OS_34. Implicit learning
Sunday, October 02nd, 2011 [15:40 - 16:40]
OS_34.1 - Individual differences in implicit learning process
Popławska, A. , Kolańczyk, A. , Sterczyński, R. & Roczniewska, M.
Warsaw School of Social Sciences and Humanities. Sopot, Poland.
Implicit learning is described as process where individual differences are minimal relative to individual differences in explicit cognition (e.g., Reber, 1993). However, some researchers found correlation between implicit learning and intuitive cognitive style (Woolhouse & Bayne, 2000), the intuition facet of the Myers-Briggs Type Indicator (Kaufman et al., 2009), NEO-PI-R openness to feelings (Norman, Price & Duff, 2005), and aspects of self- reported personality (Kaufman et al. (2010). The aim of the presented studies is to establish the role of motivation (promotion and prevention) and cognitive style (global vs. local, measured by Navon test) in artificial grammar learning task (AGL). Förster and Higgins (2005) found that promotion focus was positively correlated with global processing, whereas the reverse was true for prevention focus. In presented studies participants performed the AGL task, Navon test and their motivation was manipulated in preventive and promotive way. There was also manipulation of the instruction in AGL (liking task versus rule-conformity judgments). The results indicate that motivation has influence on implicit learning process, especially in interaction with cognitive style and instruction type. The hypothesis that the instruction in AGL task can modify influence of cognitive style and motivation on effectiveness of implicit learning is discussed.
OS_34.2 - Effects of mood on learning in the serial reaction time task
Jones, E. 1 & Norman, E. 1, 2
1 University of Bergen
2 Haukeland University Hospital
According to the mood-as-information model (Schwartz & Clore, 2003) a sad mood may facilitate a systematic processing style involving increased attention to detail. We apply this model to implicit learning in order to investigate whether the implicit system will use a negative mood to indicate it needs to learn more. Healthy participants (N=80) were trained on a serial reaction time (SRT) task where the target stimulus was always a picture of a human face. For participants in the sad mood condition, the target stimulus was always a sad face. For participants in the happy mood condition it was always a happy face. Mood was assessed with PANAS-X (Watson & Clark, 1994). RSI was either 0 or 500 ms. There was a significant interaction between mood and RSI on the amount of learning: At RSI-500 there was a trend for sad participants to show more learning than happy participants. At RSI-0 there was no effect of mood on learning. We discuss the results in relationship to the mood-as-information model, and relate our findings to a recent study by von Helversen et al. (2011). The clinical implications of our findings are also discussed.
OS_34.3 - The implicit learning of metrical and non-metrical rhythms in a serial reaction-time task
Schultz, B. 1, 2 , Stevens, C. 1 , Tillmann, B. 2 & Keller, P. 3
1 MARCS Auditory Laboratories, University of Western Sydney, Australia
2 Lyon Neuroscience Research Center, CNRS-UMR 5292, INSERM U1028, Université de Lyon, France
3 Max Plank Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
Implicit learning (IL) of musical rhythm and its properties (e.g. meter) has received minimal attention. According to the Dynamic Attending Theory (DAT; Jones, 2009), metrical frameworks facilitate temporal expectancies. It was hypothesized that learning occurs more readily for metrical patterns (MP) than non-metrical patterns (NMP). Experiment 1 used a serial reaction-time task (SRT) where learning is characterized by: RT decreases over blocks containing the exposure rhythm; RT increases when new rhythms are introduced; and RT recovery when the exposure rhythm is reintroduced. A generation task using the Process Dissociation Procedure (Jacoby, 1991) assessed IL. Experiment 1 demonstrated IL of MPs and NMPs but the presence of meter did not improve the rate of learning. However, there was evidence of metric binding: the presentation of novel rhythms with different metrical frameworks resulted in greater RT increases than novel rhythms with the same meter. The generation task indicated that learning of MPs and NMPs was implicit. Overall, Experiment 1 indicates that metrical and non-metrical rhythms can be learned implicitly and, as per DAT, metrical frameworks can facilitate temporal expectancies. Facilitation from meter in sequence-reproduction tasks is investigated in Experiment 2 using SRT and serial recall tasks. Results for Experiment 2 are forthcoming.