Varieties of sameness: the impact of relational complexity on perceptual comparisons

The fundamental relations that underlie cognitive comparisons—“same” and “different”—can be defined at multiple levels of abstraction, which vary in relational complexity. We compared response times to decide whether or not two sequentially-presented patterns, each composed of two pairs of colored squares, were the same at three levels of abstraction: perceptual, relational, and system (higher order relations). For both 150 ms and 5 s inter-stimulus intervals (ISIs), both with and without a masking stimulus, decision time increased with level of abstraction. Sameness at lower complexity levels contributed to decisions based on the higher levels. The pattern of comparison times across levels was not predictable solely from encoding times. The results indicated that relations at multiple levels of complexity can be abstracted and compared in working memory, with higher complexity levels requiring more processing time. We simulated the impact of relational complexity on response time using Learning and Inference with Schemas and Analogies (LISA), a computational model of relational comparisons based on dynamic binding of elements into roles in a relational working memory.     

The conjunction fallacy: a misunderstanding about conjunction?

It is easy to construct pairs of sentences X, Y that lead many people to ascribe higher probability to the conjunction X-and-Y than to the conjuncts X, Y. Whether an error is thereby committed depends on reasoners’ interpretation of the expressions “probability” and “and.” We report two experiments designed to clarify the normative status of typical responses to conjunction problems.     

Strategies in sentential reasoning

Four experiments examined the strategies that individuals develop in sentential reasoning. They led to the discovery of five different strategies. According to the theory proposed in the paper, each of the strategies depends on component tactics, which all normal adults possess, and which are based on mental models. Reasoners vary their use of tactics in ways that have no deterministic account. This variation leads different individuals to assemble different strategies, which include the construction of incremental diagrams corresponding to mental models, and the pursuit of the consequences of a single model step by step. Moreover, the difficulty of a problem (i.e., the number of mental models required by the premises) predisposes reasoners towards certain strategies. Likewise, the sentential connectives in the premises also bias reasoners towards certain strategies, e.g., conditional premises tend to elicit reasoning step by step whereas disjunctive premises tend to elicit incremental diagrams.     

Critique of Sharif’s and Pitt’s Models of Technology

abstract

In this article Pitt’s and Sharif’s models of technology are discussed. These models are based on two different conceptions of technology, which are technology as “instrument” and as “making use of instrument.” Sharif considers technology as a collection of empowering tools, including technoware, humanware, infoware and orgaware. On the other hand, Pitt sees technology as “humanity at work.” Based on his definition, Pitt proposes a model of technology with three components; first-order transformation, second-order transformation, and the assessment of feedback mechanism. In this article this model will be explained and criticized. After that, Sharif’s model is criticized in the light of Pitt’s theory and it will be shown that Pitt’s model provides a better understanding of different aspects of technology. For example, it will be argued how Pitt’s model is efficient in explaining dynamicity, transfer and control of technology along with its soft dimensions, while Sharif’s model is incapable of doing so. In the next part, Pitt’s model is criticized and it is shown that the mechanism of knowledge progress suggested by this model is controversial and Pitt’s framework cannot support the idea of indigenous technology. Furthermore, the ability of Pitt’s model in describing different technological phenomena is called into question, since this model provides a superficial view of the complexity of an assessment of technology’s consequences. Finally, a list is proposed that contains minimal requirements that every model of technology is expected to explain. It is incumbent on technology theoreticians to consider this list.