Statistical learning is characterized by detection of regularities in one's environment without an awareness or intention to learn, and it may play a critical role in language and social behavior. Accordingly, in this study we investigated the electrophysiological correlates of visual statistical learning in young children with autism spectrum disorder (ASD) using an event‐related potential shape learning paradigm, and we examined the relation between visual statistical learning and cognitive function. Compared to typically developing (TD) controls, the ASD group as a whole showed reduced evidence of learning as defined by N1 (early visual discrimination) and P300 (attention to novelty) components. Upon further analysis, in the ASD group there was a positive correlation between N1 amplitude difference and non‐verbal IQ, and a positive correlation between P300 amplitude difference and adaptive social function. Children with ASD and a high non‐verbal IQ and high adaptive social function demonstrated a distinctive pattern of learning. This is the first study to identify electrophysiological markers of visual statistical learning in children with ASD. Through this work we have demonstrated heterogeneity in statistical learning in ASD that maps onto non‐verbal cognition and adaptive social function.

Ecologists increasingly use threshold elemental ratios (TERs) to explain and predict organism responses to altered resource C ∶ P or C ∶ N. TER calculations are grounded in diet-dependent growth, but growth data are limited for most taxa. Thus, TERs are derived instead from bioenergetics models that rely on simplifying assumptions, such as fixed organism C ∶ P and no P excretion at peak growth. We examined stoichiometric regulation of the stream insect detritivore Pycnopsyche lepida to assess bioenergetics model assumptions and compared bioenergetics TER C ∶ P estimates to those based on growth. We fed P. lepida maple and oak leaf diets along a dietary C ∶ P gradient (molar C ∶ P range = 950-4180) and measured consumption, growth, stoichiometric homeostasis (H), and elemental assimilation and growth efficiencies over a 5-wk period in the laboratory. Pycnopsyche lepida responses to varying resource C ∶ P depended on litter identity and were strongest among oak diets, on which growth peaked at diet C ∶ P = 1620. Pycnopsyche lepida fed oak litter exhibited flexible body C ∶ P during growth and in response to altered diet C ∶ P (non-strict homeostasis; H = 4.74), low P use efficiencies, and P excretion at peak growth. These trends violated common bioenergetics model assumptions and caused deviation of estimated TER C ∶ P from C ∶ P = 1620. Bioenergetics TER C ∶ P further varied among P. lepida of differing growth status on varying diet C ∶ P (overall TER C ∶ P range = 1030-9540). Our study identifies novel effects of nutrient enrichment and litter identity on detritivore stoichiometric regulation and supports growth-based approaches for future TER calculations.

No abstract

A key characteristic of host–parasite interactions is the theft of host nutrients by the parasite, yet we lack a general framework for understanding and predicting the interplay of host and parasite nutrition that applies across biological levels of organization. The elemental nutrients (C, N, P, Fe, etc.), and ecological stoichiometry provide a framework for understanding host–parasite interactions and their relation to ecosystem functioning. Here we use the ecological stoichiometry framework to develop hypotheses and predictions regarding the relationship between elemental nutrients and host–parasite interactions. We predict that a suite of host and parasite traits, stoichiometric homeostasis, host diet stoichiometry, and biogeochemical cycling are related to disease dynamics, host immunity and resistance, and bacterial growth form determination. We show that ecological stoichiometry is capable of expanding our understanding of host–parasite interactions, and complementing other approaches such as population and community ecology, and molecular biology, for studying infectious diseases.