The ability of " looking into the future " —namely, the capacity of anticipating future states of... more 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.
We present an information-theoretic method permitting one to find structure in a problem space (h... more We present an information-theoretic method permitting one to find structure in a problem space (here, in a spatial navigation domain) and cluster it in ways that are convenient to solve different classes of control problems, which include planning a path to a goal from a known or an unknown location, achieving multiple goals and exploring a novel environment. Our generative nonparametric approach, called the generative embedded Chinese restaurant process (geCRP), extends the family of Chinese restaurant process (CRP) models by introducing a parameterizable notion of distance (or kernel) between the states to be clustered together. By using different kernels, such as the the conditional probability or joint probability of two states, the same geCRP method clusters the environment in ways that are more sensitive to different control-related information, such as goal, sub-goal and path information. We perform a series of simulations in three scenarios—an open space, a grid world with four rooms and a maze having the same structure as the Hanoi Tower—in order to illustrate the characteristics of the different clusters (obtained using different kernels) and their relative benefits for solving planning and control problems.
The central problems that goal-directed animals must solve are: ‘What do I need and Why, Where an... more The central problems that goal-directed animals must solve are: ‘What do I need and Why, Where and When can this be obtained, and How do I get it?’ or the H4W problem. Here, we elucidate the principles underlying the neuronal solutions to H4W using a combination of neurobiological and neurorobotic approaches. First, we analyse H4W from a system-level perspective by mapping its objectives onto the Distributed Adaptive Control embodied cognitive architecture which sees the generation of adaptive action in the real world as the primary task of the brain rather than optimally solving abstract problems. We next map this functional decomposition to the architecture of the rodent brain to test its consistency. Following this approach, we propose that the mammalian brain solves the H4W problem on the basis of multiple kinds of outcome predictions, integrating central representations of needs and drives (e.g. hypothalamus), valence (e.g. amygdala), world, self and task state spaces (e.g. neocortex, hippocampus and prefrontal cortex, respectively) combined with multi-modal selection (e.g. basal ganglia). In our analysis, goal-directed behaviour results from a wellstructured architecture in which goals are bootstrapped on the basis of predefined needs, valence and multiple learning, memory and planning mechanisms rather than being generated by a singular computation.
During intertemporal decisions, the preference for smaller, sooner reward over larger-delayed rew... more During intertemporal decisions, the preference for smaller, sooner reward over larger-delayed rewards (temporal discounting, TD) exhibits substantial inter-subject variability; however, it is currently unclear what are the mechanisms underlying this apparently idiosyncratic behavior. To answer this question, here we recorded and analyzed mouse movement kinematics during intertemporal choices in a large sample of participants (N = 86). Results revealed a specific pattern of decision dynamics associated with the selection of “immediate” versus “delayed” response alternatives, which well discriminated between a “discounter” versus a “farsighted” behavior—thus representing a reliable behavioral marker of TD preferences. By fitting the Drift Diffusion Model to the data, we showed that differences between discounter and farsighted subjects could be explained in terms of different model parameterizations, corresponding to the use of different choice mechanisms in the two groups. While farsighted subjects were biased toward the “delayed” option, discounter subjects were not correspondingly biased toward the “immediate” option. Rather, as shown by the dynamics of evidence accumulation over time, their behavior was characterized by high choice uncertainty.
During intertemporal choice (IT) future outcomes are usually devaluated as a function of the
dela... more During intertemporal choice (IT) future outcomes are usually devaluated as a function of the delay, a phenomenon known as temporal discounting (TD). Based on task-evoked activity, previous neuroimaging studies have described several networks associated with TD. However, given its relevance for several disorders, a critical challenge is to define a specific neural marker able to predict TD independently of task execution. To this aim, we used restingstate functional connectivity MRI (fcMRI) and measured TD during economic choices several months apart in 25 human subjects.We further explored the relationship between TD, impulsivity and decision uncertainty by collecting standard questionnaires on individual trait/ state differences. Our findings indicate that fcMRI within and between critical nodes of taskevoked neural networks associated with TD correlates with discounting behavior measured a long time afterwards, independently of impulsivity. Importantly, the nodes form an intrinsic circuit that might support all the mechanisms underlying TD, from the representation of subjective value to choice selection through modulatory effects of cognitive control and episodic prospection.
While the cerebellum's role in motor function is well recognized, the nature of its concurrent ro... more While the cerebellum's role in motor function is well recognized, the nature of its concurrent role in cognitive function remains considerably less clear. The current consensus paper gathers diverse views on a variety of important roles played by the cerebellum across a range of cognitive and emotional functions. This paper considers the cerebellum in relation to neurocognitive development, language function, working memory, executive function, and the development of cerebellar internal control models and reflects upon some of the ways in which better understanding the cerebellum's status as a “supervised learning machine” can enrich our ability to understand human function and adaptation. As all contributors agree that the cerebellum plays a role in cognition, there is also an agreement that this conclusion remains highly inferential. Many conclusions about the role of the cerebellum in cognition originate from applying known information about cerebellar contributions to the coordination and quality of movement. These inferences are based on the uniformity of the cerebellum's compositional infrastructure and its apparent modular organization. There is considerable support for this view, based upon observations of patients with pathology within the cerebellum.
Pickering & Garrod (P&G) explain dialogue dynamics in terms of forward modeling and prediction-by... more Pickering & Garrod (P&G) explain dialogue dynamics in terms of forward modeling and prediction-by-simulation mechanisms. Their theory dissolves a strict segregation between production and comprehension processes, and it links dialogue to action-based theories of joint action. We propose that the theory can also incorporate intentional strategies that increase communicative success: for example, signaling strategies that help remaining predictable and forming common ground.
Why are we scared by nonperceptual entities such as the bogeyman, and why does the bogeyman only ... more Why are we scared by nonperceptual entities such as the bogeyman, and why does the bogeyman only visit us during the night? Why does hearing a window squeaking in the night suggest to us the unlikely idea of a thief or a killer? And why is this more likely to happen after watching a horror movie? To answer these and similar questions, we need to put mind and body together again and consider the embodied nature of perceptual and cognitive inference. Predictive coding provides a general framework for perceptual inference; I propose to extend it by including interoceptive and bodily information. The resulting embodied predictive coding inference permits one to compare alternative hypotheses (e.g., is the sound I hear generated by a thief or the wind?) using the same inferential scheme as in predictive coding, but using both sensory and interoceptive information as evidence, rather than just considering sensory events. If you hear a window squeaking in the night after watching a horror movie, you may consider plausible a very unlikely hypothesis (e.g., a thief, or even the bogeyman) because it explains both what you sense (e.g., the window squeaking in the night) and how you feel (e.g., your high heart rate). The good news is that the inference that I propose is fully rational and gives minds and bodies equal dignity. The bad news is that it also gives an embodiment to the bogeyman, and a reason to fear it.
During simple perceptual decisions, sensorimotor neurons in monkey fronto-parietal cortex represe... more During simple perceptual decisions, sensorimotor neurons in monkey fronto-parietal cortex represent a decision variable that guides the transformation of sensory evidence into a motor response, supporting the view that mechanisms for decision-making are closely embedded within sensorimotor structures. Within these structures, however, decision signals can be dissociated from motor signals, thus indicating that sensorimotor neurons can play multiple and independent roles in decision-making and action selection/planning. Here we used functional magnetic resonance imaging to examine whether response-selective human brain areas encode signals for decision-making or action planning during a task requiring an arbitrary association between face pictures (male vs. female) and specific actions (saccadic eye vs. hand pointing movements). The stimuli were gradually unmasked to stretch the time necessary for decision, thus maximising the temporal separation between decision and action planning. Decision-related signals were measured in parietal and motor/premotor regions showing a preference for the planning/execution of saccadic or pointing movements. In a parietal reach region, decision-related signals were specific for the stimulus category associated with its preferred pointing response. By contrast, a saccade-selective posterior intraparietal sulcus region carried decision-related signals even when the task required a pointing response. Consistent signals were observed in the motor/premotor cortex. Whole-brain analyses indicated that, in our task, the most reliable decision signals were found in the same neural regions involved in response selection. However, decision- and action-related signals within these regions can be dissociated. Differences between the parietal reach region and posterior intraparietal sulcus plausibly depend on their functional specificity rather than on the task structure.
The ability of " looking into the future " —namely, the capacity of anticipating future states of... more 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.
We present an information-theoretic method permitting one to find structure in a problem space (h... more We present an information-theoretic method permitting one to find structure in a problem space (here, in a spatial navigation domain) and cluster it in ways that are convenient to solve different classes of control problems, which include planning a path to a goal from a known or an unknown location, achieving multiple goals and exploring a novel environment. Our generative nonparametric approach, called the generative embedded Chinese restaurant process (geCRP), extends the family of Chinese restaurant process (CRP) models by introducing a parameterizable notion of distance (or kernel) between the states to be clustered together. By using different kernels, such as the the conditional probability or joint probability of two states, the same geCRP method clusters the environment in ways that are more sensitive to different control-related information, such as goal, sub-goal and path information. We perform a series of simulations in three scenarios—an open space, a grid world with four rooms and a maze having the same structure as the Hanoi Tower—in order to illustrate the characteristics of the different clusters (obtained using different kernels) and their relative benefits for solving planning and control problems.
The central problems that goal-directed animals must solve are: ‘What do I need and Why, Where an... more The central problems that goal-directed animals must solve are: ‘What do I need and Why, Where and When can this be obtained, and How do I get it?’ or the H4W problem. Here, we elucidate the principles underlying the neuronal solutions to H4W using a combination of neurobiological and neurorobotic approaches. First, we analyse H4W from a system-level perspective by mapping its objectives onto the Distributed Adaptive Control embodied cognitive architecture which sees the generation of adaptive action in the real world as the primary task of the brain rather than optimally solving abstract problems. We next map this functional decomposition to the architecture of the rodent brain to test its consistency. Following this approach, we propose that the mammalian brain solves the H4W problem on the basis of multiple kinds of outcome predictions, integrating central representations of needs and drives (e.g. hypothalamus), valence (e.g. amygdala), world, self and task state spaces (e.g. neocortex, hippocampus and prefrontal cortex, respectively) combined with multi-modal selection (e.g. basal ganglia). In our analysis, goal-directed behaviour results from a wellstructured architecture in which goals are bootstrapped on the basis of predefined needs, valence and multiple learning, memory and planning mechanisms rather than being generated by a singular computation.
During intertemporal decisions, the preference for smaller, sooner reward over larger-delayed rew... more During intertemporal decisions, the preference for smaller, sooner reward over larger-delayed rewards (temporal discounting, TD) exhibits substantial inter-subject variability; however, it is currently unclear what are the mechanisms underlying this apparently idiosyncratic behavior. To answer this question, here we recorded and analyzed mouse movement kinematics during intertemporal choices in a large sample of participants (N = 86). Results revealed a specific pattern of decision dynamics associated with the selection of “immediate” versus “delayed” response alternatives, which well discriminated between a “discounter” versus a “farsighted” behavior—thus representing a reliable behavioral marker of TD preferences. By fitting the Drift Diffusion Model to the data, we showed that differences between discounter and farsighted subjects could be explained in terms of different model parameterizations, corresponding to the use of different choice mechanisms in the two groups. While farsighted subjects were biased toward the “delayed” option, discounter subjects were not correspondingly biased toward the “immediate” option. Rather, as shown by the dynamics of evidence accumulation over time, their behavior was characterized by high choice uncertainty.
During intertemporal choice (IT) future outcomes are usually devaluated as a function of the
dela... more During intertemporal choice (IT) future outcomes are usually devaluated as a function of the delay, a phenomenon known as temporal discounting (TD). Based on task-evoked activity, previous neuroimaging studies have described several networks associated with TD. However, given its relevance for several disorders, a critical challenge is to define a specific neural marker able to predict TD independently of task execution. To this aim, we used restingstate functional connectivity MRI (fcMRI) and measured TD during economic choices several months apart in 25 human subjects.We further explored the relationship between TD, impulsivity and decision uncertainty by collecting standard questionnaires on individual trait/ state differences. Our findings indicate that fcMRI within and between critical nodes of taskevoked neural networks associated with TD correlates with discounting behavior measured a long time afterwards, independently of impulsivity. Importantly, the nodes form an intrinsic circuit that might support all the mechanisms underlying TD, from the representation of subjective value to choice selection through modulatory effects of cognitive control and episodic prospection.
While the cerebellum's role in motor function is well recognized, the nature of its concurrent ro... more While the cerebellum's role in motor function is well recognized, the nature of its concurrent role in cognitive function remains considerably less clear. The current consensus paper gathers diverse views on a variety of important roles played by the cerebellum across a range of cognitive and emotional functions. This paper considers the cerebellum in relation to neurocognitive development, language function, working memory, executive function, and the development of cerebellar internal control models and reflects upon some of the ways in which better understanding the cerebellum's status as a “supervised learning machine” can enrich our ability to understand human function and adaptation. As all contributors agree that the cerebellum plays a role in cognition, there is also an agreement that this conclusion remains highly inferential. Many conclusions about the role of the cerebellum in cognition originate from applying known information about cerebellar contributions to the coordination and quality of movement. These inferences are based on the uniformity of the cerebellum's compositional infrastructure and its apparent modular organization. There is considerable support for this view, based upon observations of patients with pathology within the cerebellum.
Pickering & Garrod (P&G) explain dialogue dynamics in terms of forward modeling and prediction-by... more Pickering & Garrod (P&G) explain dialogue dynamics in terms of forward modeling and prediction-by-simulation mechanisms. Their theory dissolves a strict segregation between production and comprehension processes, and it links dialogue to action-based theories of joint action. We propose that the theory can also incorporate intentional strategies that increase communicative success: for example, signaling strategies that help remaining predictable and forming common ground.
Why are we scared by nonperceptual entities such as the bogeyman, and why does the bogeyman only ... more Why are we scared by nonperceptual entities such as the bogeyman, and why does the bogeyman only visit us during the night? Why does hearing a window squeaking in the night suggest to us the unlikely idea of a thief or a killer? And why is this more likely to happen after watching a horror movie? To answer these and similar questions, we need to put mind and body together again and consider the embodied nature of perceptual and cognitive inference. Predictive coding provides a general framework for perceptual inference; I propose to extend it by including interoceptive and bodily information. The resulting embodied predictive coding inference permits one to compare alternative hypotheses (e.g., is the sound I hear generated by a thief or the wind?) using the same inferential scheme as in predictive coding, but using both sensory and interoceptive information as evidence, rather than just considering sensory events. If you hear a window squeaking in the night after watching a horror movie, you may consider plausible a very unlikely hypothesis (e.g., a thief, or even the bogeyman) because it explains both what you sense (e.g., the window squeaking in the night) and how you feel (e.g., your high heart rate). The good news is that the inference that I propose is fully rational and gives minds and bodies equal dignity. The bad news is that it also gives an embodiment to the bogeyman, and a reason to fear it.
During simple perceptual decisions, sensorimotor neurons in monkey fronto-parietal cortex represe... more During simple perceptual decisions, sensorimotor neurons in monkey fronto-parietal cortex represent a decision variable that guides the transformation of sensory evidence into a motor response, supporting the view that mechanisms for decision-making are closely embedded within sensorimotor structures. Within these structures, however, decision signals can be dissociated from motor signals, thus indicating that sensorimotor neurons can play multiple and independent roles in decision-making and action selection/planning. Here we used functional magnetic resonance imaging to examine whether response-selective human brain areas encode signals for decision-making or action planning during a task requiring an arbitrary association between face pictures (male vs. female) and specific actions (saccadic eye vs. hand pointing movements). The stimuli were gradually unmasked to stretch the time necessary for decision, thus maximising the temporal separation between decision and action planning. Decision-related signals were measured in parietal and motor/premotor regions showing a preference for the planning/execution of saccadic or pointing movements. In a parietal reach region, decision-related signals were specific for the stimulus category associated with its preferred pointing response. By contrast, a saccade-selective posterior intraparietal sulcus region carried decision-related signals even when the task required a pointing response. Consistent signals were observed in the motor/premotor cortex. Whole-brain analyses indicated that, in our task, the most reliable decision signals were found in the same neural regions involved in response selection. However, decision- and action-related signals within these regions can be dissociated. Differences between the parietal reach region and posterior intraparietal sulcus plausibly depend on their functional specificity rather than on the task structure.
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Papers by Giovanni Pezzulo
approaches. First, we analyse H4W from a system-level perspective by mapping its objectives onto the Distributed Adaptive Control embodied cognitive architecture which sees the generation of adaptive action in the real world as the primary task of the brain rather than optimally solving abstract problems. We next map this functional decomposition to the architecture of the rodent brain to test its consistency. Following this approach, we propose that the mammalian brain solves the H4W problem on the basis of multiple kinds of outcome predictions, integrating central representations
of needs and drives (e.g. hypothalamus), valence (e.g. amygdala), world, self and task state spaces (e.g. neocortex, hippocampus and prefrontal cortex, respectively) combined with multi-modal selection (e.g. basal ganglia). In our analysis, goal-directed behaviour results from a wellstructured architecture in which goals are bootstrapped on the basis of predefined needs, valence and multiple learning, memory and planning mechanisms rather than being generated by a singular computation.
by high choice uncertainty.
delay, a phenomenon known as temporal discounting (TD). Based on task-evoked activity,
previous neuroimaging studies have described several networks associated with TD. However,
given its relevance for several disorders, a critical challenge is to define a specific neural
marker able to predict TD independently of task execution. To this aim, we used restingstate
functional connectivity MRI (fcMRI) and measured TD during economic choices several
months apart in 25 human subjects.We further explored the relationship between TD, impulsivity
and decision uncertainty by collecting standard questionnaires on individual trait/
state differences. Our findings indicate that fcMRI within and between critical nodes of taskevoked
neural networks associated with TD correlates with discounting behavior measured
a long time afterwards, independently of impulsivity. Importantly, the nodes form an intrinsic
circuit that might support all the mechanisms underlying TD, from the representation of subjective
value to choice selection through modulatory effects of cognitive control and episodic prospection.
approaches. First, we analyse H4W from a system-level perspective by mapping its objectives onto the Distributed Adaptive Control embodied cognitive architecture which sees the generation of adaptive action in the real world as the primary task of the brain rather than optimally solving abstract problems. We next map this functional decomposition to the architecture of the rodent brain to test its consistency. Following this approach, we propose that the mammalian brain solves the H4W problem on the basis of multiple kinds of outcome predictions, integrating central representations
of needs and drives (e.g. hypothalamus), valence (e.g. amygdala), world, self and task state spaces (e.g. neocortex, hippocampus and prefrontal cortex, respectively) combined with multi-modal selection (e.g. basal ganglia). In our analysis, goal-directed behaviour results from a wellstructured architecture in which goals are bootstrapped on the basis of predefined needs, valence and multiple learning, memory and planning mechanisms rather than being generated by a singular computation.
by high choice uncertainty.
delay, a phenomenon known as temporal discounting (TD). Based on task-evoked activity,
previous neuroimaging studies have described several networks associated with TD. However,
given its relevance for several disorders, a critical challenge is to define a specific neural
marker able to predict TD independently of task execution. To this aim, we used restingstate
functional connectivity MRI (fcMRI) and measured TD during economic choices several
months apart in 25 human subjects.We further explored the relationship between TD, impulsivity
and decision uncertainty by collecting standard questionnaires on individual trait/
state differences. Our findings indicate that fcMRI within and between critical nodes of taskevoked
neural networks associated with TD correlates with discounting behavior measured
a long time afterwards, independently of impulsivity. Importantly, the nodes form an intrinsic
circuit that might support all the mechanisms underlying TD, from the representation of subjective
value to choice selection through modulatory effects of cognitive control and episodic prospection.