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Considerable evidence suggests that human category learning recruits multiple memory systems. A popular assumption is that procedural memory is used to form stimulus-to-response mappings, whereas declarative memory is used to form and test explicit rules about category membership. The multiple systems framework has been successful in motivating and...
Citations
... Several prior studies have examined RB and II learning in the same individuals. However, these studies have not compared behavior across tasks and, instead, focused on between-subjects comparisons (musicians vs. non-musicians 36 ; Parkinson's Disease patients vs. older adults vs. younger adults 37 ), examined RB and II learning in a task-switching context 38 , or focused on how individual differences in cognitive abilities relate to RB and II learning 39 . As a result, it is unclear how the same individual approaches and learns RB and II categories. ...
... Similarly, if participants forget what their in-mind strategy was, they may not apply the strategy consistently across trials. In future research, it would be informative to increase precision of the estimation strategies participants are using, how they switch between them at a more rapid timescale 38,87 , and how strategy consistency relates to explore/exploit tendencies across individuals. These questions may be particularly well-suited for online physiological measures (e.g., pupillometry) that may capture rapid processes that may be difficult to observe directly from behavior. ...
The ability to organize variable sensory signals into discrete categories is a fundamental process in human cognition thought to underlie many real-world learning problems. Decades of research suggests that two learning systems may support category learning and that categories with different distributional structures (rule-based, information-integration) optimally rely on different learning systems. However, it remains unclear how the same individual learns these different categories and whether the behaviors that support learning success are common or distinct across different categories. In two experiments, we investigate learning and develop a taxonomy of learning behaviors to investigate which behaviors are stable or flexible as the same individual learns rule-based and information-integration categories and which behaviors are common or distinct to learning success for these different types of categories. We found that some learning behaviors are stable in an individual across category learning tasks (learning success, strategy consistency), while others are flexibly task-modulated (learning speed, strategy, stability). Further, success in rule-based and information-integration category learning was supported by both common (faster learning speeds, higher working memory ability) and distinct factors (learning strategies, strategy consistency). Overall, these results demonstrate that even with highly similar categories and identical training tasks, individuals dynamically adjust some behaviors to fit the task and success in learning different kinds of categories is supported by both common and distinct factors. These results illustrate a need for theoretical perspectives of category learning to include nuances of behavior at the level of an individual learner.
... Order effects in category likelihood judgments (Original data) (OSF) Therefore, in Crossley et al. (2018), the function f () maps spatial frequency values in perceptual space, y 1 , to spatial frequency values used to construct the Gabor filters, y ′ 1 . In our study, the raw coordinates for spatial frequency, x ′′ 1 , were transformed into spatial frequency values that could be used to construct the Gabor filter stimuli, x ′ 1 , using the following transformation, which is similar to the stimulus generating procedure carried out in Ell and Ashby (2006), ...
... To more accurately represent the position of the stimuli in perceptual space when carrying out the decision bound modeling, we used the inverse of the function employed by Crossley et al. (2018) to map the spatial frequency coordinates in stimulus generation space, x ′ 1 , into coordinates for spatial frequency in perceptual space, x 1 . That is, the raw coordinates for spatial frequency in parameter space, x ′′ 1 , were first mapped by g() to the spatial frequency values used to construct the Gabor filters, x ′ 1 , and then the spatial frequency values used to construct the Gabor filters, x ′ 1 , were mapped by f −1 () to the coordinates that more accurately represent the position of the stimuli in perceptual space, x 1 , where f −1 (x ′ 1 ) = (100/3) · (log 2 (x ′ 1 ) + 1). ...
... To construct Gabor filter stimuli similar to the ones used in this experiment, Crossley, Roeder, Helie, and Ashby (2018) first generated raw coordinates for each stimulus, (y 1 , y 2 ), from a bivariate uniform distribution on the interval (0,100). To convert the raw spatial frequency coordinates, y 1 , to spatial frequency values that could be used to construct the Gabor filter stimuli, y ′ 1 , Crossley et al. (2018) used the following nonlinear transformation defined in Treutwein, Rentschler, and Caelli (1989) f (y 1 ) = 2 (3/100)·y 1 −1 . ...
Quantum probability theory has successfully provided accurate descriptions of behavior in the areas of judgment and decision making, and here we apply the same principles to two category learning tasks, one task using information-integration categories and the other using rule-based categories. Since information-integration categories lack verbalizable descriptions, unlike rule-based ones, we assert that an information-integration categorization decision results from an intuitive probabilistic reasoning system characterized by quantum probability theory, whereas a rule-based categorization decision results from a logical, rational probabilistic reasoning system characterized classical probability theory. In our experiment, participants learn to categorize simple, visual stimuli as members of either category S or category K during an acquisition phase, and then rate the likelihood on a scale of 0 to 5 that a stimulus belongs to one category and subsequently perform the same likelihood rating for the other category during a transfer phase. Following the principle of complementarity in quantum theory, we expect the category likelihood ratings to exhibit order effects in the information-integration task, but not in the rule-based task. In the information-integration task, we found definitive order effects in the likelihood ratings. But, in the rule-based task, we found that the order effects in the likelihood ratings are not significant.
... For example, it is assumed that there is a bias toward the verbal system, with humans biased to spontaneously form task-sets. Switch costs may reflect the cognitively demanding process of reconfiguring between task-sets (Koch et al., 2018), while category learning also assumes that switching between the categorization systems is subject to interference (Crossley et al., 2018). At what point do these systems differ? ...
Humans are characterized by their ability to leverage rules for classifying and linking stimuli to context-appropriate actions. Previous studies have shown that when humans learn stimulus-response associations for two-dimensional stimuli, they implicitly form and generalize hierarchical rule structures (task-sets). However, the cognitive processes underlying structure formation are poorly understood. Across four experiments, we manipulated how trial-unique images mapped onto responses to bias spontaneous task-set formation and investigated structure learning through the lens of incidental stimulus encoding. Participants performed a learning task designed to either promote task-set formation (by “motor-clustering” possible stimulus-action rules), or to discourage it (by using arbitrary category-response mappings). We adjudicated between two hypotheses: Structure learning may promote attention to task stimuli, thus resulting in better subsequent memory. Alternatively, building task-sets might impose cognitive demands (for instance, on working memory) that divert attention away from stimulus encoding. While the clustering manipulation affected task-set formation, there were also substantial individual differences. Importantly, structure learning incurred a cost: spontaneous task-set formation was associated with diminished stimulus encoding. Thus, spontaneous hierarchical task-set formation appears to involve cognitive demands that divert attention away from encoding of task stimuli during structure learning.
... In sum, mixed pairs learning-in the flavor we investigate in this study-requires much more task switching than either pure variety of comparison. Given the costs to category learning known to arise from strategy switching (e.g., Crossley, Roeder, Helie, & Ashby, 2018;Erickson, 2008), we believe having to randomly switch between different comparison strategies incurs a comparable cost during mixed pairs learning. In further support of this point, previous work from our lab has shown that learning from a 75%/25% ratio of same-to different-category comparison did not differ from another condition with the same ratio flipped on far transfer items. ...
In accord with structural alignment theory, same-category comparison opportunities within a classification learning task should promote relational category acquisition. However, a straightforward merging of the classification paradigm with copresentation of same-category item pairs does not yield an advantage relative to an equal number of single-item exposures. In 3 experiments, we explore the hypothesis that the traditional classification learning mode (guess-and-correct) and comparison have a previously unforeseen incompatibility. In Experiment 1, we test this hypothesis by contrasting classification with supervised observational learning (passive study of labeled examples) under 3 presentation formats: same-category pairs, mixed pairs, and single-item. We find an observational advantage with same-category pairs and produce the elusive advantage over single-item exposures. In Experiment 2, we assess the generality of the learning mode effect by testing both same- and different-category comparison. The observational advantage replicates and extends to different-category comparison-although, we do not find a significant difference between the 2 types of comparison. In Experiment 3, relative to the classification mode, we find enhanced performance in an intermediate learning mode between classification and observation in which participants are instructed to make a covert category guess (without making an actual response) before seeing the correct category label. Implications and interpretations-including our interpretation that the performance emphasis inherent in classification learning undermines the benefits that arise from comparison opportunities-are discussed. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
... Behavioral studies encouraging participants to switch between hypothesis-testing and associative strategies in a perceptual category learning task show that unless participants are cued towards which type of strategy to use on a given trial, they tend to use hypothesis-testing strategies for all trials (Ashby & Crossley, 2010;Erickson, 2008). When participants are given cues on which type of strategy to use, many participants can successfully switch between associative and hypothesis-testing strategies, but this type of switching is more difficult than switching between different hypothesis-testing strategies (Crossley et al., 2018). In addition, participants will use a verbal rule when it is available, even if it produces suboptimal categorization (Noseworthy & Goode, 2011). ...
Multiple theories of category learning converge on the idea that there are two systems for categorization, each designed to process different types of category structures. The associative system learns categories that have probabilistic boundaries and multiple overlapping features through iterative association of features and feedback. The hypothesis-testing system learns rule-based categories through explicit testing of hypotheses about category boundaries. Prior research suggests that language resources are necessary for the hypothesis-testing system but not for the associative system. However, other research emphasizes the role of verbal labels in learning the probabilistic similarity-based categories best learned by the associative system. This suggests that language may be relevant for the associative system in a different way than it is relevant for the hypothesis-testing system. Thus, this study investigated the ways in which language plays a role in the two systems for category learning. In the first experiment, I tested whether language is related to an individual’s ability to switch between the associative and hypothesis-testing systems. I found that participants showed remarkable ability to switch between systems regardless of their language ability. The second experiment directly compared three dual-systems approaches to category learning and tested whether individual differences in language-related skills like vocabulary and executive function were related to category learning performance. This experiment showed different patterns of performance for each category learning approach despite considerable theoretical overlap. It also showed that performance in each approach was related to different individual difference measures. I conclude by questioning the applicability of a dual-systems model to all levels of processing and discuss ways in which future research can further elucidate the role of language in category learning for categories of different structures.
... While existing multiple-systems theories and models of categorization sometimes disagree about the number or nature of the different systems, all assume that people can switch between systems seamlessly depending on the task at hand. However, one ubiquitous task that has not received much attention is categorization system-switching (Ashby & Crossley, 2010;Crossley, Roeder, Hélie, & Ashby, 2016;Erickson, 2008): Can people flexibly switch between the different categorization systems on a trial-by-trial basis? For example, imagine you are in the basement of a building completing your laundry and a fire alarm sounds. ...
... Only 37% of the participants were able to switch categorization systems on a trial-bytrial basis (Erickson, 2008). With continuous-dimension stimuli, Crossley et al. (2016) obtained a proportion of undergraduate student switchers of about 40%. The highest proportion of undergraduate student switchers observed so far is 65.7% (Hélie, 2017). ...
... Taskswitching is a more general construct than system-switching in that the participants are asked to switch between tasks that may or may not rely on separate systems (Vandierendonck, Liefooghe, & Verbruggen, 2010). Crossley et al. (2016) showed that switch cost, a measure quantifying difficulty of trial-by-trial switching, is higher when switching between systems than when switching within a system. Older adults typically have a higher switch cost, meaning that they tend to be slower and less accurate on trials in which the This document is copyrighted by the American Psychological Association or one of its allied publishers. ...
Objective:
Numerous studies documenting cognitive deficits in Parkinson's disease (PD) revealed impairment in a variety of tasks related to memory, learning, and attention. One ubiquitous task that has not received much attention, is categorization system-switching. Categorization system-switching is a form of task-switching requiring participants to switch between different categorization systems. In this article, we explore whether older adults and people with PD show deficits in categorization system-switching.
Method:
Twenty older adults diagnosed with PD, 20 neurologically intact older adults, and 67 young adults participated in this study. Participants were first trained in rule-based (RB) and later information-integration (II) categorization separately. After training on the tasks, participants performed a block of trial-by-trial switching where the RB and II trials were randomly intermixed. Finally, the last block of trials also intermixed RB and II trials were randomly but additionally changed the location of the response buttons.
Results:
Contrary to our hypothesis, the results show no difference in accuracy between older adults and people with PD during the intermixed trial block, as well as no difference in response time (RT) switch cost. However, both groups were less accurate during intermixed trial blocks and had a higher RT switch cost when compared with young adults. In addition, the proportion of participants able to switch systems was smaller in people with PD than in young adults.
Conclusions:
The results suggest that older adults and people with PD have impaired categorization system-switching ability, and that this ability may be related to a decrease in tonic dopamine (DA) levels associated with normal aging and PD. (PsycINFO Database Record
... More recently, Crossley et al. (2017) published a new experiment again using disks with sine-wave gratings but this time adding back the background color cue and separate response buttons (4 categories) used in Erickson (2008). Crossley et al. used more training trials, and obtained a proportion of switchers close to 40%, which is higher than Ashby and Crossley (2010) and close to Erickson (2008). ...
... Systemswitching would be a special case of task-switching in which each task relies on a different categorization system. To explore this possibility, Crossley et al. (2017) also included a condition in which participants needed to switch between two different declarative strategies. The results showed that the switch cost was smaller when switching within the declarative system compared to switching between a declarative and a procedural system. ...
... In Crossley et al. (2017), a larger proportion of switchers was identified by increasing the number of training trials in the experimental session. The task-switching literature has also found some positive effects of training on taskswitching, although the switch cost was not significantly reduced. ...
Mounting evidence suggests that category learning is achieved using different psychological and biological systems. While existing multiple-system theories and models of categorization may disagree about the number or nature of the different systems, all assume that people can switch between systems seamlessly. However, little empirical data has been collected to test this assumption, and recent available data suggest that system-switching is difficult. The main goal of this article is to identify factors influencing the proportion of participants who successfully learn to switch between procedural and declarative systems on a trial-by-trial basis. Specifically, we tested the effects of preparation time and practice, two factors that have been useful in task-switching, in a system-switching experiment. The results suggest that practice and preparation time can be beneficial to system-switching (as calculated by a higher proportion of switchers and lower switch costs), especially when they are jointly present. However, this improved system-switching comes at the cost of a larger button-switch interference when changing the location of the response buttons. The article concludes with a discussion of the implications of these findings for empirical research on system-switching and theoretical work on multiple-systems of category learning.
Work on multiple-system theories of cognition mostly focused on the systems themselves, while limited work has been devoted to understanding the interactions between systems. Generally, multiple-system theories include a model-based decision system supported by the prefrontal cortex and a model-free decision system supported by the striatum. Here we propose a neurobiological model to describe the interactions between model-based and model-free decision systems in category learning. The proposed model used spiking neurons to simulate activity of the hyperdirect pathway of the basal ganglia. The hyperdirect pathway acts as a gate for the response signal from the model-free system located in the striatum. We propose that the model-free system's response is inhibited when the model-based system is in control of the response. The new model was used to simulate published data from young adults, people with Parkinson's disease, and aged-matched older adults. The simulation results further suggest that system-switching ability may be related to individual differences in executive function. A new behavioral experiment tested this model prediction. The results show that an updating score predicts the ability to switch system in a categorization task. The article concludes with new model predictions and implications of the results for research on system interactions.
Substantial evidence suggests that human category learning is governed by the interaction of multiple quali- tatively distinct neural systems. In this view, procedural memory is used to learn stimulus-response associa- tions, and declarative memory is used to apply explicit rules and test hypotheses about category membership. However, much less is known about the interaction between these systems: how is control passed between systems as they interact to influence motor resources? Here, we used fMRI to elucidate the neural correlates of switching between procedural and declarative categorization systems. We identified a key region of the cerebellum (left Crus I) whose activity was bidirectionally modulated depending on switch direction. We also identified regions of the default mode network (DMN) that were selectively connected to left Crus I during switching. We propose that the cerebellum—in coordination with the DMN—serves a critical role in passing control between procedural and declarative memory systems.
