Attention and distraction in the modular visual system of a jumping spider

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ABSTRACT

Animals must selectively attend to relevant stimuli and avoid being distracted by unimportant stimuli. Jumping spiders (Salticidae) do this by coordinating eyes with different capabilities. Objects are examined by a pair of high-acuity principal eyes, whose narrow field of view is compensated for by retinal movements. The principal eyes overlap in field of view with motion-sensitive anterior-lateral eyes (ALEs), which direct their gaze to new stimuli. Using a salticid-specific eyetracker, we monitored the gaze direction of the principal eyes as they examined a primary stimulus. We then presented a distractor stimulus visible only to the ALEs and observed whether the principal eyes reflexively shifted their gaze to it or whether this response was flexible. Whether spiders redirected their gaze to the distractor depended on properties of both the primary and distractor stimuli. This flexibility suggests that higher-order processing occurs in the management of the attention of the principal eyes.

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Fitting Linear Mixed-Effects Models Usinglme4

Steve Walker, Martin Mächler, Ben Bolker … (2022)

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A brief introduction to mixed effects modelling and multi-model inference in ecology

Xavier Harrison, Lynda Donaldson, Maria Correa-Cano … (2018)

The use of linear mixed effects models (LMMs) is increasingly common in the analysis of biological data. Whilst LMMs offer a flexible approach to modelling a broad range of data types, ecological data are often complex and require complex model structures, and the fitting and interpretation of such models is not always straightforward. The ability to achieve robust biological inference requires that practitioners know how and when to apply these tools. Here, we provide a general overview of current methods for the application of LMMs to biological data, and highlight the typical pitfalls that can be encountered in the statistical modelling process. We tackle several issues regarding methods of model selection, with particular reference to the use of information theory and multi-model inference in ecology. We offer practical solutions and direct the reader to key references that provide further technical detail for those seeking a deeper understanding. This overview should serve as a widely accessible code of best practice for applying LMMs to complex biological problems and model structures, and in doing so improve the robustness of conclusions drawn from studies investigating ecological and evolutionary questions.