It is generally accepted that, in the cognitive and neural sciences, there are both computational and mechanistic explanations. We ask how computational explanations can integrate into the mechanistic hierarchy. The problem stems from the fact that implementation and mechanistic relations have different forms. The implementation relation, from the states of an abstract computational system to the physical, implementing states is a homomorphism mapping relation. The mechanistic relation, however, is that of part/whole; the explaining features in a mechanistic explanation are the (...) components of the explanandum phenomenon and their causal organization. Moreover, each component in one level of mechanism is constituted and explained by components of an underlying level of mechanism. Hence, it seems, computational variables and functions cannot be mechanistically explained by the medium-dependent states and properties that implement them. How then, do the computational and the implementational integrate to create the mechanistic hierarchy? After explicating the general problem, we further demonstrate it through a concrete example, of reinforcement learning, in the cognitive and neural sciences. We then examine two possible solutions. On one solution, the mechanistic hierarchy embeds at the same levels computational and implementational properties. This picture fits with the view that computational explanations are mechanistic sketches. On the other solution, there are two separate hierarchies, one computational and another implementational, which are related by the implementation relation. This picture fits with the view that computational explanations are functional and autonomous explanations. It is less clear how these solutions fit with the view that computational explanations are full-fledged mechanistic explanations. Finally, we argue that both pictures are consistent with the reinforcement learning example, but that scientific practice does not align with the view that computational models are merely mechanistic sketches. (shrink)

A popular view presents explanations in the cognitive sciences as causal or mechanistic and argues that an important feature of such explanations is that they allow us to manipulate and control the explanandum phenomena. Nonetheless, whether there can be explanations in the cognitive sciences that are neither causal nor mechanistic is still under debate. Another prominent view suggests that both causal and non-causal relations of counterfactual dependence can be explanatory, but this view is open to the criticism that it is (...) not clear how to distinguish explanatory from non-explanatory relations. In this paper, I draw from both views and suggest that, in the cognitive sciences, relations of counterfactual dependence that allow manipulation and control can be explanatory even when they are neither causal nor mechanistic. Furthermore, the ability to allow manipulation can determine whether non-causal counterfactual dependence relations are explanatory. I present a preliminary framework for manipulation relations that includes some non-causal relations and use two examples from the cognitive sciences to show how this framework distinguishes between explanatory and non-explanatory, non-causal relations. The proposed framework suggests that, in the cognitive sciences, causal and non-causal relations have the same criterion for explanatory value, namely, whether or not they allow manipulation and control. (shrink)

We introduce a set of biologically and computationally motivated design choices for modeling the learning of language, or of other types of sequential, hierarchically structured experience and behavior, and describe an implemented system that conforms to these choices and is capable of unsupervised learning from raw natural-language corpora. Given a stream of linguistic input, our model incrementally learns a grammar that captures its statistical patterns, which can then be used to parse or generate new data. The grammar constructed in this (...) manner takes the form of a directed weighted graph, whose nodes are recursively (hierarchically) defined patterns over the elements of the input stream. We evaluated the model in seventeen experiments, grouped into five studies, which examined, respectively, (a) the generative ability of grammar learned from a corpus of natural language, (b) the characteristics of the learned representation, (c) sequence segmentation and chunking, (d) artificial grammar learning, and (e) certain types of structure dependence. The model's performance largely vindicates our design choices, suggesting that progress in modeling language acquisition can be made on a broad front—ranging from issues of generativity to the replication of human experimental findings—by bringing biological and computational considerations, as well as lessons from prior efforts, to bear on the modeling approach. (shrink)

We welcome Soltis' use of evolutionary signaling theory, but question his interpretations of colic as a signal of vigor and his explanation of abnormal high-pitched crying as a signal of poor infant quality. Instead, we suggest that these phenomena may be suboptimal by-products of a generally adaptive learning process by which infants adjust their crying levels in relation to parental responsiveness.

Social workers with the Dutch Child Protection Board use hypothetical questions as a means to assess the suitability of prospective adoptive parents for adoption. In particular, while talking about the future, prospective adoptive parents are assessed on their educational skills, knowledge and awareness with regard to adoption-specific problems. In our study we analysed the preliminary conversational work that has to be done in order to pose a hypothetical question. We distinguished between 1) patterns that start with an eliciting question as (...) a way of collecting topics with which to build a hypothetical question, and 2) patterns that start with a retrieving question, using themes from earlier conversation. Follow up questions are part of the preparatory work and form a bridge between the elicitation of topics and the actual hypothetical question. These follow up questions can be asked both before and after the introduction of the hypothetical question. Follow-up questions in post-position allow the social worker to challenge parents' answers to hypothetical questions. (shrink)

This paper will focus on two textual articulations that emerged in the Immanuel “Beis-Yaakov” school segregation case. The first is a declaration of the Admor from Slonim that was published when the ultra-Orthodox fathers who refused to send their daughters to an integrated school were imprisoned. The second is a letter to the Supreme Court that was written by an Ashkenazi mother whose daughter attended the “Beis Yaakov” school. A semiotic reading of the articulations reveals several opposing characteristics. The Admor’s (...) audience is determined by his choices of medium and rhetoric, which guarantee hegemonic reading, corresponding with the textual code of his interpretive community. The letter, on the other hand, represents an attempt to break through communal borders, and therefore its writer cannot expect hegemonic reading. Yet, she makes a considerable effort to employ signifiers denoting her ultra-Orthodox affiliation. In light of the hindrances that usually prevent ultra-Orthodox women from contesting the authority of the community, the letter presents a rare feminine voice, which is vigorous enough to attempt subverting under the authoriality of the Admor, and might have a long run affect on the quest for equality. (shrink)

The Transformation of Learning gives an overview of some significant advances of the cultural-historical activity theory, also known as CHAT in the educational domain. Developments are described with respect to both the theoretical framework and research. The book's main focus is on the evolution of the learning concept and school practices under the influence of cultural-historical activity theory. Activity theory has contributed to this transformation of views on learning, both conceptually and practically. It has provided us with a useful approach (...) to the understanding of learning in cultural contexts. (shrink)

We propose that food-related uncertainty is but one of multiple cues that predicts harsh conditions and may activate “incentive hope.” An evolutionarily adaptive response to these would have been to shift to a behavioral-metabolic phenotype geared toward facing hardship. In modernity, this phenotype may lead to pathologies such as obesity and hoarding. Our perspective suggests a novel therapeutic approach.