Coordinating with the Future: The Anticipatory Nature of Representation

Motor cognition in plants: from thought to real experiments

Motor cognition involves the process of planning and executing goal-directed movements and recognizing, anticipating, and interpreting others' actions. Motor cognitive functions are generally associated with the presence of a brain and are ascribed only to humans and other animal species. A growing body of evidence suggests that aneural organisms, like climbing plants, exhibit behaviors driven by the intention to achieve goals, challenging our understanding of cognition. Here, we propose an inclusive perspective under motor cognition to explain climbing plants' behavior. We will first review our empirical research based on kinematical analysis to understand movement in pea plants. Then, we situate this empirical research within the current theoretical debate aimed at extending the principles of cognition to aneural organisms. A novel comparative perspective that considers the perception-action cycle, involving transforming perceived environmental elements into intended movement patterns, is provided.

Robot Arm Reaching Based on Inner Rehearsal

Robot arm motion control is a fundamental aspect of robot capabilities, with arm reaching ability serving as the foundation for complex arm manipulation tasks. However, traditional inverse kinematics-based methods for robot arm reaching struggle to cope with the increasing complexity and diversity of robot environments, as they heavily rely on the accuracy of physical models. In this paper, we introduce an innovative approach to robot arm motion control, inspired by the cognitive mechanism of inner rehearsal observed in humans. The core concept revolves around the robot's ability to predict or evaluate the outcomes of motion commands before execution. This approach enhances the learning efficiency of models and reduces the mechanical wear on robots caused by excessive physical executions. We conduct experiments using the Baxter robot in simulation and the humanoid robot PKU-HR6.0 II in a real environment to demonstrate the effectiveness and efficiency of our proposed approach for robot arm reaching across different platforms. The internal models converge quickly and the average error distance between the target and the end-effector on the two platforms is reduced by 80% and 38%, respectively.

Deep imagination is a close to optimal policy for planning in large decision trees under limited resources

Many decisions involve choosing an uncertain course of action in deep and wide decision trees, as when we plan to visit an exotic country for vacation. In these cases, exhaustive search for the best sequence of actions is not tractable due to the large number of possibilities and limited time or computational resources available to make the decision. Therefore, planning agents need to balance breadth-considering many actions in the first few tree levels-and depth-considering many levels but few actions in each of them-to allocate optimally their finite search capacity. We provide efficient analytical solutions and numerical analysis to the problem of allocating finite sampling capacity in one shot to infinitely large decision trees, both in the time discounted and undiscounted cases. We find that in general the optimal policy is to allocate few samples per level so that deep levels can be reached, thus favoring depth over breadth search. In contrast, in poor environments and at low capacity, it is best to broadly sample branches at the cost of not sampling deeply, although this policy is marginally better than deep allocations. Our results can provide a theoretical foundation for why human reasoning is pervaded by imagination-based processes.

Self-Concern Across Scales: A Biologically Inspired Direction for Embodied Artificial Intelligence

Intelligence in current AI research is measured according to designer-assigned tasks that lack any relevance for an agent itself. As such, tasks and their evaluation reveal a lot more about our intelligence than the possible intelligence of agents that we design and evaluate. As a possible first step in remedying this, this article introduces the notion of “self-concern,” a property of a complex system that describes its tendency to bring about states that are compatible with its continued self-maintenance. Self-concern, as argued, is the foundation of the kind of basic intelligence found across all biological systems, because it reflects any such system's existential task of continued viability. This article aims to cautiously progress a few steps closer to a better understanding of some necessary organisational conditions that are central to self-concern in biological systems. By emulating these conditions in embodied AI, perhaps something like genuine self-concern can be implemented in machines, bringing AI one step closer to its original goal of emulating human-like intelligence.

Planning Catching Movements: Advantages of Expertise, Visibility and Self-Throwing

In a ball catching task, the catcher guides their hand to the ball's future trajectory. The hand may start to move even before the exact position is known, and the interceptive movement may be corrected online. Using a recent method for detecting the phases of catching movements we investigate how juggling experience, self-throwing, and delayed visibility of the ball, influence the timing of the hand's trajectory. Specifically, we analyze the time from which the goal position of the movement is known, i.e., the time from which the movement becomes smooth. Seventeen jugglers and twenty controls caught ten balls per each of eight conditions. The results indicate that experts' catching movements acquire the smooth nature of goal-directed movements earlier than novices catching movements do.

The influence of auditory rhythms on the speed of inferred motion

The present research explored the influence of isochronous auditory rhythms on the timing of movement-related prediction in two experiments. In both experiments, participants observed a moving disc that was visible for a predetermined period before disappearing behind a small, medium, or large occluded area for the remainder of its movement. In Experiment 1, the disc was visible for 1 s. During this period, participants were exposed to either a fast or slow auditory rhythm, or they heard nothing. They were instructed to press a key to indicate when they believed the moving disc had reached a specified location on the other side of the occluded area. The procedure measured the (signed) error in participants' estimate of the time it would take for a moving object to contact a stationary one. The principal results of Experiment 1 were main effects of the rate of the auditory rhythm and of the size of the occlusion on participants' judgments. In Experiment 2, the period of visibility was varied with size of the occlusion area to keep the total movement time constant for all three levels of occlusion. The results replicated the main effect of rhythm found in Experiment 1 and showed a small, significant interaction, but indicated no main effect of occlusion size. Overall, the results indicate that exposure to fast isochronous auditory rhythms during an interval of inferred motion can influence the imagined rate of such motion and suggest a possible role of an internal rhythmicity in the maintenance of temporally accurate dynamic mental representations.

Improving Mental Health and Well-Being through Informal Mindfulness Practices: An Intervention Study

BACKGROUND

Mindfulness-based programs have been shown to be effective in reducing stress, anxiety, and depression symptoms, and enhancing well-being. However, it remains unclear whether longer formal mindfulness practices are necessary to obtain such results. We therefore aimed to assess the effectiveness of a program (FOVEA, 8 weeks, 2h/week) which was only based on brief and informal practices.

METHODS

Using a switching replication design, participants (N = 139) were assigned to a FOVEA or a wait-list group, and completed the following self-report questionnaires online at three time points: perceived stress, anxiety, depression, satisfaction with life (dependent variables), and mindfulness (mediating variable). They also completed a daily practice diary.

RESULTS

Relative to the wait-list group, FOVEA participants showed significantly reduced perceived stress, anxiety, and depression, and increased satisfaction with life. These changes were completely mediated by increased mindfulness, and were maintained 2.5 months after the end of the program. The effect sizes were moderate to large.

CONCLUSIONS

These results underline the potential benefits of a mindfulness informal practices program for the general population. This type of program could constitute a first step towards more formal practices once the motivation to practice has been enhanced by the perceived benefits of brief practices.

Delayed feedback embedded in perception-action coordination cycles results in anticipation behavior during synchronized rhythmic action: A dynamical systems approach

Dancing and playing music require people to coordinate actions with auditory rhythms. In laboratory perception-action coordination tasks, people are asked to synchronize taps with a metronome. When synchronizing with a metronome, people tend to anticipate stimulus onsets, tapping slightly before the stimulus. The anticipation tendency increases with longer stimulus periods of up to 3500ms, but is less pronounced in trained individuals like musicians compared to non-musicians. Furthermore, external factors influence the timing of tapping. These factors include the presence of auditory feedback from one’s own taps, the presence of a partner performing coordinated joint tapping, and transmission latencies (TLs) between coordinating partners. Phenomena like the anticipation tendency can be explained by delay-coupled systems, which may be inherent to the sensorimotor system during perception-action coordination. Here we tested whether a dynamical systems model based on this hypothesis reproduces observed patterns of human synchronization. We simulated behavior with a model consisting of an oscillator receiving its own delayed activity as input. Three simulation experiments were conducted using previously-published behavioral data from 1) simple tapping, 2) two-person alternating beat-tapping, and 3) two-person alternating rhythm-clapping in the presence of a range of constant auditory TLs. In Experiment 1, our model replicated the larger anticipation observed for longer stimulus intervals and adjusting the amplitude of the delayed feedback reproduced the difference between musicians and non-musicians. In Experiment 2, by connecting two models we replicated the smaller anticipation observed in human joint tapping with bi-directional auditory feedback compared to joint tapping without feedback. In Experiment 3, we varied TLs between two models alternately receiving signals from one another. Results showed reciprocal lags at points of alternation, consistent with behavioral patterns. Overall, our model explains various anticipatory behaviors, and has potential to inform theories of adaptive human synchronization.

When navigating a busy sidewalk, people coordinate their behavior in an orderly manner. Other activities require people to carefully synchronize periodic actions, as in a group rowing or marching. When individuals tap in synchrony with a metronome, their taps tend to anticipate the metronome. Experiments have revealed that factors like musical expertise, the presence of a synchronizing partner, auditory feedback, and the sound travel time, all systematically affect the tendency to anticipate. While researchers have hypothesized a number of potential mechanisms for such anticipatory behavior, none have successfully accounted for all of the effects. Previous research on coupled physical systems has shown that when one system receives input from a second system, plus its own delayed signal as input, this causes system 1 to anticipate system 2. We hypothesize that the tendency to anticipate is the result of delayed communication between neurons. Our work demonstrates the ability of delay-coupled physical systems to capture human anticipation and the effect of external factors in the anticipation tendency. Our model supports the theory that delayed communication within the nervous system is crucial to understanding anticipatory coordinative behavior.

Making the Environment an Informative Place: A Conceptual Analysis of Epistemic Policies and Sensorimotor Coordination

How do living organisms decide and act with limited and uncertain information? Here, we discuss two computational approaches to solving these challenging problems: a "cognitive" and a "sensorimotor" enrichment of stimuli, respectively. In both approaches, the key notion is that agents can strategically modulate their behavior in informative ways, e.g., to disambiguate amongst alternative hypotheses or to favor the perception of stimuli providing the information necessary to later act appropriately. We discuss how, despite their differences, both approaches appeal to the notion that actions must obey both epistemic (i.e., information-gathering or uncertainty-reducing) and pragmatic (i.e., goal- or reward-maximizing) imperatives and balance them. Our computationally-guided analysis reveals that epistemic behavior is fundamental to understanding several facets of cognitive processing, including perception, decision making, and social interaction.

Action perception as hypothesis testing

We present a novel computational model that describes action perception as an active inferential process that combines motor prediction (the reuse of our own motor system to predict perceived movements) and hypothesis testing (the use of eye movements to disambiguate amongst hypotheses). The system uses a generative model of how (arm and hand) actions are performed to generate hypothesis-specific visual predictions, and directs saccades to the most informative places of the visual scene to test these predictions - and underlying hypotheses. We test the model using eye movement data from a human action observation study. In both the human study and our model, saccades are proactive whenever context affords accurate action prediction; but uncertainty induces a more reactive gaze strategy, via tracking the observed movements. Our model offers a novel perspective on action observation that highlights its active nature based on prediction dynamics and hypothesis testing.

Driving with Intuition: A Preregistered Study about the EEG Anticipation of Simulated Random Car Accidents

The study of neural pre-stimulus or "anticipatory" activity opened a new window for understanding how the brain actively constructs the forthcoming reality. Usually, experimental paradigms designed to study anticipatory activity make use of stimuli. The purpose of the present study is to expand the study of neural anticipatory activity upon the temporal occurrence of dichotomic, statistically unpredictable (random) stimuli within an ecological experimental paradigm. To this purpose, we used a simplified driving simulation including two possible, randomly-presented trial types: a car crash end trial and a no car crash end trial. Event Related Potentials (ERP) were extracted -3,000 ms before stimulus onset. We identified a fronto-central negativity starting around 1,000 ms before car crash presentation. By contrast, a whole-scalp distributed positivity characterized the anticipatory activity observed before the end of the trial in the no car crash end condition. The present data are in line with the hypothesis that the brain may also anticipate dichotomic, statistically unpredictable stimuli, relaying onto different pre-stimulus ERP activity. Possible integration with car-smart-systems is also suggested.

Participation, both a means and an end: a conceptual analysis of processes and outcomes in childhood disability

This review outlines a conceptual approach to inform research and practice aimed at supporting children whose lives are complicated by impairment and/or chronic medical conditions, and their families. 'Participation' in meaningful life activities should be an essential intervention goal, to meet the challenges of healthy growth and development, and to provide opportunities to help ensure that young people with impairments reach their full potential across their lifespan. Intervention activities and research can focus on participation as either an independent or dependent variable. The proposed framework and associated hypotheses are applicable to children and young people with a wide variety of conditions, and to their families. In taking a fresh 'non-categorical' perspective to health for children and young people, asking new questions, and exploring issues in innovative ways, we expect to learn lessons and to develop creative solutions that will ultimately benefit children with a wide variety of impairments and challenges, and their families, everywhere.

Motor imagery training enhances motor skill in children with DCD: A replication study

BACKGROUND

Children with impaired motor coordination (or DCD) have difficulty using motor imagery. We have suggested that this difficulty is explained by the internal modeling deficit (IMD) hypothesis of DCD. Our previous training study lent support for this hypothesis by showing that a computerized imagery training protocol (involving action observation, and mental- and overt-rehearsal) was equally effective to perceptual-motor therapy (PMT) in promoting motor skill acquisition.

AIMS

The study presented here was designed to replicate and extend this finding, targeting a select group of children with moderate-to-severe DCD.

METHODS AND PROCEDURES

All 36 children with DCD who participated were referred to the study and scored below the 10th percentile for their age on the Movement Assessment Battery for Children (MABC). Using a randomized control trial, the referred children were assigned randomly to one of three groups using a blocked procedure: imagery training, perceptual-motor training (PMT), and wait-list control. Motor proficiency was measured using the MABC, pre and post-training. Individual training consisted of 60-min sessions, conducted once a week for 5 weeks.

RESULTS

Results showed that the imagery protocol was equally effective as PMT in promoting motor skill acquisition, with moderate-to-large effect sizes. Individual differences showed that the majority of children in the two intervention groups improved their motor performance significantly.

CONCLUSIONS

Overall, these results further support the use of motor imagery protocols in the treatment of DCD, and tentative support for the IMD hypothesis. Developmental and dose issues in the implementation of imagery-based intervention are discussed.

Predictive Technologies: Can Smart Tools Augment the Brain's Predictive Abilities?

The ability of “looking into the future”—namely, the capacity of anticipating future states of the environment or of the body—represents a fundamental function of human (and animal) brains. A goalkeeper who tries to guess the ball's direction; a chess player who attempts to anticipate the opponent's next move; or a man-in-love who tries to calculate what are the chances of her saying yes—in all these cases, people are simulating possible future states of the world, in order to maximize the success of their decisions or actions. Research in neuroscience is showing that our ability to predict the behavior of physical or social phenomena is largely dependent on the brain's ability to integrate current and past information to generate (probabilistic) simulations of the future. But could predictive processing be augmented using advanced technologies? In this contribution, we discuss how computational technologies may be used to support, facilitate or enhance the prediction of future events, by considering exemplificative scenarios across different domains, from simpler sensorimotor decisions to more complex cognitive tasks. We also examine the key scientific and technical challenges that must be faced to turn this vision into reality.

Autonoetic consciousness: Reconsidering the role of episodic memory in future-oriented self-projection

Following the seminal work of Ingvar (1985. “Memory for the future”: An essay on the temporal organization of conscious awareness. Human Neurobiology, 4, 127–136), Suddendorf (1994. The discovery of the fourth dimension: Mental time travel and human evolution. Master's thesis. University of Waikato, Hamilton, New Zealand), and Tulving (1985. Memory and consciousness. Canadian Psychology/PsychologieCanadienne, 26, 1–12), exploration of the ability to anticipate and prepare for future contingencies that cannot be known with certainty has grown into a thriving research enterprise. A fundamental tenet of this line of inquiry is that future-oriented mental time travel, in most of its presentations, is underwritten by a property or an extension of episodic recollection. However, a careful conceptual analysis of exactly how episodic memory functions in this capacity has yet to be undertaken. In this paper I conduct such an analysis. Based on conceptual, phenomenological, and empirical considerations, I conclude that the autonoetic component of episodic memory, not episodic memory per se, is the causally determinative factor enabling an individual to project him or herself into a personal future.

How we remember what we can do

According to the motor simulation theory, the knowledge we possess of what we can do is based on simulation mechanisms triggered by an off-line activation of the brain areas involved in motor control. Action capabilities memory does not work by storing some content, but consists in the capacity, rooted in sensory-motor systems, to reenact off-line action sequences exhibiting the range of our powers. In this paper, I present several arguments from cognitive neuropsychology, but also first-person analysis of experience, against this hypothesis. The claim that perceptual access to affordances is mediated by motor simulation processes rests on a misunderstanding of what affordances are, and comes up against a computational reality principle. Motor simulation cannot provide access to affordances because (i) the affordances we are aware of at each moment are too many for their realization to be simulated by the brain and (ii) affordances are not equivalent to currently or personally feasible actions. The explanatory significance of the simulation theory must then be revised downwards compared to what is claimed by most of its advocates. One additional challenge is to determine the prerequisite, in terms of cognitive processing, for the motor simulation mechanisms to work. To overcome the limitations of the simulation theory, I propose a new approach: the direct content specification hypothesis. This hypothesis states that, at least for the most basic actions of our behavioral repertoire, the action possibilities we are aware of through perception are directly specified by perceptual variables characterizing the content of our experience. The cognitive system responsible for the perception of action possibilities is consequently far more direct, in terms of cognitive processing, than what is stated by the simulation theory. To support this hypothesis I review evidence from current neuropsychological research, in particular data suggesting a phenomenon of ‘fossilization’ of affordances. Fossilization can be defined as a gap between the capacities that are treated as available by the cognitive system and the capacities this system really has at its disposal. These considerations do not mean that motor simulation cannot contribute to explain how we gain perceptual knowledge of what we can do based on the memory of our past performances. However, when precisely motor simulation plays a role and what it is for exactly currently remain largely unknown.

The intentional stance as structure learning: a computational perspective on mindreading

Recent theories of mindreading explain the recognition of action, intention, and belief of other agents in terms of generative architectures that model the causal relations between observables (e.g., observed movements) and their hidden causes (e.g., action goals and beliefs). Two kinds of probabilistic generative schemes have been proposed in cognitive science and robotics that link to a "theory theory" and "simulation theory" of mindreading, respectively. The former compares perceived actions to optimal plans derived from rationality principles and conceptual theories of others' minds. The latter reuses one's own internal (inverse and forward) models for action execution to perform a look-ahead mental simulation of perceived actions. Both theories, however, leave one question unanswered: how are the generative models - including task structure and parameters - learned in the first place? We start from Dennett's "intentional stance" proposal and characterize it within generative theories of action and intention recognition. We propose that humans use an intentional stance as a learning bias that sidesteps the (hard) structure learning problem and bootstraps the acquisition of generative models for others' actions. The intentional stance corresponds to a candidate structure in the generative scheme, which encodes a simplified belief-desire folk psychology and a hierarchical intention-to-action organization of behavior. This simple structure can be used as a proxy for the "true" generative structure of others' actions and intentions and is continuously grown and refined - via state and parameter learning - during interactions. In turn - as our computational simulations show - this can help solve mindreading problems and bootstrap the acquisition of useful causal models of both one's own and others' goal-directed actions.

Goals reconfigure cognition by modulating predictive processes in the brain

I applaud Huang & Bargh's (H&B's) theory that places goals at the center of cognition, and I discuss two ingredients missing from that theory. First, I argue that the brains of organisms much simpler than those of humans are already configured for goal achievement in situated interactions. Second, I propose a mechanistic view of the "reconfiguration principle" that links the theory with current views in computational neuroscience.

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.

Multiplicative-cascade dynamics in pole balancing

Pole balancing is a key task for probing the prospective control that organisms must engage in for purposeful action. The temporal structure of pole-balancing behaviors will reflect the on-line operation of control mechanisms needed to maintain an upright posture. In this study, signatures of multifractality are sought and found in time series of the vertical angle of a pole balanced on the fingertip. Comparisons to surrogate time series reveal multiplicative-cascade dynamics and interactivity across scales. In addition, simulations of a pole-balancing model generating on-off intermittency [J. L. Cabrera and J. G. Milton, Phys. Rev. Lett. 89, 158702 (2002)] were analyzed. Evidence of multifractality is also evident in simulations, though comparing simulated and participant series reveals a significantly greater contribution of cross-scale interactivity for the latter. These findings suggest that multiplicative-cascade dynamics are an extension of on-off intermittency and play a role in prospective coordination.

Behavioral Implicit Communication (BIC)

A crucial part of the intelligence that smart environments should display is a specific form of social intelligence: the ability to read human behavior and its traces in terms of underlying intentions and assumptions. Such ability is crucial to enable human users to tacitly coordinate and negotiate with smart and proactive digital environments. In this paper, the authors argue that the necessary tool for this ability is behavioral and stigmergic implicit (i.e. non-conventional) communication. The authors present a basic theory of such a fundamental interactive means—the theory of Behavioral Implicit Communication (BIC).

Development of hierarchical structures for actions and motor imagery: a constructivist view from synthetic neuro-robotics study

The current paper shows a neuro-robotics experiment on developmental learning of goal-directed actions. The robot was trained to predict visuo-proprioceptive flow of achieving a set of goal-directed behaviors through iterative tutor training processes. The learning was conducted by employing a dynamic neural network model which is characterized by their multiple time-scale dynamics. The experimental results showed that functional hierarchical structures emerge through stages of developments where behavior primitives are generated in earlier stages and their sequences of achieving goals appear in later stages. It was also observed that motor imagery is generated in earlier stages compared to actual behaviors. Our claim that manipulatable inner representation should emerge through the sensory-motor interactions is corresponded to Piaget's constructivist view.

Thinking as the control of imagination: a conceptual framework for goal-directed systems

This paper offers a conceptual framework which (re)integrates goal-directed control, motivational processes, and executive functions, and suggests a developmental pathway from situated action to higher level cognition. We first illustrate a basic computational (control-theoretic) model of goal-directed action that makes use of internal modeling. We then show that by adding the problem of selection among multiple action alternatives motivation enters the scene, and that the basic mechanisms of executive functions such as inhibition, the monitoring of progresses, and working memory, are required for this system to work. Further, we elaborate on the idea that the off-line re-enactment of anticipatory mechanisms used for action control gives rise to (embodied) mental simulations, and propose that thinking consists essentially in controlling mental simulations rather than directly controlling behavior and perceptions. We conclude by sketching an evolutionary perspective of this process, proposing that anticipation leveraged cognition, and by highlighting specific predictions of our model.