In the target article, Sue Llewellyn proposes that rapid eye movement (REM) dreaming is able to encode new episodic memories through several cognitive processes that enhance the likelihood of information retrieval by providing strong associations with other remote and emotionally salient memories.
After such presentation, one great question arises: Is there any biological basis to support that the proposed connective patterns actually occur in a deeper physiological level at the neural networks involved in long-term memory encoding and dreaming (such as the mesial temporal structures and the limbic networks), or is this just another purely speculative description (according to Llewellyn, based on the ancient art of memory [AAOM] principles) about some possible structural pattern in the relationships between remote emotionally salient information, the phenomenology of dreaming, and the acquisition of new memories? The answer to such concern is actually one of the most decisive factors in predicting the long-term implications of any theoretical model in psychology. For example, very few people would deny that Freud's id/ego/superego scheme of the human psyche represented a relatively rational description of the relations between human desires, fears, and personal decisions (De Sousa Reference de Sousa2011). Nevertheless, the fact that the topological structure of Freud's theoretical model bore no resemblance to the actual hierarchical structure of the underlying biological systems at any level rendered such a scheme a simple speculative description of the observed psychological phenomena with limited scientific applications.
Interestingly, a short appraisal about the contemporary understanding of the relationships between structure and function in the human brain (Bressler & Tognoli Reference Bressler and Tognoli2006; Damoiseaux & Greicius Reference Damoiseaux and Greicius2009; Horwitz & Braun Reference Horwitz and Braun2004; McIntosh Reference McIntosh2000) reveals that the vast majority of the current neuroscience literature has departed from a static localizationist approach (Berker et al. Reference Berker, Berker and Smith1986; Von Economo Reference Von Economo1930; Wernicke Reference Wernicke1970), in which each different eloquent area of the brain is deemed to be responsible for a specific function (a paradigm clearly illustrated by the classic Brodmann's cortical maps; Pearce Reference Pearce2005), to a dynamic connectionist approach (McClelland et al. Reference McClelland, Botvinick, Noelle, Plaut, Rogers, Seidenberg and Smith2010), in which actual information is not spatially located at specific brain regions but rather can be traced to specific patterns of connections among distant clusters of neurons (Seung Reference Seung2009). In fact, Llewellyn's proposal closely follows the current hodotopic model of brain functions (De Benedictis & Duffau Reference de Benedictis and Duffau2011), according to which the human brain would operate based on the activity of a plastic network of cortical functional epicenters (topical organization) connected by both short-local and large-scale white-matter fibers (hodological organization). In such a framework, not only memories, but a variety of other higher cognitive functions (such as language, attention, memory, and decision making), would emerge from the dynamic interaction between parallel streams of information flowing between highly interconnected neuronal clusters (Litwin-Kumar & Doiron Reference Litwin-Kumar and Doiron2012) organized in a widely distributed circuit modulated by key central nodes. Such parallel processing and local recurrent activity would, therefore, give rise to neuroplasticity and enable the encoding of new information as the overall patterns in the strength of the intrinsic connections of such network change over time (Polack & Contreras Reference Polack and Contreras2012; Turrigiano & Nelson Reference Turrigiano and Nelson2004). This new paradigm for understanding brain functions has led to an amazing and challenging mapping task (the so-called Human Connectome Project), which compares in complexity to (and, according to some authors, even exceeds) that of mapping the human genome (Sporns Reference Sporns2011b; Toga et al. Reference Toga, Clark, Thompson, Shattuck and van Horn2012).
From a theoretical standpoint, modeling this type of information processing has required a new set of mathematical and conceptual tools that involve fuzzy logic and probabilistic outcomes (Brainerd & Reyna Reference Brainerd and Reyna2001) (instead of the classic Boolean logic with its two-valued deterministic outcomes), as well as nonlinear (chaotic) dynamic systems and stochastic processes (Afraimovich et al. Reference Afraimovich, Young, Muezzinoglu and Rabinovich2011), instead of classic linear functions. According to such models, the complexity of higher cognitive functions would emerge not by data processing involving hierarchical trees of propositional calculus (with a branching trend of information from specific to general categories in progressive logical order), but rather by a comparative pattern analysis of the different features of the sensorial input performed by parallel, distributed, and interconnected networks (McClelland & Rogers Reference McClelland and Rogers2003). It has already been shown that nonlinear (chaotic) dynamics can be successfully used to describe, represent, and model several cognitive and neural functions (Korn & Faure Reference Korn and Faure2003), such as neurons' single-cell firing patterns (Huber et al. Reference Huber, Krieg, Dewald and Braun2000), neural network synchronization (Elbert et al. Reference Elbert, Ray, Kowalik, Skinner, Graf and Birbaumer1994), autonomic nervous system response to systemic physiological stimuli (Magrans et al. Reference Magrans, Gomis, Caminal and Wagner2010), electroencephalographic analysis (Abásolo et al. Reference Abásolo, James and Hornero2007), synchronic pattern and noise modulation in adaptive motor control in the cerebellum (Tokuda et al. Reference Tokuda, Han, Aihara, Kawato and Schweighofer2010), and even higher cognitive processes (Aiello Reference Aiello2012) and complex psychiatric disorders (Uhlhaas & Singer Reference Uhlhaas and Singer2012).
By combining the classic psychological AAOM principles (visualization, bizarre association, organization, narration, embodiment, and location) with the most recent findings in the neuroscience of memory and emotions, Llewellyn has demonstrated that, unlike the Freudian psyche model, such principles are solidly grounded on the neurobiology of memory and dreaming. The advantages of such compatible framework go far beyond the simple desire for interdisciplinary uniformity regarding the conceptual structures (as well as an universally accepted nomenclature) employed in the study of dreams and memory by both psychology and neuroscience. Indeed, if a psychological model survives such a compatibility test, that means its theoretical structure is universally valid and can, therefore, be successfully applied also to the formulation of scoring systems that can then be used in very practical clinical studies involving, for example, frontline neurosurgical interventional trials for a wide range of neurological conditions affecting memory (such as Alzheimer's disease and other forms of dementia; Laxton & Lozano Reference Laxton and Lozano2012; Laxton et al. Reference Laxton, Tang-Wai, McAndrews, Zumsteg, Wennberg, Keren, Wherrett, Naglie, Hamani, Smith and Lozano2010).
In summary, there is nothing new in stating the apparently obvious fact (which has been clearly noticed and properly described since early antiquity; Harrisson Reference Harrisson2010) that there seems to be a close connection between human dreams, imagination, and memory. The great trump and uniqueness of Llewellyn's article is having analyzed such a close relationship on the basis of two leading-edge paradigms in neuroscience: the concepts of hodotopic organization and the nonlinear dynamics of brain neural networks.
In the target article, Sue Llewellyn proposes that rapid eye movement (REM) dreaming is able to encode new episodic memories through several cognitive processes that enhance the likelihood of information retrieval by providing strong associations with other remote and emotionally salient memories.
After such presentation, one great question arises: Is there any biological basis to support that the proposed connective patterns actually occur in a deeper physiological level at the neural networks involved in long-term memory encoding and dreaming (such as the mesial temporal structures and the limbic networks), or is this just another purely speculative description (according to Llewellyn, based on the ancient art of memory [AAOM] principles) about some possible structural pattern in the relationships between remote emotionally salient information, the phenomenology of dreaming, and the acquisition of new memories? The answer to such concern is actually one of the most decisive factors in predicting the long-term implications of any theoretical model in psychology. For example, very few people would deny that Freud's id/ego/superego scheme of the human psyche represented a relatively rational description of the relations between human desires, fears, and personal decisions (De Sousa Reference de Sousa2011). Nevertheless, the fact that the topological structure of Freud's theoretical model bore no resemblance to the actual hierarchical structure of the underlying biological systems at any level rendered such a scheme a simple speculative description of the observed psychological phenomena with limited scientific applications.
Interestingly, a short appraisal about the contemporary understanding of the relationships between structure and function in the human brain (Bressler & Tognoli Reference Bressler and Tognoli2006; Damoiseaux & Greicius Reference Damoiseaux and Greicius2009; Horwitz & Braun Reference Horwitz and Braun2004; McIntosh Reference McIntosh2000) reveals that the vast majority of the current neuroscience literature has departed from a static localizationist approach (Berker et al. Reference Berker, Berker and Smith1986; Von Economo Reference Von Economo1930; Wernicke Reference Wernicke1970), in which each different eloquent area of the brain is deemed to be responsible for a specific function (a paradigm clearly illustrated by the classic Brodmann's cortical maps; Pearce Reference Pearce2005), to a dynamic connectionist approach (McClelland et al. Reference McClelland, Botvinick, Noelle, Plaut, Rogers, Seidenberg and Smith2010), in which actual information is not spatially located at specific brain regions but rather can be traced to specific patterns of connections among distant clusters of neurons (Seung Reference Seung2009). In fact, Llewellyn's proposal closely follows the current hodotopic model of brain functions (De Benedictis & Duffau Reference de Benedictis and Duffau2011), according to which the human brain would operate based on the activity of a plastic network of cortical functional epicenters (topical organization) connected by both short-local and large-scale white-matter fibers (hodological organization). In such a framework, not only memories, but a variety of other higher cognitive functions (such as language, attention, memory, and decision making), would emerge from the dynamic interaction between parallel streams of information flowing between highly interconnected neuronal clusters (Litwin-Kumar & Doiron Reference Litwin-Kumar and Doiron2012) organized in a widely distributed circuit modulated by key central nodes. Such parallel processing and local recurrent activity would, therefore, give rise to neuroplasticity and enable the encoding of new information as the overall patterns in the strength of the intrinsic connections of such network change over time (Polack & Contreras Reference Polack and Contreras2012; Turrigiano & Nelson Reference Turrigiano and Nelson2004). This new paradigm for understanding brain functions has led to an amazing and challenging mapping task (the so-called Human Connectome Project), which compares in complexity to (and, according to some authors, even exceeds) that of mapping the human genome (Sporns Reference Sporns2011b; Toga et al. Reference Toga, Clark, Thompson, Shattuck and van Horn2012).
From a theoretical standpoint, modeling this type of information processing has required a new set of mathematical and conceptual tools that involve fuzzy logic and probabilistic outcomes (Brainerd & Reyna Reference Brainerd and Reyna2001) (instead of the classic Boolean logic with its two-valued deterministic outcomes), as well as nonlinear (chaotic) dynamic systems and stochastic processes (Afraimovich et al. Reference Afraimovich, Young, Muezzinoglu and Rabinovich2011), instead of classic linear functions. According to such models, the complexity of higher cognitive functions would emerge not by data processing involving hierarchical trees of propositional calculus (with a branching trend of information from specific to general categories in progressive logical order), but rather by a comparative pattern analysis of the different features of the sensorial input performed by parallel, distributed, and interconnected networks (McClelland & Rogers Reference McClelland and Rogers2003). It has already been shown that nonlinear (chaotic) dynamics can be successfully used to describe, represent, and model several cognitive and neural functions (Korn & Faure Reference Korn and Faure2003), such as neurons' single-cell firing patterns (Huber et al. Reference Huber, Krieg, Dewald and Braun2000), neural network synchronization (Elbert et al. Reference Elbert, Ray, Kowalik, Skinner, Graf and Birbaumer1994), autonomic nervous system response to systemic physiological stimuli (Magrans et al. Reference Magrans, Gomis, Caminal and Wagner2010), electroencephalographic analysis (Abásolo et al. Reference Abásolo, James and Hornero2007), synchronic pattern and noise modulation in adaptive motor control in the cerebellum (Tokuda et al. Reference Tokuda, Han, Aihara, Kawato and Schweighofer2010), and even higher cognitive processes (Aiello Reference Aiello2012) and complex psychiatric disorders (Uhlhaas & Singer Reference Uhlhaas and Singer2012).
By combining the classic psychological AAOM principles (visualization, bizarre association, organization, narration, embodiment, and location) with the most recent findings in the neuroscience of memory and emotions, Llewellyn has demonstrated that, unlike the Freudian psyche model, such principles are solidly grounded on the neurobiology of memory and dreaming. The advantages of such compatible framework go far beyond the simple desire for interdisciplinary uniformity regarding the conceptual structures (as well as an universally accepted nomenclature) employed in the study of dreams and memory by both psychology and neuroscience. Indeed, if a psychological model survives such a compatibility test, that means its theoretical structure is universally valid and can, therefore, be successfully applied also to the formulation of scoring systems that can then be used in very practical clinical studies involving, for example, frontline neurosurgical interventional trials for a wide range of neurological conditions affecting memory (such as Alzheimer's disease and other forms of dementia; Laxton & Lozano Reference Laxton and Lozano2012; Laxton et al. Reference Laxton, Tang-Wai, McAndrews, Zumsteg, Wennberg, Keren, Wherrett, Naglie, Hamani, Smith and Lozano2010).
In summary, there is nothing new in stating the apparently obvious fact (which has been clearly noticed and properly described since early antiquity; Harrisson Reference Harrisson2010) that there seems to be a close connection between human dreams, imagination, and memory. The great trump and uniqueness of Llewellyn's article is having analyzed such a close relationship on the basis of two leading-edge paradigms in neuroscience: the concepts of hodotopic organization and the nonlinear dynamics of brain neural networks.