Knowledge and processing speed as determinants of associative learning.

Abstract

In 4 experiments with over 2,000 subjects, the hypothesis was examined that cognitive processing variables measuring breadth of declarative knowledge and information processing speed were related to learning on a paired-associates task. Across all experiments, breadth of verbal knowledge was found to be a strong predictor of learning overall and a strong predictor of the increment in learning benefit due to increases in study time. Mnemonic strategy training improved learning but also served to enhance the relationship between knowledge and learning. Memory search speed also predicted learning, but primarily under conditions of high information flow, as a result of short (.5 seconds per pair) study. High-Knowledge subjects and Fast Memory-Search subjects were also quicker at retrieving the answer, when they knew the answer, but High-Knowledge subjects took longer in retrieving an answer under conditions of uncertainty. Results are discussed in terms of a general model of associative learning in which encoding is viewed as a process of generating links by constructing elaborations of the terms studied.Recently, a number of studies in the experimental literature have used individual differences (correlational) methods to identify hypothetical cognitive processing components. This union between what were once distinct disciplines has been expressed in two ways. In one, the validity of theory-based components, knowledge structures, or processing mechanisms, is tested by correlating individuals' performances on sets of tasks presumed either to engage or not to engage the component. This approach, originally suggested by Underwood (1975), has been applied in areas such as memory (Underwood, Boruch, & Malmi, 1978), imagery (Kosslyn, Development of this article was supported by the Air Force Learning Abilities Measurement Program (LAMP), a multi-year program of basic research conducted at the Air Force Human Resources Laboratory (AFHRL) and sponsored by the Air Force Office of Scientific Research and AFHRL. The goals of the program are to specify the basic parameters of learning ability, to develop techniques for the assessment of individuals' knowledge and skill levels, and to explore the feasibility of a model-based system of psychological assessment. Support was provided by AFHRL and the

Individual differences in criterion-based dropout learning in old age: the role of processing speed and verbal knowledge

“…Regarding possible mechanisms of how verbal knowledge affects individual differences in learning, there are some studies that provided evidence for a positive relation between verbal knowledge and verbal learning (Jones et al 2005;Kyllonen et al 1991;Rast 2011). Rast (2011 emphasized, in particular, the role of verbal knowledge for learning rates in old age.…”

Individual differences in current events knowledge: Contributions of ability, personality, and interests

“…Laboratory studies provide further evidence for the role of preexisting knowledge in learning. As examples, vocabulary facilitates paired-associates learning (e.g., Kyllonen & Tirre, 1988;Kyllonen, Tirre, & Christal, 1991;Thurstone, 1938), and there have been many reports of positive effects of prior knowledge on learning from text (e.g., Spilich et al, 1979;see Hambrick & Engle, 2002, for a review). As another example, in a longitudinal study, Hambrick (2003) found that preexisting knowledge of basketball facilitated acquisition of new knowledge about basketball over a season.…”

Exploring the Latent Structure of Strength‐related Attitude Attributes

“…And most of its effects appear to be primarily ability-based in character. Knowledge has been shown to enhance recall (e.g., Cooke, Atlas, Lane, & Berger, 1993;Fiske, Lau, & Smith, 1990;McGraw & Pinney, 1990;Schneider, Gruber, Gold, & Opwis, 1993), improve comprehension (Eckhardt, Wood, & Jacobvitz, 1991;Engle, Nations, & Cantor, 1990), increase the speed of judgments (e.g., Fiske et al, 1990;Paull & Glencross, 1997), improve cue utilization in decision tasks (Paull & Glencross, 1997), enable appropriate inferences (Pearson, Hansen, & Gordon, 1979), facilitate the objective processing of attituderelevant information (Biek, Wood, & Chaiken, 1996) and the learning of new topic-relevant information (Hansen, 1984;Kyllonen, Tirre, & Christal, 1991;Willoughby, Waller, Wood, & MacKinnon, 1993), and 18 enable the generation of effective counterarguments to a persuasive appeal (Wood, 1982;Wood, Rhodes, & Biek, 1995). Thus, although knowledge seems to enable people to perform various relevant cognitive tasks more effectively, we see no reason to suppose that it should, in and of itself, motivate people to engage in any behavior.…”