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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.


Main factors which specify the nature of predictive processes across different contexts. It is important to note that these do not represent mutually independent and orthogonal dimensions, but may in some contexts strongly overlap and interact. Furthermore, some additional features such as, e.g., type of information used for formulating expectations, might also be of relevance when specifying the exact nature of the predictive phenomenon of interest.
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Figure 2: Main factors which specify the nature of predictive processes across different contexts. It is important to note that these do not represent mutually independent and orthogonal dimensions, but may in some contexts strongly overlap and interact. Furthermore, some additional features such as, e.g., type of information used for formulating expectations, might also be of relevance when specifying the exact nature of the predictive phenomenon of interest.

Mentions: In the previous paragraphs we have introduced the basic terminology which describes different facets of predictive processing and indicated some inconsistencies in its usage. However, even if this terminology was to become more uniform and agreed upon, it would still not solve all existing problems in this area, as a more elaborate systematization which could account for differences between different levels, timescales or types (e.g., implicit and explicit) of predictions would still be lacking. Specifically, the nature and strength of predictions varies greatly in different contexts and may be influenced by different factors, e.g., the strength of the relationship between different events, frequency or context of their occurrence, etc. While in some situations expectations can be formulated in a rather unspecific manner and be restricted to a selected set of event features, e.g., sensory modality or location of an incoming stimulus, in others they may be very specific and pertain to the exact stimulus identity as well as the timing of its appearance. In addition, although a separation between implicit anticipations expressed through habits (behavior) and explicit ones which include representations of the predicted future states has been suggested (Pezzulo, 2008), it is still not clear whether these should be considered as a dichotomy or if it would be more appropriate to posit a continuous distribution of representations characterized by different degree of explicitness. Furthermore, prediction can take place on different temporal scales. First, expectations can be formulated based on the knowledge gained through long-term experience (Bar, 2007) or learning triggered by short-term exposure to non-random patterns (Schubotz, 2007). Second, it is possible to predict events which are expected to occur in different moments in the future, e.g., those expected to occur within seconds-range in contrast to those which may occur in the distant future. Long-term prediction is usually used “offline” and is not necessarily coupled with any immediately relevant or running process in contrast to short-term prediction which is more likely to be used “online” for regulating the ongoing behavior, as exemplified in motor control where it is coupled to the current sensorimotor cycle (Pezzulo et al., 2008). Consequently, prediction occurring on shorter timescales is typically more accurate when compared to long-term planning. In this context it is important to note that the timescale of prediction should not be confused with the concept of temporal expectations, namely a foresight of when something will occur (Nobre et al., 2007), which interact with expectations about other event properties in order to optimize our behavior. In addition, it is possible to generate multiple expectations pertaining to different points in space and time, as done in hierarchical predictive systems (Pezzulo et al., 2008) which capture the hierarchical organization of cognitive processes, the neural system and behavior (Dehaene and Changeux, 1997; Friston et al., 2006; Grafton and Hamilton, 2007; Kiebel et al., 2008, 2009). Furthermore, it has been shown that multiple expectations pertaining to the same event occurring at one point in time may also be formulated across different brain systems. For example, Ritter et al. (1999) demonstrated how rare predictable tones may be classified as violations of expectations at a preattentive, lower level of cognitive processing and, in the same time, as expected events at a higher level. Expectations of such different type and specificity could be mediated through different mechanisms or, alternatively, be based on the same types of processes partially implemented within different brain regions. Understanding how such different types of predictions are coded in the brain will be crucial in understanding their mutual relations and potential interactions. In summary, in describing different aspects of prediction, it is always important to clearly specify as many features of such processing as possible. As indicated in this paragraph and Figure 2, there exist numerous factors which may be of relevance in this context. Although it may sometimes be difficult to clearly specify all of them, it is nevertheless important to try, as this may substantially aid general understanding and future progress within the field.


Prediction, cognition and the brain.

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

Main factors which specify the nature of predictive processes across different contexts. It is important to note that these do not represent mutually independent and orthogonal dimensions, but may in some contexts strongly overlap and interact. Furthermore, some additional features such as, e.g., type of information used for formulating expectations, might also be of relevance when specifying the exact nature of the predictive phenomenon of interest.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Main factors which specify the nature of predictive processes across different contexts. It is important to note that these do not represent mutually independent and orthogonal dimensions, but may in some contexts strongly overlap and interact. Furthermore, some additional features such as, e.g., type of information used for formulating expectations, might also be of relevance when specifying the exact nature of the predictive phenomenon of interest.
Mentions: In the previous paragraphs we have introduced the basic terminology which describes different facets of predictive processing and indicated some inconsistencies in its usage. However, even if this terminology was to become more uniform and agreed upon, it would still not solve all existing problems in this area, as a more elaborate systematization which could account for differences between different levels, timescales or types (e.g., implicit and explicit) of predictions would still be lacking. Specifically, the nature and strength of predictions varies greatly in different contexts and may be influenced by different factors, e.g., the strength of the relationship between different events, frequency or context of their occurrence, etc. While in some situations expectations can be formulated in a rather unspecific manner and be restricted to a selected set of event features, e.g., sensory modality or location of an incoming stimulus, in others they may be very specific and pertain to the exact stimulus identity as well as the timing of its appearance. In addition, although a separation between implicit anticipations expressed through habits (behavior) and explicit ones which include representations of the predicted future states has been suggested (Pezzulo, 2008), it is still not clear whether these should be considered as a dichotomy or if it would be more appropriate to posit a continuous distribution of representations characterized by different degree of explicitness. Furthermore, prediction can take place on different temporal scales. First, expectations can be formulated based on the knowledge gained through long-term experience (Bar, 2007) or learning triggered by short-term exposure to non-random patterns (Schubotz, 2007). Second, it is possible to predict events which are expected to occur in different moments in the future, e.g., those expected to occur within seconds-range in contrast to those which may occur in the distant future. Long-term prediction is usually used “offline” and is not necessarily coupled with any immediately relevant or running process in contrast to short-term prediction which is more likely to be used “online” for regulating the ongoing behavior, as exemplified in motor control where it is coupled to the current sensorimotor cycle (Pezzulo et al., 2008). Consequently, prediction occurring on shorter timescales is typically more accurate when compared to long-term planning. In this context it is important to note that the timescale of prediction should not be confused with the concept of temporal expectations, namely a foresight of when something will occur (Nobre et al., 2007), which interact with expectations about other event properties in order to optimize our behavior. In addition, it is possible to generate multiple expectations pertaining to different points in space and time, as done in hierarchical predictive systems (Pezzulo et al., 2008) which capture the hierarchical organization of cognitive processes, the neural system and behavior (Dehaene and Changeux, 1997; Friston et al., 2006; Grafton and Hamilton, 2007; Kiebel et al., 2008, 2009). Furthermore, it has been shown that multiple expectations pertaining to the same event occurring at one point in time may also be formulated across different brain systems. For example, Ritter et al. (1999) demonstrated how rare predictable tones may be classified as violations of expectations at a preattentive, lower level of cognitive processing and, in the same time, as expected events at a higher level. Expectations of such different type and specificity could be mediated through different mechanisms or, alternatively, be based on the same types of processes partially implemented within different brain regions. Understanding how such different types of predictions are coded in the brain will be crucial in understanding their mutual relations and potential interactions. In summary, in describing different aspects of prediction, it is always important to clearly specify as many features of such processing as possible. As indicated in this paragraph and Figure 2, there exist numerous factors which may be of relevance in this context. Although it may sometimes be difficult to clearly specify all of them, it is nevertheless important to try, as this may substantially aid general understanding and future progress within the field.

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.