention is more based on an action or goal mechanism. We reviewed empirical evidence and theoretical accounts of different domains such as motor skills, tool use, and action memory supporting the idea that such delayed motor intentions are rather represented through a goal-based mechanism. The specific role of this goal-based mechanism is to envision the future in an implementation-neutral mode to flexibly and efficiently retrieve an adapted action to environmental constraints. This goal-based account offers an interesting alternative to reshape the classical models about the representations of delayed motor intention. We also discuss how this account can be applied to practical activities in daily life situations.

Stochastic accumulation of feature information in perception and memory

It is now well established that the time course of perceptual processing influences the first second or so of performance in a wide variety of cognitive tasks. Over the last 20 years, there has been a shift from modeling the speed at which a display is processed, to modeling the speed at which different features of the display are perceived and formalizing how this perceptual information is used in decision making. The first of these models (Lamberts, 1995) was implemented to fit the time course of performance in a speeded perceptual categorization task and assumed a simple stochastic accumulation of feature information. Subsequently, similar approaches have been used to model performance in a range of cognitive tasks including identification, absolute identification, perceptual matching, recognition, visual search, and word processing, again assuming a simple stochastic accumulation of feature information from both the stimulus and representations held in memory. These models are typically fit to data from signal-to-respond experiments whereby the effects of stimulus exposure duration on performance are examined, but response times (RTs) and RT distributions have also been modeled. In this article, we review this approach and explore the insights it has provided about the interplay between perceptual processing, memory retrieval, and decision making in a variety of tasks. In so doing, we highlight how such approaches can continue to usefully contribute to our understanding of cognition.