Attenuating age-related learning deficits: Emotional valenced feedback interacts with task complexity.

Gorlick, Marissa A.; Giguère, Gyslain; Glass, Brian D.; Nix, Brittany N.; Mather, Mara; Maddox, W. Todd

doi:10.1037/a0030071

Cited by 13 publications

(30 citation statements)

References 55 publications

(94 reference statements)

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“…These include: healthy adult humans (Ashby and Maddox, 2005, 2010; Grimm and Maddox, 2013; Ashby, 2014; Smith et al, 2014), human children, and older adults (Ridderinkhof et al, 2002; Filoteo and Maddox, 2004; Filoteo et al, 2005a; Racine et al, 2006; Minda et al, 2008; Maddox et al, 2010; Huang-Pollock et al, 2011; Gorlick et al, 2012), non-human animals (Smith et al, 2004, 2010, 2011, 2012a,b), various neuropsychological patient groups (Knowlton and Squire, 1993; Knowlton et al, 1994; Squire and Knowlton, 1995; Knowlton, 1999; Keri, 2003; Filoteo et al, 2005b; Filoteo and Maddox, 2007), as well as using brain imaging techniques such as fMRI (Poldrack et al, 1999, 2001; Cincotta and Seger, 2000, 2007; Poldrack and Packard, 2003; Aron et al, 2004; Poldrack and Rodriguez, 2004; Shohamy et al, 2004; Seger and Cincotta, 2005; Seger and Cincotta, 2006; Nomura et al, 2007; Nomura and Reber, 2008; Seger, 2008; Helie et al, 2010; Seger and Miller, 2010; Waldschmidt and Ashby, 2011) and EEG (Folstein and Van Petten, 2004). …”

Section: Single System Vs Multiple Systems Of Category Learningmentioning

confidence: 99%

Toward a dual-learning systems model of speech category learning

Chandrasekaran¹,

Koslov²,

Maddox³

2014

Front. Psychol.

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More than two decades of work in vision posits the existence of dual-learning systems of category learning. The reflective system uses working memory to develop and test rules for classifying in an explicit fashion, while the reflexive system operates by implicitly associating perception with actions that lead to reinforcement. Dual-learning systems models hypothesize that in learning natural categories, learners initially use the reflective system and, with practice, transfer control to the reflexive system. The role of reflective and reflexive systems in auditory category learning and more specifically in speech category learning has not been systematically examined. In this article, we describe a neurobiologically constrained dual-learning systems theoretical framework that is currently being developed in speech category learning and review recent applications of this framework. Using behavioral and computational modeling approaches, we provide evidence that speech category learning is predominantly mediated by the reflexive learning system. In one application, we explore the effects of normal aging on non-speech and speech category learning. Prominently, we find a large age-related deficit in speech learning. The computational modeling suggests that older adults are less likely to transition from simple, reflective, unidimensional rules to more complex, reflexive, multi-dimensional rules. In a second application, we summarize a recent study examining auditory category learning in individuals with elevated depressive symptoms. We find a deficit in reflective-optimal and an enhancement in reflexive-optimal auditory category learning. Interestingly, individuals with elevated depressive symptoms also show an advantage in learning speech categories. We end with a brief summary and description of a number of future directions.

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Section: Single System Vs Multiple Systems Of Category Learningmentioning

confidence: 99%

Toward a dual-learning systems model of speech category learning

Chandrasekaran¹,

Koslov²,

Maddox³

2014

Front. Psychol.

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“…Unbeknownst to the participant, after ten consecutive correct responses the rule changes and cognitive control must be used to disengage from the first rule and adopt a new rule. Interestingly, recent research in our laboratory demonstrates that these cognitive deficits, which are considered a hallmark of age-related cognitive declines, can be attenuated when learning is placed in a social context ( Figure 1 ; Gorlick et al, 2013 ). Importantly, the efficacy of social feedback depends on a complex three way interaction between task demands [high cognitive control demand ( Figure 1 , top): three dimensional stimuli each with four possible values, low cognitive control demand ( Figure 1 , bottom): two dimensional stimuli each with two possible values] social feedback valence [positive happy face ( Figure 1 , left), negative angry face ( Figure 1 , right)] and age (younger, older).…”

Section: Introductionmentioning

confidence: 96%

“… An adapted schematic of the task from Gorlick et al (2013) . This task directly manipulated task demands on cognitive control.…”

Section: Introductionmentioning

confidence: 99%

“…Though this paradigm offers a potential method for attenuating age-related learning deficits without cognitive training or pharmaceutical interventions, it is still unclear how these findings extend to age-related deficits in automatic procedural category learning tasks. Rule-based task performance, such as that seen in the WCST and Gorlick et al (2013) , is considered deliberative because verbalizable rules are maintained by the central executive and used to guide performance (e.g., “categorize by color”). On the other hand, procedural information integration task performance is characterized by slow and incremental learning.…”

Section: Introductionmentioning

confidence: 99%

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Social incentives improve deliberative but not procedural learning in older adults

Gorlick

Maddox

2015

Front. Psychol.

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Age-related deficits are seen across tasks where learning depends on asocial feedback processing, however plasticity has been observed in some of the same tasks in social contexts suggesting a novel way to attenuate deficits. Socioemotional selectivity theory suggests this plasticity is due to a deliberative motivational shift toward achieving well-being with age (positivity effect) that reverses when executive processes are limited (negativity effect). The present study examined the interaction of feedback valence (positive, negative) and social salience (emotional face feedback – happy; angry, asocial point feedback – gain; loss) on learning in a deliberative task that challenges executive processes and a procedural task that does not. We predict that angry face feedback will improve learning in a deliberative task when executive function is challenged. We tested two competing hypotheses regarding the interactive effects of deliberative emotional biases on automatic feedback processing: (1) If deliberative emotion regulation and automatic feedback are interactive we expect happy face feedback to improve learning and angry face feedback to impair learning in older adults because cognitive control is available. (2) If deliberative emotion regulation and automatic feedback are not interactive we predict that emotional face feedback will not improve procedural learning regardless of valence. Results demonstrate that older adults show persistent deficits relative to younger adults during procedural category learning suggesting that deliberative emotional biases do not interact with automatic feedback processing. Interestingly, a subgroup of older adults identified as potentially using deliberative strategies tended to learn as well as younger adults with angry relative to happy feedback, matching the pattern observed in the deliberative task. Results suggest that deliberative emotional biases can improve deliberative learning, but have no effect on procedural learning.

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“…The following hypothesis about the emotional changes in humans during the mental task performance with different difficulty levels are validated in this study: 1) Negative (low valence) emotion is associated with high cognitive load for young adults [1,2]; 2) Humans tend to have high arousal in performing the cognitive tasks as the arousal level positively correlates with the speed of mental processing, and higher arousal also enhances the memory storage and retrieval [3]; 3) humans tend to have low dominance under time pressure and in performing the high complexity tasks [4].…”

Section: Introductionmentioning

confidence: 99%

EEG-Based Emotion Monitoring in Mental Task Performance

2014

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Abstract-Emotional engagement during mental tasks performance when the difficulty level of mental tasks increases is studied using Electroencephalogram (EEG) recorded by Emotiv Epoch device. A real-time EEG-based emotion recognition algorithm using Valence-Arousal-Dominance emotion model is applied. An experiment with 5 levels of workload is proposed and carried out with 7 subjects. The mental tasks are given to the participants to solve addition problems of increasing complexity. For each task, 3 min is given to complete additions with 2 min rest between the tasks sessions. Performance score for each participant is recorded as well. Emotions are recognized from the EEG during each task using the subjectdependent emotion recognition algorithm. The algorithm consists from feature extraction and classification parts. The classifier is trained for each participant using short calibration session with emotion induction before the experiment. The combined features obtained from the training session are used to train a SVM classifier. The following hypotheses are confirmed. Participants have mostly negative, high arousal, low dominance emotions during the mental tasks performance under the stress since high arousal can speed up the mental process and enhance information retrieval, and as the limited time is given to perform the tasks, the feeling of losing control is dominated in the participants that corresponds to low dominance on the scale of Valence-Arousal-Dominance emotion model. At the same time it is found that when the difficulty level of mental tasks increases under the stress, people tend to be more negative and more aroused.

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Attenuating age-related learning deficits: Emotional valenced feedback interacts with task complexity.

Cited by 13 publications

References 55 publications

Toward a dual-learning systems model of speech category learning

Toward a dual-learning systems model of speech category learning

Social incentives improve deliberative but not procedural learning in older adults

EEG-Based Emotion Monitoring in Mental Task Performance

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