Decision‐making Systems in Human Memory Modelling

Kybernetes ◽  
1990 ◽  
Vol 19 (5) ◽  
pp. 29-43 ◽  
Author(s):  
Simeon J. Mrchev
Keyword(s):  
Decision Making ◽  
Human Memory ◽  
Memory Modelling

Conditioning and Associative Learning

2021 ◽  
Author(s):  
Alice Mason ◽  
Elliot Andrew Ludvig ◽  
Christopher R Madan
Keyword(s):  
Decision Making ◽  
Associative Learning ◽  
Operant Conditioning ◽  
Paired Associate ◽  
Human Memory ◽  
Paired Associate Learning ◽  
Memory Process ◽  
Associate Learning ◽  
Episodic Memories ◽  
Predictive Relationship

Associative learning is the process whereby humans and other animals learn the predictive relationship between cues in their environment. This process underlies simple forms of learning from rewards, such as classical and operant conditioning. In this chapter, we introduce the basics of associative learning and discuss the role that memory processes play in the establishment and maintenance of this learning. We then discuss the role that associative learning plays in human memory, including through paired associate learning, the enhancement of memory by reward, and the formation of episodic memories. Finally, we illustrate how the memory process influences choice in decision-making, where associative learning allows people to learn the values of different options. We conclude with some suggestions about how models of associative learning, memory, and choice can be integrated into a single theoretical framework.

scholarly journals Self-protecting motivation, indexed by self-threat, modifies retrieval-induced-forgetting and confidence in employment decision bias against out-group targets

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Shaohang Lui ◽  
Christopher Kent ◽  
Josie Briscoe
Keyword(s):  
Decision Making ◽  
Retrieval Practice ◽  
Human Memory ◽  
Employment Decisions ◽  
Decision Bias ◽  
Employment Decision ◽  
Personal Strengths ◽  
Induced Forgetting ◽  
Recruitment Practice ◽  
Positive Traits

AbstractHuman memory is malleable by both social and motivational factors and holds information relevant to workplace decisions. Retrieval-induced forgetting (RIF) describes a phenomenon where retrieval practice impairs subsequent memory for related (unpracticed) information. We report two RIF experiments. Chinese participants received a mild self-threat manipulation (Experiment 2) or not (Experiment 1) before an ethnicity-RIF task that involved practicing negative traits of either in-group (Chinese) or an out-group (Japanese) target. After a subsequent memory test, participants selected their preferred applicant for employment. RIF scores correspond to forgetting of unpracticed positive traits of one target (Rp−) relative to the recall of practiced negative traits of the other target (Rp+). Enhanced forgetting of positive traits was found in both experiments for both targets. Across experiments, a significant target by threat interaction showed that target ethnicity modified RIF (an ethnicity-RIF effect). Inducing a self-protecting motivation enhanced RIF effects for the out-group (Japanese) target. In a subsequent employment decision, there was a strong bias to select the in-group target, with the confidence in these decisions being associated with RIF scores. This study suggests that rehearsing negative traits of minority applicants can affect metacognitive aspects of employment decisions, possibly by shaping the schemas available to the majority (in-group) employer. To disrupt systemic racism, recruitment practices should aim to offset a human motivation to protect one-self, when exposed to a relatively mild threat to self-esteem. Discussing the negative traits of minority applicants is a critical, and sensitive, aspect of decision-making that warrants careful practice. These data suggest that recruiting individuals should be reminded of their personal strengths in this context, not their vulnerabilities, to secure their decision-making for fairer recruitment practice.

A Human Memory Process Modeling

2020 ◽  
Vol 14 (2) ◽  
pp. 179-193
Author(s):  
Rahul Shrivastava ◽  
Prabhat Kumar ◽  
Sudhakar Tripathi
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Process Modeling ◽  
Sensory Information ◽  
Human Memory ◽  
Procedural Memory ◽  
Semantic Knowledge ◽  
Memory Process ◽  
Inference Mechanism ◽  
Proposed Model

Background: The cognitive models based agents proposed in the existing patents are not able to create knowledge by themselves. They also did not have the inference mechanism to take decisions and perform planning in novel situations. Objective: This patent proposes a method to mimic the human memory process for decision making. Methods: The proposed model simulates the functionality of episodic, semantic and procedural memory along with their interaction system. The sensory information activates the activity nodes which is a binding of concept and the sensory values. These activated activity nodes are captured by the episodic memory in the form of an event node. Each activity node has some participation strength in each event depending upon its involvement among other events. Recalling of events and frequent usage of some coactive activity nodes constitute the semantic knowledge in the form of associations between the activity nodes. The model also learns the actions in context to the activity nodes by using reinforcement learning. The proposed model uses an energy-based inference mechanism for planning and decision making. Results: The proposed model is validated by deploying it in a virtual war game agent and analysing the results. The obtained results show that the proposed model is significantly associated with all the biological findings and theories related to memories. Conclusion: The implementation of this model allows humanoid and game agents to take decisions and perform planning in novel situations.

Ion-Molecular Memory Model. Basic Notions. Types of Memory (review)

10.12737/2754 ◽  
2013 ◽  
Vol 20 (4) ◽  
pp. 165-170 ◽  
Author(s):  
Герасимов ◽  
I. Gerasimov ◽  
Яшин ◽  
A. Yashin
Keyword(s):  
Human Memory ◽  
Memory Model ◽  
Molecular Memory ◽  
Storage Devices ◽  
Literary Sources ◽  
History Of ◽  
Definition Of ◽  
Living Objects ◽  
Memory Modelling

The review presents the history of the known approaches, concepts and theories of memory, first of all the human, as properties perceive, save, retrieve and reproduce information important for life. The review is written with a specific aim designation: precedes the developed author´s concept of ion-molecular memory model. In the introduction, the authors note that it is reasonably consider memory as a property and the living and non-living objects. Definition of structural memory is presented. It is noted that the review is dedicated to the human memory as biological (according to I.P. Amsharin) - the supreme manifestation of the nature of bio-objects. The authors give a basic definition of the memory elements as information operand: receivers, analyzers, analytical systems, selectors, transmitters, storage devices, media, and library memory. Classification of types of memory as conceptual, oriented to the task research: creation of ion-molecular memory model is presented. As an example, the authors present the definition of the classification of memory on the parameter of time storage of the information. In the aspect of the review of the existing models of memory the authors identified three basic types which simulate associative (distributed) memory, the so-called working memory, i.e. operational situational memory, and other, different, memory models: from temporary to sensory memory. In conclusion, it is shown that in the memory modelling the authors used various mathematical and physical principles: neural networks, holography, fractals, and many sections of non-linear dynamics. The content of this review is based on the analysis of numerous literary sources.

scholarly journals A framework for computational models of human memory

2019 ◽  
Author(s):  
Matthew A Kelly ◽  
Robert West
Keyword(s):  
Data Structure ◽  
Knowledge Representation ◽  
Computational Models ◽  
Reaction Times ◽  
Learning Rule ◽  
Knowledge Structure ◽  
Human Memory ◽  
Memory Models ◽  
Representation Scheme ◽  
Memory Modelling

We present analysis of existing memory models, examining how models represent knowledge, structure memory, learn, make decisions, and predict reaction times. On the basis of this analysis, we propose a theoretical framework that characterizes memory modelling in terms of six key decisions: (1) choice of knowledge representation scheme, (2) choice of data structure, (3) choice of associative architecture, (4) choice of learning rule, (5) choice of time variant process, and (6) choice of response decision criteria. This framework is both descriptive and proscriptive: we intend to both describe the state of the literature and outline what we believe is the most fruitful space of possibilities for the development of future memory models.

Discarding optimality: Throwing out the baby with the bathwater?

2018 ◽  
Vol 41 ◽  
Author(s):  
Patrick Simen ◽  
Fuat Balcı
Keyword(s):  
Decision Making ◽  
Perceptual Decision Making ◽  
Reward Rate ◽  
Perceptual Decision ◽  
Standard Observer ◽  
Rate Maximization ◽  
Reward Rate Maximization ◽  
Descriptive Standard ◽  
Observer Model

AbstractRahnev & Denison (R&D) argue against normative theories and in favor of a more descriptive “standard observer model” of perceptual decision making. We agree with the authors in many respects, but we argue that optimality (specifically, reward-rate maximization) has proved demonstrably useful as a hypothesis, contrary to the authors’ claims.

LPCD framework: Analytical tool or psychological model?

2018 ◽  
Vol 41 ◽  
Author(s):  
David Danks
Keyword(s):  
Decision Making ◽  
Analytical Tool ◽  
Mathematical Framework ◽  
Bayesian Decision ◽  
Perceptual Decision Making ◽  
Psychological Model ◽  
Psychological Reality ◽  
Psychological Theories ◽  
Target Article ◽  
Bayesian Decision Making

AbstractThe target article uses a mathematical framework derived from Bayesian decision making to demonstrate suboptimal decision making but then attributes psychological reality to the framework components. Rahnev & Denison's (R&D) positive proposal thus risks ignoring plausible psychological theories that could implement complex perceptual decision making. We must be careful not to slide from success with an analytical tool to the reality of the tool components.

Elaborating the role of reflection and individual differences in the study of folk-economic beliefs

2018 ◽  
Vol 41 ◽  
Author(s):  
Kevin Arceneaux
Keyword(s):  
Decision Making ◽  
Individual Differences ◽  
Cognitive Processes ◽  
Evolutionary History ◽  
Behavioral Responses ◽  
History Of ◽  
Economic Beliefs

AbstractIntuitions guide decision-making, and looking to the evolutionary history of humans illuminates why some behavioral responses are more intuitive than others. Yet a place remains for cognitive processes to second-guess intuitive responses – that is, to be reflective – and individual differences abound in automatic, intuitive processing as well.

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