The effect of blocking inter-trial interval on sequential effects in absolute identification

Abstract

Sequential effects are ubiquitous in decision-making, but no more than in the absolute identification task where participants must identify stimuli from a set of items that vary on a single dimension. A number of competing explanations for these sequential effects have been proposed, and recently Matthews and Stewart [(2009a). The effect of inter-stimulus interval on sequential effects in absolute identification. The Quarterly Journal of Experimental Psychology, 62, 2014–2029] showed that manipulations of the time between decisions is useful in discriminating between these accounts. We use a Bayesian hierarchical regression model to show that inter-trial interval has an influence on behaviour when it varies across different blocks of trials, but not when it varies from trial to trial. We discuss the implications of both our and Matthews and Stewart's results on the effect of inter-trial interval for theories of sequential effects.

Keywords:

• Absolute identification
• Sequential effects
• Bayesian inference
• Inter-trial interval
• Decision-making

Chris Donkin's contribution to this research was supported by his Australian Research Council Discovery Early Career Award (DE130100129) and Discovery Project (DP130100124). We also wish to thank Scott Brown for his helpful comments on an earlier version of this manuscript.

Notes

¹It is also possible that the stimuli,which were different in our experiments, are the cause of the difference between the two results. Our initial replication of the two experiments was so similar that we doubt this possibility, but future empirical work is needed.

²A standard 2 (between-blocks vs. within-block) x 2 (0 ms vs. 2000 ms ITI) ANOVA analysis on the α ₁ regression coefficients also indicates a significant interaction, F(1,38) = 4.38, p = .043. However, whether or not p < .05 is conditional on the removal of an outlying participant. We prefer the Hierarchical Bayesian approach, as the hierarchical structure provides a more elegant means of dealing with this atypical participant. The one-step nature of the Bayesian regression also takes into account uncertainty in our estimates of α ₁ parameters, unlike the two-step regression and then ANOVA analysis.