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Mimicry has evolved in a wide range of organisms and encompasses diverse tactics for defence, foraging, pollination and social parasitism. Here, I report an extraordinary case of egg mimicry by a fungus, whereby the fungus gains competitor-free habitat in termite nests. Brown fungal balls, called 'termite balls', are frequently found in egg piles of Reticulitermes termites. Phylogenetic analysis illustrated that termite-ball fungi isolated from different hosts (Reticulitermes speratus, Reticulitermes flavipes and Reticulitermes virginicus) were all very similar, with no significant molecular differences among host species or geographical locations. I found no significant effect of termite balls on egg survivorship. The termite-ball fungus rarely kills termite eggs in natural colonies. Even a termite species (Reticulitermes okinawanus) with no natural association with the fungus tended termite balls along with its eggs when it was experimentally provided with termite balls. Dummy-egg bioassays using glass beads showed that both morphological and chemical camouflage were necessary to induce tending by termites. Termites almost exclusively tended termite balls with diameters that exactly matched their egg size. Moreover, scanning electron microscopic observations revealed sophisticated mimicry of the smooth surface texture of eggs. These results provide clear evidence that this interaction is beneficial only for the fungus, i.e. termite balls parasitically mimic termite eggs.
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PMID:Termite-egg mimicry by a sclerotium-forming fungus. 1672 Mar 92

Two similar classes of evidence-accumulation model have dominated theorizing about rapid binary choice: diffusion models and racing accumulator pairs. Donkin, Brown, Heathcote, and Wagenmakers (2011) examined mimicry between the Ratcliff diffusion (RD; Ratcliff & Smith, 2004) and the linear ballistic accumulator (LBA; Brown & Heathcote, 2008), the 2 least similar models from each class that provide a comprehensive account of a set benchmark phenomena in rapid binary choice. Where conditions differed only in the rate of evidence accumulation (the most common case in past research), simulations showed the models supported equivalent psychological inferences. In contrast, differences in 2 other parameters of key psychological interest, response caution (the amount of information required for a decision), and nondecision time, traded-off when fitting 1 model to data simulated from the other, implying the potential for divergent inferences about latent cognitive processes. However, Donkin, Brown, Heathcote, and Wagenmakers did not find such inconsistencies between fits of the RD and LBA models in a survey of data sets from paradigms using a range of experimental manipulations. We examined a further data set, collected by Dutilh, Vandekerckhove, Tuerlinckx, and Wagenmakers (2009), which used a manipulation not surveyed by Donkin, Brown, Heathcote, and Wagenmakers's practice. Dutilh et al.'s RD model fits indicated that practice had large effects on all three types of parameters. We show that in this case the LBA provides a different and simpler account of practice effects. Implications for evidence accumulation modelling are discussed.
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PMID:Diffusion versus linear ballistic accumulation: different models for response time with different conclusions about psychological mechanisms? 2268 61

Jones and Dzhafarov (2014) proved the linear ballistic accumulator (LBA) and diffusion model (DM) of speeded choice become unfalsifiable if 2 assumptions are removed: that growth rate variability between trials follows a Gaussian distribution and that this distribution is invariant under certain experimental manipulations. The former assumption is purely technical and has never been claimed as a theoretical commitment, and the latter is logically and empirically suspect. Heathcote, Wagenmakers, and Brown (2014) questioned the distinction between theoretical and technical assumptions and argued that only the predictions of the whole model matter. We respond that it is valuable to understand how a model's predictions depend on each of its assumptions to know what is critical to an explanation and to generalize principles across phenomena or domains. Smith, Ratcliff, and McKoon (2014) claimed unfalsifiability of the generalized DM relies on parameterizations with negligible diffusion and proposed a theoretical commitment to simple growth-rate distributions. We respond that a lower bound on diffusion would be a new, ad hoc assumption, and restrictions on growth-rate distributions are only theoretically justified if one supplies a model of what determines growth-rate variability. Finally, we summarize a simulation of the DM that retains the growth-rate invariance assumption, requires the growth-rate distribution to be unimodal, and maintains a contribution of diffusion as large as in past fits of the standard model. The simulation demonstrates mimicry between models with different theoretical assumptions, showing the problems of excess flexibility are not limited to the cases to which Smith et al. objected. (PsycINFO Database Record (c) 2014 APA, all rights reserved).
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PMID:Analyzability, ad hoc restrictions, and excessive flexibility of evidence-accumulation models: reply to two critical commentaries. 2534 15