Rather than passively 'waiting' to be activated by sensations, it is proposed that the human brain is continuously busy generating predictions that approximate the relevant future. Building on previous work, this proposal posits that rudimentary information is extracted rapidly from the input to derive analogies linking that input with representations in memory. The linked stored representations then activate the associations that are relevant in the specific context, which provides focused predictions. These predictions facilitate perception and cognition by pre-sensitizing relevant representations. Predictions regarding complex information, such as those required in social interactions, integrate multiple analogies. This cognitive neuroscience framework can help explain a variety of phenomena, ranging from recognition to first impressions, and from the brain's 'default mode' to a host of mental disorders. General framework When we are immersed in the world of neuroscience findings, the brain might seem like a collection of many little modules, each expert in a specific task. Is it possible that, instead, one can account for much of the brain's operation using a small set of unifying principles? One such principle could be that the brain is proactive in that it regularly anticipates the future, a proposal that has been promoted in the past in different forms and contexts. Specifically, I propose that the cognitive brain relies on memory-based predictions, and these predictions are generated continually either based on gist information gleaned from the senses or driven by thought. The emphasis in this proposal is on the analogical link to memory and the role of associations in predictions, as well as on the idea that we use rudimentary information to generate these predictions efficiently. Furthermore, by developing this framework using a cognitive neuroscience approach and a minimalistic terminology, key concepts can directly be tested and used in empirical and theoretical future research. The proposed account integrates three primary components. The first is associations, which are formed by a lifetime of extracting repeating patterns and statistical regularities from our environment, and storing them in memory. The second is the concept of analogies, whereby we seek correspondence between a novel input and existing representations in memory (e.g. 'what does this look like?'). Finally, these analogies activate associated representations that translate into predictions Each of these key components -associations, analogies and predictions -has been the focus of rich and active research for a long time. By connecting these concepts in one unifying principle of memory-based predictions, the framework proposed here builds on this valuable background to emphasize the functional coherence between the three processes. To make the underlying mechanism more explicit, I will elaborate on each of the elements that mediate the generation of predictions. I will start with the proposal that the foundation of predictions is provided by the associative nature of memory organization. Associations as the building blocks of predictions How does our experience translate into focused, testable predictions? The answer proposed is that memory is used to generate predictions via associative activation. In memory, our experiences are represented in structures that cluster together related information. For example, objects that tend to appear together are linked on some level, and these representations include properties that are inherent to and typical of that same experience. Such structures have been termed 'context frames' Taken together, the associative nature of memory makes it possible to take advantage of frequent trends in the environment to help interpret and anticipate immediate and future events. One basis for this proposal is provided by the literature on priming, with its various types (e.g. perceptual, semantic and contextual). These studies support

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Natural objects provide partially redundant information to the brain through different sensory modalities. For example, voices and faces both give information about the speech content, age, and gender of a person. Thanks to this redundancy, multimodal recognition is fast, robust, and automatic. In unimodal perception, however, only part of the information about an object is available. Here, we addressed whether, even under conditions of unimodal sensory input, crossmodal neural circuits that have been shaped by previous associative learning become activated and underpin a performance benefit. We measured brain activity with functional magnetic resonance imaging before, while, and after participants learned to associate either sensory redundant stimuli, i.e. voices and faces, or arbitrary multimodal combinations, i.e. voices and written names, ring tones, and cell phones or brand names of these cell phones. After learning, participants were better at recognizing unimodal auditory voices that had been paired with faces than those paired with written names, and association of voices with faces resulted in an increased functional coupling between voice and face areas. No such effects were observed for ring tones that had been paired with cell phones or names. These findings demonstrate that brief exposure to ecologically valid and sensory redundant stimulus pairs, such as voices and faces, induces specific multisensory associations. Consistent with predictive coding theories, associative representations become thereafter available for unimodal perception and facilitate object recognition. These data suggest that for natural objects effective predictive signals can be generated across sensory systems and proceed by optimization of functional connectivity between specialized cortical sensory modules.

Dynamic causal modelling (DCM) has been applied recently to eventrelated responses (ERPs) measured with EEG/MEG. DCM attempts to explain ERPs using a network of interacting cortical sources and waveform differences in terms of coupling changes among sources. The aim of this work was to establish the validity of DCM by assessing its reproducibility across subjects. We used an oddball paradigm to elicit mismatch responses. Sources of cortical activity were modelled as equivalent current dipoles, using a biophysical informed spatiotemporal forward model that included connections among neuronal subpopulations in each source. Bayesian inversion provided estimates of changes in coupling among sources and the marginal likelihood of each model. By specifying different connectivity models we were able to evaluate three different hypotheses: differences in the ERPs to rare and frequent events are mediated by changes in forward connections (F-model), backward connections (B-model) or both (FB-model). The results were remarkably consistent over subjects. In all but one subject, the forward model was better than the backward model. This is an important result because these models have the same number of parameters (i.e., the complexity). Furthermore, the FB-model was significantly better than both, in 7 out of 11 subjects. This is another important result because it shows that a more complex model (that can fit the data more accurately) is not necessarily the most likely model. At the group level the FB-model supervened. We discuss these findings in terms of the validity and usefulness of DCM in characterising EEG/ MEG data and its ability to model ERPs in a mechanistic fashion. © 2007 Elsevier Inc. All rights reserved.

Comparative evaluation of two applications for

Attention acts, through cortical feedback pathways, to enhance the response of cells encoding expected or predicted information. Such observations are inconsistent with the predictive coding theory of cortical function which proposes that feedback acts to suppress information predicted by higher-level cortical regions. Despite this discrepancy, this article demonstrates that the predictive coding model can be used to simulate a number of the effects of attention. This is achieved via a simple mathematical rearrangement of the predictive coding model, which allows it to be interpreted as a form of biased competition model. Nonlinear extensions to the model are proposed that enable it to explain a wider range of data. Keywords: neural networks; cortical circuits; cortical feedback; attention; binding problem 1

Both perceptual inference and motor responses are shaped by learned probabilities. For example, stimulus-induced responses in sensory cortices and preparatory activity in premotor cortex reflect how (un)expected a stimulus is. This is in accordance with predictive coding accounts of brain function, which posit a fundamental role of prediction errors for learning and adaptive behavior. We used functional magnetic resonance imaging and recent advances in computational modeling to investigate how (failures of) learned predictions about visual stimuli influence subsequent motor responses. Healthy volunteers discriminated visual stimuli that were differentially predicted by auditory cues. Critically, the predictive strengths of cues varied over time, requiring subjects to continuously update estimates of stimulus probabilities. This online inference, modeled using a hierarchical Bayesian learner, was reflected behaviorally: speed and accuracy of motor responses increased significantly with predictability of the stimuli. We used nonlinear dynamic causal modeling to demonstrate that striatal prediction errors are used to tune functional coupling in cortical networks during learning. Specifically, the degree of striatal trial-by-trial prediction error activity controls the efficacy of visuomotor connectionsandthustheinfluenceofsurprisingstimulionpremotoractivity.Thisfindingsubstantiallyadvancesourunderstandingofstriatalfunction and provides direct empirical evidence for formal learning theories that posit a central role for prediction error-dependent plasticity.