Despite its strengths, Leech et al.'s model fails to address the important benefits that derive from self-explanation and task feedback in analogical reasoning development. These components encourage explicit, self-reflective processes that do not necessarily link to knowledge accretion. We wonder, therefore, what mechanisms can be included within a connectionist framework to model self-reflective involvement and its beneficial consequences.

An item that stands out (is isolated) from its context is better remembered than an item consistent with the context. This isolation effect cannot be accounted for by increased attention, because it occurs when the isolated item is presented as the first item, or by impoverished memory of nonisolated items, because the isolated item is better remembered than a control list consisting of equally different items. The isolation effect is seldom experimentally or theoretically related to the primacy or the recency (...) effects—that is, the improved performance on the first few and last items, respectively, on the serial position curve. The primacy effect cannot easily be accounted for by rehearsal in short‐term memory because it occurs when rehearsal is eliminated. This article suggests that the primacy, the recency, and the isolation effects can be accounted for by experience‐dependent synaptic plasticity in neural cells. Neurological empirical data suggest that the threshold that determines whether cells will show long‐term potentiation (LTP) or long‐term depression (LTD) varies as a function of recent postsynaptic activity and that synaptic plasticity is bounded. By implementing an adaptive LTP‐LTD threshold in an artificial neural network, the various aspects of the isolation, the primacy, and the recency effects are accounted for, whereas none of these phenomena are accounted for if the threshold is constant. This theory suggests a possible link between the cognitive and the neurological levels. (shrink)

Leech et al.'s model proposes representing relations as primed transformations rather than as structured representations (explicit representations of relations and their roles dynamically bound to fillers). However, this renders the model unable to explain several developmental trends (including relational integration and all changes not attributable to growth in relational knowledge). We suggest looking to an alternative computational model that learns structured representations from examples.

The remarkable successes of the physical sciences have been built on highly general quantitative laws, which serve as the basis for understanding an enormous variety of specific physical systems. How far is it possible to construct universal principles in the cognitive sciences, in terms of which specific aspects of perception, memory, or decision making might be modelled? Following Shepard (e.g., ), it is argued that some universal principles may be attainable in cognitive science. Here, 2 examples are proposed: the simplicity (...) principle (which states that the cognitive system prefers patterns that provide simpler explanations of available data); and the scale‐invariance principle, which states that many cognitive phenomena are independent of the scale of relevant underlying physical variables, such as time, space, luminance, or sound pressure. This article illustrates how principles may be combined to explain specific cognitive processes by using these principles to derive SIMPLE, a formal model of memory for serial order (), and briefly mentions some extensions to models of identification and categorization. This article also considers the scope and limitations of universal laws in cognitive science. (shrink)

The development of analogical reasoning has traditionally been understood in terms of theories of adult competence. This approach emphasizes structured representations and structure mapping. In contrast, we argue that by taking a developmental perspective, analogical reasoning can be viewed as the product of a substantially different cognitive ability – relational priming. To illustrate this, we present a computational (here connectionist) account where analogy arises gradually as a by-product of pattern completion in a recurrent network. Initial exposure to a situation primes (...) a relation that can then be applied to a novel situation to make an analogy. Relations are represented as transformations between states. The network exhibits behaviors consistent with a broad range of key phenomena from the developmental literature, lending support to the appropriateness of this approach (using low-level cognitive mechanisms) for investigating a domain that has normally been the preserve of high-level models. Furthermore, we present an additional simulation that integrates the relational priming mechanism with deliberative controlled use of inhibition to demonstrate how the framework can be extended to complex analogical reasoning, such as the data from explicit mapping studies in the literature on adults. This account highlights how taking a developmental perspective constrains the theory construction and cognitive modeling processes in a way that differs substantially from that based purely on adult studies, and illustrates how a putative complex cognitive skill can emerge out of a simple mechanism. (shrink)