When high working memory capacity is and is not beneficial for predicting nonlinear processes

Abstract

Predicting the development of dynamic processes is vital in many areas of life. Previous findings are inconclusive as to whether higher working memory capacity (WMC) is always associated with using more accurate prediction strategies, or whether higher WMC can also be associated with using overly complex strategies that do not improve accuracy. In this study, participants predicted a range of systematically varied nonlinear processes based on exponential functions where prediction accuracy could or could not be enhanced using well-calibrated rules. Results indicate that higher WMC participants seem to rely more on well-calibrated strategies, leading to more accurate predictions for processes with highly nonlinear trajectories in the prediction region. Predictions of lower WMC participants, in contrast, point toward an increased use of simple exemplar-based prediction strategies, which perform just as well as more complex strategies when the prediction region is approximately linear. These results imply that with respect to predicting dynamic processes, working memory capacity limits are not generally a strength or a weakness, but that this depends on the process to be predicted.

Extrapolation accuracy underestimates rule learning: Evidence from the function-learning paradigm

“…In function-learning experiments, participants learn to predict continuous output (y-values) from continuous input (x-values) variables. To do so, participants are presented with an input value (for example, a time point; Fischer and Holt, 2016), and then predict the corresponding outcome value.…”

Functional opponency in working memory capacity predicts cognitive flexibility in problem solving.

“…Mental set problem-solving tasks thus require flexibility in "overcoming" a suboptimal approach that is strongly activated by prior experience solving similar problems (i.e., "breaking" mental set). For example, Beilock and DeCaro (2007) found that high WMs were more likely to persist in using a complex problem-solving strategy despite the availability of simpler, more efficient alternatives (see Fischer & Holt, 2017, for a similar finding). Insight problems are problems that have a high probability of triggering a "faulty" initial problem representation (i.e., a representation that has a low probability of activating the knowledge needed to solve the problem; Ohlsson, 1992).…”

“…high WMs also persist in using suboptimal strategies (e.g., Beilock & DeCaro, 2007;Fischer & Holt, 2017;Richmond et al, 2015). To address this apparent inconsistency in the literature, the present research adopted a multifaceted view of WMC (Shipstead et al, 2014; see also Unsworth et al, 2014) to advance a novel theory of functional opponency in which different WMC mechanisms interact in ways that both support and constrain cognitive flexibility.…”

“…For example, individuals with higher WMC (high WMs) demonstrate greater fluid intelligence (Gf)-the ability to solve novel reasoning problems Oberauer, Schulze, Wilhelm, & Süß, 2005). High WMs are also better able to implement complex, cognitively-demanding strategies than lower WMC individuals (low WMs; Barrett, Tugade, & Engle, 2004;Fischer, & Holt, 2017;Richmond, Redick, & Braver, 2015;Thomassin, Gonthier, Guerraz, & Roulin, 2015). These abilities enable high WMs to better adapt to novel or changing task demands (Bleckley, Durso, Crutchfield, Engle, & Khanna, 2003;Colflesh & Conway, 2007;Gulbinaite, van Rijn, & Cohen, 2014;Rummel & Boywitt, 2014;Shipstead & Broadway, 2013;Weldon, Mushlin, Kim, & Sohn, 2013;Wiley, Jarosz, Cushen, & Colflesh, 2011)-a hallmark of cognitive flexibility (Collins & Koechlin, 2012;Ionescu, 2012).…”

“…However, a superior capacity to restrict memory and attention to goal-relevant information may also lead high WMs to overlook potentially useful information (e.g., Campbell, Hasher, & Thomas, 2010;Woehrle & Magliano, 2012; see Amer, Campbell, & Hasher, 2016). For example, high WMs demonstrate greater bias for complex solutions that have worked in the past, while overlooking new, simpler solutions to problems (Beilock & DeCaro, 2007;DeCaro, Thomas, & Beilock, 2008; see also Fischer, & Holt, 2017;Ricks, Turley-Ames, & Wiley, 2007). This tendency can lead to cognitive inflexibility, or mental set (Bilalić, McLeod, & Gobet, 2010;Schultz & Searleman, 2002).…”