Sampling capacity underlies individual differences in human associative learning.

Experiment 1

A modified version of the negative patterning task developed by Redhead and Pearce (1995 ; A+/B+/C+, AB+/AC+/BC+, ABC–) was used. In this experiment participants were required to learn that a single stimulus (A+) and a double compound (BC+) predicted one outcome but the configuration of all three stimuli (ABC–) predicted an absence of outcome. As each separate stimulus was paired with outcome as frequently as it was paired with the absence of an outcome, solving this discrimination depended on learning about configurations of stimuli as opposed to learning about the separate stimuli. Ability to learn this discrimination was compared to ability to learn a linear discrimination, in which a single stimulus (D–) and a double compound (EF–) were paired with no outcome while a triple compound (GHI+) was paired with the outcome. More important, the compounds in this linear discrimination did not share any stimuli in common and the discrimination could be acquired without learning about the configurations of the co-occurring stimuli.

Participants’ response time to incongruent stimuli in the Navon task were used to provide an indication of whether participants focused more readily on the global or local stimuli. Participants focusing on global stimuli should respond faster on incongruent trials when asked to identify the global cue. Participants focusing on the local stimuli will respond faster on incongruent trials when asked to identify the local cue. As the global and local stimuli are the same on congruent trials, these trials were not used to calculate the tendency toward local or global processing. This is because participants can respond to the global stimulus in a block of trials where they have been asked to identify the local stimuli and their response will appear correct. Congruent trials however were included in the test procedure to ensure that participants were not using an explicit rule, such as the local stimulus is always the opposite of the global stimulus.

There were 41 university students who participated for course credit or were paid £5 for their participation. Thirty-two of the participants were female. The average age was 20.32 years ( SEM = 0.40) years and average working memory capacity, as tested by digit span ( Lezak, 1995 ) was 7.50 ( SEM = 0.11) digits.

Negative patterning

The learning task was embedded in a cover story describing bacteria and cell growth. The stimuli, as shown in Figure 1 , were nine brightly colored shapes presented in squares on a black grid of 6 × 6 squares, measuring a total of 52 mm × 52 mm. The shapes (measuring 8.50 mm × 8.50 mm) were: upright triangle, upside down triangle, square, circle, kite, pentagon, diagonal, and cross. The remaining squares in the grid were filled with darkly colored circles. Assignment of shape to experimental stimulus was partially counterbalanced, such that each shape was assigned to a different stimulus position giving nine configurations. The occurrence of outcome was shown by cells growing to cover the computer screen.

Stimuli used in the negative patterning tasks in Experiment 1 and Experiment 2. Left: stimulus presentation on a single stimulus trial (i.e., A); right: stimulus presentation on a double stimulus compound trial (i.e., BC).

Navon task

The Navon task, presented on computer (programmed in Visual Basic, Microsoft), used four separate stimuli, shown in Figure 2 . All stimuli consisted of white letters presented on a black background. Stimuli were presented in a square with the large letters spanning 55 mm × 55 mm (6.30° × 6.30°) and the small letters spanning approximately 5 mm × 5 mm (0.60° × 0.60°). A stimulus size, toward the upper boundary of that identified as capable of inducing a global precedence effect ( Kinchla & Wolfe, 1979 ) was used to allow for a reasonable likelihood of observing individual difference in global tendency. If stimuli are particularly small, all participants might be expected to show a global advantage. With very large stimuli, all participants might be expected to show a local advantage.

Stimuli used in the Navon task, showing congruent (left) and incongruent (right) stimuli.

Navon task

Participants were informed that they would be presented with a series of large letters composed of small letters and on successive blocks they would be asked to identify the large letter or the small letter presented. Participants were warned that the letters would be presented for a very short period of time.

Participants completed eight blocks of 16 trials. Each trial block contained eight S stimuli and eight H stimuli. Half the stimuli were congruent, half incongruent. The order of stimuli presentation was randomized within each block. On half of the trial blocks participants identified the small letters, on the other half the large letters. Trial blocks alternated and half of the participants started by identifying the large letters whereas half started by identifying the small letters.

On each trial a fixation point was presented in the center of the screen for 500 ms. This was followed by a stimulus presented for 40 ms. A mask replaced the stimulus and remained on the screen until participants made a response using the S or H using keys on the keyboard. Following their response there was a 300-ms intertrial interval. Response time, measured from stimulus onset and response accuracy were recorded.

Learning task

Participants’ judgments of the probability that cell growth would follow a stimulus or stimuli compound were recorded. Average judgment over the first two trials were used for the initial trial block, average judgments over four consecutive trials were used to calculate Trial Blocks 2, 3, and 4.

Judgments of outcome likelihood over training are shown in Figure 3 . This analysis addresses whether participants differed in their ability to acquire a configural discrimination: the negative patterning discrimination. Analysis of judgments of outcome likelihood following the two compound stimuli in the negative patterning discrimination, BC and ABC, provides a simple assessment of differences in such ability. These compounds share two stimuli in common and predict different outcomes, placing a requirement on participants to learn about the combinations of stimuli presented in the compound. Ability to acquire the discrimination between BC and ABC was compared to ability acquiring the discrimination between EF and GHI. To learn the discrimination between EF and GHI, there is no requirement to learn about the combination of stimuli presented in the compound.

Judgments of outcome likelihood over trial blocks for Experiment 1, showing the local group (left) and the global group (right), for the negative patterning discrimination (top) and linear discrimination (bottom).

Differences in judgments for single stimuli and double compounds

Learning about the single stimulus A was not used to investigate the central hypothesis although we did find that at the end of training participants judged A as more likely to be followed by the outcome than BC, consistent with predictions of a configural models (e.g., Pearce, 1987 ). As shown in Figure 3 , at the start of training participants did not differ in their judgments of outcome likelihood following A compared to BC, t (39) < 1, p = .91. This was the case for both the local, t (19) = 1.53, p = .14 and the global, t (19) = 1.83, p = .08 groups. At the end of training, participants judged A as somewhat more likely to be followed by the outcome than BC, t (39) = 2.11, p = .05, d = 0.42, 95% CI [0.02, 1.14]. However, split by group, neither the local, t (19) = 1.53, p = .14, nor the global, t (19) = 1.98, p = .06 group showed a statistically significant difference in judgments of A and BC. Independent samples t tests for the last trial block of training revealed that the two participant groups did not differ in their judgments of outcome likelihood following A, t (38) < 1, p = .52 or BC, t (38) < 1, p = .38. As we have not used learning with A to investigate our central hypothesis, it should be noted that, on average, all participants learned the discrimination between A and ABC better than they learned the discrimination between BC and ABC and, at the end of training, all participants gave judgments of outcome likelihood of above five (average) following A. Only four participants gave higher judgments of outcome likelihood following BC than A. This should provide confidence that participants, acquiring the BC-ABC discrimination, did not fail to acquire the A–ABC discrimination.

Differences in discrimination learning

To address group differences in discrimination learning, analysis focused on the discrimination between double and triple compounds. A four way repeated-measures analysis of variance (ANOVA) was conducted on judgments of the outcome likelihood with the factors of outcome (cell growth vs. no cell growth), discrimination (linear vs. negative patterning), trial block (first vs. last), and group (local vs. global). This analysis revealed a significant four way interaction, F (1, 38) = 8.20, MSE = 1.44, p < .01, η p 2 = 0.18). To understand this interaction analyses of judgments of outcome likelihood, with the factors of discrimination (linear vs. negative patterning), outcome (cell growth vs. no cell growth), and group (local vs. global), have been conducted for the first and last trial block.

On the first trial block of training, shown in Figure 3 , a three way ANOVA revealed no significant main effect of outcome, F (1, 38) < 1, p = .94, but a significant interaction between outcome and group, F (1, 38) = 5.11, MSE = 2.11, p = .05, η p 2 = 0.12 and between outcome and discrimination, F (1, 38) = 12.66, MSE = 1.36, p < .001, η p 2 = 0.25. Paired samples t tests for the local group, showed no significant difference between initial judgments of BC and ABC, t (19) < 1, p = .86 or EF and GHI, t (19) = 1.93, p = .07. Showing an expected pattern of generalization on the basis of similarity, the global group judged ABC as more likely to be followed by the outcome than BC, t (19) = 3.10, p < .01, d = 0.85, 95% CI [0.24, 1.43], but did not give significantly different judgments for EF compared to GHI, t (19) = 1.18, p = .25. The relatively strong judgments following ABC at the start of training are expected given the similarity between the compounds in the negative patterning discrimination. The difference in judgments is in the opposite direction to the training contingencies and therefore could not be seen to contribute to the final discrimination.

On the last trial block of training, shown in Figure 3 , a three way ANOVA revealed a significant three way interaction between outcome, discrimination, and group, F (1, 38) = 7.01, MSE = 1.65, p < .01, η p 2 = 0.16. In the linear discrimination there was no significant interaction between outcome and group, F (1, 38) < 1, p = .88. All participants judged GHI (7.49, SEM = 0.21) as more likely to be followed by the outcome than EF (2.36; SEM = 0.19), t (39) = 19.92, p < .001, d > 2. In the negative patterning discrimination, there was a significant interaction between stimulus and group, F (1, 38) = 5.85, MSE = 3.63, p < .05, η p 2 = 0.13. Paired samples t tests revealed that on the final trial block of training, the global group judged the outcome to be significantly more likely to follow BC than ABC, t (19) = 6.89, p < .001, d > 2. In contrast, for the local group, judgments of outcome likelihood following BC were not significantly higher than judgments following ABC, t (19) = 1.99, p = .06. This analysis indicates that on the final trial block of training both groups were discriminating between EF and GHI in the linear discrimination. In contrast, only the global group learned to discriminate between BC and ABC in the negative patterning discrimination.

The previous analysis divided participants into local and global groups to allow detailed exploration of learning. We also look at the relationship between discrimination and global processing as a continuous variable. To do this discrimination difference scores were calculated to give a single measure of discrimination learning. This was used to assess the proportion of variance in discrimination learning that was accounted for by the global processing score. A linear and a negative patterning discrimination score was calculated for the first trial block and for the last trial block of training. The linear discrimination score was calculated as the judgment of outcome likelihood following GHI minus the judgment following EF. The negative patterning discrimination score was calculated as the judgment of outcome likelihood following BC minus the judgment following ABC. Discrimination scores for the first trial block of training were then subtracted from discrimination scores on the last trial block of training to give an indication of the magnitude of change in discrimination over training.

The relationship between global processing score and discrimination difference score for the negative patterning and linear discrimination is shown in Figure 4 . The global processing score significantly predicted acquisition of the negative patterning discrimination, β = 0.36, t (38) = 2.40, p = .05, 95% CI [0.03, 0.34], and explained a significant proportion of variance in acquisition of the negative patterning discrimination, R 2 = 0.13, F (1, 38) = 5.74, p = .05. Global processing score did not predict acquisition of the linear discrimination, β = 0.15, t (38) < 1, p = .34, or explain a significant proportion of variance, R 2 = 0.02, F (1, 38) < 1, p = .34. Including digit span in the model did not increase the proportion of variance in acquisition of the negative patterning discrimination explained, R 2 change = 0.02, F (1, 37) < 1, p = .38 or increase the proportion of variance in acquisition of the linear discrimination explained, R 2 change = 0.01, F (1, 37) < 1, p = .57. Digit span was not correlated with global processing score, r (40) < .01, p = .98.

The relationship between discrimination difference score and global score for the negative patterning (left) and linear (right) discriminations in Experiment 1, showing line of best fit.

Overall, the results demonstrate considerable individual difference in ability to solve a negative patterning discrimination, in absolute terms and relative to the limited individual difference in ability to solve a linear discrimination. This does not appear to be explained simply in terms of working memory capacity. Global processing was correlated with ability to solve

Experiment 2

In Experiment 2 we asked whether the relationship between global processing and configural learning is static, or whether short term experiences can have a similar influence on ability to learn a configural discrimination. Experience identifying global targets in the Navon task has been found to enhance recognition of faces ( Gao, Flevaris, Robertson, & Bentin, 2011 ; Macrae & Lewis, 2002 ; Perfect, 2003 ). Facial recognition is acknowledged to be dependent on global or configural processing ( Bartlett & Searcy, 1993 ; Diamond & Carey, 1986 ; Leder & Bruce, 1998 , 2000 ; Maurer et al., 2002 ; Tanaka & Farah, 1993 ; Tanaka, Kiefer, & Bukach, 2004 ; Tanaka & Sengco, 1997 ; Young, Hellawell, & Hay, 1987 ). Providing experience identifying local targets impairs the recognition of faces ( Macrae & Lewis, 2002 ; Perfect, 2003 ) but enhances recognition of features ( Weston & Perfect, 2005 ).

Given these findings we might anticipate that providing participants with experience identifying targets at one or other level may influence their tendency to process the elements of compounds or configurations and thereby influence discrimination learning. Therefore, experience identifying global targets may be expected to enhance a participant’s ability to learn a negative patterning discrimination, while experience identifying local targets may be expected to impair ability to learn a negative patterning discrimination.

There were 40 university students who participated for course credit or were paid £5 for their participation. Twenty-four participants were female. The average participant age was 22.33 years ( SEM = 0.50) and the average digit span was 7.76 digits ( SEM = 0.09). Eighteen participants completed the local pretraining and 22 participants completed the global pretraining.