Abstract: Foragers can often show a broad range of strategies when searching for resources. The simplest foraging strategy is to search randomly within a habitat; however, foragers can often assess habitat quality over various spatial scales and use this information to keep themselves in, or direct themselves to, regions of high resource abundance or low predation risk. We investigated models that describe a population of consumers competing for a renewable resource that is distributed among discrete patches. Our aim was to identify what foraging strategy or strategies are expected to persist within a population, where strategies differ in the degree of habitat assessment (i.e. none, local, or global). We were interested in how the optimal strategies are dependent on the cost of assessment and habitat structure (i.e. the variation in renewal rates and predation risks among patches). The models showed that the simple random foraging strategy (i.e. make no habitat assessments) often persisted even when the cost of habitat assessment was low. Persistence could occur when habitat assessment and population dynamics generated an ideal free distribution because it could be exploited by the random foragers. Habitat assessment was more advantageous when consumers could not achieve ideal free distributions, which was more likely as patches became less productive. When productivity was low we sometimes observed the situation where different foraging strategies generated resource heterogeneities that promoted their coexistence, and this could occur even when all patches were intrinsically identical.

Abstract: In search of the minimal requirements for transitive reasoning, a simple neural network was trained and tested on the non-verbal version of the conventional “five-term-series task” – a paradigm used with human adults, children and a variety of non-human species. The transitive performance of the network was analogous in several aspects to that reported for children and animals. The three effects usually associated with transitive choices i.e. “symbolic distance”, “lexical marking” and “end-anchor”, were also clearly shown by the neural network. In a second experiment, where the training conditions were manipulated, the network failed to match the behavioural pattern reported for human adults in the test following an ordered presentation of the premises. However, it mimicked young children's performance when tested with a novel comparison term. Although we do not intend to suggest a new model of transitive inference, we conclude, in line with other authors, that a simple error-correcting rule can generate transitive behaviour similar to the choice pattern of children and animals in the binary form of the five-term-series task without requiring high-order logical or paralogical abilities. The analysis of the training history and of the final internal structure of the network reveals the associative strategy employed. However, our results indicate that the scope of the associative strategy used by the network might be limited. The extent to which the conventional five-term-series task, in absence of appropriate manipulations of training and testing conditions, is suitable to detect cognitive differences across species is also discussed on the basis of our results.