Manual THE BEHAVIORAL BLUEPRINT 4: HUMAN PREDICTABILITY AND THE FUTURE

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Such results should not be surprising, given the wide range of contexts which afford predictions. One way to understand predictive processing in perception is to conceptualize anticipation as a bias signal Rees and Frith, which improves the computational efficiency of a specific area.

This description may be useful, as it points to three elements which need to be specified in order to understand such a phenomenon: Within sites of prediction such as, e. These effects are reflected in the elicitation of particular event-related anticipatory components, e. Evidence for the claim that improved speed and accuracy of processing expected stimuli reflects preparatory effects in the relevant sensory cortices potentially coupled with the inhibitory effects in other sensory modalities Brunia, comes from studies which show comparable patterns of activity in stimulus perception and anticipation.

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For example, findings showing that actual somatosensory stimulation and anticipation of such stimulation engage the same network Carlsson et al. A similar pre-activation of areas involved in processing relevant events has been show in other domains, e. In addition to understanding preparatory effects in relevant sensory cortices, it is important to describe how these effects are initiated and controlled.

In an attempt to answer this question, Gomez et al. An important role of orbitofrontal as well as medial prefrontal cortex in formulating expectations about incoming visual objects which is crucial for object recognition has also been suggested by Kveraga et al. On the other hand, dorsolateral prefrontal cortex was hypothesized to be implicated in sustaining the activation of the sensory and motor cortices Gomez et al.

Similarly, Brunia suggested a crucial role of prefrontal cortex in organizing anticipatory behavior by activating cortico-cortical and thalamo-cortical loops to sensory and motor areas after the preparatory set had been established. Along these lines, Liang et al. Therefore, these authors argued that synchronized oscillations in prefrontal cortex represent a plausible candidate for sustaining visual anticipation, proposing that such anticipatory control develops as a consequence of accumulating prior experience.

And, although this distinction may generally prove to be useful, it may not always be easy to incorporate. For example, it is by now well established that predictive processing is not a phenomenon restricted to two levels of brain processing one source and one site , but one which occurs across multiple levels of hierarchy.

According to the predictive coding model, visual processing can be described as an integration of top-down expectations and bottom-up stimulus information occurring across multiple levels within a hierarchical architecture Rao and Ballard, ; Friston, ; Friston and Kiebel, In this view, top-down expectancy biases are communicated through cortical feedback connections, while feedforward ones convey error signals which indicate the goodness-of-fit of predictions and incoming stimulus information, namely the difference residual error between top-down and bottom-up signals. While the importance of prediction errors in this framework will be described in the later sections, at this point it is important to notice that most levels within such a hierarchy can be considered as both sources and sites of predictions.

Furthermore, it is not always so that high-level associative areas have to be responsible for generating predictions about the incoming input. 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, ; Wolpert and Flanagan, ; Wolpert et al.

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, ; Wolpert et al. 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.

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, , 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, 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, The predictive process is initiated by a copy of the motor command, i. 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.

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, ; Iwanaga and Nittono, , 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, ; Adachi et al.

On a more general level, it has been demonstrated that internal models are, in essence, predictive Bays et al. 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, and subsequently evaluated through a comparison with the actual sensory input available after the movement Figure 3. Prediction in motor control. Although the internal model framework has originally been developed within the motor domain, it has in recent decades proved to be useful for explaining different phenomena well beyond this field.

For example, it was recognized rather early that one class of forward models can mimic or approximate some aspects of the environment using the collected sensory knowledge such as, e. In a similar fashion, initially motivated by findings implicating the motor system in some forms of perceptual processing Schubotz and von Cramon, a , b , c , , Schubotz and von Cramon suggested a joint, so-called sensorimotor forward model, account unifying the perceptual and motor domain. Such emulation is enabled by the creation of internal, sensorimotor or even amodal forward models which can be exploited for making predictions about future states of the modeled space, be that the body or the environment Schubotz, Although suggesting that the prediction of both internal and external events can be supported through highly comparable computations implemented within the motor system, this view does not automatically assume that the models supporting perceptual and motor processing should be completely identical.

While motor processing requires development of highly accurate and precise models Blakemore et al. In the previous sections different parts of the brain have been associated with predictive processing, specifically different sensory cortices, the thalamus, the prefrontal cortex and the motor system. These sections described only a subset of contexts which afford predictability, most of which were limited to short timescales. In addition, it is important to mention that a pivotal role in prediction on longer timescales can be associated with the prefrontal cortex which is, together with medial temporal regions, especially the hippocampus Eichenbaum and Fortin, ; Lisman and Redish, , and posterior cerebral cortices including the lateral parietal and temporal regions, the precuneus and the retrosplenial cortex , crucial for imagining the future as well as remembering the past Schacter et al.

However, although this region is also typically considered as the key region implicated in planning Fuster, , the contributions of the parietal cortices should also be acknowledged in this context Ruby et al. Furthermore, a more central role of lateral parietal, together with premotor regions can be posited for formulating temporal expectations Coull and Nobre, ; Coull, In addition, it is important to mention other brain regions which have been associated with predictive processing, e. Not questioning the validity of these or accounts previously specified, it is still important to note one danger which can be associated with considering all of these accounts together, without clearly specifying the type of predictive processing they refer to.

Specifically, if one was to try and summarize all brain areas which have so far been mentioned as incorporating some aspect of predictive processing, these would include: In other words, the whole brain. And, while it may be true that different aspects of prediction can be captured across the whole brain or nervous system itself, it does not imply that they share an equivalent role. It is important to note that this outline is simplified as it lacks both anatomical and functional specificity.

Nevertheless, it may be useful as an illustration and an invitation to further elaborate specific roles of the marked regions. In summary, there are different ways of conceptualizing and differentiating the role of different brain areas in prediction. One way is to differentiate between sources and sites of predictions, as shown in the example of perception. In this view, higher-level areas such as lateral, medial, orbital prefrontal and premotor regions could be considered as sources which formulate expectations and communicate them to lower-level, typically sensory areas.

However, as previously elaborated, this view can only be considered as a very rough simplification.

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Furthermore, it may be useful to consider numerous dimensions which have previously been discussed and suggested to be relevant in defining the nature of predictive phenomena. This includes, for example, the timescale of prediction, as some regions may be more relevant for short-term, e. In addition, the cognitive domain, e. An alternative way of determining which levels and types of predictions are associated with certain brain areas is to specify more holistic models.

Previously described predictive coding model can be viewed as such. Importantly, such general nature of brain processing can then account for many phenomena across domains and processes, e. An important aspect of this and other models of prediction relates to testing the validity of posited expectations by comparing them to the realized events. Potential outcomes of such testing will be described in the following section. Although the process of formulating expectations is interesting in its own right, it is also quite fascinating to consider what happens once the external event occurs, especially in cases where it does not meet the initial expectations.

In the previous sections it was suggested that expected stimuli matches are processed in a more efficient manner than the unexpected ones mismatches , as indicated by more accurate and faster reactions to these events. However, efficiency should not be confused with relevance or associated priority. On the contrary, given that these represent pure confirmations of correctly formulated expectations and signal correct learning, matches carry little informational value and are therefore not relevant for the system. Consequently, an expected event does not need to be explicitly represented or communicated to higher cortical areas which have processed all of its relevant features prior to its occurrence.

In contrast, errors of prediction have much greater value Friston and Stephan, , as they may signal unsuccessful learning, a major change in the surroundings or noise and smaller changes in the body or the environment, corresponding to normal plant or world drifts which typically occur over time Grush, For example, errors of prediction which are irrelevant for the current mental set or reflect noise in the environment can be registered and ignored, allowing the individual to reorient himself to the task at hand Escera et al. However, when these are relevant and informative, e. Therefore, the cost associated with processing these events may in the end turn to be beneficial, as it can lead to an adaptive reaction to the changing environment.

Such significance of deviant events for cognitive processing and behavior is reflected on the level of our nervous system which is highly sensitive to novel events, changes in the environment and other types of errors in prediction Corbetta and Shulman, ; Friston et al. Importantly, not only are novel or unexpected events preferentially detected, but also encoded, as demonstrated by the identified novelty advantage in memory Knight and Nakada, ; Kishiyama et al.

This may explain why prediction errors or the discrepancies between expected and realized events have been postulated as one of the main learning forces. Specifically, associative learning theories Rescorla and Wagner, ; Schultz et al. At the neuronal level, these discrepancies can be translated into changes in synaptic weights using specific learning computational rules, leading to changes in the model and subsequent more accurate predictions Wolpert et al. Neurons in different brain structures have been shown to code prediction errors stemming from different sources, e.

In addition to this direct link between prediction errors and learning, a somewhat more indirect one may be mediated through increased attentional resources being diverted towards the perceived prediction error Wills et al. On a somewhat different note, although it has previously been suggested that errors in behavior could be organized hierarchically Krigolson and Holroyd, , it is not clear what such hierarchical structure includes and whether different levels of hierarchy may somehow interact.

Interestingly, it has also been shown that the detection of semantic violations in language might be somehow restricted by the processing of syntactic structure Friederici et al. This line of research comparing and mutually relating different types and sources of errors will surely become more and more important in the future as it may reveal interesting and important insights about both regular and violated predictive processing within and across different contexts.

The question of how errors of prediction are processed online relates strongly to the general issue of the integration of top-down and bottom-up information which has been posited to rely on error-minimalization mechanisms Grossberg, ; Mumford, ; Ullman, ; Friston, ; Kveraga et al. According to the predictive view, expectations mediated through feedback connections represent top-down information which are compared and integrated with bottom-up signals communicated through feedforward connections, a process accomplished through specific synchronization patterns visible across different levels of the hierarchy Kveraga et al.

It has already been described that mismatches which are detected through such a comparison elicit more pronounced responses which get communicated to the next level in the hierarchy using feedforward connections. The size of such mismatches prediction error is suggested to reflect surprise which the brain tries to minimize in order to maintain present and future stability Friston and Stephan, In contrast, matches produce non-salient responses and their overall processing is suppressed.

In this view, postulated predictions act as a form of perceptual filter, as their accuracy determines which information is suppressed at an earlier processing stage match and what is communicated to a higher level mismatch. It has been suggested that this conceptualization may be incompatible with current theories of attention which posit an enhancement of stimulus-driven activity that it is consistent with top-down bias communicated through feedback connections Desimone and Duncan, ; Summerfield and Egner, However, it has recently been demonstrated that this may not be the case, as the predictive coding model can be considered mathematically equivalent with a particular form of biased competition model of attention Spratling, a , b.

It is plausible to expect that the near future will being a formulation of an unifying framework bridging seemingly contradictory attentional and predictive phenomena, given that both of these reflect comparable processing biases implemented within the same hierarchical brain architecture. An additional open issue concerns the differences in dynamics of processing events which confirm and violate previous expectations.

Although it was previously mentioned that more elaborate processing should follow the presentation of mismatches, Summerfield and Koechlin have suggested a more refined hypothesis according to which match-suppression should occur in lower-level hierarchical areas in contrast to match-enhancement which is to be expected in higher-level regions.

In accordance with this, these authors demonstrated how processing expected stimuli preferentially engages ventral prefrontal and orbitofrontal cortex Summerfield and Koechlin, However, the importance of these regions has previously been identified in the completely opposite context of detecting violations of expectations Nobre et al. In addition to identifying neural regions which preferentially process matches and mismatches, future research may benefit from investigating neural synchrony between relevant cortical regions across different levels of hierarchy Kveraga et al.

Clearly, much more research will be needed in order to clarify these issues. Even if this statement is too strong, the relevance of prediction in cognitive and neural processing can still not be overestimated. Prediction allows us to direct our behavior towards the future, while remaining well-grounded and guided by the information pertaining to the present and the past. Furthermore, predictive processing represents one of the key features of many cognitive functions and is mediated through a wide selection of mechanisms expressed in numerous cortical and subcortical levels.

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Such benefits are widely acknowledged and have in recent years been greatly investigated. However, although a lot is known about this type of processing, numerous open questions remain. Many of these can be identified with respect to each specific account or model incorporating or positing predictive mechanisms.

Even more importantly, it seems even more difficult to reconciliate manifold views and theories which emphasize the importance of different brain systems implicated in predictive processing or bring together findings stemming from different domains, especially those aimed at exploring expectations of various types, specificities and temporal structure. An even bigger challenge will include comparing and reconciling predictive with non-predictive mechanisms of brain and cognitive functioning: Bringing these together will be an important experimental and theoretical task for the future.


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The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors thank Heike Schmidt-Duderstedt for help with the figures. Event-related potentials elicited by unexpected visual stimuli after voluntary actions. Cortical control of motor sequences. Attenuated human auditory middle latency response and evoked Hz response to self-initiated sounds. Neural systems engaged by planning: Investigation of sequence processing: Visual objects in context.

Attenuation of self- generated tactile sensations is predictive, not postdictive. Dissociable networks for the expectancy and perception of emotional stimuli in the human brain. How do we predict the consequences of our actions? A functional imaging study.

Efference copy and its limitations. Neural aspects of anticipatory behavior. Learning the structure of event sequences. Attention and structure in sequence learning. Sequential learning in non-human primates. Neural systems for visual orienting and their relationships to spatial working memory. Control of goal-directed and stimulus-driven attention in the brain. Neural substrates of mounting temporal expectation. Dissociating explicit timing from temporal expectation with fMRI. A hierarchical neuronal network for planning behavior. Probabilistic word pre-activation during language comprehension inferred from electrical brain activity.

A dual role for prediction error in associative learning. Neural mechanisms of selective visual attention. Motion integration and postdiction in visual awareness. The neurobiology of memory based predictions. New evidence for prediction in human vision. Involuntary attention and distractibility as evaluated with event-related brain potentials.

Top-down facilitation of visual object recognition: Dynamic anticipatory processing of hierarchical sequential events: Computational constraints on syntactic processing in a nonhuman primate. Encoding touch and the orbitofrontal cortex. Sequential effects of syntactic and semantic information. The Developmental Psychology of Planning: Only 2 left in stock - order soon. Provide feedback about this page. There's a problem loading this menu right now. Get fast, free shipping with Amazon Prime. Get to Know Us. English Choose a language for shopping.

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How To Communicate Climate Change Uncertainty

AmazonGlobal Ship Orders Internationally. Amazon Inspire Digital Educational Resources. Amazon Rapids Fun stories for kids on the go. Particularly when talking about complex topics like global climate change, it is important to find effective ways to communicate inherently uncertain information. Too often discussions of climate science uncertainty convey the mistaken impression that scientists are hopelessly confused about this complicated subject, when in fact the uncertainties about exactly how much warmer the planet will be in years do not change the very high confidence scientists have that human-made emissions of greenhouse gases are warming the planet and are likely to continue doing so.

Although such terms have greatly permeated public discourse on climate change, there is evidence that suggests people interpret such probability descriptors more subjectively than scientists intend. Among other recommendations, the researchers suggested that the IPCC consider including the associated range of probabilities whenever a probability descriptor is used, rather than only publishing a key to the terminology. Climate change uncertainties vary in type and significance, and they are difficult to convey without seeming to minimize the importance or understanding of the issue.

One of the first key tasks for communicators is to put that uncertainty into context by helping audiences understand what is known with a high degree of confidence and what is relatively poorly understood. In particular, scientists found that the general public interprets certain common words differently than do the scientists who used them. Table 4 above shows a list of common words used to describe climate change that mean different things to scientists and the general public.

Such phrases can easily translate as unreliable climate science to the greater public. Using the word considerable to describe uncertainty creates a disparity in meaning between common language and science. This word is subject to varying interpretations. Most critically, communicators should suggest neither more, nor less scientific certainty about climate change than actually exists.

When significant uncertainty remains about a specific effect, they should explain why that uncertainty exists e. Cherry blossoms have begun to appear seven to ten days earlier in Michigan than they did three decades ago, leaving them susceptible to potentially devastating spring frosts. And because a cherry tree can take up to a decade to bear fruit and typically has only a year cycle of productivity, the farmers needed both extended and highly localized climate change information. A group of agricultural experts, economists, climate scientists, and others began working to bring these cherry growers and other stakeholders information about climate change on a very local level.

Instead these researchers needed to determine a wide range of climate scenarios for that region extending through the rest of the century. Further, they needed to communicate to the farmers their level of confidence per scenario. The farmers could then decide how to proceed, choosing to invest in wind machines or other frost protection, plant a hardier variety of cherry, switch to a different crop, or get out of farming altogether based on shifts in probability.

Their livelihood depends on making sound decisions using the best available, yet still uncertain, scientific information. It is also important to recognize and emphasize that scientific uncertainty alone is not an adequate justification for inaction or business-as-usual policies and behaviors.

Rather, it suggests that, at a minimum, it would be prudent to develop contingency plans and adopt adaptive management strategies. Governor Arnold Schwarzenegger of California referred to the principle with a metaphor when he said: We go with the majority, the large majority The precautionary principle is a key consideration for making decisions under uncertainty, and it is useful to address potential harms that are outside of the environmental arena as well, as the example above-left illustrates.

Over the last decade, CRED researchers have been working with African farmers to interpret climate forecasts for use in agricultural decisions.


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