Criterion learning in rule-based categorization: Simulation of neural mechanism and new data

Hélie, Sébastien; Ell, Shawn W.; Filoteo, J. Vincent; Maddox, W. Todd

doi:10.1016/j.bandc.2015.01.009

Cited by 10 publications

(10 citation statements)

References 62 publications

(103 reference statements)

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“…However, our results suggest that criterion placement itself may require a nonverbalizable implicit learning process, particularly in the case of unequal category base-rates. In this regard, the current results correspond to recent computational neuroscience work arguing for associative learning of criterion placement [43]. It is clear from our current results that participants were limited in their criterion adjustment in response to base-rates when implicit learning was disrupted.…”

Section: Discussionsupporting

confidence: 88%

Base-rate sensitivity through implicit learning

Wismer

Bohil

2017

PLoS ONE

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Two experiments assessed the contributions of implicit and explicit learning to base-rate sensitivity. Using a factorial design that included both implicit and explicit learning disruptions, we tested the hypothesis that implicit learning underlies base-rate sensitivity from experience (and that explicit learning contributes comparatively little). Participants learned to classify two categories of simple stimuli (bar graph heights) presented in a 3:1 base-rate ratio. Participants learned either from “observational” training to disrupt implicit learning or “response” training which supports implicit learning. Category label feedback on each trial was followed either immediately or after a 2.5 second delay by onset of a working memory task intended to disrupt explicit reasoning about category membership feedback. Decision criterion values were significantly larger following response training, suggesting that implicit learning underlies base-rate sensitivity. Disrupting explicit processing had no effect on base-rate learning as long as implicit learning was supported. These results suggest base-rate sensitivity develops from experience primarily through implicit learning, consistent with separate learning systems accounts of categorization.

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Section: Discussionsupporting

confidence: 88%

Base-rate sensitivity through implicit learning

Wismer

Bohil

2017

PLoS ONE

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“…Accordingly, people attempt to apply rules in unfamiliar categorization tasks, even when this is an unadaptive strategy . However, rule learning requires stimulus representations that can support the proposal and testing of dimensional rules (Ashby et al, 1998;Hélie et al, 2015); that is, it requires a representation in terms of separable dimensions. If such a representation can be learned during a categorization task, then rule-based category learning can be applied in most circumstances.…”

mentioning

confidence: 99%

“…As rule-based category learning is thought to depend on selective attention to separable dimensions (Ashby et al, 1998;Hélie et al, 2015), unknown morphed dimensions cannot support rule-based category learn- ing. Instead, morphed faces seem to change in a variety of different shape dimensions at the same time.…”

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confidence: 99%

Novel representations that support rule-based categorization are acquired on-the-fly during category learning

Soto

Ashby

2019

Psychological Research

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Humans learn categorization rules that are aligned with separable dimensions through a rulebased learning system, which makes learning faster and easier to generalize than categorization rules that require integration of information from different dimensions. Recent research suggests that learning to categorize objects along a completely novel dimension changes its perceptual representation, making it more separable and discriminable. Here we asked whether such newly learned dimensions could support rule-based category learning. One group received extensive categorization training and a second group did not receive such training. Later, both groups were trained in a task that made use of the category-relevant dimension, and then tested in an analogical transfer task (Experiment 1) and a button-switch interference task (Experiment 2). We expected that only the group with extensive pre-training (with well-learned dimensional representations) would show evidence of rule-based behavior in these tasks. Surprisingly, both groups performed as expected from rule-based learning. A third experiment tested whether a single session (less than one hour) of training in a categorization task would facilitate learning in a task requiring executive function. There was a substantial learning advantage for a group with brief pre-training with the relevant dimension. We hypothesize that extensive experience with separable dimensions is not required for rule-based category learning; rather, the rulebased system may learn representations "on the fly" that allow rule application. We discuss what kind of neurocomputational model might explain these data best.

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“…However, it is possible that participants learn the BC bound during training, even though they are never asked to contrast these categories. We argue that this possibility is unlikely because (1) participants typically learn only the information that is required to perform the task [ 5 , 50 ] and (2) the participants never receive feedback about the BC bound, which would make the criterion difficult to learn [ 51 ]. Still this possibility is not completely ruled out by the current experiments.…”

Section: Discussionmentioning

confidence: 99%

“…For example, the procedural learning mechanism in COVIS (used to learn II structures) is a radial–basis function connectionist network (computationally equivalent to a Gaussian mixture model, which is a generative model) that learns a within-category representation. Importantly, despite the task goal being identical (e.g., classification), neurocomputational models that have been applied to RB and II structures implicitly echo the hypothesis that RB structures promote between–category representations whereas II structures promote within–category representations [ 3 , 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

The effect of training methodology on knowledge representation in categorization

2017

Self Cite

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Category representations can be broadly classified as containing within–category information or between–category information. Although such representational differences can have a profound impact on decision–making, relatively little is known about the factors contributing to the development and generalizability of different types of category representations. These issues are addressed by investigating the impact of training methodology and category structures using a traditional empirical approach as well as the novel adaptation of computational modeling techniques from the machine learning literature. Experiment 1 focused on rule–based (RB) category structures thought to promote between–category representations. Participants learned two sets of two categories during training and were subsequently tested on a novel categorization problem using the training categories. Classification training resulted in a bias toward between–category representations whereas concept training resulted in a bias toward within–category representations. Experiment 2 focused on information-integration (II) category structures thought to promote within–category representations. With II structures, there was a bias toward within–category representations regardless of training methodology. Furthermore, in both experiments, computational modeling suggests that only within–category representations could support generalization during the test phase. These data suggest that within–category representations may be dominant and more robust for supporting the reconfiguration of current knowledge to support generalization.

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Criterion learning in rule-based categorization: Simulation of neural mechanism and new data

Cited by 10 publications

References 62 publications

Base-rate sensitivity through implicit learning

Base-rate sensitivity through implicit learning

Novel representations that support rule-based categorization are acquired on-the-fly during category learning

The effect of training methodology on knowledge representation in categorization

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