Two categorization arguments pose particular problems for localist connectionist models. The internal representations of localist networks do not reflect the variability within categories in the environment, whereas networks with distributed internal representations do reflect this essential feature of categories. We provide a real biological example of perceptual categorization in the monkey that seems to require population coding (i.e., distributed internal representations).

As endemic measles is eliminated through immunization, countries must determine the risk factors for the importation of measles into highly immunized populations to target control measures. Despite eliminating endemic measles, New Zealand suffers from outbreaks after introductions from abroad, enabling us to use it as a model for measles introduction risk. We used a generalized linear model to analyze risk factors for 1137 measles cases from 2007 to June 2014, provide estimates of national immunity levels, and model measles importation risk. People of European ethnicity made up the majority of measles cases. Age is a positive risk factor, particularly 0–2-year-olds and 5–17-year-old Europeans, along with increased wealth. Pacific islanders were also at greater risk, but due to 0–2-year-old cases. Despite recent high measles, mumps, and rubella vaccine immunization coverage, overall population immunity against measles remains ~90% and is lower in people born between 1982 and 2005. Greatest measles importation risk is during December, and countries predicted to be sources have historical connections and highest travel rates (Australia and UK), followed by Asian countries with high travel rates and higher measles incidences. Our results suggest measles importation due to travel is seeding measles outbreaks, and immunization levels are insufficient to continue to prevent outbreaks because of heterogeneous immunity in the population, leaving particular age groups at risk.

Relational priming is argued to be a deeply inadequate model of analogy-making because of its intrinsic inability to do analogies where the base and target domains share no common attributes and the mapped relations are different. Leech et al. rely on carefully handcrafted representations to allow their model to make a complex analogy, seemingly unaware of the debate on this issue fifteen years ago. Finally, they incorrectly assume the existence of fixed, context-independent relations between objects.

This book is an excellent manifesto for future work in child development. It presents a multidisciplinary approach that clearly demonstrates the value of integrating modeling, neuroscience, and behavior to explore the mechanisms underlying development and to show how internal context-dependent representations arise and are modified during development. Its only major flaw is to have given short shrift to the study of the role of genetics on development.

Natura non facit saltum (Nature does not make leaps) was the lovely aphorism on which Darwin based his work on evolution. It applies as much to the formation of mental representations as to the formation of species, and therein lies our major disagreement with the SOC model proposed by Perruchet & Vinter.

Dijkstra and van Heuven have made an admirable attempt to develop a new model of bilingual memory, the BIA+. Their article presents a clear and well-reasoned theoretical justification of their model, followed by a description of their model. The BIA+ is, as the name implies, an extension of the Bilingual Interactive Activation (BIA) model (Dijkstra and van Heuven, 1998; Van Heuven, Dijkstra and Grainger, 1998; etc), which was itself an adaptation to bilingual memory of McClelland and Rumelhart's (1981) Interactive Activation model of monolingual memory.

What new implications does the dynamical hypothesis have for cognitive science? The short answer is: none. The target article is basically an attack on traditional symbolic artificial intelligence (AI) and differs very little from prior connectionist criticisms of it. For the past 10 years, the connectionist community has been well aware of the necessity of using (and understanding) dynamically evolving, recurrent network models of cognition.

The fixed-feature viewpoint Schyns et al. are opposing is not a widely held theoretical position but rather a working assumption of cognitive psychologists – and thus a straw man. We accept their demonstration of new-feature acquisition, but question its ubiquity in category learning. We suggest that new-feature learning (at least in adults) is rarer and more difficult than the authors suggest.