Modulation of competing memory systems by distraction

Abstract

Different forms of learning and memory depend on functionally and anatomically separable neural circuits [Squire, L. R. (1992) Psychol. Rev. 99, 195-231]. Declarative memory relies on a medial temporal lobe system, whereas habit learning relies on the striatum [Cohen, N. J. & Eichenbaum, H. (1993) Memory, Amnesia, and the Hippocampal System (MIT Press, Cambridge, MA)]. How these systems are engaged to optimize learning and behavior is not clear. Here, we present results from functional neuroimaging showing that the presence of a demanding secondary task during learning modulates the degree to which subjects solve a problem using either declarative memory or habit learning. Dual-task conditions did not reduce accuracy but reduced the amount of declarative learning about the task. Medial temporal lobe activity was correlated with task performance and declarative knowledge after learning under single-task conditions, whereas performance was correlated with striatal activity after dual-task learning conditions. These results demonstrate a fundamental difference in these memory systems in their sensitivity to concurrent distraction. The results are consistent with the notion that declarative and habit learning compete to mediate task performance, and they suggest that the presence of distraction can bias this competition. These results have implications for learning in multitask situations, suggesting that, even if distraction does not decrease the overall level of learning, it can result in the acquisition of knowledge that can be applied less flexibly in new situations.

Fig. 1.

Schematic of experimental design and task structure. (a) Tone-counting task: Participants kept a running count of the number of high tones in a stream of high- and low-pitched tones. This task was subsequently used as the secondary task. (b) Participants learned to predict weather outcomes (rain or sun) for two different cities. Three seconds were allowed for responding, after which feedback was provided. During training, high and low tones were played. Participants ignored the tones on the ST blocks and counted the high tones on DT blocks. (c) After five trials of weather prediction, subjects indicated how many high tones they had counted (or selected the high number on ST blocks). ST and DT order was counterbalanced across participants. (d) On baseline trials, participants pressed with their index finger in response to the stimulus. One run of weather prediction consisted of 10 cycles of five weather trials and three baseline trials. (e) During the probe test, participants predicted weather, as during training, but did not receive feedback. No tones were played during the probe trials. During the probe run, trials of task learned under ST and DT conditions were intermixed. RESP, response; ITI, intertrial interval.

Fig. 2.

Behavioral results. The percentages of correct responses are shown. (a) PCT performance during training runs 1 and 2 for ST and DT. (b) PCT performance during the probe test. (c) Cue-selection scores. Scores ranged from 1 to 4 (chance = 2.5). Error bars are standard errors.

Fig. 3.

Correlations between accuracy and brain activity during the probe task. (a) Activity in the right hippocampus was significantly correlated with performance of the PCT learned under ST conditions (SVC, P < 0.05). (b) During the ST, but not the DT, activity was significantly correlated with performance. (c) Activity in the left putamen was correlated with performance of the PCT learned under DT conditions (SVC, P < 0.05). (d) During DT, but not ST, activity was significantly correlated with performance. Regression lines and P values plotted are from the robust regression results. The x axes in b and d represent signal change (arbitrary units). MNI coordinates are displayed under the figure. (L = R in images).

Fig. 4.

Correlations between declarative knowledge and brain activity during the probe task. (a) Regions correlated with declarative cue knowledge of PCT learned under ST conditions (SVC, P < 0.05). Significant correlations were found in both left and right MTLs during performance on items learned under ST conditions. Correlations between declarative knowledge and brain activity for items learned under DT conditions were not significant. Regression lines and P values plotted are from the robust regression results. The x axes represent signal change (arbitrary units), and the y axes represent declarative knowledge z scores. The absolute difference between cue estimates and actual values and the cue-selection scores were transformed to z scores and averaged separately for the ST and DT scores (therefore, the significant difference between declarative knowledge for items learned under ST and DT conditions is not apparent in these figures). This composite score represented a general measure of each participant’s flexible knowledge of cue–outcome associations. (b) Activity in the right MTL correlated with cue knowledge during ST performance. (c) Activity in the left MTL correlated with cue knowledge during ST performance. MNI coordinates are displayed under the figure. (L = R in images).