Elsevier

Neuropsychologia

Volume 93, Part A, December 2016, Pages 13-20
Neuropsychologia

A Neurophysiological examination of quality of learning in a feedback-based learning task

https://doi.org/10.1016/j.neuropsychologia.2016.10.001Get rights and content

Highlights

  • Steeper reduction in FRN to negative feedback was associated with faster learning.

  • Changes in FCP amplitude were not found related to individual learning slopes.

  • FRN in the first block of trials predicted learning outcomes.

  • Smaller FRN to negative feedback was associated with better learning outcomes.

  • Larger FRN to positive feedback was associated with better learning outcomes.

  • FCP in the first block of trials predicted learning outcomes.

  • Larger FCP to negative feedback was associated with better learning outcomes.

Abstract

The efficiency with which one processes external feedback contributes to the speed and quality of one’s learning. Previous findings that the feedback related negativity (FRN) event related potential (ERP) is modulated by learning outcomes suggested that this ERP reflects the extent to which feedback is used by the learner to improve performance. To further test this suggestion, we measured whether the FRN and the fronto-central positivity (FCP) that follows it are modulated by learning slopes, and as a function of individual differences in learning outcomes. Participants were tasked with learning names (non-words) of 42 novel objects in a two-choice feedback-based visual learning task. The items were divided into three sets of 14 items, each presented in five learning blocks and a sixth test block. Individual learning slopes based on performance on the task, as well as FRN and FCP slopes based on positive and negative feedback related activation in each block were created for 53 participants. Our data pointed to an interaction between slopes of the FRN elicited by negative feedback and learning slopes, such that a sharper decrease in the amplitude of the FRN to negative feedback was associated with sharper learning slopes. We further examined the predictive power of the FRN and FCP elicited in the training blocks on the learning outcomes as measured by performance on the test blocks. We found that small FRN to negative feedback, large FRN to positive feedback, and large FCP to negative feedback in the first training block predicted better learning outcomes. These results add to the growing evidence that the processes giving rise to the FRN and FCP are sensitive to individual differences in the extent to which feedback is used for learning.

Introduction

Feedback processing is a component of Self-regulated learning, a construct that refers to the process by which individuals actively engage in their learning experience by planning, setting goals, self-monitoring, and using external feedback to adjust goals, performance or strategies (Butler and Winne, 1995, De la Fuente and Martínez, 2007, Efklides, 2011, Greene and Azevedo, 2010, Winne and Nesbit, 2010, Zimmerman and Schunk, 2008). A link between self-regulated learning and academic success has been reported in several studies (Abar and Loken, 2010; De Corte et al., 2000; Pressley and McCormick, 1995; Schunk and Zimmerman, 1994, 1998; Zimmerman, 1994,1998; Zimmerman and Schunk, 2008), where “high self-regulators” were found to be more effective learners than “low self-regulators”. As feedback processing a critical component of self-regulated learning, the efficient use of external feedback is crucial for becoming a successful self-regulated learner, and in turn, for achieving academic success (Blair, 2002, Zimmerman and Chunk, 1989). The study of the relationship between feedback processing and learning has been enhanced by the discovery some twenty years ago of an event related potential associated with performance monitoring and feedback processing. The feedback related negativity, known as fERN or FRN is an event related potential (ERP) elicited by feedback in various tasks in which feedback guides response choice, and learning. This ERP component peaks at about 250–300 ms following the presentation of a feedback stimulus (Miltner et al., 1997), when information about the accuracy of a choice or action is unknown to the learner until it is communicated by an external source. Converging evidence points to the anterior cingulate cortex (ACC) as the generator of the FRN (Carter et al., 1998, Critchley et al., 2005, Dehaene et al., 1994, Holroyd et al., 1998, Kiehl et al., 2000, Ladouceur et al., 2007, Mathalon et al., 2003, Mars et al., 2005, Menon et al., 2001, van Veen and Carter, 2002).

A growing number of studies examine the FRN with the goal of elucidating the link between feedback processing and learning. Such studies employ feedback-based learning tasks in which the processing of positive and negative feedback is assessed in connection with learning outcomes (Arbel et al., 2013, Arbel et al., 2014, Eppinger et al., 2009, Krigolson et al., 2009, Luft, 2014, Pietschmann et al., 2008, Sailer et al., 2010, van der Helden et al., 2010, Van den Bos et al., 2009), and with within-task changes in decision making (Chase et al., 2011, Frank et al., 2005). Most studies that have reported a connection between the FRN and learning (see review by Luft (2014)) demonstrated this connection by showing a relationship between the FRN amplitude and response adjustment by the learner. For example, van der Helden et al. (2010), who examined the FRN using a motor sequence learning task reported that negative feedback to incorrect responses which were later modified was associated with larger FRN, than negative feedback provided to incorrect responses which were later repeated. These results were interpreted to suggest that the FRN is associated with effective adjustments of performance. Similarly, Cohen et al. (2007) found that the FRN was associated with a change of response after a loss, such that its amplitude was larger on “loss” trials after which participants changed their response, in comparison with “loss” trials after which participants repeated the action that previously resulted in unfavorable outcomes. Others have examined the extent to which the FRN was sensitive to learning outcomes in declarative learning tasks where associations between stimuli had to be learned and retained. Arbel et al. (2013) reported that in a feedback-based four-choice word learning task, FRN associated with positive feedback was sensitive to subsequent learning, such that words that were subsequently recalled elicited larger FRN to positive feedback during the learning process. Ernst and Steinhauser (2012), who studied the FRN in a multiple-choice declarative learning task, reported that the FRN amplitude associated with negative feedback was modulated by learning outcomes, such that smaller FRN amplitude was elicited in relation to successful learning.

Findings of a relationship between the FRN and learning can be interpreted to suggest that the FRN is an index of the extent to which feedback is used by the learner to improve performance and learning. Arbel et al. (2014) proposed the utility account of the FRN, positing that the FRN is a marker of the degree of utilization of the feedback by the learner. According to this account, the feedback-receiver extracts information from the feedback to improve learning and performance, and uses it to evaluate progress toward the goal. One prediction derived from this account is that if the FRN is indeed a marker of the use of feedback, it should show differentiation between individuals who are efficient at extracting information from feedback to facilitate learning and those who are not. Findings pertaining to this prediction are limited and inconsistent. In a study by Santesso et al. (2008), who employed a probabilistic reward learning task, FRN was examined in two groups of individuals who were classified as “learners” and “non-learners” based on their performance on the task. In their study, learning was defined as a growing bias to select stimuli with high reward probability measured by change in response bias from the first block and the combination of the second and third blocks. Their results suggested that the FRN to reward (positive feedback associated with the selection of a stimulus with high reward probability) was more positive among “learners” when compared with “non-learners”, and showed a positive correlation with a positive change in response bias (i.e., learning), indicating that a greater reward related positivity was associated with better learning. It is important to note that in Santesso et al. (2008) FRN was only examined for reward feedback (positive feedback).

Results pertaining to learning related differences in the FRN elicited by negative feedback were reported by Bellebaum and Daum (2008), who examined the FRN as participants performed a reward probabilistic task that was governed by a rule. Participants were informed that by finding and applying the rule, they were likely to increase their gains. Trials were divided into pre-learning and post-learning trials for each of the learners, and into two parts for non-learners based on the learners’ data. Findings suggested that the amplitude of the FRN to negative feedback increased from the pre-learning trials to the post-learning trials only among participants who learned the rule. Different findings were reported in a study by Sailer, Florian, Fischmeister, and Bauer (2010), who tasked participants with learning the correct sequence of response outcomes, with the goal of maximizing their gains and reducing their losses. Participants were classified as “high learners” and “low-learners” based on their performance on the task, and trials were split into half to create “early phase” and “late phase” conditions. FRN was found to be overall smaller among “high learners”, and to show a reduction in amplitude from early trials to later trials in the two groups. In a study by Luft et al. (2013), participants were divided into two groups (“high” and “low” learners) based on performance on a time estimation task. Their data suggested that the FRN was not different between the two groups. It is worth noting that in Luft et al. (2013) the FRN was examined across all trials, while in other reports (e.g., Bellebaum and Daum, 2008; Sailer et al., 2010; Santesso et al., 2008) FRN was examined in two different periods in the leaning process. Given that some reports detected a difference between groups of learners only at a later portion of the task, it is possible that an existing difference was missed in Luft et al. (2013), because a distinction between early and late trials was not made.

The inconsistency among the reported studies can stem from various factors, ranging from those related to task and participants, to factors related to the manner by which the FRN was measured and analyzed. It is important to consider the possible differences in the processing of feedback in a probabilistic learning task (e.g., Bellebaum and Daum, 2008; Sailer et al., 2010), in which participants learn to map a specific response to its probable outcome (feedback), and in a declarative word leaning task (e.g., Arbel et al., 2013, Arbel et al., 2014), in which participants learn correct associations through a trial-and-error process guided by feedback. We argue that the “late phase” of each of the learning tasks captures a different type of feedback processing inherent in the nature of the learning task, with the late phase in a probabilistic learning task capturing the processing of an expected negative feedback that is no longer beneficial for learning, and with the same phase in a declarative learning task capturing the processing of a still very informative negative feedback. This difference stems from the fact that in most probabilistic learning tasks, outcomes are not 100% mapped to a particular response, and learners come to expect occasional negative feedback even after they have optimized their responses. In that sense, when early learning phases are compared with late phases, the comparison is between negative feedback that is informative for task performance and an expected negative feedback that is no longer contributing to learning.

Another possible contributor to the varying findings is the classification of participants into learning groups, with some studies reporting the differences between “learners” and “non-learners” (e.g., Bellebaum and Daum, 2008; Santesso et al., 2008), and others comparing “high learners” with “low learners” (e.g., Sailer et al., 2010; Luft et al., 2013). In the comparison of “learners” with “non-learners”, information is obtained about the processing of feedback by all participants who achieved a learning criterion, regardless of how fast or how well they have learned, with those who did not achieve a learning criterion. One may claim that whereas “low learners” extract information from feedback but at a slower rate, or with greater effort, when compared with “high learners”, “non-learners” fail to extract relevant information from feedback. Comparison between reports that treat learning as a categorical variable is therefore challenging.

The choice of method for FRN measurement is another important candidate for inconsistent reports. While some studies measure the FRN as the most negative peak in a specified time window (e.g., Santesso et al., 2008), others used base-to-peak-to-peak measure by subtracting the average of preceding and proceeding positivity from the largest negativity (e.g., Sailer et al., 2010), calculated area measurement (e.g., Bellebaum and Daum, 2008), or conducted principal component analysis (e.g., Arbel et al., 2013, Arbel et al., 2014). A particular concern related to the variation in FRN measurement is the extent to which the FRN is measured independently, or in combination with another feedback related ERP component that follows it in time, namely the fronto-central positivity (FCP).

The FCP, first described by Butterfield and Mangels (2003) peaks about 350 ms following the presentation of the feedback, and is maximal at the same fronto-central electrode site (FCz) as the FRN. It is larger for negative feedback, and is assumed to reflect an attentional orienting process that proceeds the initial processing of feedback. Although the FCP can be detected in numerous FRN related reports, it has not been studied extensively. Butterfield and Mangels (2003), who examined the FCP in feedback-based learning tasks, reported that it was modulated by learning, such that a larger FCP amplitude was found to be associated with successful learning.

To further examine the FRN and FCP in relation to learning, the present study offers an evaluation of the relationship between these components and learning, by treating the feedback related ERPs and learning as continuous variables, and participants as a single cohort. The FRN and FCP to positive and negative feedback were examined in a feedback-based two-choice declarative learning task presented in a block-design. Each stimulus in this design was presented only once within a block, allowing for an examination of the learning process from the first presentation of a stimulus to its sixth presentation. In addition, unbeknown to the learners, in the first block of trials, positive and negative feedback stimuli were presented at an equal probability. This unique design allowed for the examination of the predictive value of the FRN and FCP elicited by equally probable positive and negative feedback in the first block of trials on learning outcomes. Temporal principal component analysis (TPCA) was employed to allow the separation of components which may overlap in time, and to avoid a selection of a time window that may either capture only part of the activity of interest or a combination of several activities.

Section snippets

Participants

Sixty healthy young adults (50 females) from the Boston area participated in the study after signing a consent form. Participants were right handed individuals, between the ages of 18 and 35 (M=24.6, SD=3.1), with normal or corrected vision, who reported that they had no history of head injury or other neurological deficits, and that English was their predominant language. Participants were paid for their participation. ERP data of seven participants were excluded from the ERP analysis due to

Behavioral data

Accuracy level on Round 1 was 0.5 across all participants due to the design of the task. Mean accuracy rate was found to be 0.6 (SD=0.08) on Round 2, 0.7 (SD=0.1) on Round 3, 0.77 (SD=0.1) on Round 4, 0.83 (SD=0.11) on Round 5, and 0.84 (SD=0.1) on Round 6. A generalized estimating equation analysis of accuracy data of all participants with an unstructured working correlation matrix revealed a Round effect, χ2 (5)=620.36, p<0.001, confirming that there was a significant increase in accuracy

Discussion

In the present study, the slope of the FRN to negative feedback was found related to individual differences in the slope of learning, such that a faster decrease in FRN amplitude to negative feedback during the learning process was associated with a sharper learning slope (faster learning). Moreover, activation associated with positive and negative feedback during the first round of the learning task was found to be a predictor of learning outcomes, such that smaller FRN to negative feedback,

Conclusions

The results of the study suggest that individual differences in feedback processing as reflected by the FRN and FCP are related to individual differences in learning. More specifically, individual slopes created by the change in FRN amplitude to negative feedback over the course of the learning task were found related to individual learning slopes. Additionally, individual differences in the processing of the initial negative and positive feedback were found associated with learning outcomes.

Acknowledgement

This study was funded by a Faculty Research Fellowship from the MGH Institute of Health Professions, Boston, MA, United States (Grant no. 300259) to Yael Arbel.

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