Academic journal article Psychonomic Bulletin & Review

Converging Measures of Workload Capacity

Academic journal article Psychonomic Bulletin & Review

Converging Measures of Workload Capacity

Article excerpt

Does processing more than one stimulus concurrently impede or facilitate performance relative to processing just one stimulus? This fundamental question about workload capacity was surprisingly difficult to address empirically until Townsend and Nozawa (1995) developed a set of nonparametric analyses called systems factorial technology. We develop an alternative parametric approach based on the linear ballistic accumulator decision model (Brown & Heathcote, 2008), which uses the model's parameter estimates to measure processing capacity. We show that these two methods have complementary strengths, and that, in a data set where participants varied greatly in capacity, the two approaches provide converging evidence.

(ProQuest: ... denotes formulae omitted.)

Choices are often made on the basis of multiple attributes of an object or objects. An important question in this context is what happens to people's capacity for information processing as workload-the number of to-beprocessed stimuli-increases. Workload capacity is commonly measured using the redundant-target detection task (e.g., Miller, 1982, 1991; Townsend & Nozawa, 1995). In this task, participants are presented with two targets (denoted as the redundant-target or "AB" condition), one target (either A or B), or no targets. Typically, the order of display conditions (i.e., no, one, or two targets) is randomized within blocks of trials, and participants are asked to respond positively if they detect either target and negatively if they observe neither target. Workload capacity can be assessed by comparing the time to make positive responses in the redundant- and single-target conditions.

Standard analyses, such as mean response time (RT) comparisons, provide ambiguous evidence about whether adding additional targets (i.e., increasing workload) reduces, improves, or has no effect on the efficiency of processing. For example, in a system where each stimulus is processed in parallel by separate and independent channels, mean RT reduces as the number of target stimuli increases, apparently indicating an increase in capacity, when capacity is in fact unlimited in the sense that processing in each channel is unaffected by processing in other channels.1 To avoid such ambiguities, Townsend and Nozawa (1995) developed systems factorial technology, a set of nonparametric analyses based on the entire distribution of RTs for correct responses (for applications, see Eidels, Townsend, & Algom, 2010; Eidels, Townsend, & Pomerantz, 2008; Wenger & Townsend, 2001).

Here we elaborate a parametric model of rapid choice, the linear ballistic accumulator (LBA; Brown & Heathcote, 2008), to provide an alternative perspective on workload capacity. Brown and Heathcote (2008) showed that the LBA provides a comprehensive account of benchmark phenomena in single-target rapid-choice tasks. Moreover, the LBA is a psychologically plausible process model, with interpretable parameters that account for the quality of sensory input and for participants' response strategy. Because of its mathematical simplicity, the LBA has already been applied in areas ranging from the neural basis of rapid choice (e.g., Ho, Brown, & Serences, 2009) to eye movements (Ludwig, Farrell, Ellis, & Gilchrist, 2009). We take advantage of that simplicity to extend the LBA to the redundant-target task. We show that nonparametric and parametric approaches have complementary advantages that together provide converging evidence about workload capacity in cognitive systems.

The Capacity Coefficient: A Nonparametric Measure

Townsend and Nozawa's (1995) capacity coefficient, C(t), compares a measure of the entire distribution of RT for correct responses in the double-target condition and the single-target conditions. The measure is based on the survivor function, S(t) = 1 - F(t), where F(t) is the cumulative density function, the probability of a response occurring before time t. …

Search by... Author
Show... All Results Primary Sources Peer-reviewed

Oops!

An unknown error has occurred. Please click the button below to reload the page. If the problem persists, please try again in a little while.