Modeling aspects of human memory for scientific study

Zhang model is developed through a series of assumptions and hypotheses by reverse engineering. The model intends to integrate everything we know about the human memory into a single memory model. It was first proposed in 2004, which posited the memory process and function of sleep. Based on this model, Zhang further hypothesized a continual activation theory, which explained several mysteries that were related with human brains: such as, dreaming, restless legs syndrome, schizophrenia and sudden infant death syndrome. In this article, the original Zhang model is revised and numerous new assumptions and hypotheses are proposed. As a result, the revised Zhang Model will cover many subjects, such as: attention, emotion, memory classification, working memory, temporary memory, learning, sleep, dream synthesis, amnesia and hallucination. This is a self-published paper. If you use this article in research, please use the following citation: Zhang, J. (2016): Towards a comprehensive model of human memory,

Journal of Mathematical Physics, 2022

Human memory is an incredibly complex system of vast capacity but often unreliable. Measuring memory for realistic material, such as narratives, is quantitatively challenging as people rarely remember narratives verbatim. Cognitive psychologists developed experimental paradigms involving randomly collected lists of items that make possible quantitative measures of performance in memory tasks, such as recall and recognition. Here, we describe a set of mathematical models designed to predict the results of these experiments. The models are based on simple underlying assumptions and surprisingly agree with experimental results quite well, in addition to that they exhibit quite interesting mathematical behavior that can partially be understood analytically.

Current Opinion in Neurobiology, 1999

2018

Understanding the links between brain and behavior is a central goal of computational cognitive neuroscience. We present a framework for simultaneous modeling of behavioral and neuroimaging data in the context of human memory acquisition and forgetting. Using a Hidden Markov Model of memory that can account for both behavioral and functional magnetic resonance imaging (fMRI) observations, we show that we can predict memory performance in held-out data at a level well-above chance and that we can surpass the predictions made by fMRI data alone as well as those made by variants of established behavioral models. This work highlights a path for better understanding the relationship between neural data and latent cognitive processes and advances a model of memory whose predictive ability could enable model-augmented learning environments.

2015

Models of human recognition memory frequently differentiate between processes of recollection, as the retrieval of qualitative information about a past event, and familiarity, as a more continuous process that matches a currently perceived stimulus against an accumulation of many similar past experiences. Cognitive models have made important advances in our understanding of memory functioning, but have at times struggled to account for some more nuanced empirical findings. Recently, computational models of the brain have provided an alternative, but complementary perspective by simulating networks of neurons that communicate with one another and learn to associate external events with internal patterns of activation. The research presented for this thesis combines behavioral and computational methodologies to investigate aspects of recognition memory and related cognitive domains that have to date been poorly understood. The current research is presented as four peer-reviewed papers...

New Memory Science, 2020

"There appears to be something mysterious about memory powers, failures and disparities that draw attention, compared to any of our other cognitive abilities." Jane Austen One thing we don't care to think over is our memory. We use our memory every day, but don't think about how we use it. Schools are requiring students to memorize multiplication tables, poems, birth dates, death of important people and what they did through their life, wars and many others. Every aspect of our daily behavior and life is affected in one way or another by our capabilities to remember past events and experiences. We need memory as a prerequisite for life, learning, and for self-protection. Without memory, we cannot face the present, or plan for the future. Example: Imagine a person with no ability to remember; If this person had been spoken to, he would not understand your words since the vocabulary of language and the meaning of words would be forgotten. Furthermore, if called by his name, he would not answer because would simply forget. If this person wakes up, he will not know how to wash his face or wear his clothes. Simple, everyday actions must be learned and stored in our memory and remembered when needed. This paper will discuss and explain what memory is, how we store information and recall it, experiments conducted on memory, and differences between remembering and recognition, short-and long-term memory...

1976

This dissertation, using the methodology of cognitive psychology, addressed several questions about the structure and process of human long-term memory (LTM). First, several arguments were presented for viewing LTM as a dynamic network structure. Within this framework, the major structural question addressed in this dissertation is whether there are isolable. LTM sub-structures. Several possible partitions of LTM were considered, and a specific multi layered LTM hypothesis was developed. An assumption of this hypothesis which was tested is that there are isolable. lexical (word) and semantic (concept) memories. Previous work relevant to this issue was reviewed. VThile associative retrieval is a natural type of processing in a network structure $ whether there are more complex, constructive, but still automatic retrieval processes (i.e. procedural retrieval) was the major processing question addressed in this dissertation. Two types of associative retrieval processes-intersection and generate-test-were described, and several notions about procedural retrieval were outlined. Previous experimental work addressed at related questions was reviewed. The approach used to address these issues was to require subjects to make simple timed responses to experimentally presented material. They retrieved one of several types of information about one of several words on each of several hundred randomly sequenced trials. A general process model was constructed which was assumed to reflect the flow of information processing required to complete the task, and reaction time (RT) data were used to analyze the retrieval stage. Of special interes

Annual Review of Psychology, 1997

We review current computational models of hippocampal function in learning and memory, concentrating on those that make strongest contact with psychological issues and behavioral data. Some models build upon Marr's early theories for modeling hippocampal field CA3's putative role in the fast, temporary storage of episodic memories. Other models focus on hippocampal involvement in incrementally learned associations, such as classical conditioning. More recent efforts have attempted to bring functional interpretations of the hippocampal region in closer contact with underlying anatomy and physiology. In reviewing these psychobiological models, three major themes emerge. First, computational models provide the conceptual glue to bind together data from multiple levels of analysis. Second, models serve as important tools to integrate data from both animal and human studies. Third, previous psychological models that capture important behavioral principles of memory provide an important top-down constraint for developing computational models of the neural bases of these behaviors.

Hippocampus, 1994

The authors draw together the results of a series of detailed computational studies and show how they are contributing to the development of a theory of hippocampal function. A new part of the theory introduced here is a quantitative analysis of how backprojections from the hippocampus to the neocortex could lead to the recall of recent memories. The theory is then compared with other theories of hippocampal function. First, what is computed by the hippocampus is considered. The hypothesis the authors advocate, on the basis of the effects of damage to the hippocampus and neuronal activity recorded in it, is that it is involved in the formation of new memories by acting as an intermediate-term buffer store for information about episodes, particularly for spatial, but probably also for some nonspatial, information. The authors analyze how the hippocampus could perform this function, by producing a computational theory of how it operates, based on neuroanatomical and neurophysiological information about the different neuronal systems contained within the hippocampus. Key hypotheses are that the CA3 pyramidal cells operate as a single autoassociation network to store new episodic information as it arrives via a number of specialized preprocessing stages from many association areas of the cerebral cortex, and that the dentate granule cell/mossy fiber system is important, particularly during learning, to help to produce a new pattern of firing in the CA3 cells for each episode. The computational analysis shows how many memories could be stored in the hippocampus and how quickly the CA3 autoassociation system would operate during recall. The analysis is then extended to show how the CA3 system could be used to recall a whole episodic memory when only a fragment of it is presented. It is shown how this recall could operate using modified synapses in backprojection pathways from the hippocampus to the cerebral neocortex, resulting in reinstatement of neuronal activity in association areas of the cerebral neocortex similar to that present during the original episode. The recalled information in the cerebral neocortex could then be used by the neocortex in the formation of long-term memories. 01994 Wiley-Liss, Inc.