Introduction

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-2.1 Experiment 1: Retention of information during continuous recognition of pictures, faces, fractals and trigrams

2.1.1 Introduction

The goal of mathematically modelling the time course of memory retention is over 100 years old (e.g. Ebbinghaus, 1964; see Rubin and Wenzel, 1996). The purposes of obtaining a function, or collection of functions, to describe the process whereby memory performance declines from almost 100% accuracy to chance performance, are both practical and theoretical. The ability to accurately predict individuals’ retention of information is of practical importance, whilst the elucidation of the function(s) would reveal important information regarding the components contributing to memory output.

As has been detailed in Chapter 1, attempts to evaluate retention of information using list-based memory tasks have been complicated by the presence of serial position effects. In a typical list-based task, the list of items is presented for memorisation, followed by a recognition or recall task. Plotting memory performance against serial position of study reveals that items towards the start and the end of lists are remembered better than those in the middle (primacy and recency effects), typically resulting in a U-shaped curve. This is observed even for lists as short as 4 items (e.g. Korsnes, Magnussen, &

Reinvang, 1996; Wright, Santiago, Sands, Kendrick, & Cook, 1985) but is more

pronounced for lists long enough to enable the plotting of the time course of retention.

An alternative method of assessing memory that avoids this complication is the continuous recognition paradigm, first used by Shepard and Teghtsoonian (1961). By intermixing study and test trials in a continuous stream of information, the authors were able to examine recognition at a relatively ‘steady state’. Each stimulus occurs twice, once as a novel stimulus and then as an ‘old’ stimulus, and participants are required to distinguish between the two. An initial unscored buffer of trials serves to prevent primacy effects, and after this period a steady state is assumed to have been reached. Whilst this is not always strictly the case, as demonstrated by Shepard and Teghtsoonian’s (1961) own discovery that false alarm rates very gradually increased throughout the experiment, the effects of serial position are minimal compared with those observed in list-based tasks. The separation of study and test trials is normally controlled, and the number of trials intervening between the two is known as the ‘lag’. Because the stimuli intervening between study and test are randomly selected, study-test pairs of the same lag throughout the experiment are assumed to be equivalent, and hit rates can be obtained for each lag. By the inclusion of a wide range of lags in an experiment it is possible to plot a retention curve of performance against lag.

This experimental paradigm has been used to good effect by Rubin et al.

(1999) in the search for precise functions for the retention of information, tested by both recall and recognition. By using a very wide range of lags (0, 1, 2, 4, 7, 12, 21, 35, 59 and 99), many repetitions of each lag (27), and using a large number of participants (100 per condition), the authors achieved very precise retention curves for both recall and recognition of trigrams. By fitting the data obtained to a wide range of functions, informed by the authors’ previous fitting of data from 100 years of previous memory experiments (Rubin & Wenzel, 1996), a series of exponentials was selected as the best fitting function. This function, y =

a1e^-t/T1 + a2e^-t/T2 + a3e^-t/T3, contains three time constants (T1, T2, and T3). Of these T1 was set at 1.15, and T3 was infinite. T2 varied according to the memory measure employed, being 27.55 for cued-recall and remember-know recognition, whereas 13.38 was better for old-new recognition, reflecting the different shaped curves plotted. Coupling this difference with the apparent difference between functions obtained for most data sets and those from studies of autobiographical memory in Rubin and Wenzel’s (1996) study, it may be inferred that different memory processes produce retention curves that differ qualitatively as well as quantitatively.

Adopting the methodology of Rubin et al. (1999) seems to offer a precise and powerful method of comparing retention of information for different classes of stimuli. In the current experiment, a range of visual stimuli was tested in order to systematically compare and contrast the retention curves produced. In addition to the trigrams used by Rubin and colleagues, cartoon pictures, algorithm-generated fractals, and parametric face-like stimuli were also tested. The “clipart”

cartoon images were chosen because they represented common objects, and are a stimulus type employed frequently in studies of visual memory (Barbarotto, Laiacona, Macchi, & Capitani, 2002; Biederman & Cooper, 1991; Hornak, Duncan, & Gaffan, 2002; Proverbio, Burco, del Zotto, & Zani, 2004; Snodgrass &

Vanderwart, 1980; van Turennout, Bielamowicz, & Martin, 2003; Wan, Aggleton,

& Brown, 1999). They were selected from several different basic level categories, with many exemplars from each (e.g. 10 oranges, 10 umbrellas, etc.). The fractals were chosen as an example of abstract stimuli that would be resistant to naming. As they are relatively complex, but do not resemble commonly encountered objects, recognition of these stimuli must largely rely on visual discrimination. Unlike the picture stimulus set, the fractal stimulus set can be considered to be composed of stimuli of a single category, and what is tested, therefore, is true recognition of individuals from a homogenous group, rather than

discrimination between categories (Goldstein & Chance, 1970). Fractals similar to those used here have been used by Miyashita et al. (1993) in the study of stimulus-selectivity for complex visual forms of IT neurones. The face-like stimuli were also generated in such a way that they could be considered a homogenous category. These stimuli had a similar configuration to human faces, but were generated from a series of manipulated ellipses, whose parameters could be precisely controlled (Prof Andrew Derrington, personal communication). Whilst, as with the fractals, recognition of these stimuli might be expected to be based on visual discrimination as individual stimuli were relatively homogenous, participants’ familiarity with the configuration of the features might be expected to result in a different recognition profile.

In addition to the expected differences between recognition of the different sets of visual stimuli, a difference between the visual stimuli and the verbal trigram stimuli was also expected. Whilst the findings of Ward et al. (2005) suggest that the form of memory for verbal and visual memory is similar, a previous continuous recognition task comparing the two modalities suggests that verbally encoded stimuli are recognised faster and more accurately than abstract visual stimuli (Doty & Savakis, 1997). Whether this finding, obtained with common 4-letter words, could be generalised to the more complex digit-letter-digit trigrams, was a matter of some considerable interest.