Generating models of attentional cueing and inhibition of return with genetic programming

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@Article{Bartlett:2026:cogsys,

• author = "Laura K. Bartlett and Noman Javed and Dmitry Bennett and Peter C. R. Lane and Fernand Gobet",
• title = "Generating models of attentional cueing and inhibition of return with genetic programming",
• journal = "Cognitive Systems Research",
• year = "2026",
• volume = "95",
• pages = "101420",
• month = jan,
• keywords = "genetic algorithms, genetic programming, GEMS, Attention, Cueing, Inhibition of return",
• ISSN = "1389-0417",
• URL = "https://researchonline.lse.ac.uk/id/eprint/130161/",
• URL = "https://www.sciencedirect.com/science/article/pii/S1389041725000981",
• DOI = "10.1016/j.cogsys.2025.101420",
• size = "9 pages",
• abstract = "The cueing task is a robust experimental paradigm for investigating attention. A centrally presented valid cue, correctly indicating the location of an upcoming target stimulus, leads to quicker responses than an invalid cue. A feature of this paradigm is that increasing the delay between a peripheral cue and a target reverses this effect, where responses become slower for a valid cue, a phenomenon termed inhibition of return (IOR). Using GEMS, a system that uses genetic programming techniques, we generated potential strategies underlying the facilitation and IOR effects in the cueing paradigm. Models were generated for three experiments differing in their experimental designs, all with good fit to behavioural data. Our approach helps address current issues in the field of attention regarding how it is defined and what mechanisms underlie it. Additional benefits and limitations of this method are discussed.",
• notes = "also known as \cite{BARTLETT2026101420}",

}

Genetic Programming entries for Laura Bartlett Noman Javed Dmitry Bennett Peter C R Lane Fernand Gobet

Citations