Francesco Donnarumma | Consiglio Nazionale delle Ricerche (CNR) (original) (raw)

Papers by Francesco Donnarumma

IEEE Transactions on Systems, Man, and Cybernetics: Systems

We advance a novel computational model of multiagent, cooperative joint actions that is grounded ... more We advance a novel computational model of multiagent, cooperative joint actions that is grounded in the cognitive framework of active inference. The model assumes that to solve a joint task, such as pressing together a red or blue button, two (or more) agents engage in a process of interactive inference. Each agent maintains probabilistic beliefs about the joint goal (e.g., Should we press the red or blue button?) and updates them by observing the other agent's movements, while in turn selecting movements that make his own intentions legible and easy to infer by the other agent (i.e., sensorimotor communication). Over time, the interactive inference aligns both the beliefs and the behavioral strategies of the agents, hence ensuring the success of the joint action. We exemplify the functioning of the model in two simulations. The first simulation illustrates a "leaderless" joint action. It shows that when two agents lack a strong preference about their joint task goal, they jointly infer it by observing each other's movements. In turn, this helps the interactive alignment of their beliefs and behavioral strategies. The second simulation illustrates a "leader-follower" joint action. It shows that when one agent ("leader") knows the true joint goal, it uses sensorimotor communication to help the other agent ("follower") infer it, even if doing this requires selecting a more costly individual plan. These simulations illustrate that interactive inference supports successful multi-agent joint actions and reproduces key cognitive and behavioral dynamics of "leaderless" and "leaderfollower" joint actions observed in human-human experiments. In sum, interactive inference provides a cognitively inspired, formal framework to realize cooperative joint actions and consensus in multi-agent systems.

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013

This video presents experiments conducted within the final review meeting demonstration session o... more This video presents experiments conducted within the final review meeting demonstration session of the AIRobots project. AIRobots started at 2010 and the final review meeting took place on 22 of March, 2013. The presented experiments cover a wide area of the challenges related with aerial industrial inspection. In particular, multiple test-cases related with both vision-based and contact-based inspection and in general physical interaction are shown. It is highlighted that these experiments were recorded live during the project demonstration and evaluation process.

2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2020

The functional analysis of a novel instrumentation for Brain-Computer Interfaces (BCI) is carried... more The functional analysis of a novel instrumentation for Brain-Computer Interfaces (BCI) is carried out. This consists of a wireless wearable helmet with only 8 dry electrodes. The brain signals to be measured through an electroencephalography are related to the sensorimotor cortex. The final aim is to distinguish between different motor imagery tasks. Furthermore, this analysis also takes into account the discrimination between two executed movements. Features are extracted from the brain signals by means of a Common Spatial Pattern algorithm. Then, two different classifiers are employed to process the brain signals, namely the Random Forest, and the Support Vector Machine with Gaussian kernel. Their performance was compared in terms of classification accuracy and the best accuracy resulted equal to about 80% when distinguishing between left and right imagined movement, classified by means of the Random Forest. The results of this study aim at giving a contribution to the building of wearable BCIs for daily life applications.

2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2019

In this paper, the accuracy in classifying Motor Imagery (MI) tasks for a Brain-Computer Interfac... more In this paper, the accuracy in classifying Motor Imagery (MI) tasks for a Brain-Computer Interface (BCI) is analyzed. Electroencephalographic (EEG) signals were taken into account, notably by employing one channel per time. Four classes were to distinguish, i.e. imagining the movement of left hand, right hand, feet, or tongue. The dataset ”2a” of BCI Competition IV (2008) was considered. Brain signals were processed by applying a short-time Fourier transform, a common spatial pattern filter for feature extraction, and a support vector machine for classification. With this work, the aim is to give a contribution to the development of wearable MI-based BCIs by relying on single channel EEG.

Current Biology, 2021

Hippocampal place cells encode global location but not connectivity in a complex space Highlights... more Hippocampal place cells encode global location but not connectivity in a complex space Highlights d Rats flexibly navigate in a four-room maze where connectivity changes d Place cell firing fields are not specifically altered by changes in connectivity d Single or ensemble place cell activity does not encode changes in connectivity d Place cells can uniquely map identical connected compartments Authors É l eonore Duvelle, Roddy M. Grieves,

Measurement, 2021

Abstract The number of elderly people is increasing, and heart diseases are a major issue in a he... more Abstract The number of elderly people is increasing, and heart diseases are a major issue in a healthy aging of population. Indeed, the possibility of hospital care is limited and the avoidance of crowded hospitals recently became even more essential. Meanwhile, the possibility to exploit e-health technology for home care would be desirable. In this framework, the concept design of a soft sensor for measuring cardiovascular risk of a patient in real time is here reported. ECG, blood oxygenation, body temperature, and data acquired from patients’ interviews are processed to extract characterizing features. These are then classified to assess the cardiovascular risk. Experimental results show that patients’ classification accuracy can be as high as 80% when employing a random forest classifier, even with few data employed for training. Finally, method evaluation was extended by exploiting further data and by means of a noise robustness test.

Scientific Reports, 2020

An amendment to this paper has been published and can be accessed via a link at the top of the pa... more An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Studies of neural population dynamics of cell activity from monkey motor areas during reaching sh... more Studies of neural population dynamics of cell activity from monkey motor areas during reaching show that it mostly represents the generation and timing of motor behavior. We compared neural dynamics in dorsal premotor cortex (PMd) during the performance of a visuomotor task executed individually or cooperatively and during an observation task. In the visuomotor conditions, monkeys applied isometric forces on a joystick to guide a visual cursor in different directions, either alone or jointly with a conspecific. In the observation condition, they observed the cursor's motion guided by the partner. We found that in PMd neural dynamics were widely shared across action execution and observation, with cursor motion directions more accurately discriminated than task types. This suggests that PMd encodes spatial aspects irrespective of specific behavioral demands. Furthermore, our results suggest that largest components of premotor population dynamics, which have previously been sugges...

The moral decisions we make during this period, such as deciding whether to comply with quarantin... more The moral decisions we make during this period, such as deciding whether to comply with quarantine rules, have unprecedented societal effects. We simulate the "escape from Milan" that occurred on March 7th-8th 2020, when many travelers moved from a high-risk zone (Milan) to southern regions of Italy (Campania and Lazio) immediately after an imminent lockdown was announced. Our simulations show that fewer than 50 active cases might have caused the sudden spread of the virus observed afterwards in these regions. The surprising influence of the actions of few individuals on societal dynamics challenges our cognitive expectations -- as in normal conditions, collective dynamics are rather robust to the decisions of few "cheaters". This situation therefore requires novel educational strategies that increase our awareness and understanding of the unprecedented effects of our individual moral decisions.

2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT), 2019

The present work is a first step in building a wearable system to monitor the heart functionality... more The present work is a first step in building a wearable system to monitor the heart functionality of a patient and assess the cardiovascular risk by means of non-invasive measurements, such as electrocardiogram (ECG), heart rate, blood oxygenation, and body temperature. Also clinic data obtained by means of a patient interview are taken into account. In this feasibility study, measures from a pre-existing dataset are exploited. They are processed with a machine learning algorithm. Features are first extracted from the measures collected with the wearable sensors. Then, these features are employed together with clinic data to classify the patients health status. A Random Forest classifier was employed and the algorithm was characterized considering different setups. The best accuracy resulted equal to 78.6% in distinguishing three classes of patients, namely healthy, unhealthy non-critical, and unhealthy critical patients.

Scientific reports, Jan 12, 2018

Converging evidence shows that hand-actions are controlled at the level of synergies and not sing... more Converging evidence shows that hand-actions are controlled at the level of synergies and not single muscles. One intriguing aspect of synergy-based action-representation is that it may be intrinsically sparse and the same synergies can be shared across several distinct types of hand-actions. Here, adopting a normative angle, we consider three hypotheses for hand-action optimal-control: sparse-combination hypothesis (SC) - sparsity in the mapping between synergies and actions - i.e., actions implemented using a sparse combination of synergies; sparse-elements hypothesis (SE) - sparsity in synergy representation - i.e., the mapping between degrees-of-freedom (DoF) and synergies is sparse; double-sparsity hypothesis (DS) - a novel view combining both SC and SE - i.e., both the mapping between DoF and synergies and between synergies and actions are sparse, each action implementing a sparse combination of synergies (as in SC), each using a limited set of DoFs (as in SE). We evaluate thes...

PLOS Computational Biology, 2016

How do humans and other animals face novel problems for which predefined solutions are not availa... more How do humans and other animals face novel problems for which predefined solutions are not available? Human problem solving links to flexible reasoning and inference rather than to slow trial-and-error learning. It has received considerable attention since the early days of cognitive science, giving rise to well known cognitive architectures such as SOAR and ACT-R, but its computational and brain mechanisms remain incompletely known. Furthermore, it is still unclear whether problem solving is a "specialized" domain or module of cognition, in the sense that it requires computations that are fundamentally different from those supporting perception and action systems. Here we advance a novel view of human problem solving as probabilistic inference with subgoaling. In this perspective, key insights from cognitive architectures are retained such as the importance of using subgoals to split problems into subproblems. However, here the underlying computations use probabilistic inference methods analogous to those that are increasingly popular in the study of perception and action systems. To test our model we focus on the widely used Tower of Hanoi (ToH) task, and show that our proposed method can reproduce characteristic idiosyncrasies of human problem solvers: their sensitivity to the "community structure" of the ToH and their difficulties in executing so-called "counterintuitive" movements. Our analysis reveals that subgoals have two key roles in probabilistic inference and problem solving. First, prior beliefs on (likely) useful subgoals carve the problem space and define an implicit metric for the problem at hand-a metric to which humans are sensitive. Second, subgoals are used as waypoints in the probabilistic problem solving inference and permit to find effective solutions that, when unavailable, lead to problem solving deficits. Our study thus suggests that a probabilistic inference scheme enhanced with subgoals provides a comprehensive framework to study problem solving and its deficits.

Journal of The Royal Society Interface, 2015

Non-verbal communication is the basis of animal interactions. In dyadic leader–follower interacti... more Non-verbal communication is the basis of animal interactions. In dyadic leader–follower interactions, leaders master the ability to carve their motor behaviour in order to ‘signal’ their future actions and internal plans while these signals influence the behaviour of follower partners, who automatically tend to imitate the leader even in complementary interactions. Despite their usefulness, signalling and imitation have a biomechanical cost, and it is unclear how this cost–benefits trade-off is managed during repetitive dyadic interactions that present learnable regularities. We studied signalling and imitation dynamics (indexed by movement kinematics) in pairs of leaders and followers during a repetitive, rule-based, joint action. Trial-by-trial Bayesian model comparison was used to evaluate the relation between signalling, imitation and pair performance. The different models incorporate different hypotheses concerning the factors (past interactions versus online movements) influen...

Biological cybernetics, Jan 14, 2015

Recent theories of mindreading explain the recognition of action, intention, and belief of other ... more 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" proposa...

International Journal of Neural Systems, 2015

There is wide consensus that the prefrontal cortex (PFC) is able to exert cognitive control on be... more There is wide consensus that the prefrontal cortex (PFC) is able to exert cognitive control on behavior by biasing processing toward task-relevant information and by modulating response selection. This idea is typically framed in terms of top-down influences within a cortical control hierarchy, where prefrontal-basal ganglia loops gate multiple input–output channels, which in turn can activate or sequence motor primitives expressed in (pre-)motor cortices. Here we advance a new hypothesis, based on the notion of programmability and an interpreter–programmer computational scheme, on how the PFC can flexibly bias the selection of sensorimotor patterns depending on internal goal and task contexts. In this approach, multiple elementary behaviors representing motor primitives are expressed by a single multi-purpose neural network, which is seen as a reusable area of "recycled" neurons (interpreter). The PFC thus acts as a "programmer" that, without modifying the netwo...

Journal of The Royal Society Interface, 2015

It has long been recognized that humans (and possibly other animals) usually break problems down ... more It has long been recognized that humans (and possibly other animals) usually break problems down into smaller and more manageable problems using subgoals. Despite a general consensus that subgoaling helps problem solving, it is still unclear what the mechanisms guiding online subgoal selection are during the solution of novel problems for which predefined solutions are not available. Under which conditions does subgoaling lead to optimal behaviour? When is subgoaling better than solving a problem from start to finish? Which is the best number and sequence of subgoals to solve a given problem? How are these subgoals selected during online inference? Here, we present a computational account of subgoaling in problem solving. Following Occam's razor, we propose that good subgoals are those that permit planning solutions and controlling behaviour using less information resources, thus yielding parsimony in inference and control. We implement this principle using approximate probabili...

Frontiers in Neuroscience, 2012

How do animals select their actions in uncertain and volatile environments? Converging evidence i... more How do animals select their actions in uncertain and volatile environments? Converging evidence indicates that there are multiple mechanisms of choice, habitual, goal-directed, and Pavlovian, which compete over time. Researchers are using multiple techniques --animal and human experiments, computational modeling-- to understand how these multiple controllers compete, in which situations one controller dominates over the other, and what are the

Adaptive Behavior, 2013

Recent experimental evidence indicates that animals can use mental simulation to make decisions a... more Recent experimental evidence indicates that animals can use mental simulation to make decisions about the actions to take during goal-directed navigation. The principal brain areas found to be active during this process are the hippocampus, the ventral striatum and the sensory-motor cortex. In this paper, we present a computational model that includes biological aspects of this circuit and explains mechanistically how it may be used to imagine and evaluate future events. Its most salient characteristic is that choices about actions are made by simulating movements and their sensory effects using the same brain areas that are active during overt execution. More precisely, the simulation of an action (e.g., walking) creates a new sensory pattern that is evaluated in the same way as real inputs. The model is validated in a navigation task in which a simulated rat is placed in a complex maze. We show that hippocampal and striatal cells are activated to simulate paths, to retrieve their ...

IEEE Transactions on Systems, Man, and Cybernetics: Systems

We advance a novel computational model of multiagent, cooperative joint actions that is grounded ... more We advance a novel computational model of multiagent, cooperative joint actions that is grounded in the cognitive framework of active inference. The model assumes that to solve a joint task, such as pressing together a red or blue button, two (or more) agents engage in a process of interactive inference. Each agent maintains probabilistic beliefs about the joint goal (e.g., Should we press the red or blue button?) and updates them by observing the other agent's movements, while in turn selecting movements that make his own intentions legible and easy to infer by the other agent (i.e., sensorimotor communication). Over time, the interactive inference aligns both the beliefs and the behavioral strategies of the agents, hence ensuring the success of the joint action. We exemplify the functioning of the model in two simulations. The first simulation illustrates a "leaderless" joint action. It shows that when two agents lack a strong preference about their joint task goal, they jointly infer it by observing each other's movements. In turn, this helps the interactive alignment of their beliefs and behavioral strategies. The second simulation illustrates a "leader-follower" joint action. It shows that when one agent ("leader") knows the true joint goal, it uses sensorimotor communication to help the other agent ("follower") infer it, even if doing this requires selecting a more costly individual plan. These simulations illustrate that interactive inference supports successful multi-agent joint actions and reproduces key cognitive and behavioral dynamics of "leaderless" and "leaderfollower" joint actions observed in human-human experiments. In sum, interactive inference provides a cognitively inspired, formal framework to realize cooperative joint actions and consensus in multi-agent systems.

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013

This video presents experiments conducted within the final review meeting demonstration session o... more This video presents experiments conducted within the final review meeting demonstration session of the AIRobots project. AIRobots started at 2010 and the final review meeting took place on 22 of March, 2013. The presented experiments cover a wide area of the challenges related with aerial industrial inspection. In particular, multiple test-cases related with both vision-based and contact-based inspection and in general physical interaction are shown. It is highlighted that these experiments were recorded live during the project demonstration and evaluation process.

2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2020

The functional analysis of a novel instrumentation for Brain-Computer Interfaces (BCI) is carried... more The functional analysis of a novel instrumentation for Brain-Computer Interfaces (BCI) is carried out. This consists of a wireless wearable helmet with only 8 dry electrodes. The brain signals to be measured through an electroencephalography are related to the sensorimotor cortex. The final aim is to distinguish between different motor imagery tasks. Furthermore, this analysis also takes into account the discrimination between two executed movements. Features are extracted from the brain signals by means of a Common Spatial Pattern algorithm. Then, two different classifiers are employed to process the brain signals, namely the Random Forest, and the Support Vector Machine with Gaussian kernel. Their performance was compared in terms of classification accuracy and the best accuracy resulted equal to about 80% when distinguishing between left and right imagined movement, classified by means of the Random Forest. The results of this study aim at giving a contribution to the building of wearable BCIs for daily life applications.

2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2019

In this paper, the accuracy in classifying Motor Imagery (MI) tasks for a Brain-Computer Interfac... more In this paper, the accuracy in classifying Motor Imagery (MI) tasks for a Brain-Computer Interface (BCI) is analyzed. Electroencephalographic (EEG) signals were taken into account, notably by employing one channel per time. Four classes were to distinguish, i.e. imagining the movement of left hand, right hand, feet, or tongue. The dataset ”2a” of BCI Competition IV (2008) was considered. Brain signals were processed by applying a short-time Fourier transform, a common spatial pattern filter for feature extraction, and a support vector machine for classification. With this work, the aim is to give a contribution to the development of wearable MI-based BCIs by relying on single channel EEG.

Current Biology, 2021

Hippocampal place cells encode global location but not connectivity in a complex space Highlights... more Hippocampal place cells encode global location but not connectivity in a complex space Highlights d Rats flexibly navigate in a four-room maze where connectivity changes d Place cell firing fields are not specifically altered by changes in connectivity d Single or ensemble place cell activity does not encode changes in connectivity d Place cells can uniquely map identical connected compartments Authors É l eonore Duvelle, Roddy M. Grieves,

Measurement, 2021

Abstract The number of elderly people is increasing, and heart diseases are a major issue in a he... more Abstract The number of elderly people is increasing, and heart diseases are a major issue in a healthy aging of population. Indeed, the possibility of hospital care is limited and the avoidance of crowded hospitals recently became even more essential. Meanwhile, the possibility to exploit e-health technology for home care would be desirable. In this framework, the concept design of a soft sensor for measuring cardiovascular risk of a patient in real time is here reported. ECG, blood oxygenation, body temperature, and data acquired from patients’ interviews are processed to extract characterizing features. These are then classified to assess the cardiovascular risk. Experimental results show that patients’ classification accuracy can be as high as 80% when employing a random forest classifier, even with few data employed for training. Finally, method evaluation was extended by exploiting further data and by means of a noise robustness test.

Scientific Reports, 2020

An amendment to this paper has been published and can be accessed via a link at the top of the pa... more An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Studies of neural population dynamics of cell activity from monkey motor areas during reaching sh... more Studies of neural population dynamics of cell activity from monkey motor areas during reaching show that it mostly represents the generation and timing of motor behavior. We compared neural dynamics in dorsal premotor cortex (PMd) during the performance of a visuomotor task executed individually or cooperatively and during an observation task. In the visuomotor conditions, monkeys applied isometric forces on a joystick to guide a visual cursor in different directions, either alone or jointly with a conspecific. In the observation condition, they observed the cursor's motion guided by the partner. We found that in PMd neural dynamics were widely shared across action execution and observation, with cursor motion directions more accurately discriminated than task types. This suggests that PMd encodes spatial aspects irrespective of specific behavioral demands. Furthermore, our results suggest that largest components of premotor population dynamics, which have previously been sugges...

The moral decisions we make during this period, such as deciding whether to comply with quarantin... more The moral decisions we make during this period, such as deciding whether to comply with quarantine rules, have unprecedented societal effects. We simulate the "escape from Milan" that occurred on March 7th-8th 2020, when many travelers moved from a high-risk zone (Milan) to southern regions of Italy (Campania and Lazio) immediately after an imminent lockdown was announced. Our simulations show that fewer than 50 active cases might have caused the sudden spread of the virus observed afterwards in these regions. The surprising influence of the actions of few individuals on societal dynamics challenges our cognitive expectations -- as in normal conditions, collective dynamics are rather robust to the decisions of few "cheaters". This situation therefore requires novel educational strategies that increase our awareness and understanding of the unprecedented effects of our individual moral decisions.

2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT), 2019

The present work is a first step in building a wearable system to monitor the heart functionality... more The present work is a first step in building a wearable system to monitor the heart functionality of a patient and assess the cardiovascular risk by means of non-invasive measurements, such as electrocardiogram (ECG), heart rate, blood oxygenation, and body temperature. Also clinic data obtained by means of a patient interview are taken into account. In this feasibility study, measures from a pre-existing dataset are exploited. They are processed with a machine learning algorithm. Features are first extracted from the measures collected with the wearable sensors. Then, these features are employed together with clinic data to classify the patients health status. A Random Forest classifier was employed and the algorithm was characterized considering different setups. The best accuracy resulted equal to 78.6% in distinguishing three classes of patients, namely healthy, unhealthy non-critical, and unhealthy critical patients.

Scientific reports, Jan 12, 2018

Converging evidence shows that hand-actions are controlled at the level of synergies and not sing... more Converging evidence shows that hand-actions are controlled at the level of synergies and not single muscles. One intriguing aspect of synergy-based action-representation is that it may be intrinsically sparse and the same synergies can be shared across several distinct types of hand-actions. Here, adopting a normative angle, we consider three hypotheses for hand-action optimal-control: sparse-combination hypothesis (SC) - sparsity in the mapping between synergies and actions - i.e., actions implemented using a sparse combination of synergies; sparse-elements hypothesis (SE) - sparsity in synergy representation - i.e., the mapping between degrees-of-freedom (DoF) and synergies is sparse; double-sparsity hypothesis (DS) - a novel view combining both SC and SE - i.e., both the mapping between DoF and synergies and between synergies and actions are sparse, each action implementing a sparse combination of synergies (as in SC), each using a limited set of DoFs (as in SE). We evaluate thes...

PLOS Computational Biology, 2016

How do humans and other animals face novel problems for which predefined solutions are not availa... more How do humans and other animals face novel problems for which predefined solutions are not available? Human problem solving links to flexible reasoning and inference rather than to slow trial-and-error learning. It has received considerable attention since the early days of cognitive science, giving rise to well known cognitive architectures such as SOAR and ACT-R, but its computational and brain mechanisms remain incompletely known. Furthermore, it is still unclear whether problem solving is a "specialized" domain or module of cognition, in the sense that it requires computations that are fundamentally different from those supporting perception and action systems. Here we advance a novel view of human problem solving as probabilistic inference with subgoaling. In this perspective, key insights from cognitive architectures are retained such as the importance of using subgoals to split problems into subproblems. However, here the underlying computations use probabilistic inference methods analogous to those that are increasingly popular in the study of perception and action systems. To test our model we focus on the widely used Tower of Hanoi (ToH) task, and show that our proposed method can reproduce characteristic idiosyncrasies of human problem solvers: their sensitivity to the "community structure" of the ToH and their difficulties in executing so-called "counterintuitive" movements. Our analysis reveals that subgoals have two key roles in probabilistic inference and problem solving. First, prior beliefs on (likely) useful subgoals carve the problem space and define an implicit metric for the problem at hand-a metric to which humans are sensitive. Second, subgoals are used as waypoints in the probabilistic problem solving inference and permit to find effective solutions that, when unavailable, lead to problem solving deficits. Our study thus suggests that a probabilistic inference scheme enhanced with subgoals provides a comprehensive framework to study problem solving and its deficits.

Journal of The Royal Society Interface, 2015

Non-verbal communication is the basis of animal interactions. In dyadic leader–follower interacti... more Non-verbal communication is the basis of animal interactions. In dyadic leader–follower interactions, leaders master the ability to carve their motor behaviour in order to ‘signal’ their future actions and internal plans while these signals influence the behaviour of follower partners, who automatically tend to imitate the leader even in complementary interactions. Despite their usefulness, signalling and imitation have a biomechanical cost, and it is unclear how this cost–benefits trade-off is managed during repetitive dyadic interactions that present learnable regularities. We studied signalling and imitation dynamics (indexed by movement kinematics) in pairs of leaders and followers during a repetitive, rule-based, joint action. Trial-by-trial Bayesian model comparison was used to evaluate the relation between signalling, imitation and pair performance. The different models incorporate different hypotheses concerning the factors (past interactions versus online movements) influen...

Biological cybernetics, Jan 14, 2015

Recent theories of mindreading explain the recognition of action, intention, and belief of other ... more 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" proposa...

International Journal of Neural Systems, 2015

There is wide consensus that the prefrontal cortex (PFC) is able to exert cognitive control on be... more There is wide consensus that the prefrontal cortex (PFC) is able to exert cognitive control on behavior by biasing processing toward task-relevant information and by modulating response selection. This idea is typically framed in terms of top-down influences within a cortical control hierarchy, where prefrontal-basal ganglia loops gate multiple input–output channels, which in turn can activate or sequence motor primitives expressed in (pre-)motor cortices. Here we advance a new hypothesis, based on the notion of programmability and an interpreter–programmer computational scheme, on how the PFC can flexibly bias the selection of sensorimotor patterns depending on internal goal and task contexts. In this approach, multiple elementary behaviors representing motor primitives are expressed by a single multi-purpose neural network, which is seen as a reusable area of "recycled" neurons (interpreter). The PFC thus acts as a "programmer" that, without modifying the netwo...

Journal of The Royal Society Interface, 2015

It has long been recognized that humans (and possibly other animals) usually break problems down ... more It has long been recognized that humans (and possibly other animals) usually break problems down into smaller and more manageable problems using subgoals. Despite a general consensus that subgoaling helps problem solving, it is still unclear what the mechanisms guiding online subgoal selection are during the solution of novel problems for which predefined solutions are not available. Under which conditions does subgoaling lead to optimal behaviour? When is subgoaling better than solving a problem from start to finish? Which is the best number and sequence of subgoals to solve a given problem? How are these subgoals selected during online inference? Here, we present a computational account of subgoaling in problem solving. Following Occam's razor, we propose that good subgoals are those that permit planning solutions and controlling behaviour using less information resources, thus yielding parsimony in inference and control. We implement this principle using approximate probabili...

Frontiers in Neuroscience, 2012

How do animals select their actions in uncertain and volatile environments? Converging evidence i... more How do animals select their actions in uncertain and volatile environments? Converging evidence indicates that there are multiple mechanisms of choice, habitual, goal-directed, and Pavlovian, which compete over time. Researchers are using multiple techniques --animal and human experiments, computational modeling-- to understand how these multiple controllers compete, in which situations one controller dominates over the other, and what are the

Adaptive Behavior, 2013

Recent experimental evidence indicates that animals can use mental simulation to make decisions a... more Recent experimental evidence indicates that animals can use mental simulation to make decisions about the actions to take during goal-directed navigation. The principal brain areas found to be active during this process are the hippocampus, the ventral striatum and the sensory-motor cortex. In this paper, we present a computational model that includes biological aspects of this circuit and explains mechanistically how it may be used to imagine and evaluate future events. Its most salient characteristic is that choices about actions are made by simulating movements and their sensory effects using the same brain areas that are active during overt execution. More precisely, the simulation of an action (e.g., walking) creates a new sensory pattern that is evaluated in the same way as real inputs. The model is validated in a navigation task in which a simulated rat is placed in a complex maze. We show that hippocampal and striatal cells are activated to simulate paths, to retrieve their ...