Probabilistic Classification Learning Task Research Articles

With or without Feedback?-How the Presence of Feedback Affects Processing in Children with Developmental Language Disorder.

Language acquisition depends on the ability to process and learn probabilistic information, often through the integration of performance feedback. Children with developmental language disorder (DLD) have demonstrated weaknesses in both probabilistic learning and feedback processing, but the individual effects of each skill are poorly understood in this population. This study examined school-aged children with DLD (n = 29) and age- and gender-matched children with typical development (TD; n = 44) on a visual probabilistic classification learning task presented with and without feedback. In the feedback-based version of the task, children received performance feedback on a trial-by-trial basis during the training phase of the task. In the feedback-free version, children responded after seeing the correct choice marked with a green border and were not presented with feedback. Children with TD achieved higher accuracy than children with DLD following feedback-based training, while the two groups achieved similar levels of accuracy following feedback-free training. Analyses of event-related potentials (ERPs) provided insight into stimulus encoding processes. The feedback-free task was dominated by a frontal slow wave (FSW) and a late parietal component (LPC) which were not different between the two groups. The feedback-based task was dominated by a parietal slow wave (PSW) and an LPC, both of which were found to be larger in the TD than in the DLD group. In combination, results suggest that engagement with feedback boosts learning in children with TD, but not in children with DLD. When the need to process feedback is eliminated, children with DLD demonstrate behavioral and neurophysiological responses similar to their peers with TD.

Feedback Processing During Probabilistic Learning in Children With Developmental Language Disorder: An Event-Related Potential Study.

The purpose of this study was to examine feedback processing within the context of probabilistic learning in children with and without developmental language disorder (DLD). The probabilistic category learning task required 28 children ages 8-13 years old to classify novel cartoon animals that differed in five binary features into one of two categories. Performance feedback guided incremental learning of the stimuli classifications. Feedback processing was compared between children with DLD and age-matched children with typical development (TD) by measuring the magnitude of feedback-related event-related potentials. Additionally, the likelihood of each group to repeat a classification of a stimulus following positive feedback ("stay" behavior) and change a classification following negative feedback ("switch" behavior) served as a measure of the consequence of feedback processing. Children with DLD achieved lower classification accuracy on all learning outcomes compared to their peers with TD. Children with DLD were less likely than those with TD to demonstrate "stay" behavior or to repeat a correct response following positive feedback. "Switch" behavior or changing an incorrect response following negative feedback was found to be at chance level in both groups. Electrophysiological data indicated that children with DLD had a smaller feedback-related negativity effect (i.e., smaller differential processing of positive and negative feedback) when compared to children with TD. Although no differences were found between the two groups in the amplitude of the P3a, strong positive correlations were found between "stay/switch" behavior and the P3a for children in the TD group only. Children with DLD do not appear to benefit from incremental corrective feedback to the same extent as their peers with TD. Processing differences are captured in the initial stages of feedback evaluation and in translating information carried by the feedback to inform future actions.

Category learning in autism: Are some situations better than others?

Autism is diagnosed according to atypical social-communication and repetitive behaviors. However, autistic individuals are also distinctive in the high variability of specific abilities such as learning. Having been characterized as experiencing great difficulty with learning, autistics have also been reported to learn spontaneously in exceptional ways. These contrasting accounts suggest that some situations may be better than others for learning in autism. We tested this possibility using a probabilistic category learning task with four learning situations differing either in feedback intensity or information presentation. Two learning situations compared high- versus low-intensity feedback, while two other learning situations without external feedback compared isolated sequentially presented information versus arrays of simultaneously presented information. We assessed the categorization and generalization performance of 54 autistic and 52 age-matched typical school-age children after they learned in different situations. We found that children in both groups were able to learn and generalize novel probabilistic categories in all four learning situations. However, across and within groups, autistic children were advantaged by simultaneously presented information while typical children were advantaged by high-intensity feedback when learning. These findings question some common aspects of autism interventions (e.g., frequent intense feedback, minimized simplified information), and underline the importance of improving our current understanding of how and when autistics learn optimally. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Stress-induced bias of multiple memory systems during retrieval depends on training intensity

Stressful events promote a shift from hippocampus-dependent ‘cognitive’ learning towards dorsal striatum-dependent ‘habit’ learning. Beyond modulating the recruitment of multiple memory systems during learning, recent evidence suggests that stress may also affect which of these memory systems is employed during retrieval, thereby affecting the nature of remembering. However, while some studies reported increased reliance on ‘habit’ memory retrieval after stress, other studies suggested even a bias towards ‘cognitive’ memory retrieval after stress. In the present experiment, we tested the hypothesis that the nature of the stress effect on the control of memory retrieval depends on the extent of initial training. To this end, participants completed a probabilistic classification learning (PCL) task that can be solved by both the ‘cognitive’ and the ‘habit’ memory systems, which is reflected in the engagement of specific behavioral strategies. Critically, participants received either moderate (100 trials) or intensive (200 trials) training in the PCL task. Participants then underwent a stress protocol or a non-stressful control procedure, before they completed a retrieval version of the PCL task. The effectiveness of the stress manipulation was verified by increases in salivary cortisol and autonomic arousal. Our results further revealed that participants who received moderate training showed, during retrieval, a stress-induced shift towards strategies indicative of the dorsal striatal ‘habit’ memory system. After prolonged training, however, stress did not affect which memory system guided retrieval. The present results indicate that the effect of stress on the engagement of multiple memory systems during retrieval is critically dependent on the extent of initial training and, by inference, on the strength of the multiple memory traces established during learning.

Stress-induced modulation of multiple memory systems during retrieval requires noradrenergic arousal

Stress has been shown to favor dorsal striatum-dependent ‘habit’ memory over hippocampus-dependent ‘cognitive’ memory during learning. Here, we investigated whether stress may modulate the engagement of these ‘cognitive’ and ‘habit’ systems also during memory retrieval and if so, whether such a stress-induced shift in the control of memory retrieval depends on noradrenergic activation. To this end, participants acquired a probabilistic classification learning (PCL) task that can be solved by both the ‘cognitive’ and the ‘habit’ system, reflected in the distinct behavioral strategies. Twenty-four hours later, participants received either the beta-adrenergic receptor antagonist propranolol or a placebo before they underwent a psychosocial stressor or a non-stressful control manipulation, followed by a retrieval version of the PCL task. Overall, participants showed a practice-dependent shift from ‘cognitive’ to ‘habit’ memory. Stressed participants that had received a placebo fell back to a ‘cognitive’ strategy during retrieval, which was linked to an impairment in retrieval performance. Propranolol blocked this stress-induced shift towards the less efficient strategy. Moreover, our results showed that salivary cortisol was related to the retrieval strategy only when paralleled by increased autonomic arousal. Together, these results indicate that stress effects on the modulation of multiple memory system during retrieval necessitate noradrenergic arousal, with relevant implications for retrieval performance under stress.

Stress-induced cortisol modulates the control of memory retrieval towards the dorsal striatum.

Stress can modulate the recruitment of multiple memory systems during learning, favouring dorsal striatal "habit" learning over hippocampal "cognitive" learning. Here, we tested whether stress may also bias the engagement of "cognitive" and "habit" systems during retrieval and thereby affect the nature of remembering. To this end, participants first performed a probabilistic classification learning task that can be solved by both the "cognitive" and the "habit" system. Twenty-four hours later, participants underwent either a stress manipulation or a non-stressful control procedure before they completed a retention test for the previously learned task in the MRI scanner. During this retention test, stress-induced cortisol levels were linked to a relative bias towards behavioural strategies indicative for the "habit" system. At the neural level, stress led to increased dorsal striatal activity during retrieval. Elevated cortisol levels were directly correlated with increased activity in the dorsal striatum and further linked to reduced functional connectivity between the hippocampus and the amygdala, which is assumed to orchestrate the stress-related shift from "cognitive" to "habitual" control. Together, our data suggest that stress may bias the contributions of multiple memory systems also at retrieval, in a manner that promotes dorsal striatal "habit" processes and most likely driven by cortisol.

Does Acute Stress Impact Declarative and Procedural Learning?

It is well established that acute stress can influence memory function, yet its influence may differ across memory systems. Whereas stress sometimes exerts a negative influence on declarative learning, it does not necessarily harm learning in general, as demonstrated in the case of procedural learning. Probabilistic category learning is mediated by the striatum, but delaying feedback by a few seconds shifts learning to become more hippocampal-dependent. Here, we examined the influence of acute stress on this type of learning, under different conditions that favor either procedural-based (immediate feedback) vs. declarative-based (delayed feedback) learning. Sixty-two participants randomly assigned to either stress or non-stress groups, performed a probabilistic category learning task, in which they were instructed to learn associations between cues and outcomes under different feedback conditions (immediate feedback, short-delayed feedback, and long-delayed feedback). Acute stress was induced by the Maastricht Acute Stress Test (MAST), and stress levels were gauged by Galvanic Skin Response (GSR) measures and a self-reported questionnaire. Results showed that although the MAST was effective in inducing stress, this did not harm learning in either of the feedback conditions. These findings suggest that not all hippocampal-based learning types are negatively influenced by stress.

Modeling the Effect of Speed Emphasis in Probabilistic Category Learning

For many decisions in life, there is uncertainty about the outcome even if the stimulus is a familiar one. Under these circumstances, people often rely upon previous experience to make a choice. In general, more accurate responding is achieved when one considers decision-relevant information for longer. There are practical limits, however, to how long one can deliberate, and so decision-makers must balance decision speed against accuracy. Traditionally, the overall speed with which people respond has been understood in terms of changes in the evidence threshold used for decision-making. However, more recent findings suggest that speed pressure may also affect the quality of information people recruit for making decisions. The current study investigated the effects of emphasizing either response speed or response accuracy on decision-making in a probabilistic category learning task. Strikingly, conclusions from two model-based analyses of performance depended on whether data were analyzed at the level of individuals or the group average. For the group-level analyses, speed pressure selectively influenced evidence threshold. For the individual-level analyses, speed pressure affected both evidence threshold as well as the quality of information driving decision-making for four out of six participants. Our findings are consistent with recent results that identify common brain areas associated with setting evidence thresholds decision-making and representing category-level information in trial-by-trial associative learning.

Combining error-driven models of associative learning with evidence accumulation models of decision-making.

As people learn a new skill, performance changes along two fundamental dimensions: Responses become progressively faster and more accurate. In cognitive psychology, these facets of improvement have typically been addressed by separate classes of theories. Reductions in response time (RT) have usually been addressed by theories of skill acquisition, whereas increases in accuracy have been explained by associative learning theories. To date, relatively little work has examined how changes in RT relate to changes in response accuracy, and whether these changes can be accounted for quantitatively within a single theoretical framework. The current work examines joint changes in accuracy and RT in a probabilistic category learning task. We report a model-based analysis of changes in the shapes of RT distributions for different category responses at the level of individual stimuli over the course of learning. We show that changes in performance are determined solely by changes in the quality of information entering the decision process. We then develop a new model that combines an associative learning front end with a sequential sampling model of the decision process, showing that the model provides a good account of all aspects of the learning data. We conclude by discussing potential extensions of the model and future directions for theoretical development that are opened up by our findings.

Glucocorticoids, Noradrenergic Arousal, and the Control of Memory Retrieval

Glucocorticoids and noradrenaline can enhance memory consolidation but impair memory retrieval. Beyond their effects on quantitative memory performance, these major stress mediators bias the engagement of multiple memory systems toward "habitual" control during learning. However, if and how glucocorticoids and noradrenaline may also affect which memory system is recruited during recall, thereby affecting the control of retrieval, remain largely unknown. To address these questions, we trained healthy participants in a probabilistic classification learning task, which can be supported both by cognitive and habitual strategies. Approximately 24 hr later, participants received a placebo, hydrocortisone, yohimbine (an α2-adrenoceptor antagonist increasing noradrenergic stimulation), or both drugs before they completed a recall test for the probabilistic classification learning task. During training, all groups showed a practice-dependent shift toward more habitual strategies, reflecting an "automatization" of behavior. In the recall test, after a night of sleep, this automatization was even more pronounced in the placebo group, most likely due to offline consolidation processes and with beneficial effects on recall performance. Hydrocortisone or yohimbine intake abolished this further automatization, preventing the shift to a more efficient memory system and leading, in particular in the hydrocortisone group, to impaired recall performance. Our results suggest that glucocorticoids and noradrenergic stimulation may modulate the engagement of different strategies at recall and link the well-known stress hormone-induced retrieval deficit to a change in the system controlling memory retrieval.

Probabilistic Category Learning and Striatal Functional Activation in Psychosis Risk.

Psychosis risk is associated with striatal dysfunction, including a previous behavioral study that found that psychosis risk is associated with impaired performance on a probabilistic category learning task (PCLT; ie, the Weather Prediction Task), a task strongly associated with striatal activation. The current study examined whether psychosis risk based on symptom levels was associated with both poor behavioral performance and task-related physiological dysfunction in specific regions of the striatum while performing the PCLT. There were 2 groups of participants: psychosis risk (n = 21) who had both (a) extreme levels of self-reported psychotic-like beliefs and experiences and (b) interview-rated current attenuated psychotic symptoms (APS); and a comparison group (n = 20) who had average levels of self-reported psychotic-like beliefs and experiences. Participants completed the PCLT during fMRI scanning. The current research replicated previous work finding behavioral PCLT deficits at the end of the task in psychosis risk. Furthermore, as expected, the psychosis risk group exhibited decreased striatal activation on the task, especially in the associative striatum. The psychosis risk group also displayed decreased activation in a range of cortical regions connected to the associative striatum. In contrast, the psychosis risk group exhibited greater activation predominantly in cortical regions not connected to the associative striatum. Psychosis risk was associated with both behavioral and striatal dysfunction during performance on the PCLT, suggesting that behavioral and imaging measures using this task could be a marker for psychosis risk.

Depression Reduces Accuracy While Parkinsonism Slows Response Time for Processing Positive Feedback in Patients with Parkinson's Disease with Comorbid Major Depressive Disorder Tested on a Probabilistic Category-Learning Task.

Major depressive disorder (MDD) is the most common non-motor manifestation of Parkinson’s disease (PD) affecting 50% of patients. However, little is known about the cognitive correlates of MDD in PD. Using a computer-based cognitive task that dissociates learning from positive and negative feedback, we tested four groups of subjects: (1) patients with PD with comorbid MDD, (2) patients with PD without comorbid MDD, (3) matched patients with MDD alone (without PD), and (4) matched healthy control subjects. Furthermore, we used a mathematical model of decision-making to fit both choice and response time data, allowing us to detect and characterize differences between the groups that are not revealed by cognitive results. The groups did not differ in learning accuracy from negative feedback, but the MDD groups (PD patients with MDD and patients with MDD alone) exhibited a selective impairment in learning accuracy from positive feedback when compared to the non-MDD groups (PD patients without MDD and healthy subjects). However, response time in positive feedback trials in the PD groups (both with and without MDD) was significantly slower than the non-PD groups (MDD and healthy groups). While faster response time usually correlates with poor learning accuracy, it was paradoxical in PD groups, with PD patients with MDD having impaired learning accuracy and PD patients without MDD having intact learning accuracy. Mathematical modeling showed that both MDD groups (PD with MDD and MDD alone) were significantly slower than non-MDD groups in the rate of accumulation of information for stimuli trained by positive feedback, which can lead to lower response accuracy. Conversely, modeling revealed that both PD groups (PD with MDD and PD alone) required more evidence than other groups to make responses, thus leading to slower response times. These results suggest that PD patients with MDD exhibit cognitive profiles with mixed traits characteristic of both MDD and PD, furthering our understanding of both PD and MDD and their often-complex comorbidity. To the best of our knowledge, this is the first study to examine feedback-based learning in PD with MDD while controlling for the effects of PD and MDD.

Feedback-based probabilistic category learning is selectively impaired in attention/hyperactivity deficit disorder

Although Attention-Deficit Hyperactivity Disorder (ADHD) is closely linked to executive function deficits, it has recently been attributed to procedural learning impairments that are quite distinct from the former. These observations challenge the ability of the executive function framework solely to account for the diverse range of symptoms observed in ADHD. A recent neurocomputational model emphasizes the role of striatal dopamine (DA) in explaining ADHD’s broad range of deficits, but the link between this model and procedural learning impairments remains unclear. Significantly, feedback-based procedural learning is hypothesized to be disrupted in ADHD because of the involvement of striatal DA in this type of learning. In order to test this assumption, we employed two variants of a probabilistic category learning task known from the neuropsychological literature. Feedback-based (FB) and paired associate-based (PA) probabilistic category learning were employed in a non-medicated sample of ADHD participants and neurotypical participants. In the FB task, participants learned associations between cues and outcomes initially by guessing and subsequently through feedback indicating the correctness of the response. In the PA learning task, participants viewed the cue and its associated outcome simultaneously without receiving an overt response or corrective feedback. In both tasks, participants were trained across 150 trials. Learning was assessed in a subsequent test without a presentation of the outcome or corrective feedback. Results revealed an interesting disassociation in which ADHD participants performed as well as control participants in the PA task, but were impaired compared with the controls in the FB task. The learning curve during FB training differed between the two groups. Taken together, these results suggest that the ability to incrementally learn by feedback is selectively disrupted in ADHD participants. These results are discussed in relation to both the ADHD dopaminergic dysfunction model and recent findings implicating procedural learning impairments in those with ADHD.

Cerebellar tDCS does not improve performance in probabilistic classification learning

In this study, the role of the cerebellum in a cognitive learning task using transcranial direct current stimulation (tDCS) was investigated. Using a weather prediction task, subjects had to learn the probabilistic associations between a stimulus (a combination of cards) and an outcome (sun or rain). This task is a variant of a probabilistic classification learning task, for which it has been reported that prefrontal tDCS enhances performance. Using a between-subject design, all 30 subjects learned to improve their performance with increasing accuracies and shortened response times over a series of 500 trials. Subjects also became more confident in their prediction during the experiment. However, no differences in performance and learning were observed between subjects receiving sham stimulation (n = 10) or anodal stimulation (2 mA for 20 min) over either the right cerebellum (n = 10) or the left prefrontal cortex (n = 10). This suggests that stimulating the brain with cerebellar tDCS does not readily influence probabilistic classification performances, probably due to the rather complex nature of this cognitive task.

A tutorial on particle filters

This tutorial aims to provide an accessible introduction to particle filters, and sequential Monte Carlo (SMC) more generally. These techniques allow for Bayesian inference in complex dynamic state-space models and have become increasingly popular over the last decades. The basic building blocks of SMC–sequential importance sampling and resampling–are discussed in detail with illustrative examples. A final example presents a particle filter for estimating time-varying learning rates in a probabilistic category learning task.

Generalized Anxiety Disorder and Social Anxiety Disorder, but Not Panic Anxiety Disorder, Are Associated with Higher Sensitivity to Learning from Negative Feedback: Behavioral and Computational Investigation.

Anxiety disorders, including generalized anxiety disorder (GAD), social anxiety disorder (SAD), and panic anxiety disorder (PAD), are a group of common psychiatric conditions. They are characterized by excessive worrying, uneasiness, and fear of future events, such that they affect social and occupational functioning. Anxiety disorders can alter behavior and cognition as well, yet little is known about the particular domains they affect. In this study, we tested the cognitive correlates of medication-free patients with GAD, SAD, and PAD, along with matched healthy participants using a probabilistic category-learning task that allows the dissociation between positive and negative feedback learning. We also fitted all participants' data to a Q-learning model and various actor-critic models that examine learning rate parameters from positive and negative feedback to investigate effects of valence vs. action on performance. SAD and GAD patients were more sensitive to negative feedback than either PAD patients or healthy participants. PAD, SAD, and GAD patients did not differ in positive-feedback learning compared to healthy participants. We found that Q-learning models provide the simplest fit of the data in comparison to other models. However, computational analysis revealed that groups did not differ in terms of learning rate or exploration values. These findings argue that (a) not all anxiety spectrum disorders share similar cognitive correlates, but are rather different in ways that do not link them to the hallmark of anxiety (higher sensitivity to negative feedback); and (b) perception of negative consequences is the core feature of GAD and SAD, but not PAD. Further research is needed to examine the similarities and differences between anxiety spectrum disorders in other cognitive domains and potential implementation of behavioral therapy to remediate cognitive deficits.

Implicit learning on a probabilistic classification task in adults and adolescents with Bulimia Nervosa

BackgroundDysfunction in frontostriatal circuits likely contributes to impaired regulatory control in Bulimia Nervosa (BN), resulting in binge-eating and purging behaviors that resemble maladaptive habits. Less is known about the implicit learning processes of these circuits, which may contribute to habit formation. MethodsWe compared 52 adolescent and adult females with BN to 55 healthy matched-controls during performance of a probabilistic classification learning task, one form of implicit learning. Groups were compared in accuracy and response times, using mixed-models with block, age, and diagnosis as predictors, corrected for multiple comparisons with confounds covaried. ResultsBN participants showed differences in performance on a probabilistic classification learning task that varied by age. Adolescents with BN initially performed as accurately as healthy adolescents, but showed poorer perseverance over time. Adults with BN initially performed less accurately than healthy adults, but improved to perform equivalently. Symptom severity was associated with poorer accuracy in both adults and adolescents with BN. ConclusionsFrontostriatal dysfunction may underlie abnormalities in regulatory control and probabilistic classification learning in BN, likely contributing to the dysregulation of implicitly learned, maladaptive binge-eating and purging behaviors. Such dysfunction in BN may progress with increasing age, first manifesting in poor regulatory control over behaviors and then expanding to implicit learning processes that may underlie habitual behaviors.

The Role of Informative and Ambiguous Feedback in Avoidance Behavior: Empirical and Computational Findings

Avoidance behavior is a critical component of many psychiatric disorders, and as such, it is important to understand how avoidance behavior arises, and whether it can be modified. In this study, we used empirical and computational methods to assess the role of informational feedback and ambiguous outcome in avoidance behavior. We adapted a computer-based probabilistic classification learning task, which includes positive, negative and no-feedback outcomes; the latter outcome is ambiguous as it might signal either a successful outcome (missed punishment) or a failure (missed reward). Prior work with this task suggested that most healthy subjects viewed the no-feedback outcome as strongly positive. Interestingly, in a later version of the classification task, when healthy subjects were allowed to opt out of (i.e. avoid) responding, some subjects (“avoiders”) reliably avoided trials where there was a risk of punishment, but other subjects (“non-avoiders”) never made any avoidance responses at all. One possible interpretation is that the “non-avoiders” valued the no-feedback outcome so positively on punishment-based trials that they had little incentive to avoid. Another possible interpretation is that the outcome of an avoided trial is unspecified and that lack of information is aversive, decreasing subjects’ tendency to avoid. To examine these ideas, we here tested healthy young adults on versions of the task where avoidance responses either did or did not generate informational feedback about the optimal response. Results showed that provision of informational feedback decreased avoidance responses and also decreased categorization performance, without significantly affecting the percentage of subjects classified as “avoiders.” To better understand these results, we used a modified Q-learning model to fit individual subject data. Simulation results suggest that subjects in the feedback condition adjusted their behavior faster following better-than-expected outcomes, compared to subjects in the no-feedback condition. Additionally, in both task conditions, “avoiders” adjusted their behavior faster following worse-than-expected outcomes, and treated the ambiguous no-feedback outcome as less rewarding, compared to non-avoiders. Together, results shed light on the important role of ambiguous and informative feedback in avoidance behavior.

The influence of trial order on learning from reward vs. punishment in a probabilistic categorization task: experimental and computational analyses.

Previous research has shown that trial ordering affects cognitive performance, but this has not been tested using category-learning tasks that differentiate learning from reward and punishment. Here, we tested two groups of healthy young adults using a probabilistic category learning task of reward and punishment in which there are two types of trials (reward, punishment) and three possible outcomes: (1) positive feedback for correct responses in reward trials; (2) negative feedback for incorrect responses in punishment trials; and (3) no feedback for incorrect answers in reward trials and correct answers in punishment trials. Hence, trials without feedback are ambiguous, and may represent either successful avoidance of punishment or failure to obtain reward. In Experiment 1, the first group of subjects received an intermixed task in which reward and punishment trials were presented in the same block, as a standard baseline task. In Experiment 2, a second group completed the separated task, in which reward and punishment trials were presented in separate blocks. Additionally, in order to understand the mechanisms underlying performance in the experimental conditions, we fit individual data using a Q-learning model. Results from Experiment 1 show that subjects who completed the intermixed task paradoxically valued the no-feedback outcome as a reinforcer when it occurred on reinforcement-based trials, and as a punisher when it occurred on punishment-based trials. This is supported by patterns of empirical responding, where subjects showed more win-stay behavior following an explicit reward than following an omission of punishment, and more lose-shift behavior following an explicit punisher than following an omission of reward. In Experiment 2, results showed similar performance whether subjects received reward-based or punishment-based trials first. However, when the Q-learning model was applied to these data, there were differences between subjects in the reward-first and punishment-first conditions on the relative weighting of neutral feedback. Specifically, early training on reward-based trials led to omission of reward being treated as similar to punishment, but prior training on punishment-based trials led to omission of reward being treated more neutrally. This suggests that early training on one type of trials, specifically reward-based trials, can create a bias in how neutral feedback is processed, relative to those receiving early punishment-based training or training that mixes positive and negative outcomes.

Procedural learning: A developmental study of motor sequence learning and probabilistic classification learning in school-aged children

In this study, we investigated motor and cognitive procedural learning in typically developing children aged 8–12 years with a serial reaction time (SRT) task and a probabilistic classification learning (PCL) task. The aims were to replicate and extend the results of previous SRT studies, to investigate PCL in school-aged children, to explore the contribution of declarative knowledge to SRT and PCL performance, to explore the strategies used by children in the PCL task via a mathematical model, and to see whether performances obtained in motor and cognitive tasks correlated. The results showed similar learning effects in the three age groups in the SRT and in the first half of the PCL tasks. Participants did not develop explicit knowledge in the SRT task whereas declarative knowledge of the cue–outcome associations correlated with the performances in the second half of the PCL task, suggesting a participation of explicit knowledge after some time of exposure in PCL. An increasing proportion of the optimal strategy use with increasing age was observed in the PCL task. Finally, no correlation appeared between cognitive and motor performance. In conclusion, we extended the hypothesis of age invariance from motor to cognitive procedural learning, which had not been done previously. The ability to adopt more efficient learning strategies with age may rely on the maturation of the fronto-striatal loops. The lack of correlation between performance in the SRT task and the first part of the PCL task suggests dissociable developmental trajectories within the procedural memory system.