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How sequence learning creates explicit knowledge: the role of response–stimulus interval

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Abstract

Destrebecqz and Cleeremans (Psychon Bull Rev 8:343–350, 2001; Attention and implicit learning. John Benjamins Publishing Company, Amsterdam, pp 181–213, 2003) reported that increasing the response–stimulus interval (RSI) during incidental sequence learning improved participants’ ability to discriminate old and new sequences in a recognition test. However, the original experimental design confounded RSI effects during training and test. I therefore repeated the experiment with an improved design in which RSI was varied systematically during the training phase and the recognition task. Participants learned a sequence of response locations either incidentally or intentionally. As a result, sequence recognition was not affected by the RSI manipulations in the group of incidental learners. With intentional learning instructions, recognition was unaffected by training RSI, but a long RSI in the test phase improved recognition performance over a short RSI. Response latencies while executing the test sequences indicated no effect of training RSI on sequence learning. However, sequence knowledge was expressed more readily when the RSI in the test phase matched the RSI in the training phase.

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Notes

  1. In addition to a recognition test each participant performed two versions of a free-generation task which involved manual reproduction of the training sequence. Since we did not include a generation task in our own study, we will focus on recognition test results in the remainder of this report.

  2. Welch’s (1947) t test was used for the 0-ms training RSI condition because equality of variance could not be assumed.

  3. A six-point rating scale is commonly used in studies of sequence recognition (e.g., Destrebecqz & Cleeremans, 2001; Norman et al., 2006; Rünger, Nagy, & Frensch, 2009; Shanks & Johnstone, 1999; Shanks & Perruchet, 2002), but there is the valid concern that this scale does not meet the psychometric requirements for parametric analysis. I therefore analyzed recognition judgments with the sensitivity index d′ from signal-detection theory (Macmillan & Creelman, 1991) as the dependent variable. In an ANOVA with the between-subjects variables instruction, training RSI, and recognition RSI, the only significant effect was that of instruction, F(1, 181) = 6.07, MSE = 0.72. In particular, the Instruction × Recognition RSI interaction failed to reach significance, F(1, 181) = 1.91, MSE = 0.72, P = 0.17. Nevertheless, the pattern of mean d′ values was in close agreement with the pattern of mean recognition ratings (cf. Fig. 4). Pooled across recognition test RSIs, mean d′ was numerically lower with the 500-ms training RSI than with the 0-ms RSI for both incidental learners (0.37 vs. 0.53) and intentional learners (0.73 vs. 0.78). Thus, there was no indication that a longer training RSI improved sequence recognition. Pooled across training RSIs, incidental learners performed slightly worse with the 500-ms recognition RSI than with the 0-ms RSI (0.38 vs. 0.51). In contrast, sequence discrimination of intentional learners was better with the long recognition RSI (0.86 vs. 0.65).

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Acknowledgments

This research was supported by NIH Grant P01 NS044393.

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  1. Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA, 93106-9660, USA

    Dennis Rünger

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Rünger, D. How sequence learning creates explicit knowledge: the role of response–stimulus interval. Psychological Research 76, 579–590 (2012). https://doi.org/10.1007/s00426-011-0367-y

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