Automaticity and Control in Prospective Memory: A Computational Model

International audienceProspective memory (PM) refers to our ability to realize delayed intentions. In event-based PM paradigms, participants must act on an intention when they detect the occurrence of a pre-established cue. Some theorists propose that in such paradigms PM responding can only occur when participants deliberately initiate processes for monitoring their environment for appropriate cues. Others propose that perceptual processing of PM cues can directly trigger PM responding in the absence of strategic monitoring, at least under some circumstances. In order to address this debate, we present a computational model implementing the latter account, using a parallel distributed processing (interactive activation) framework. In this model PM responses can be triggered directly as a result of spreading activation from units representing perceptual inputs. PM responding can also be promoted by top-down monitoring for PM targets. The model fits a wide variety of empirical findings from PM paradigms, including the effect of maintaining PM intentions on ongoing response time and the intention superiority effect. The model also makes novel predictions concerning the effect of stimulus degradation on PM performance, the shape of response time distributions on ongoing and prospective memory trials, and the effects of instructing participants to make PM responses instead of ongoing responses or alongside them. These predictions were confirmed in two empirical experiments. We therefore suggest that PM should be considered to result from the interplay between bottom-up triggering of PM responses by perceptual input, and top-down monitoring for appropriate cues. We also show how the model can be extended to simulate encoding new intentions and subsequently deactivating them, and consider links between the model's performance and results from neuroimaging. Citation: Gilbert SJ, Hadjipavlou N, Raoelison M (2013) Automaticity and Control in Prospective Memory: A Computational Model. PLoS ONE 8(3): e59852

Effects of stimulus degradation.

<p>Mean response time for PM miss, correct ongoing, and PM hit trials, alongside accuracy for ongoing trials and PM hit rate. Results are shown separately for the model using its standard settings (blue bars) and degraded input settings (red bars).</p

Schematic illustration of experimental stimuli in standard input and degraded input conditions.

<p>Schematic illustration of experimental stimuli in standard input and degraded input conditions.</p

Model architecture.

<p>Only connections between units representing the letter ‘A’ are shown, for simplicity; analogous connections existed for representations of ‘B’ and ‘C’.</p

Model performance.

<p>Mean response time, PM hit rate, and ongoing accuracy in ‘No monitoring’, ‘Standard monitoring’, and ‘High monitoring’ settings.</p

Mean response times for ongoing and PM miss trials in the two groups.

<p>The task switching group shows a significant intention superiority effect (i.e. faster responses for PM miss than ongoing trials) but there is no significant difference in the dual task group. Error bars indicate 95% confidence intervals for the within-subjects comparison between the two conditions for each group, using Loftus and Masson’s <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0059852#pone.0059852-Loftus1" target="_blank">[49]</a> method.</p

Empirical data.

<p>Mean response times are shown for PM miss, correct ongoing, and PM hit trials, alongside accuracy for ongoing trials and PM hit rate. Results are shown separately for the standard stimulus condition (blue bars) and degraded stimulus condition (red bars). Error bars indicate 95% confidence intervals for the within-subjects comparison between standard stimulus and degraded stimulus conditions, using Loftus and Masson’s <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0059852#pone.0059852-Loftus1" target="_blank">[49]</a> method. See Fig. 3 for equivalent data from the model.</p

Results of Experiment 2, presented separately for the task switching and dual task groups, along with statistical comparisons between the two groups.

<p>Results of Experiment 2, presented separately for the task switching and dual task groups, along with statistical comparisons between the two groups.</p

Response time distributions for the model’s simulation of correct ongoing, PM miss, and PM hit trials.

<p>Coefficient of variation (CV), i.e. standard deviation divided by mean, is also shown.</p

Response time distributions for correct ongoing, PM miss, and PM hit trials.

<p>Distributions have been averaged over participants using Ratcliff’s <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0059852#pone.0059852-Ratcliff1" target="_blank">[28]</a> method, with 10 bins. Coefficient of variation (CV), i.e. standard deviation divided by mean, is also shown. As in the model’s simulations (Fig. 4), CV is greatest for ongoing trials, intermediate for PM miss trials, and least for PM hit trials.</p