You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Modeling language and cognition with deep unsupervised learning: a tutorial overview 
Chapter Title:  DISCUSSION 
1. Abstract of Cited Paper (BIB001):  Time underlies many interesting human behaviors. Thus, the question of how to represent time in connectionist models is very important. One approach is to represent time implicitly by its effects on processing rather than explicitly (as in a spatial representation). The current report develops a proposal along these lines first described by Jordan (1986) which involves the use of recurrent links in order to provide networks with a dynamic memory. In this approach, hidden unit patterns are fed back to themselves; the internal representations which develop thus reflect task demands in the context of prior internal states. A set of simulations is reported which range from relatively simple problems (temporal version of XOR) to discovering syntactic/semantic features for words. The networks are able to learn interesting internal representations which incorporate task demands with memory demands; indeed, in this approach the notion of memory is inextricably bound up with task processing. These representations reveal a rich structure, which allows them to be highly context-dependent while also expressing generalizations across classes of items. These representations suggest a method for representing lexical categories and the type/token distinction. 
2. Abstract of Cited Paper (BIB002):  For over 200 years, philosophers and mathematicians have be en using probability theory to describe human cognition. While the theory of prob abilities was first developed as a means of analyzing games of chance, it quickly took on a larger and deeper significance as a formal account of how rational agents should reason in situations of uncertainty (Gigerenzer et al., 1989; Hacking, 1975). Our goal in this ch apter is to illustrate the kinds of computational models of cognition that we can build if we assume that human learning and inference approximately follow the principles of Bayesian probabilistic inference, and to explain some of the mathematical ideas and techniques underlying those models 
3. Abstract of Cited Paper (BIB003):  We propose a non-linear generative model for human motion data that uses an undirected model with binary latent variables and real-valued “visible” variables that represent joint angles. The latent and visible variables at each time step receive directed connections from the visible variables at the last few time-steps. Such an architecture makes on-line inference efficient and allows us to use a simple approximate learning procedure. After training, the model finds a single set of parameters that simultaneously capture several different kinds of motion. We demonstrate the power of our approach by synthesizing various motion sequences and by performing on-line filling in of data lost during motion capture. 
4. Abstract of Cited Paper (BIB004):  The Temporal Restricted Boltzmann Machine (TRBM) is a probabilistic model for sequences that is able to successfully model (i.e., generate nice-looking samples of) several very high dimensional sequences, such as motion capture data and the pixels of low resolution videos of balls bouncing in a box. The major disadvantage of the TRBM is that exact inference is extremely hard, since even computing a Gibbs update for a single variable of the posterior is exponentially expensive. This difficulty has necessitated the use of a heuristic inference procedure, that nonetheless was accurate enough for successful learning. In this paper we introduce the Recurrent TRBM, which is a very slight modification of the TRBM for which exact inference is very easy and exact gradient learning is almost tractable. We demonstrate that the RTRBM is better than an analogous TRBM at generating motion capture and videos of bouncing balls. 
5. Abstract of Cited Paper (BIB005):  Multilayer Neural Networks trained with the backpropagation algorithm constitute the best example of a successful Gradient-Based Learning technique. Given an appropriate network architecture, Gradient-Based Learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional Neural Networks, that are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation, recognition, and language modeling. A new learning paradigm, called Graph Transformer Networks (GTN), allows such multi-module systems to be trained globally using Gradient-Based methods so as to minimize an overall performance measure. Two systems for on-line handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of Graph Transformer Networks. A Graph Transformer Network for reading bank check is also described. It uses Convolutional Neural Network character recognizers combined with global training techniques to provides record accuracy on business and personal checks. It is deployed commercially and reads several million checks per day. 
6. Abstract of Cited Paper (BIB006):  The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology - namely, Behaviorism and evolutionary psychology - that set aside mechanistic explanations or make use of optimality assumptions. Through these comparisons, we identify a number of challenges that limit the rational program's potential contribution to psychological theory. Specifically, rational Bayesian models are significantly unconstrained, both because they are uninformed by a wide range of process-level data and because their assumptions about the environment are generally not grounded in empirical measurement. The psychological implications of most Bayesian models are also unclear. Bayesian inference itself is conceptually trivial, but strong assumptions are often embedded in the hypothesis sets and the approximation algorithms used to derive model predictions, without a clear delineation between psychological commitments and implementational details. Comparing multiple Bayesian models of the same task is rare, as is the realization that many Bayesian models recapitulate existing (mechanistic level) theories. Despite the expressive power of current Bayesian models, we argue they must be developed in conjunction with mechanistic considerations to offer substantive explanations of cognition. We lay out several means for such an integration, which take into account the representations on which Bayesian inference operates, as well as the algorithms and heuristics that carry it out. We argue this unification will better facilitate lasting contributions to psychological theory, avoiding the pitfalls that have plagued previous theoretical movements.

Literature Review:
Understanding how cognition and language might emerge from neural computation is certainly one of the most exciting frontiers in cognitive neuroscience. In this tutorial overview we discussed a recent step forward in connectionist modeling, which allows the emergence of hierarchical representations in a deep neural network learning a generative model of the sensory data. We started by reviewing the theoretical foundations of deep learning, which rely on the framework of probabilistic graphical models to derive efficient inference and learning algorithms over hierarchically organized energy-based models. We then provided a step-by-step tutorial on how to practically perform a complete deep learning simulation, covering the main aspects related to the training, testing and analysis of deep belief networks. In our presentation we focused on examples that require the progressive extraction of abstract representations from sensory data and that are therefore representative of a wide range of cognitive processes. In particular, we showed how deep learning can be applied to the classic letter and word perception problem of  . In addition to providing a useful toy example of modeling based on deep learning, the emergent properties of the model revisit key aspects of the seminal IAM and suggest a very promising research direction for developing a full-blown deep learning model of visual word recognition. Indeed, up-scaling the present toy model is likely to be successful because deep learning is particularly suited to capture features hierarchies over large training datasets with great pattern variability. This aspect was present in two additional problems that complemented our tutorial with more realistic simulations, that is, handwritten digit recognition BIB006 and visual numerosity perception . Together, the various simulations illustrate the strength of the deep learning approach to cognitive modeling. Deep unsupervised learning extracts increasingly more abstract representations of the world, with the important consequence that explanatory factors behind the sensory data can be shared across tasks. The hierarchical architecture captures higher order structure of input data that might be invisible at the lower levels and it efficiently exploits features re-use. The idea that learned internal representations at the deepest layers can be easily "read-out" is consistent with the notion of "explicitness of information" articulated by BIB001 , who argued that explicitness is tightly related to the processing system which uses it. Within this perspective, the degree of explicitness is better linked to the usability of information rather than to its form (i.e., how quickly it can be accessed, retrieved or in some other manner put to use). This idea has been further extended by  , who proposed to take into account also the multi-track usability of stored information: "Truly explicit items of information should be usable in a wide variety of ways, that is, not restricted to use in a single task " (p. 198) . Note that this conception of abstract representations that can be shared across tasks or even across domains is particularly useful in the context of modeling language processing. Efficient generative learning in neural networks is a recent breakthrough in machine learning and its potential has yet to be fully unfolded. In particular, the extension of RBMs to the temporal domain BIB005 BIB004 ) is a very promising avenue for research. Indeed, generative networks that learn the temporal dynamics of the data could anticipate relevant events in the environment, using the history of the system as context to make accurate predictions about the incoming information, as proposed by the predictive coding framework   . Learning and processing of sequential information is also a key aspect of cognition and it is particularly ubiquitous in language processing BIB002 ). An initial exploration of this direction is the use of the Recurrent Temporal RBM BIB005 for learning orthographic structure from letter sequences (Testolin et al., 2012, submitted) . It is worth noting that deep generative network models of cognition can offer a unified theoretical framework that encompasses classic connectionism and the structured Bayesian approach to cognition. Structured Bayesian models of cognition (for reviews see  assume that human learning and inference approximately follow the principles of Bayesian probabilistic inference and they have been used in the last few years to address a number of issues in cognitive science, including language processing  , for review). However, Bayesian models are typically formulated at the level of "computational theory"  ) rather than at the process level that characterizes other cognitive modeling paradigms like connectionism (for further discussion see  BIB007 . This implies limits on the phenomena that can be studied with the Bayesian approach, because only problems of inductive inference or that contain an inductive component are naturally expressed in Bayesian terms BIB003 . In contrast, computational models of cognition based on deep neural networks and generative learning implement the probabilistic approach in a neural-like architecture and can provide an emergentist explanation of structured representations that is in line with the connectionist tradition . Their probabilistic formulation not only allows to deal with ambiguity of sensory input and with the intrinsic uncertainty of environmental dynamics, but it also provides a coherent theory about how learning can integrate new evidence to refine beliefs of the model. Importantly, there is no need to have an external signal that guides learning, because the aim is to reproduce incoming information as accurately as possible by discovering its hidden causes (that is, learning can be seen as a stochastic inference problem). In conclusion, we believe that the focus on deep architectures and generative learning represents a crucial step forward for the connectionist modeling enterprise, because it offers a more plausible model of cortical learning as well as way to bridge the gap between emergentist connectionist models and structured Bayesian models of cognition.