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Prediction, cognition and the brain.

Bubic A, von Cramon DY, Schubotz RI - Front Hum Neurosci (2010)

Bottom Line: Analogously, it has been suggested that predictive processing represents one of the fundamental principles of neural computations and that errors of prediction may be crucial for driving neural and cognitive processes as well as behavior.Furthermore, we discuss the process of testing the validity of postulated expectations by matching these to the realized events and compare the subsequent processing of events which confirm to those which violate the initial predictions.We conclude by suggesting that, although a lot is known about this type of processing, there are still many open issues which need to be resolved before a unified theory of predictive processing can be postulated with regard to both cognitive and neural functioning.

View Article: PubMed Central - PubMed

Affiliation: Department of Cognitive Neurology, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany.

ABSTRACT
The term "predictive brain" depicts one of the most relevant concepts in cognitive neuroscience which emphasizes the importance of "looking into the future", namely prediction, preparation, anticipation, prospection or expectations in various cognitive domains. Analogously, it has been suggested that predictive processing represents one of the fundamental principles of neural computations and that errors of prediction may be crucial for driving neural and cognitive processes as well as behavior. This review discusses research areas which have recognized the importance of prediction and introduces the relevant terminology and leading theories in the field in an attempt to abstract some generative mechanisms of predictive processing. Furthermore, we discuss the process of testing the validity of postulated expectations by matching these to the realized events and compare the subsequent processing of events which confirm to those which violate the initial predictions. We conclude by suggesting that, although a lot is known about this type of processing, there are still many open issues which need to be resolved before a unified theory of predictive processing can be postulated with regard to both cognitive and neural functioning.

No MeSH data available.


Prediction in motor control. Based on the efference copy of the motor command, a forward model is formulated and used for predicting the consequences of one's own actions. These predictions are compared with the incoming sensory input which can result either in a “match” in case predictions were correctly formulated, or a “mismatch”, signaling an error in prediction.
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Figure 3: Prediction in motor control. Based on the efference copy of the motor command, a forward model is formulated and used for predicting the consequences of one's own actions. These predictions are compared with the incoming sensory input which can result either in a “match” in case predictions were correctly formulated, or a “mismatch”, signaling an error in prediction.

Mentions: In addition to the general account presented above, more specific suggestions emphasizing a crucial role of certain systems and regions of the brain, primarily the motor system and especially the cerebellum (Jeannerod, 2001; Wolpert and Flanagan, 2001; Wolpert et al., 2003; Schubotz, 2007), in predictive processing have also been proposed. Functionally, it has been suggested that the prediction of future states of the body or the environment arises from mimicking their respective dynamics through the use of internal models (Johnson-Laird, 1983; Wolpert et al., 1995; Grush, 2004). The internal model approach was originally developed in the motor domain where it went beyond explaining the release of motor commands acting on the musculoskeletal system and introduced another level of computations which essentially entail internal simulations of different aspects of sensorimotor processing (Wolpert et al., 2003) accomplished by internal models. The initial development of the internal model framework was motivated by demonstrations from the experimental work of Sperry, who proposed that a corollary discharge from an action command modulates the visual perception of movement (Sperry, 1950), as well as from von Holst and Mittelstaedt who first described how the discrimination of self- produced and externally applied stimuli may occur through the interaction between sensory feedback signals following an action and an efference copy of the action command (von Holst and Mittelstaedt, 1950). Although addressing somewhat different issues and introducing different terminology, these two findings were the first to demonstrate how the system predicts self-generated sensory signals, an idea which has been greatly pursued in the last decades within the framework of internal models. These models simulate the dynamics of the motor system in order to, in case of inverse models, deduce the motor command which lead to a certain outcome or, in case of forward models, predict the expected sensory consequences of the executed movement (Wolpert and Miall, 1996). The predictive process is initiated by a copy of the motor command, i.e., an efference copy, while the term corollary discharge is typically used to describe the output of the predictor, namely the expected sensory consequences of the produced action. In this context, it has been experimentally demonstrated that expected sensory consequences of self-generated movements get processed in an attenuated fashion both in the auditory and the somatosensory domain (Martikainen et al., 2005; Bäß et al., 2009; Hesse et al., 2010). In contrast, sensory outcomes of self- generated actions which violate expectations formulated based on motor signals elicit deviance-related event-related potentials of the EEG and cause behavioral delay (Waszak and Herwig, 2007; Iwanaga and Nittono, 2010), indicating that they are processed as deviant events. Importantly, although these effects occur as responses to violations related to different types of movements, it has recently been demonstrated that they are especially accentuated in cases of voluntary actions (Nittono, 2006; Adachi et al., 2007). On a more general level, it has been demonstrated that internal models are, in essence, predictive (Bays et al., 2006) and, in addition to distinguishing between self-generated and externally produced movements, may be used to estimate the current or predict the future state of the system (Miall and Wolpert, 1996) as well as estimate more general context variables (Wolpert and Flanagan, 2001). Computationally, the expectations formulized within the internal models could be optimized in a Bayesian fashion, through weighted combinations of priors and sensory likelihoods (Kording and Wolpert, 2006) and subsequently evaluated through a comparison with the actual sensory input available after the movement (Figure 3).


Prediction, cognition and the brain.

Bubic A, von Cramon DY, Schubotz RI - Front Hum Neurosci (2010)

Prediction in motor control. Based on the efference copy of the motor command, a forward model is formulated and used for predicting the consequences of one's own actions. These predictions are compared with the incoming sensory input which can result either in a “match” in case predictions were correctly formulated, or a “mismatch”, signaling an error in prediction.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2904053&req=5

Figure 3: Prediction in motor control. Based on the efference copy of the motor command, a forward model is formulated and used for predicting the consequences of one's own actions. These predictions are compared with the incoming sensory input which can result either in a “match” in case predictions were correctly formulated, or a “mismatch”, signaling an error in prediction.
Mentions: In addition to the general account presented above, more specific suggestions emphasizing a crucial role of certain systems and regions of the brain, primarily the motor system and especially the cerebellum (Jeannerod, 2001; Wolpert and Flanagan, 2001; Wolpert et al., 2003; Schubotz, 2007), in predictive processing have also been proposed. Functionally, it has been suggested that the prediction of future states of the body or the environment arises from mimicking their respective dynamics through the use of internal models (Johnson-Laird, 1983; Wolpert et al., 1995; Grush, 2004). The internal model approach was originally developed in the motor domain where it went beyond explaining the release of motor commands acting on the musculoskeletal system and introduced another level of computations which essentially entail internal simulations of different aspects of sensorimotor processing (Wolpert et al., 2003) accomplished by internal models. The initial development of the internal model framework was motivated by demonstrations from the experimental work of Sperry, who proposed that a corollary discharge from an action command modulates the visual perception of movement (Sperry, 1950), as well as from von Holst and Mittelstaedt who first described how the discrimination of self- produced and externally applied stimuli may occur through the interaction between sensory feedback signals following an action and an efference copy of the action command (von Holst and Mittelstaedt, 1950). Although addressing somewhat different issues and introducing different terminology, these two findings were the first to demonstrate how the system predicts self-generated sensory signals, an idea which has been greatly pursued in the last decades within the framework of internal models. These models simulate the dynamics of the motor system in order to, in case of inverse models, deduce the motor command which lead to a certain outcome or, in case of forward models, predict the expected sensory consequences of the executed movement (Wolpert and Miall, 1996). The predictive process is initiated by a copy of the motor command, i.e., an efference copy, while the term corollary discharge is typically used to describe the output of the predictor, namely the expected sensory consequences of the produced action. In this context, it has been experimentally demonstrated that expected sensory consequences of self-generated movements get processed in an attenuated fashion both in the auditory and the somatosensory domain (Martikainen et al., 2005; Bäß et al., 2009; Hesse et al., 2010). In contrast, sensory outcomes of self- generated actions which violate expectations formulated based on motor signals elicit deviance-related event-related potentials of the EEG and cause behavioral delay (Waszak and Herwig, 2007; Iwanaga and Nittono, 2010), indicating that they are processed as deviant events. Importantly, although these effects occur as responses to violations related to different types of movements, it has recently been demonstrated that they are especially accentuated in cases of voluntary actions (Nittono, 2006; Adachi et al., 2007). On a more general level, it has been demonstrated that internal models are, in essence, predictive (Bays et al., 2006) and, in addition to distinguishing between self-generated and externally produced movements, may be used to estimate the current or predict the future state of the system (Miall and Wolpert, 1996) as well as estimate more general context variables (Wolpert and Flanagan, 2001). Computationally, the expectations formulized within the internal models could be optimized in a Bayesian fashion, through weighted combinations of priors and sensory likelihoods (Kording and Wolpert, 2006) and subsequently evaluated through a comparison with the actual sensory input available after the movement (Figure 3).

Bottom Line: Analogously, it has been suggested that predictive processing represents one of the fundamental principles of neural computations and that errors of prediction may be crucial for driving neural and cognitive processes as well as behavior.Furthermore, we discuss the process of testing the validity of postulated expectations by matching these to the realized events and compare the subsequent processing of events which confirm to those which violate the initial predictions.We conclude by suggesting that, although a lot is known about this type of processing, there are still many open issues which need to be resolved before a unified theory of predictive processing can be postulated with regard to both cognitive and neural functioning.

View Article: PubMed Central - PubMed

Affiliation: Department of Cognitive Neurology, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany.

ABSTRACT
The term "predictive brain" depicts one of the most relevant concepts in cognitive neuroscience which emphasizes the importance of "looking into the future", namely prediction, preparation, anticipation, prospection or expectations in various cognitive domains. Analogously, it has been suggested that predictive processing represents one of the fundamental principles of neural computations and that errors of prediction may be crucial for driving neural and cognitive processes as well as behavior. This review discusses research areas which have recognized the importance of prediction and introduces the relevant terminology and leading theories in the field in an attempt to abstract some generative mechanisms of predictive processing. Furthermore, we discuss the process of testing the validity of postulated expectations by matching these to the realized events and compare the subsequent processing of events which confirm to those which violate the initial predictions. We conclude by suggesting that, although a lot is known about this type of processing, there are still many open issues which need to be resolved before a unified theory of predictive processing can be postulated with regard to both cognitive and neural functioning.

No MeSH data available.