Currently, there is widespread skepticism that higher cognitive processes, given their apparent flexibility and globality, could be carried out by specialized computational devices, or modules. This skepticism is largely due to Fodor's influential definition of modularity. From the rather flexible catalogue of possible modular features that Fodor originally proposed has emerged a widely held notion of modules as rigid, informationally encapsulated devices that accept highly local inputs and whose operations are insensitive to context. It is a mistake, however, to equate such features with computational devices in general and therefore to assume, as Fodor does, that higher cognitive processes must be non-computational. Of the many possible non-Fodorean architectures, one is explored here that offers possible solutions to computational problems faced by conventional modular systems: an 'enzymatic' architecture. Enzymes are computational devices that use lock-and-key template matching to identify relevant information (substrates), which is then operated upon and returned to a common pool for possible processing by other devices. Highly specialized enzymes can operate together in a common pool of information that is not pre-sorted by information type. Moreover, enzymes can use molecular 'tags' to regulate the operations of other devices and to change how particular substrates are construed and operated upon, allowing for highly interactive, context-specific processing. This model shows how specialized, modular processing can occur in an open system, and suggests that skepticism about modularity may largely be due to failure to consider alternatives to the standard model.

Information about environmental dangers is valuable and the cost of transmitting such information to social partners is minimal. Furthermore, an information transmitter who helps a social partner to avoid harm can later reap the benefits of a continued social relationship, and does not have to pay the costs of helping an injured friend. This cost-benefit assymetry suggests there is a positive selective pressure on a propensity to socially transmit information about danger at especially high rates compared to information that is not about danger. We call this predicted propensity a warning bias. Here we report the results of tests for this bias using data from the social networking site Twitter. Two coders rated each of 13,203 tweets (publicly-shared 140 character utterances) for whether or not each tweet contained information about danger. The number of retweets for each tweet indexes that tweet's transmission rate, and was our outcome variable. Results of negative binomial regressions showed that tweets about danger have up to 3.13 times as many retweets as tweets that are not about danger.