Secondary-task effects on classification learning

Why do individuals with Williams syndrome or Down syndrome fail the Weather Prediction Task?

Williams syndrome (WS) and Down syndrome (DS) are two neurodevelopmental disorders with distinct genetic origins characterized by mild to moderate intellectual disability. Individuals with WS or DS exhibit impaired hippocampus-dependent place learning and enhanced striatum-dependent spatial response learning. Here, we used the Weather Prediction Task (WPT), which can be solved using hippocampus- or striatum-dependent learning strategies, to determine whether individuals with WS or DS exhibit similar profiles outside the spatial domain. Only 10% of individuals with WS or DS solved the WPT. We further assessed whether a concurrent memory task could promote reliance on procedural learning to solve the WPT in individuals with WS but found that the concurrent task did not improve performance. To understand how the probabilistic cue-outcome associations influences WPT performance, and whether individuals with WS or DS can ignore distractors, we assessed performance using a visual learning task with differing reward contingencies, and a modified WPT with unpredictive cues. Both probabilistic feedback and distractors negatively impacted the performance of individuals with WS or DS. These findings are consistent with deficits in hippocampus-dependent learning and executive functions, and reveal the importance of congruent feedback and the minimization of distractors to optimize learning in these two populations.

When and how do children solve the Weather Prediction Task?

The Weather Prediction Task (WPT) can be solved by adults using several strategies dependent on different memory systems. One developmental study reported that 8-12-year-old children can solve WPT-like tasks but, because of inadequate analyses, the cognitive processes involved in solving the task have not been established. The present study aimed to determine at what age children can first solve the WPT and identify the strategies used by children of different ages. We tested 3-12-year-old typically developing children and 20-30-year-old adults on a modified WPT. We performed detailed analyses of performance for each pattern of cue-outcome associations to decipher the strategies used by participants. None of the 3-5.5-year-old children solved the task. About one third of 5.5-7.5-year-old children performed above chance, relying only on the two most predictive cues. In contrast, about 80% of 7.5-12-year-old children performed above chance, relying on a conditional hierarchical strategy. Similar to 20-30-year-old adults, 7.5-12-year-old children considered the highly predictive cues primarily and the less predictive cues secondarily. These findings indicate that the learning strategies used to solve the WPT evolve from middle to late childhood and reflect an increasing ability to use a conditional strategy concomitant with the development of the hippocampus-dependent memory system.

Pay attention and you might miss it: Greater learning during attentional lapses

Attentional lapses have been found to impair everything from basic perception to learning and memory. Yet, despite the well-documented costs of lapses on cognition, recent work suggests that lapses might unexpectedly confer some benefits. One potential benefit is that lapses broaden our learning to integrate seemingly irrelevant content that could later prove useful-a benefit that prior research focusing only on goal-relevant memory would miss. Here, we measure how fluctuations in sustained attention influence the learning of seemingly goal-irrelevant content that competes for attention with target content. Participants completed a correlated flanker task in which they categorized central targets (letters or numbers) while ignoring peripheral flanking symbols that shared hidden probabilistic relationships with the targets. We found that across participants, higher rates of attentional lapses correlated with greater learning of the target-flanker relationships. Moreover, within participants, learning was more evident during attentional lapses. These findings address long-standing theoretical debates and reveal a benefit of attentional lapses: they expand the scope of learning and decisions beyond the strictly relevant.

What Is the Weather Prediction Task Good for? A New Analysis of Learning Strategies Reveals How Young Adults Solve the Task

The Weather Prediction Task (WPT) was originally designed to assess probabilistic classification learning. Participants were believed to gradually acquire implicit knowledge about cue–outcome association probabilities and solve the task using a multicue strategy based on the combination of all cue–outcome probabilities. However, the cognitive processes engaged in the resolution of this task have not been firmly established, and despite conflicting results, the WPT is still commonly used to assess striatal or procedural learning capacities in various populations. Here, we tested young adults on a modified version of the WPT and performed novel analyses to decipher the learning strategies and cognitive processes that may support above chance performance. The majority of participants used a hierarchical strategy by assigning different weights to the different cues according to their level of predictability. They primarily based their responses on the presence or absence of highly predictive cues and considered less predictive cues secondarily. However, the influence of the less predictive cues was inconsistent with the use of a multicue strategy, since they did not affect choices when both highly predictive cues associated with opposite outcomes were present simultaneously. Our findings indicate that overall performance is inadequate to draw conclusions about the cognitive processes assessed by the WPT. Instead, detailed analyses of performance for the different patterns of cue–outcome associations are essential to determine the learning strategies used by participants to solve the task.

Short-Term Classification Learning Promotes Rapid Global Improvements of Information Processing in Human Brain Functional Connectome

Classification learning is a preeminent human ability within the animal kingdom but the key mechanisms of brain networks regulating learning remain mostly elusive. Recent neuroimaging advancements have depicted human brain as a complex graph machinery where brain regions are nodes and coherent activities among them represent the functional connections. While long-term motor memories have been found to alter functional connectivity in the resting human brain, a graph topological investigation of the short-time effects of learning are still not widely investigated. For instance, classification learning is known to orchestrate rapid modulation of diverse memory systems like short-term and visual working memories but how the brain functional connectome accommodates such modulations is unclear. We used publicly available repositories (openfmri.org) selecting three experiments, two focused on short-term classification learning along two consecutive runs where learning was promoted by trial-by-trial feedback errors, while a further experiment was used as supplementary control. We analyzed the functional connectivity extracted from BOLD fMRI signals, and estimated the graph information processing in the cerebral networks. The information processing capability, characterized by complex network statistics, significantly improved over runs, together with the subject classification accuracy. Instead, null-learning experiments, where feedbacks came with poor consistency, did not provoke any significant change in the functional connectivity over runs. We propose that learning induces fast modifications in the overall brain network dynamics, definitely ameliorating the short-term potential of the brain to process and integrate information, a dynamic consistently orchestrated by modulations of the functional connections among specific brain regions.

The benefit of interleaved presentation in category learning is independent of working memory

We tested whether working memory (WM) resources were necessary for the interleaved presentation benefit over blocked presentation in category learning. We examined category learning in the Kornell and Bjork [2008. Learning concepts and categories: Is spacing the "enemy of induction"? Psychological Science, 19(6), 585] artistic style task while participants performed the numerical Stroop task as a dual task in order to interfere with WM maintenance and WM dependent executive functions. In addition, we evaluated whether individual differences in WM capacity (WMC), assessed via complex span tasks, would affect learning. The results revealed a superiority for interleaved presentation in both single-task and dual-task conditions, as well as superior performance for participants with relatively high WMC. Importantly, there was no interaction between the presence of the dual task and interleaving, or WMC and interleaving, indicating that the benefits of interleaving are independent of WM. We also probed participants' metacognitive judgments about whether blocking or interleaving was superior for learning, and found that most participants reported blocking was more effective, contrary to the reality that interleaving led to the best performance. These results support theories of the interleaving effect that are independent of working memory resources and pose a challenge to theories that rely on working memory mediated comparisons of items across trials.

Enhanced Avoidance Habits in Relation to History of Early-Life Stress

The effect of stress on the balance between goal-directed behavior and stimulus-response habits has been demonstrated in a number of studies, but the extent to which stressful events that occur during development affect the balance between these systems later in life is less clear. Here, we examined whether individuals with a history of early-life stress (ELS) show a bias toward avoidance habits on an instrumental learning task as adults. Participants (N = 189 in Experiment 1 and N = 112 in Experiment 2) were undergraduate students at the University of California, Los Angeles. In Experiment 1, we hypothesized that a history of ELS and a longer training phase would be associated with greater avoidance habits. Participants learned to make button-press responses to visual stimuli in order to avoid aversive auditory outcomes. Following a training phase involving extensive practice of the responses, participants were tested for habitual responding using outcome devaluation. After completing the instrumental learning task, participants provided retrospective reports of stressful events they experienced during their first 16 years of life. We did not observe evidence for an effect of the length of training, but we did observe an effect of ELS, with greater stress predicting greater odds of performing the avoidance habit. In Experiment 2, we sought to replicate the effect of ELS observed in Experiment 1, and we also tested whether the presence of distraction during training would increase avoidance habit performance. We replicated the effect of ELS but we did not observe evidence of an effect of distraction. Taken together, these data lend support to the hypothesis that stress occurring during development can have lasting effects on the balance between goal-directed behavior and stimulus-response habits in humans. Enhancement of avoidance habits may help explain the higher levels of negative health outcomes such as heart and liver disease that have been observed in individuals with a history of ELS. Some of the negative health behaviors that contribute to these negative health outcomes, e.g., overeating and substance use, may be performed initially to avoid feelings of distress and then transition to being performed habitually.

Procedural and declarative learning in dyslexia

The procedural deficit hypothesis claims that impaired procedural learning is at least partly responsible for the deficits in learning to read seen in children with developmental dyslexia. This study used a reading ability-matched design to examine group differences in both procedural and declarative learning. Both children with dyslexia and typically developing children demonstrated procedural learning on a serial reaction time task, although learning in the typically developing group increased at a greater rate towards the end of the task compared with children with dyslexia. However, these results do not provide strong evidence for the procedural deficit hypothesis, because poorer procedural learning in the group with dyslexia may reflect impairments in motor learning, rather than sequence specific procedural learning. In addition, neither group showed a relationship between procedural learning and reading ability.

Evidence of stable individual differences in implicit learning

There is a fundamental psychological and neuropsychological distinction between explicit and implicit memory, and it has been proposed that whereas there are stable trait individual differences in explicit memory ability, there are not such differences across people for implicit learning. There is, however, little evidence about whether or not there are stable trait differences in implicit learning. Here we performed a test-retest reliability study with healthy young adults in which they performed four implicit learning tasks (artificial grammar learning, probabilistic classification, serial response, and implicit category learning) twice, about a week apart. We found medium (by Cohen's guidelines) test-retest reliability for three of the tasks: probabilistic classification, serial response, and implicit category learning, suggesting that differences in implicit learning ability are more stable than originally thought. In addition, implicit learning on all tasks was unrelated to explicit measures: we did not find any correlation between implicit learning measures and independent measures of IQ, working memory, or explicit learning ability. These findings indicate that implicit learning, like explicit learning, varies reliably across individuals.

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.

Contributions of the hippocampus to feedback learning

Humans learn about the world in a variety of manners, including by observation, by associating cues in the environment, and via feedback. Across species, two brain structures have been predominantly involved in these learning processes: the hippocampus--supporting learning via observation and paired association--and the striatum--critical for feedback learning. This simple dichotomy, however, has recently been challenged by reports of hippocampal engagement in feedback learning, although the role of the hippocampus is not fully understood. The purpose of this experiment was to characterize the hippocampal response during feedback learning by manipulating varying levels of memory interference. Consistent with prior reports, feedback learning recruited the striatum and midbrain. Notably, feedback learning also engaged the hippocampus. The level of activity in these regions was modulated by the degree of memory interference, such that the greatest activation occurred during the highest level of memory interference. Importantly, the accuracy of information learned via feedback correlated with hippocampal activation and was reduced by the presence of high memory interference. Taken together, these findings provide evidence of hippocampal involvement in feedback learning by demonstrating both its relevance for the accuracy of information learned via feedback and its susceptibility to interference.

Paired-Associate and Feedback-Based Weather Prediction Tasks Support Multiple Category Learning Systems

It remains unclear whether probabilistic category learning in the feedback-based weather prediction task (FB-WPT) can be mediated by a non-declarative or procedural learning system. To address this issue, we compared the effects of training time and verbal working memory, which influence the declarative learning system but not the non-declarative learning system, in the FB and paired-associate (PA) WPTs, as the PA task recruits a declarative learning system. The results of Experiment 1 showed that the optimal accuracy in the PA condition was significantly decreased when the training time was reduced from 7 to 3 s, but this did not occur in the FB condition, although shortened training time impaired the acquisition of explicit knowledge in both conditions. The results of Experiment 2 showed that the concurrent working memory task impaired the optimal accuracy and the acquisition of explicit knowledge in the PA condition but did not influence the optimal accuracy or the acquisition of self-insight knowledge in the FB condition. The apparent dissociation results between the FB and PA conditions suggested that a non-declarative or procedural learning system is involved in the FB-WPT and provided new evidence for the multiple-systems theory of human category learning.

Habit Formation and the Striatum

Data from experimental animals and human subjects has provided convergent evidence for the key role of the striatum in the formation of stimulus-response habits. Habits can be distinguished from associative memories that support goal-directed actions based on their insensitivity to reward devaluation and contingency degradation. Behavior on many instrumental learning tasks can be supported by both declarative knowledge and habits, and these contributions shift with the amount of training. This shift appears to be accompanied by the involvement of different cortico-striatal loops in controlling behavior. Factors that encourage the shift toward and maintenance of habits include learning under conditions of stress, distraction, and interval or probabilistic schedules of reinforcement.

The effect of early-life stress on memory systems supporting instrumental behavior

People experiencing early-life stress (ELS) exhibit increased incidence of behaviors that lead to addiction and obesity as adults. Many of these behaviors may be viewed as resulting from an overreliance on habits as opposed to goal-directed instrumental behavior. This increased habitization may result from alterations in the interactions between dorsolateral striatum-dependent and hippocampus-dependent learning systems. As an initial examination of this idea, we investigated the effect of ELS on instrumental learning and extinction. In Experiment 1, we examined the effect of ELS in two groups of people, one trained on a continuous reinforcement schedule and one trained on a partial reinforcement schedule. We found that people who experienced ELS had a diminished effect of the partial reinforcement schedule on extinction. In Experiment 2, we again manipulated reinforcement schedule and also challenged declarative memory by requiring subjects to perform a concurrent task. We found that the declarative challenge did not affect extinction responding in the non-ELS group. In a moderate-ELS group, we observed a diminished sensitivity to the reinforcement schedule during extinction only under divided attention. In the high-ELS group, we observed a reduced sensitivity to reinforcement schedule even in the absence of the declarative memory challenge, consistent with Experiment 1. Our results suggest that ELS reduces the tendency to use declarative, hippocampus-dependent memory in instrumental tasks in favor of habits. ELS may affect hippocampal development, thus altering the interaction between memory systems and potentially contributing to poor health outcomes.

Imminent danger? Probabilistic classification learning of threat-related information in obsessive-compulsive disorder

A tendency to overestimate threat has been shown in individuals with OCD. We tested the hypothesis that this bias in judgment is related to difficulties in learning probabilistic associations between events. Thirty participants with OCD and 30 matched healthy controls completed a learning experiment involving 2 variants of a probabilistic classification learning task. In the neutral weather-prediction task, rainy and sunny weather had to be predicted. In the emotional task danger of an epidemic from virus infection had to be predicted (epidemic-prediction task). Participants with OCD were as able as controls to improve their prediction of neutral events across learning trials but scored significantly below healthy controls on the epidemic-prediction task. Lower performance on the emotional task variant was significantly related to a heightened tendency to overestimate threat. Biased information processing in OCD might thus hamper corrective experiences regarding the probability of threatening events.

Impaired automatization of a cognitive skill in first-degree relatives of patients with schizophrenia

We studied healthy, first-degree relatives of patients with schizophrenia to test the hypothesis that deficits in cognitive skill learning are associated with genetic liability to schizophrenia. Using the Weather Prediction Task (WPT), 23 healthy controls and 10 adult first-degree Relatives Of Schizophrenia (ROS) patients were examined to determine the extent to which cognitive skill learning was automated using a dual-task paradigm to detect subtle impairments in skill learning. Automatization of a skill is the ability to execute a task without the demand for executive control and effortful behavior and is a skill in which schizophrenia patients possess a deficit. ROS patients did not differ from healthy controls in accuracy or reaction time on the WPT either during early or late training on the single-task trials. In contrast, the healthy control and ROS groups were differentially affected during the dual-task trials. Our results demonstrate that the ROS group did not automate the task as well as controls and continued to rely on controlled processing even after extensive practice. This suggests that adult ROS patients may engage in compensatory strategies to achieve normal levels of performance and support the hypothesis that impaired cognitive skill learning is associated with genetic risk for schizophrenia.

A neurocomputational theory of how explicit learning bootstraps early procedural learning

It is widely accepted that human learning and memory is mediated by multiple memory systems that are each best suited to different requirements and demands. Within the domain of categorization, at least two systems are thought to facilitate learning: an explicit (declarative) system depending largely on the prefrontal cortex, and a procedural (non-declarative) system depending on the basal ganglia. Substantial evidence suggests that each system is optimally suited to learn particular categorization tasks. However, it remains unknown precisely how these systems interact to produce optimal learning and behavior. In order to investigate this issue, the present research evaluated the progression of learning through simulation of categorization tasks using COVIS, a well-known model of human category learning that includes both explicit and procedural learning systems. Specifically, the model's parameter space was thoroughly explored in procedurally learned categorization tasks across a variety of conditions and architectures to identify plausible interaction architectures. The simulation results support the hypothesis that one-way interaction between the systems occurs such that the explicit system "bootstraps" learning early on in the procedural system. Thus, the procedural system initially learns a suboptimal strategy employed by the explicit system and later refines its strategy. This bootstrapping could be from cortical-striatal projections that originate in premotor or motor regions of cortex, or possibly by the explicit system's control of motor responses through basal ganglia-mediated loops.

A matched filter hypothesis for cognitive control

The prefrontal cortex exerts top-down influences on several aspects of higher-order cognition by functioning as a filtering mechanism that biases bottom-up sensory information toward a response that is optimal in context. However, research also indicates that not all aspects of complex cognition benefit from prefrontal regulation. Here we review and synthesize this research with an emphasis on the domains of learning and creative cognition, and outline how the appropriate level of cognitive control in a given situation can vary depending on the organism's goals and the characteristics of the given task. We offer a matched filter hypothesis for cognitive control, which proposes that the optimal level of cognitive control is task-dependent, with high levels of cognitive control best suited to tasks that are explicit, rule-based, verbal or abstract, and can be accomplished given the capacity limits of working memory and with low levels of cognitive control best suited to tasks that are implicit, reward-based, non-verbal or intuitive, and which can be accomplished irrespective of working memory limitations. Our approach promotes a view of cognitive control as a tool adapted to a subset of common challenges, rather than an all-purpose optimization system suited to every problem the organism might encounter.

The curse of planning: dissecting multiple reinforcement-learning systems by taxing the central executive

A number of accounts of human and animal behavior posit the operation of parallel and competing valuation systems in the control of choice behavior. In these accounts, a flexible but computationally expensive model-based reinforcement-learning system has been contrasted with a less flexible but more efficient model-free reinforcement-learning system. The factors governing which system controls behavior-and under what circumstances-are still unclear. Following the hypothesis that model-based reinforcement learning requires cognitive resources, we demonstrated that having human decision makers perform a demanding secondary task engenders increased reliance on a model-free reinforcement-learning strategy. Further, we showed that, across trials, people negotiate the trade-off between the two systems dynamically as a function of concurrent executive-function demands, and people's choice latencies reflect the computational expenses of the strategy they employ. These results demonstrate that competition between multiple learning systems can be controlled on a trial-by-trial basis by modulating the availability of cognitive resources.

Brain activity across the development of automatic categorization: a comparison of categorization tasks using multi-voxel pattern analysis

Previous evidence suggests that relatively separate neural networks underlie initial learning of rule-based and information-integration categorization tasks. With the development of automaticity, categorization behavior in both tasks becomes increasingly similar and exclusively related to activity in cortical regions. The present study uses multi-voxel pattern analysis to directly compare the development of automaticity in different categorization tasks. Each of the three groups of participants received extensive training in a different categorization task: either an information-integration task, or one of two rule-based tasks. Four training sessions were performed inside an MRI scanner. Three different analyses were performed on the imaging data from a number of regions of interest (ROIs). The common patterns analysis had the goal of revealing ROIs with similar patterns of activation across tasks. The unique patterns analysis had the goal of revealing ROIs with dissimilar patterns of activation across tasks. The representational similarity analysis aimed at exploring (1) the similarity of category representations across ROIs and (2) how those patterns of similarities compared across tasks. The results showed that common patterns of activation were present in motor areas and basal ganglia early in training, but only in the former later on. Unique patterns were found in a variety of cortical and subcortical areas early in training, but they were dramatically reduced with training. Finally, patterns of representational similarity between brain regions became increasingly similar across tasks with the development of automaticity.

Learning the exception to the rule: model-based FMRI reveals specialized representations for surprising category members

Category knowledge can be explicit, yet not conform to a perfect rule. For example, a child may acquire the rule "If it has wings, then it is a bird," but then must account for exceptions to this rule, such as bats. The current study explored the neurobiological basis of rule-plus-exception learning by using quantitative predictions from a category learning model, SUSTAIN, to analyze behavioral and functional magnetic resonance imaging (fMRI) data. SUSTAIN predicts that exceptions require formation of specialized representations to distinguish exceptions from rule-following items in memory. By incorporating quantitative trial-by-trial predictions from SUSTAIN directly into fMRI analyses, we observed medial temporal lobe (MTL) activation consistent with 2 predicted psychological processes that enable exception learning: item recognition and error correction. SUSTAIN explains how these processes vary in the MTL across learning trials as category knowledge is acquired. Importantly, MTL engagement during exception learning was not captured by an alternate exemplar-based model of category learning or by standard contrasts comparing exception and rule-following items. The current findings thus provide a well-specified theory for the role of the MTL in category learning, where the MTL plays an important role in forming specialized category representations appropriate for the learning context.

The dimensionality of perceptual category learning: a state-trace analysis

State-trace analysis was used to investigate the effect of concurrent working memory load on perceptual category learning. Initial reanalysis of Zeithamova and Maddox (2006, Experiment 1) revealed an apparently two-dimensional state-trace plot consistent with a dual-system interpretation of category learning. However, three modified replications of the original experiment found evidence of a single resource underlying the learning of both rule-based and information integration category structures. Follow-up analyses of the Zeithamova and Maddox data, restricted to only those participants who had learned the category task and performed the concurrent working memory task adequately, revealed a one-dimensional plot consistent with a single-resource interpretation and the results of the three new experiments. The results highlight the potential of state-trace analysis in furthering our understanding of the mechanisms underlying category learning.

Human category learning 2.0

During the 1990s and early 2000s, cognitive neuroscience investigations of human category learning focused on the primary goal of showing that humans have multiple category-learning systems and on the secondary goals of identifying key qualitative properties of each system and of roughly mapping out the neural networks that mediate each system. Many researchers now accept the strength of the evidence supporting multiple systems, and as a result, during the past few years, work has begun on the second generation of research questions-that is, on questions that begin with the assumption that humans have multiple category-learning systems. This article reviews much of this second generation of research. Topics covered include (1) How do the various systems interact? (2) Are there different neural systems for categorization and category representation? (3) How does automaticity develop in each system? and (4) Exactly how does each system learn?

Interactions between declarative and procedural-learning categorization systems

Two experiments tested whether declarative and procedural memory systems operate independently or inhibit each other during perceptual categorization. Both experiments used a hybrid category-learning task in which perfect accuracy could be achieved if a declarative strategy is used on some trials and a procedural strategy is used on others. In the two experiments, only 2 of 53 participants learned a strategy of this type. In Experiment 1, most participants appeared to use simple explicit rules, even though control participants reliably learned the procedural component of the hybrid task. In Experiment 2, participants pre-trained either with the declarative or procedural component and then transferred to the hybrid categories. Despite this extra training, no participants in either group learned to categorize the hybrid stimuli with a strategy of the optimal type. These results are inconsistent with the most prominent single- and multiple-system accounts of category learning. They also cannot be explained by knowledge partitioning, or by the hypothesis that the failure to learn was due to high switch costs. Instead, these results support the hypothesis that declarative and procedural memory systems interact during category learning.

Distinguishing the contributions of implicit and explicit processes to performance of the weather prediction task

Examinations of the cognitive neuroscience of category learning frequently rely on probabilistic classification-learning tasks-namely, the weather prediction task (WPT)-to study the neural mechanisms of implicit learning. Accumulating evidence suggests that the task also depends on explicit-learning processes. The present investigation manipulated the WPT to assess the specific contributions of implicit- and explicit-learning processes to performance, with a particular focus on how the contributions of these processes change as the task progresses. In Experiment 1, a manipulation designed to disrupt implicit-learning processes had no effect on classification accuracy or the distribution of individual response strategies. In Experiment 2, by contrast, a manipulation designed to disrupt explicit-learning processes substantially reduced classification accuracy and reduced the number of participants who relied on a correct response strategy. The present findings suggest that WPT learning is not an effective tool for investigating nondeclarative learning processes.

Fear relevancy, strategy use, and probabilistic learning of cue-outcome associations

The goal of this study was to determine how the fear relevancy of outcomes during probabilistic classification learning affects behavior and strategy use. Novel variants of the "weather prediction" task were created, in which cue cards predicted either looming fearful or neutral outcomes in a between-groups design. Strategy use was examined by goodness-of-fit estimates of response patterns across trial blocks to mathematical models of simple, complex, and nonidentifiable strategies. Participants in the emotional condition who were fearful of the outcomes had greater skin conductance responses compared with controls and performed worse, used suboptimal strategies, and had less insight into the predictive cue features during initial learning. In contrast, nonfearful participants in the emotional condition used more optimal strategies than the other groups by the end of the two training days. Results have implications for understanding how individual differences in fear relevancy alter the impact of emotion on feedback-based learning.

Dimensions in data: testing psychological models using state-trace analysis

Cognitive science is replete with fertile and forceful debates about the need for one or more underlying mental processes or systems to explain empirical observations. Such debates can be found in many areas, including learning, memory, categorization, reasoning and decision-making. Multiple-process models are often advanced on the basis of dissociations in data. We argue and illustrate that using dissociation logic to draw conclusions about the dimensionality of data is flawed. We propose that a more widespread adoption of 'state-trace analysis'--an approach that overcomes these flaws--could lead to a re-evaluation of the need for multiple-process models and to a re-appraisal of how these models should be formulated and tested.

Challenging the role of implicit processes in probabilistic category learning

Considerable interest in the hypothesis that different cognitive tasks recruit qualitatively distinct processing systems has led to the proposal of separate explicit (declarative) and implicit (procedural) systems. A popular probabilistic category learning task known as the weather prediction task is said to be ideally suited to examine this distinction because its two versions, "observation" and "feedback," are claimed to recruit the declarative and procedural systems, respectively. In two experiments, we found results that were inconsistent with this interpretation. In Experiment 1, a concurrent memory task had a detrimental effect on the implicit (feedback) version of the task. In Experiment 2, participants displayed comparable and accurate insight into the task and their judgment processes in the feedback and observation versions. These findings have important implications for the study of probabilistic category learning in both normal and patient populations.

Category learning and the memory systems debate

A substantial and growing body of evidence from cognitive neuroscience supports the concept of multiple memory systems (MMS). However, the existence of multiple systems has been questioned by theorists who instead propose that dissociations can be accounted for within a single memory system. We present convergent evidence from neuroimaging and neuropsychological studies of category learning in favor of the existence of MMS for category learning and declarative knowledge. Whereas single-system theorists have argued that their approach is more parsimonious because it only postulates a single form of memory representation, we show that the MMS approach is superior in its ability to account for a broad range of data from psychology and neuroscience.