Mathematical transcription of the ‘time‐based resource sharing’ theory of working memory

The time-based resource sharing (TBRS) model is a prominent model of working memory that is both predictive and simple. TBRS is a mainstream decay-based model and the most susceptible to competition with interference-based models. A connectionist implementation of TBRS, TBRS*, has recently been developed. However, TBRS* is an enriched version of TBRS, making it difficult to test general characteristics resulting from TBRS assumptions. Here, we describe a novel model, TBRS2, built to be more transparent and simple than TBRS*. TBRS2 is minimalist and allows only a few parameters. It is a straightforward mathematical transcription of TBRS that focuses exclusively on the activation level of memory items as a function of time. Its simplicity makes it possible to derive several theorems from the original TBRS and allows several variants of the refresh process to be tested without relying on particular architectures.

[1]  Gregory L. Murphy Formal Approaches in Categorization: The contribution (and drawbacks) of models to the study of concepts , 2011 .

[2]  P. Barrouillet,et al.  On the law relating processing to storage in working memory. , 2011, Psychological review.

[3]  Dennis Norris,et al.  How do Computational Models Help us Develop Better Theories , 2017 .

[4]  Vinciane Gaillard,et al.  Working memory span development: a time-based resource-sharing model account. , 2009, Developmental psychology.

[5]  P. Barrouillet,et al.  The Time-Based Resource-Sharing Model of Working Memory , 2020, Working Memory.

[6]  F. Mathy,et al.  What’s magic about magic numbers? Chunking and data compression in short-term memory , 2012, Cognition.

[7]  Michael F. Bunting,et al.  Working memory span tasks: A methodological review and user’s guide , 2005, Psychonomic bulletin & review.

[8]  O. Kvalheim Latent Variable , 1992, The SAGE Encyclopedia of Research Design.

[9]  S. Lewandowsky,et al.  Computational Models as Aids to Better Reasoning in Psychology , 2010 .

[10]  Stephan Lewandowsky,et al.  Modeling working memory: a computational implementation of the Time-Based Resource-Sharing theory , 2011, Psychonomic bulletin & review.

[11]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[12]  P. Barrouillet,et al.  Visual and spatial working memory are not that dissociated after all: a time-based resource-sharing account. , 2009, Journal of experimental psychology. Learning, memory, and cognition.

[13]  Holger R. Maier,et al.  Recent Advances and Future Challenges for Artificial Neural Systems in Geotechnical Engineering Applications , 2009, Adv. Artif. Neural Syst..

[14]  J. Bolhuis,et al.  Exponential decay of spatial memory of rats in a radial maze. , 1986, Behavioral and neural biology.

[15]  Kenneth Hugdahl,et al.  A Standard Computerized Version of the Reading Span Test in Different Languages , 2008 .

[16]  Pierre Barrouillet,et al.  The Quarterly Journal of Experimental Psychology Phonological Similarity Effect in Complex Span Task , 2022 .

[17]  R. Engle,et al.  The role of prefrontal cortex in working-memory capacity, executive attention, and general fluid intelligence: An individual-differences perspective , 2002, Psychonomic bulletin & review.

[18]  G. A. Miller THE PSYCHOLOGICAL REVIEW THE MAGICAL NUMBER SEVEN, PLUS OR MINUS TWO: SOME LIMITS ON OUR CAPACITY FOR PROCESSING INFORMATION 1 , 1956 .

[19]  Randall W Engle,et al.  Working memory, short-term memory, and general fluid intelligence: a latent-variable approach. , 1999, Journal of experimental psychology. General.

[20]  David J. Therriault,et al.  A latent variable analysis of working memory capacity, short-term memory capacity, processing speed, and general fluid intelligence , 2002 .

[21]  P. Barrouillet,et al.  Time-related decay or interference-based forgetting in working memory? , 2008, Journal of experimental psychology. Learning, memory, and cognition.

[22]  R. Engle,et al.  Is working memory capacity task dependent , 1989 .

[23]  T. Braver,et al.  The Role of Frontopolar Cortex in Subgoal Processing during Working Memory , 2002, NeuroImage.

[24]  Stephan Lewandowsky,et al.  Turning simple span into complex span: Time for decay or interference from distractors? , 2010, Journal of experimental psychology. Learning, memory, and cognition.

[25]  Mariano Sigman,et al.  Frontiers in Computational Neuroscience Computational Neuroscience Neurophysiological Bases of Exponential Sensory Decay and Top-down Memory Retrieval: a Model , 2022 .

[26]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[27]  Richard P. Heitz,et al.  Complex working memory span tasks and higher-order cognition: A latent-variable analysis of the relationship between processing and storage , 2009, Memory.

[28]  A. Baddeley Working memory: looking back and looking forward , 2003, Nature Reviews Neuroscience.

[29]  H. Swanson,et al.  The Relationship Between Working Memory and Mathematical Problem Solving in Children at Risk and Not at Risk for Serious Math Difficulties , 2004 .

[30]  Wayne A. Wickelgren,et al.  Time, interference, and rate of presentation in short-term recognition memory for items , 1970 .

[31]  Thomas S. Redick,et al.  Measuring Working Memory Capacity With Automated Complex Span Tasks , 2012 .

[32]  P. Barrouillet,et al.  Time constraints and resource sharing in adults' working memory spans. , 2004, Journal of experimental psychology. General.

[33]  Sophie Portrat,et al.  Promoting the experimental dialogue between working memory and chunking: Behavioral data and simulation , 2015, Memory & Cognition.

[34]  P. Barrouillet,et al.  On the proper reading of the TBRS model: reply to Oberauer and Lewandowsky (2014) , 2014, Front. Psychol..

[35]  J. D. E. Gabrieli,et al.  Integration of diverse information in working memory within the frontal lobe , 2000, Nature Neuroscience.

[36]  I. J. Myung,et al.  When a good fit can be bad , 2002, Trends in Cognitive Sciences.